Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes/issues, but are citable by Digital Object Identifier (DOI).
Display Method:
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210609002
Abstract:
Electrospray technology can solve the problems of long membrane formation period, thicker membrane layer and low flux of traditional chitosan nanofiltration membranes. In this study, the NH2-UIO-66(Zr)/chitosan positively charged nanofiltration membrane based on NH2-UIO-66(Zr)/chitosan was successfully prepared by combining the direct membrane-forming properties of chitosan with electrospray technology and introducing metal-organic frameworks (MOFs). This membrane has achieved efficient separation and enrichment of Ni2+. The morphology, structure and properties of membrane were investigated by SEM, EDS, Zeta potential, water contact angle measurements, etc. The results showed that compared with the traditional coating process, the permeability of the chitosan composite membrane prepared by the electrospray method was increased by 634% and the membrane can still maintain a good rejection. However, pure chitosan matrix membranes are usually affected by trade-off effect, and it is difficult to further improve the separation performance. The introduction of NH2-UIO-66 (Zr) as a filler can effectively alleviate the trade-off effect. Hybridization improved the hydrophilicity of the separation layer and formed a special membrane surface structure, thereby increasing permeability of the composite membrane. Experiments showed that the limit of NH2-UiO-66(Zr) loading was 5% (the mass fraction of MOFs to chitosan). The optimized NH2-UIO-66(Zr)/chitosan membrane had a higher flux than pristine chitosan membrane (increased by 38%). The prepared membrane exhibited a high NiCl2 rejection of 92% and a water permeability of 4.7×10−5 L/(m2·h·Pa) (test pressure: 4×105 Pa; feed concentration: 0.5 g/L). In addition, the hybrid membrane exhibited excellent mechanical strength and had the potential to separate a single solution of MgCl2, ZnCl2 and Pb(NO3)2.
Electrospray technology can solve the problems of long membrane formation period, thicker membrane layer and low flux of traditional chitosan nanofiltration membranes. In this study, the NH2-UIO-66(Zr)/chitosan positively charged nanofiltration membrane based on NH2-UIO-66(Zr)/chitosan was successfully prepared by combining the direct membrane-forming properties of chitosan with electrospray technology and introducing metal-organic frameworks (MOFs). This membrane has achieved efficient separation and enrichment of Ni2+. The morphology, structure and properties of membrane were investigated by SEM, EDS, Zeta potential, water contact angle measurements, etc. The results showed that compared with the traditional coating process, the permeability of the chitosan composite membrane prepared by the electrospray method was increased by 634% and the membrane can still maintain a good rejection. However, pure chitosan matrix membranes are usually affected by trade-off effect, and it is difficult to further improve the separation performance. The introduction of NH2-UIO-66 (Zr) as a filler can effectively alleviate the trade-off effect. Hybridization improved the hydrophilicity of the separation layer and formed a special membrane surface structure, thereby increasing permeability of the composite membrane. Experiments showed that the limit of NH2-UiO-66(Zr) loading was 5% (the mass fraction of MOFs to chitosan). The optimized NH2-UIO-66(Zr)/chitosan membrane had a higher flux than pristine chitosan membrane (increased by 38%). The prepared membrane exhibited a high NiCl2 rejection of 92% and a water permeability of 4.7×10−5 L/(m2·h·Pa) (test pressure: 4×105 Pa; feed concentration: 0.5 g/L). In addition, the hybrid membrane exhibited excellent mechanical strength and had the potential to separate a single solution of MgCl2, ZnCl2 and Pb(NO3)2.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210824001
Abstract:
Exhaled breath condensate (EBC) is a kind of respiratory lining fluid, which is easy to collect and non-invasive. EBC is considered to be the ideal sample for the study of pulmonary diseases. Proteomics is one of the novel methods to develop disease biomarkers, and the proteomics of EBC is widely studied due to its tremendous biological potential. It can reflect different disease status by analyzing the components of EBC protein, explore potential biomarkers, and improve the diagnostic ability of lung cancer and other diseases. In this study, an EBC proteomics method based on data independent acquisition (DIA) was established to overcome the disadvantage of low protein concentration of EBC, and 2052 proteins were identified. On this basis, the weighted gene co-expression network analysis (WGCNA) was carried out. WGCNA is a novel bioinformatic analysis technology, which allows multiple analysis of different omics information. A total of 61 hub proteins were screened by cluster analysis, and the hub proteins were analyzed by gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) and protein-protein interactions (PPIs) analysis. The results showed that the hub proteins mainly existed in the nucleus and cytoplasm, and participated in the metabolic pathways related to human diseases, which indicated that the hub proteins could reflect the disease status and hold the potential to be biomarkers. In conclusion, the DIA-based EBC proteomics combined with WGCNA analysis, could effectively explore the potential biological functions of EBC, which could be applied to large-scale clinical research and contribute to the exploration of biomarkers in the future.
Exhaled breath condensate (EBC) is a kind of respiratory lining fluid, which is easy to collect and non-invasive. EBC is considered to be the ideal sample for the study of pulmonary diseases. Proteomics is one of the novel methods to develop disease biomarkers, and the proteomics of EBC is widely studied due to its tremendous biological potential. It can reflect different disease status by analyzing the components of EBC protein, explore potential biomarkers, and improve the diagnostic ability of lung cancer and other diseases. In this study, an EBC proteomics method based on data independent acquisition (DIA) was established to overcome the disadvantage of low protein concentration of EBC, and 2052 proteins were identified. On this basis, the weighted gene co-expression network analysis (WGCNA) was carried out. WGCNA is a novel bioinformatic analysis technology, which allows multiple analysis of different omics information. A total of 61 hub proteins were screened by cluster analysis, and the hub proteins were analyzed by gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) and protein-protein interactions (PPIs) analysis. The results showed that the hub proteins mainly existed in the nucleus and cytoplasm, and participated in the metabolic pathways related to human diseases, which indicated that the hub proteins could reflect the disease status and hold the potential to be biomarkers. In conclusion, the DIA-based EBC proteomics combined with WGCNA analysis, could effectively explore the potential biological functions of EBC, which could be applied to large-scale clinical research and contribute to the exploration of biomarkers in the future.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210523001
Abstract:
Taking the high-salt wastewater from coal chemical industry as the research object, the simulation calculation of the occurrence state of zinc ions in the high-salt system was implemented, in which the trend of the proportion change of various forms of Zn2+ with different pH value、temperature、and NaCl concentration was obtained.Using Na2S as precipitant, the effects of stirring speed, initial pH value, dosage of sodium sulfide and other process conditions on the performance of deep removal of zinc ions were researched. The results indicate that adjusting the initial pH value of high-salt organic wastewater to neutral or above is beneficial to the removal of zinc ions by sulfide precipitation method.When the reaction temperature is 25℃、the reaction time is 20 min、the stirring speed is 300r/min、the initial pH=8、and n (Zn2+): n (S2-)=1:1, the wastewater discharging standard of the residual liquid is satisfied.
Taking the high-salt wastewater from coal chemical industry as the research object, the simulation calculation of the occurrence state of zinc ions in the high-salt system was implemented, in which the trend of the proportion change of various forms of Zn2+ with different pH value、temperature、and NaCl concentration was obtained.Using Na2S as precipitant, the effects of stirring speed, initial pH value, dosage of sodium sulfide and other process conditions on the performance of deep removal of zinc ions were researched. The results indicate that adjusting the initial pH value of high-salt organic wastewater to neutral or above is beneficial to the removal of zinc ions by sulfide precipitation method.When the reaction temperature is 25℃、the reaction time is 20 min、the stirring speed is 300r/min、the initial pH=8、and n (Zn2+): n (S2-)=1:1, the wastewater discharging standard of the residual liquid is satisfied.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220107001
Abstract:
In order to study the variation of the crack growth behavior of oxygen-sensitive materials under the interaction of creep-oxidation, the physical mechanism of dynamic embrittlement was used to establish a mathematical model of creep coupled oxidation damage. The creep-oxidation crack growth of nickel-based alloy was analyzed by Abaqus and Voronoi diagram techniques. Meanwhile, the effects of load level, Grain boundary direction at initial crack, oxygen diffusion rate and creep properties on crack growth were analyzed. Results show that when the load is small, the oxidation promotion effect is significant when the crack propagates; creep gradually tends to dominate when the load increases. Since oxygen is easier to diffuse, the crack initiation time of straight grain boundary cracks is shorter than that of oblique grain boundary cracks. As the oxygen diffusion rate increases, the crack initiation time decreases, and the increase of load will cause the crack initiation time of the straight grain boundary cracks to stabilize. Creep constitutive parameters have almost no effect on the law of crack initiation time with load. The better the creep property of the materials or service conditions, the more obvious the effect of oxidation.
In order to study the variation of the crack growth behavior of oxygen-sensitive materials under the interaction of creep-oxidation, the physical mechanism of dynamic embrittlement was used to establish a mathematical model of creep coupled oxidation damage. The creep-oxidation crack growth of nickel-based alloy was analyzed by Abaqus and Voronoi diagram techniques. Meanwhile, the effects of load level, Grain boundary direction at initial crack, oxygen diffusion rate and creep properties on crack growth were analyzed. Results show that when the load is small, the oxidation promotion effect is significant when the crack propagates; creep gradually tends to dominate when the load increases. Since oxygen is easier to diffuse, the crack initiation time of straight grain boundary cracks is shorter than that of oblique grain boundary cracks. As the oxygen diffusion rate increases, the crack initiation time decreases, and the increase of load will cause the crack initiation time of the straight grain boundary cracks to stabilize. Creep constitutive parameters have almost no effect on the law of crack initiation time with load. The better the creep property of the materials or service conditions, the more obvious the effect of oxidation.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210515006
Abstract:
Porcine epidemic diarrhea virus can infect pigs at different ages and cause porcine epidemic diarrhea, leading to heavy economic losses to the pig industry around the world. There is currently no effective treatment for porcine epidemic diarrhea, and vaccination seems to be its key preventive appoach. In order to develop an effective porcine epidemic diarrhea virus subunit vaccine, a tandem epitope subunit (EC) was assembled from the COE region (E1), S1D region (E2), and C-terminal region (E3) of spike protein, and the M3 region (E4) of membrane protein, and a baculovirus expression system was constructed for the production of subunit vaccine EC. The reported candidate subunits COE and S1 were used as positive controls. Three target proteins of EC, COE and S1 were produced by baculovirus expression vector systems in insect cell line Sf9, and purified with a nickel affinity chromatography column, respectively. The immunogenicity of the different subunit vaccines was evaluated on BALB/c mouse. The results showed that the EC, COE and S1 gene sequences were successfully inserted into the baculovirus genome. All the three proteins could be expressed and secreted into culture supernatant. Compared with the subunit vaccines of COE and S1, subunit vaccine EC could stimulate mouse to produce larger amount of specific immunoglobulin G, interferon-γ and tumor necrosis factor-α than controls. The above results indicate that each subunit vaccine can stimulate the humoral and cellular immunity of mouse, and the immunogenicity of subunit vaccine EC is much stronger.
Porcine epidemic diarrhea virus can infect pigs at different ages and cause porcine epidemic diarrhea, leading to heavy economic losses to the pig industry around the world. There is currently no effective treatment for porcine epidemic diarrhea, and vaccination seems to be its key preventive appoach. In order to develop an effective porcine epidemic diarrhea virus subunit vaccine, a tandem epitope subunit (EC) was assembled from the COE region (E1), S1D region (E2), and C-terminal region (E3) of spike protein, and the M3 region (E4) of membrane protein, and a baculovirus expression system was constructed for the production of subunit vaccine EC. The reported candidate subunits COE and S1 were used as positive controls. Three target proteins of EC, COE and S1 were produced by baculovirus expression vector systems in insect cell line Sf9, and purified with a nickel affinity chromatography column, respectively. The immunogenicity of the different subunit vaccines was evaluated on BALB/c mouse. The results showed that the EC, COE and S1 gene sequences were successfully inserted into the baculovirus genome. All the three proteins could be expressed and secreted into culture supernatant. Compared with the subunit vaccines of COE and S1, subunit vaccine EC could stimulate mouse to produce larger amount of specific immunoglobulin G, interferon-γ and tumor necrosis factor-α than controls. The above results indicate that each subunit vaccine can stimulate the humoral and cellular immunity of mouse, and the immunogenicity of subunit vaccine EC is much stronger.
, Available online
, doi: 10.14135/j.cnki.1006-3080.2021092400
Abstract:
Clostridium histolyticum collagenase H (ColH) recognizes the Y-Gly of collagen and hydrolyzes it into small peptides. The high molecular weight ColH(116 kDa) secreting strain was successfully constructed by fusing colH gene with signal peptide sequence of outer membrane protein A. In this study, we found that the secretion of ColH was affected by the position of the signal peptide at the N-terminal, and the presence of excess amino acid fragments at the N-terminal significantly reduced the secretion function of the signal peptide-guided collagenase. Orthogonal experiment and single factor experiment were used to optimize the induction conditions and medium additives to improve the secretory expression. Under the conditions of inducing temperature of 25 ℃, the cell density(OD600) of 0.9, IPTG concentration of 0.1 mmol/L, liquid volume of 20%, magnesium ion concentration of 10 mmol/L, and 2% glycine were added at 2.5 h after induction, the highest extracellular collagenase activity was 0.68 U/mL after induction for 20 h, which was 38.1 times of that of before optimization, and the secretory expression level was remarkably increased. Glycine added into the culture medium is a common strategy to promote the secretion of recombinant protein. Experimental results demonstrated that the amount and time of glycine added after induction showed the greatest influence on the secretion of collagenase. The addition of calcium and magnesium ions in the medium can promote the growth of E.coli, the results also showed that only the addition of magnesium ion can promote the secretion of ColH.
Clostridium histolyticum collagenase H (ColH) recognizes the Y-Gly of collagen and hydrolyzes it into small peptides. The high molecular weight ColH(116 kDa) secreting strain was successfully constructed by fusing colH gene with signal peptide sequence of outer membrane protein A. In this study, we found that the secretion of ColH was affected by the position of the signal peptide at the N-terminal, and the presence of excess amino acid fragments at the N-terminal significantly reduced the secretion function of the signal peptide-guided collagenase. Orthogonal experiment and single factor experiment were used to optimize the induction conditions and medium additives to improve the secretory expression. Under the conditions of inducing temperature of 25 ℃, the cell density(OD600) of 0.9, IPTG concentration of 0.1 mmol/L, liquid volume of 20%, magnesium ion concentration of 10 mmol/L, and 2% glycine were added at 2.5 h after induction, the highest extracellular collagenase activity was 0.68 U/mL after induction for 20 h, which was 38.1 times of that of before optimization, and the secretory expression level was remarkably increased. Glycine added into the culture medium is a common strategy to promote the secretion of recombinant protein. Experimental results demonstrated that the amount and time of glycine added after induction showed the greatest influence on the secretion of collagenase. The addition of calcium and magnesium ions in the medium can promote the growth of E.coli, the results also showed that only the addition of magnesium ion can promote the secretion of ColH.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210823001
Abstract:
Organic molecules with fluorine usually possess unique physical, chemical, and biological properties, thus playing an important role in material science and pharmaceutical chemistry. Meanwhile, functionalization of organic molecules via C−H activation has drawn a broad attention in recent years. Therefore, C−H fluorination for the synthesis of fluorine-containing molecules is a very important and challenging project in organic synthesis. Directing groups such as pyridine and amide have been utilized to facilitate C−H fluorinations, however, most of the directing groups are usually installed into the substrates before the fluorination and uninstalled after the fluorination, thus reducing the step economy of the reaction. Carboxylic group is ubiquitous in organic molecules and it can dramatically increase the step economy if it is employed as native directing group. Indeed, it has been utilized as directing group in C−H activations such as arylation, olefination, acetoxylation. While carboxylic group directed C−H fluorination remains a challenge. In this research, by using the carboxylic group as a directing group, after optimization of the reaction conditions including additive, solvent, fluorination reagent and ligand, we realized the Pd-catalyzed ortho-C(sp2)-H fluorination of benzoic acid, which affords the ortho-mono-fluorinated product in up to 13% isolated yield. A pyridone ligand with a nitro group at the C-5 position and an amide group at the C-3 position was found to be able to promote this transformation. We believe that these results will benefit future development of carboxylic group directed C−H fluorination.
Organic molecules with fluorine usually possess unique physical, chemical, and biological properties, thus playing an important role in material science and pharmaceutical chemistry. Meanwhile, functionalization of organic molecules via C−H activation has drawn a broad attention in recent years. Therefore, C−H fluorination for the synthesis of fluorine-containing molecules is a very important and challenging project in organic synthesis. Directing groups such as pyridine and amide have been utilized to facilitate C−H fluorinations, however, most of the directing groups are usually installed into the substrates before the fluorination and uninstalled after the fluorination, thus reducing the step economy of the reaction. Carboxylic group is ubiquitous in organic molecules and it can dramatically increase the step economy if it is employed as native directing group. Indeed, it has been utilized as directing group in C−H activations such as arylation, olefination, acetoxylation. While carboxylic group directed C−H fluorination remains a challenge. In this research, by using the carboxylic group as a directing group, after optimization of the reaction conditions including additive, solvent, fluorination reagent and ligand, we realized the Pd-catalyzed ortho-C(sp2)-H fluorination of benzoic acid, which affords the ortho-mono-fluorinated product in up to 13% isolated yield. A pyridone ligand with a nitro group at the C-5 position and an amide group at the C-3 position was found to be able to promote this transformation. We believe that these results will benefit future development of carboxylic group directed C−H fluorination.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210616001
Abstract:
Since LSTM network can effectively deal with time series, it has been widely used to analyze stock prices and future trends. However, the important parameters in the LSTM network are usually determined by experience, which is highly subjective, or the optimal value cannot be determined due to the influence of calculation cost, resulting in the reduction of the fitting ability of the model. Aiming at the above problems, this paper proposes an improved particle swarm algorithm to optimize the key parameters of LSTM network. By reducing the influence of human factors and optimizing the forecasting process, a stock price forecasting model with higher forecasting accuracy is constructed. By utilizing a dynamic multi-swarm particle swarm optimizer to avoid local optimization, the obtained model can improve the optimization performance of PSO algorithm. At the same time, aiming at the problem of high dimension, large noise and data redundancy in the stock market data, which lead to the increase of model training cost and the decrease of prediction performance, a feature selection model is further constructed based on a variety of feature selection algorithms to complete the filtering and screening of index features and build a perfect prediction index system. Finally, it is shown via experimental results that the proposed model can attain higher prediction accuracy and universal applicability in stock price prediction.
Since LSTM network can effectively deal with time series, it has been widely used to analyze stock prices and future trends. However, the important parameters in the LSTM network are usually determined by experience, which is highly subjective, or the optimal value cannot be determined due to the influence of calculation cost, resulting in the reduction of the fitting ability of the model. Aiming at the above problems, this paper proposes an improved particle swarm algorithm to optimize the key parameters of LSTM network. By reducing the influence of human factors and optimizing the forecasting process, a stock price forecasting model with higher forecasting accuracy is constructed. By utilizing a dynamic multi-swarm particle swarm optimizer to avoid local optimization, the obtained model can improve the optimization performance of PSO algorithm. At the same time, aiming at the problem of high dimension, large noise and data redundancy in the stock market data, which lead to the increase of model training cost and the decrease of prediction performance, a feature selection model is further constructed based on a variety of feature selection algorithms to complete the filtering and screening of index features and build a perfect prediction index system. Finally, it is shown via experimental results that the proposed model can attain higher prediction accuracy and universal applicability in stock price prediction.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210511001
Abstract:
With the rapid progress of seawater desalination technology, concentrated brine has not been effectively used due to the lack of mature development and treatment process. Direct emissions of concentrated brine are seriously harmful to the ecological environment. At the same time, massive emissions of carbon dioxide have exacerbated the greenhouse effect. Aiming at the resource utilization of concentrated brine and carbon dioxide, combined with the previous research basis of using calcium hydroxide as the precipitant to prepare magnesium hydroxide, this paper proposes a carbonization technical process to prepare magnesium bicarbonate using magnesium hydroxide as the precursor from low magnesium systems. The research focused on the carbonization process of magnesium hydroxide, investigating the influence of liquid-solid ratio, temperature, carbon dioxide gas velocity, stirring speed, and raw material difference on the carbonization process, and monitoring the change of calcium and magnesium ion concentration and pH value during the carbonization process. It was found that under room temperature and atmospheric pressure, the carbonization effect was the best when the liquid-solid ratio was 40, the carbon dioxide gas velocity was 400 mL/min, and the stirring speed was 300 r/min, the concentration of Mg2+ in the carbonization solution from magnesium hydroxide (AR) was 0.315 mol/L and there existd no Ca2+, the concentration of Mg2+ in the carbonization solution from self-made magnesium hydroxide was 0.203 mol/L, the concentration of Ca2+ was 3.2×10−4 mol/L, calcium impurities were basically separated. The particle size and dispersion of magnesium hydroxide had significant impact on the carbonization effect, smaller particle size and better dispersion promoted the carbonization process. The shrinking core model of magnesium hydroxide was briefly discussed at the same time, the fits of the carbonization process at different slurry temperatures showed good linearity.
With the rapid progress of seawater desalination technology, concentrated brine has not been effectively used due to the lack of mature development and treatment process. Direct emissions of concentrated brine are seriously harmful to the ecological environment. At the same time, massive emissions of carbon dioxide have exacerbated the greenhouse effect. Aiming at the resource utilization of concentrated brine and carbon dioxide, combined with the previous research basis of using calcium hydroxide as the precipitant to prepare magnesium hydroxide, this paper proposes a carbonization technical process to prepare magnesium bicarbonate using magnesium hydroxide as the precursor from low magnesium systems. The research focused on the carbonization process of magnesium hydroxide, investigating the influence of liquid-solid ratio, temperature, carbon dioxide gas velocity, stirring speed, and raw material difference on the carbonization process, and monitoring the change of calcium and magnesium ion concentration and pH value during the carbonization process. It was found that under room temperature and atmospheric pressure, the carbonization effect was the best when the liquid-solid ratio was 40, the carbon dioxide gas velocity was 400 mL/min, and the stirring speed was 300 r/min, the concentration of Mg2+ in the carbonization solution from magnesium hydroxide (AR) was 0.315 mol/L and there existd no Ca2+, the concentration of Mg2+ in the carbonization solution from self-made magnesium hydroxide was 0.203 mol/L, the concentration of Ca2+ was 3.2×10−4 mol/L, calcium impurities were basically separated. The particle size and dispersion of magnesium hydroxide had significant impact on the carbonization effect, smaller particle size and better dispersion promoted the carbonization process. The shrinking core model of magnesium hydroxide was briefly discussed at the same time, the fits of the carbonization process at different slurry temperatures showed good linearity.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210506010
Abstract:
A three-dimensional model for a random packed bed filter was established by coupling computational fluid dynamics (CFD) and discrete element method (DEM). To ensure more accurate simulation results can be obtained, the interactions of liquid-solid, particle-granule and particle-particle were taken into consideration. The filtration performance including filtration efficiency, pressure drop and impurity holding capacity were carefully analyzed, and particle deposition distribution and morphology were also numerically investigated. The simulation results of filtration efficiency have a good agreement with the experimental results. The deviation of the pressure drop is within the allowable error range of the Ergun equation. The impurity holding capacity is represented by the deposition uniformity obtained by simulation results, which increases with the superficial velocity. Correlation of deposition uniformity for granular bed filters is presented and it has good prediction accuracy. The results show that cascade filtration has both a high filtration efficiency and a low pressure drop by combining deep bed filtration and surface filtration. The quality factor of the cascade filters are greater than those with a single-layer filters. The simulation analysis of particle deposition morphology and distribution shows that particles mainly deposit on the surface of single-layer filter packed with fine granules, resulting in its small holding capacity. As for the cascade filter, the fine granular layer ensures high filtration efficiency while coarse granular layer provides large impurity holding capacity.
A three-dimensional model for a random packed bed filter was established by coupling computational fluid dynamics (CFD) and discrete element method (DEM). To ensure more accurate simulation results can be obtained, the interactions of liquid-solid, particle-granule and particle-particle were taken into consideration. The filtration performance including filtration efficiency, pressure drop and impurity holding capacity were carefully analyzed, and particle deposition distribution and morphology were also numerically investigated. The simulation results of filtration efficiency have a good agreement with the experimental results. The deviation of the pressure drop is within the allowable error range of the Ergun equation. The impurity holding capacity is represented by the deposition uniformity obtained by simulation results, which increases with the superficial velocity. Correlation of deposition uniformity for granular bed filters is presented and it has good prediction accuracy. The results show that cascade filtration has both a high filtration efficiency and a low pressure drop by combining deep bed filtration and surface filtration. The quality factor of the cascade filters are greater than those with a single-layer filters. The simulation analysis of particle deposition morphology and distribution shows that particles mainly deposit on the surface of single-layer filter packed with fine granules, resulting in its small holding capacity. As for the cascade filter, the fine granular layer ensures high filtration efficiency while coarse granular layer provides large impurity holding capacity.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211014001
Abstract:
The service life of metalworking fluids can be shortened by existence of microorganism, and it is necessary to explore the composition of microbial communities in metalworking fluids. Three different methods were measured to determine the best one to separate microorganisms from metal working fluids. The concentration of microorganisms can be increased by the mikrocount combi method which has the optimal separation result. Under the help of Illumina MiSeq high-throughput sequencing, the composition of the microbial diversity of metalworking fluid samples at the 6 levels of phylum, class, order, family, genus, and species were completed respectively. Moreover, bacteria were detected in six groups of samples, while fungus were discovered in only two groups. Meanwhile, it was easier for bacteria to thrive in metalworking fluids than fungi, and fungi was only existed in samples with high bacterial contamination. A total of 2 phyla, 2 classes, 5 orders, 6 families, 10 genera and 14 species of bacteria were detected in all samples, while 4 phyla, 8 classes, 10 orders, 14 families, 15 genera and 17 species of fungi were also detected, which means that the fungal diversity is more abundant. Citrobacter_freundii_g_Citrobacter, unclassified_g_Citrobacter, unclassified_f_Enterobacteriaceae were identified as the dominant bacteria, and most of bacteria detected were Gram-negative. The composition of the metalworking fluid will affect the type of bacteria. All detected bacteria can destroy the stability of the metal working fluid through different ways, which shorten its service life. The dominant fungi were unclassified_k_Fungi and Fusarium_petroliphilum. The health of operators will be harmed by metalworking fluids with microbial contamination.
The service life of metalworking fluids can be shortened by existence of microorganism, and it is necessary to explore the composition of microbial communities in metalworking fluids. Three different methods were measured to determine the best one to separate microorganisms from metal working fluids. The concentration of microorganisms can be increased by the mikrocount combi method which has the optimal separation result. Under the help of Illumina MiSeq high-throughput sequencing, the composition of the microbial diversity of metalworking fluid samples at the 6 levels of phylum, class, order, family, genus, and species were completed respectively. Moreover, bacteria were detected in six groups of samples, while fungus were discovered in only two groups. Meanwhile, it was easier for bacteria to thrive in metalworking fluids than fungi, and fungi was only existed in samples with high bacterial contamination. A total of 2 phyla, 2 classes, 5 orders, 6 families, 10 genera and 14 species of bacteria were detected in all samples, while 4 phyla, 8 classes, 10 orders, 14 families, 15 genera and 17 species of fungi were also detected, which means that the fungal diversity is more abundant. Citrobacter_freundii_g_Citrobacter, unclassified_g_Citrobacter, unclassified_f_Enterobacteriaceae were identified as the dominant bacteria, and most of bacteria detected were Gram-negative. The composition of the metalworking fluid will affect the type of bacteria. All detected bacteria can destroy the stability of the metal working fluid through different ways, which shorten its service life. The dominant fungi were unclassified_k_Fungi and Fusarium_petroliphilum. The health of operators will be harmed by metalworking fluids with microbial contamination.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210608001
Abstract:
Based on the capacity expansion and revamping of a 1.8×106 t/a Davy methanol production project of an energy and chemical company, the model was established and analyzed. A multilayer cross one-dimensional quasi-homogeneous mathematical model was established for the adiabatic heat transfer of methanol radial reactor. It is used in Aspen Plus to simulate the series-parallel coupling process of dual radial reactor. It is found that increasing the area of the central pipe hole can effectively reduce the pressure drop of the perforation at large flow rate, and lowering the inlet temperature is effective for reducing the hot spot temperature, but it will increase the circulating gas flow rate. Changing the ratio of feed gas actually changes the connection mode of the reactor and increases the methanol production of the 2# reactor. The optimum production conditions were obtained when the fresh gas ratio was 0.800 and the entry temperature was 235.0 ℃, and the production capacity reached 130% of the original process.
Based on the capacity expansion and revamping of a 1.8×106 t/a Davy methanol production project of an energy and chemical company, the model was established and analyzed. A multilayer cross one-dimensional quasi-homogeneous mathematical model was established for the adiabatic heat transfer of methanol radial reactor. It is used in Aspen Plus to simulate the series-parallel coupling process of dual radial reactor. It is found that increasing the area of the central pipe hole can effectively reduce the pressure drop of the perforation at large flow rate, and lowering the inlet temperature is effective for reducing the hot spot temperature, but it will increase the circulating gas flow rate. Changing the ratio of feed gas actually changes the connection mode of the reactor and increases the methanol production of the 2# reactor. The optimum production conditions were obtained when the fresh gas ratio was 0.800 and the entry temperature was 235.0 ℃, and the production capacity reached 130% of the original process.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210717001
Abstract:
Mesenchymal stem cells (MSCs) are promising candidates for cell therapy and tissue engineering. However, the currently self-developed serum-free medium (SFM) have problems in obtaining sufficient number of clinically available cells in vitro. A breakthrough may be found by fully studying the key medium components which influence cell behavior. In this study, the effect and mechanism of hepatocyte growth factor (HGF) on cell proliferation was investigated. In this paper, the self-made serum-free medium at the early stage from laboratory was used as the control group, adding different concentrations of HGF and finding the most effective one. Cell counting kit-8 and growth curve were used to evaluate its proliferation. And cell cycle transition was also analyzed by flow cytometer. Moreover, western blotting was used to detect the expression levels of cyclin D1, integrin and the related signaling pathway. The results showed that 10 ng/mL HGF could significantly increase MSCs proliferation and viability in SFM. The effect of HGF occurred via active AKT-mTOR pathway and increased the synthesis of cyclin D1 protein, accelerating cell cycle from G1 phase to S phase progression, and promoting the proliferation of MSCs. HGF could enhance the expression of integrin, which is helpful to signaling transduction. After adding the inhibitor of FAK and AKT, phosphorylation of the corresponding protein decreased. Finally, after adding HGF, MSCs could still express surface marker including CD44, CD73, CD90, and CD105, and led to osteogenesis and adipogenesis differentiation. Thus, these findings have important implications for utilizing HGF to regulate cell behavior and expand clinical-grade MSCs in vitro.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210427005
Abstract:
In the field of production scheduling, due to the inffluence of many factors such as production technology, each production process usually requires multiple machines to simultaneously participate in processing. Meanwhile, the number of workpieces to be processed is large, and each type of workpiece needs to be processed in batches for shortening the production cycle. Aiming at the above problems, in a job shop environment, this paper adopts a variable batching scheme according to the load of the machines involved in each processing process, and proposes a non-mixed multi-processor combined production batch scheduling model and integrate the brainstorming algorithm to search the shortest Processing time. Moreover, an improved brainstorming algorithm is proposed by introducing greedy thinking and dynamic discussion mechanism. The number of discussions is changed adaptively with the iteration and the global search and local search are utilized to strengthen the search ability of the proposed algorithm. Finally, it is shown via the test results that the improved brainstorming algorithm is more efficient and convergent than the basic brainstorming algorithm.
In the field of production scheduling, due to the inffluence of many factors such as production technology, each production process usually requires multiple machines to simultaneously participate in processing. Meanwhile, the number of workpieces to be processed is large, and each type of workpiece needs to be processed in batches for shortening the production cycle. Aiming at the above problems, in a job shop environment, this paper adopts a variable batching scheme according to the load of the machines involved in each processing process, and proposes a non-mixed multi-processor combined production batch scheduling model and integrate the brainstorming algorithm to search the shortest Processing time. Moreover, an improved brainstorming algorithm is proposed by introducing greedy thinking and dynamic discussion mechanism. The number of discussions is changed adaptively with the iteration and the global search and local search are utilized to strengthen the search ability of the proposed algorithm. Finally, it is shown via the test results that the improved brainstorming algorithm is more efficient and convergent than the basic brainstorming algorithm.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210731001
Abstract:
Based on computational particle fluid dynamics (CPFD), an industrial grade polyethylene fluidized bed model in cold state was established to simulate the gas-solid properties in fluidized bed, and the image processing function of MATLAB was used to calculate the bubble size in the fluidized bed by matching the image pixels with the simulated grid. The effects of gas velocity and initial material quantity on the gas-solid flow characteristics of industrial grade polyethylene fluidized bed were studied from the perspectives of fluidized bed flow structure, particles and bubble. The results show that compared with the equipment in pilot and experimental stages, the side-wall effect in the industrial fluidized bed is obviously weak, and the dense-phase zone presents a more uniform particle distribution. The gas velocity has a great influence on the gas-solid flow in the fluidized bed. Under the operating condition of 0.46 m/s gas velocity, the particle distribution in the dense-phase zone is the most uniform, and the bulk flow becomes steady. The initial bed height mainly affects the height of the dense phase zone after fluidization, but has little effect on the bed expansion rate.
Based on computational particle fluid dynamics (CPFD), an industrial grade polyethylene fluidized bed model in cold state was established to simulate the gas-solid properties in fluidized bed, and the image processing function of MATLAB was used to calculate the bubble size in the fluidized bed by matching the image pixels with the simulated grid. The effects of gas velocity and initial material quantity on the gas-solid flow characteristics of industrial grade polyethylene fluidized bed were studied from the perspectives of fluidized bed flow structure, particles and bubble. The results show that compared with the equipment in pilot and experimental stages, the side-wall effect in the industrial fluidized bed is obviously weak, and the dense-phase zone presents a more uniform particle distribution. The gas velocity has a great influence on the gas-solid flow in the fluidized bed. Under the operating condition of 0.46 m/s gas velocity, the particle distribution in the dense-phase zone is the most uniform, and the bulk flow becomes steady. The initial bed height mainly affects the height of the dense phase zone after fluidization, but has little effect on the bed expansion rate.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210531001
Abstract:
Hydrophobic coatings were prepared in xylene/ethyl acetate solvent by using fluorosiloxane modified epoxy resin as the matrix. The influence of fluorosiloxane content and solvent type on hydrophobicity as well as micromorphology of the coatings were investigated. The results show that with the increase of fluorosiloxane content, the contact angle of the coating enhances. When only using xylene as the solvent, the surface of the coating is relatively smooth, and the maximum contact angle is 105.0°. In xylene/ethyl acetate system, 5~15 µm micropores are formed attributing to the difference of solvent volatilization rate. Meanwhile, 0.1~0.6 µm bumps are generated in those micropores along with phase separation of fluorosiloxane and epoxy segments according to nucleation-growth mechanism. The surface energy of coatings reduces with the spontaneous migration of F and Si to the outer surface in the process of film formation. When the content of fluorosiloxane segments increases to 30%, much more air could be trapped in the micropores and protrusions. The highest contact angle of the coating rises to about 115.5° and the hydrophobicity is improved obviously. The coating also exhibits high adhesion of 5B and hardness of 6H, suggesting its excellent application performance.
Hydrophobic coatings were prepared in xylene/ethyl acetate solvent by using fluorosiloxane modified epoxy resin as the matrix. The influence of fluorosiloxane content and solvent type on hydrophobicity as well as micromorphology of the coatings were investigated. The results show that with the increase of fluorosiloxane content, the contact angle of the coating enhances. When only using xylene as the solvent, the surface of the coating is relatively smooth, and the maximum contact angle is 105.0°. In xylene/ethyl acetate system, 5~15 µm micropores are formed attributing to the difference of solvent volatilization rate. Meanwhile, 0.1~0.6 µm bumps are generated in those micropores along with phase separation of fluorosiloxane and epoxy segments according to nucleation-growth mechanism. The surface energy of coatings reduces with the spontaneous migration of F and Si to the outer surface in the process of film formation. When the content of fluorosiloxane segments increases to 30%, much more air could be trapped in the micropores and protrusions. The highest contact angle of the coating rises to about 115.5° and the hydrophobicity is improved obviously. The coating also exhibits high adhesion of 5B and hardness of 6H, suggesting its excellent application performance.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210530002
Abstract:
Coupled process of mineralization of CaCl2 waste by reaction extraction crystallization would be beneficial for waste recycling and mineralization of CO2, which has a broad application prospect. The key to low cost operation of reaction extraction crystallization coupling mineralization process is the effective regeneration of organic amine extractant. Solid acid catalyst was used to strengthen the pyrolysis regeneration process to realize the regeneration of organic amine.At the same time, the coupled process also producedvaluable HCl gas, improving the economyof the process. The effect of heating temperature, carrier gas flow, stirring speed, diluent amount and catalyst amount on the thermal dissociation of trioctylamine hydrochloride by 5A molecular sieve were investigated. The results showed that the pyrolysis of trioctylamine hydrochloride catalyzed by 5A molecular sieve conformed to the first-order kinetic model. The thermal dissociation reaction rate was accelerated and the conversion rate increased with thermal dissociation temperature, carrier gas flow, diluent naphthalene and catalyst amount, and the effect of rotational speed on the thermal dissociation reaction was not obvious. Considering the conversion rate and energy consumption, the optimized pyrolysis conditions were as follows: reaction temperature 180 ℃, carrier gas flow 300 mL/min, rotating speed 150 r/min, mass ratio of triactylamine hydrochloride to naphthalene 1∶4, mass ratio to 5A molecular sieve 10∶1, 4-hour conversion rate is 95%, and 8 h conversion rate is 99%. The 5 cycles experiments showed that 5A zeolite still had good catalytic activity.
Coupled process of mineralization of CaCl2 waste by reaction extraction crystallization would be beneficial for waste recycling and mineralization of CO2, which has a broad application prospect. The key to low cost operation of reaction extraction crystallization coupling mineralization process is the effective regeneration of organic amine extractant. Solid acid catalyst was used to strengthen the pyrolysis regeneration process to realize the regeneration of organic amine.At the same time, the coupled process also producedvaluable HCl gas, improving the economyof the process. The effect of heating temperature, carrier gas flow, stirring speed, diluent amount and catalyst amount on the thermal dissociation of trioctylamine hydrochloride by 5A molecular sieve were investigated. The results showed that the pyrolysis of trioctylamine hydrochloride catalyzed by 5A molecular sieve conformed to the first-order kinetic model. The thermal dissociation reaction rate was accelerated and the conversion rate increased with thermal dissociation temperature, carrier gas flow, diluent naphthalene and catalyst amount, and the effect of rotational speed on the thermal dissociation reaction was not obvious. Considering the conversion rate and energy consumption, the optimized pyrolysis conditions were as follows: reaction temperature 180 ℃, carrier gas flow 300 mL/min, rotating speed 150 r/min, mass ratio of triactylamine hydrochloride to naphthalene 1∶4, mass ratio to 5A molecular sieve 10∶1, 4-hour conversion rate is 95%, and 8 h conversion rate is 99%. The 5 cycles experiments showed that 5A zeolite still had good catalytic activity.
Theoretical Calculation and Optimal Design of Gas Water Heater to Realize Zero Waiting for Hot Water
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211222001
Abstract:
The function between time and water temperature of the gas water heater was established using the heat transfer model, and the water temperature of hot water pipe was calculated. The importance of intelligent water pump system for water saving was analyzed. Finally, several practical methods to shorten the waiting time of hot water were given: (1) Raise the water outlet temperature of the gas water heater; (2) Shorten the length of the water pipe from the gas water heater to the bathroom faucet; (3) Install the hot water pipe; (4) Accelerate the pumping speed of the hot water circulation pump; (5) Open the circulating pump all the time, keep the gas water heater open and the water circulation in the hot water pipe when not using hot water.
The function between time and water temperature of the gas water heater was established using the heat transfer model, and the water temperature of hot water pipe was calculated. The importance of intelligent water pump system for water saving was analyzed. Finally, several practical methods to shorten the waiting time of hot water were given: (1) Raise the water outlet temperature of the gas water heater; (2) Shorten the length of the water pipe from the gas water heater to the bathroom faucet; (3) Install the hot water pipe; (4) Accelerate the pumping speed of the hot water circulation pump; (5) Open the circulating pump all the time, keep the gas water heater open and the water circulation in the hot water pipe when not using hot water.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210609003
Abstract:
Certain amounts of sulfide are present in gas field produced water, which brings negative effects on environment and human health. It is essential to remove sulfide first considering this. Since the electrochemical oxidation method has the advantages of high efficiency without secondary pollution, we introduced it into the sulfide removal process under the conditions of high salinity and hardness. Based on the cyclic voltammetry characteristics, sulfide removal efficiency, and economic analysis, Ti/RuO2-SnO2-IrO2 was selected as the anode. The removal of sulfide by two-dimensional electrochemical oxidation follows zero-order kinetics, which is name that, residual sulfide concentration in solution has a linear relationship with time in all reaction stages. Sulfide exists in the form of HS− in the solution. For the reason that H+ will be released when HS− is oxidized, it is necessary to select a suitable initial pH and control the reaction time. The sulfide removal ratio and corresponding energy consumption of simulated gas field produced water with 300 mg/L sulfide and 2.50% (mass fraction) NaCl were above 99.2% and 55.2 kWh/kg (calculated by S2− of per kilogram), respectively, with the electrode distance of 5 cm, current density of 200 A/m2, aeration rate of 1 L/min, the initial pH of 9−10, and running time of 35 min. Moreover, reversing cathode and anode was found to effectively solve the problem of scaling on cathode caused by high Ca2+ and Mg2+ concentration.
Certain amounts of sulfide are present in gas field produced water, which brings negative effects on environment and human health. It is essential to remove sulfide first considering this. Since the electrochemical oxidation method has the advantages of high efficiency without secondary pollution, we introduced it into the sulfide removal process under the conditions of high salinity and hardness. Based on the cyclic voltammetry characteristics, sulfide removal efficiency, and economic analysis, Ti/RuO2-SnO2-IrO2 was selected as the anode. The removal of sulfide by two-dimensional electrochemical oxidation follows zero-order kinetics, which is name that, residual sulfide concentration in solution has a linear relationship with time in all reaction stages. Sulfide exists in the form of HS− in the solution. For the reason that H+ will be released when HS− is oxidized, it is necessary to select a suitable initial pH and control the reaction time. The sulfide removal ratio and corresponding energy consumption of simulated gas field produced water with 300 mg/L sulfide and 2.50% (mass fraction) NaCl were above 99.2% and 55.2 kWh/kg (calculated by S2− of per kilogram), respectively, with the electrode distance of 5 cm, current density of 200 A/m2, aeration rate of 1 L/min, the initial pH of 9−10, and running time of 35 min. Moreover, reversing cathode and anode was found to effectively solve the problem of scaling on cathode caused by high Ca2+ and Mg2+ concentration.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210429004
Abstract:
Hemophilia is an X chromosome-linked bleeding disorder disease caused by a lack of coagulation factor. Hemophilia patients suffer from spontaneous bleedings that may occur in different organs and tissues. Hemophilic arthropathy (HA) is the primary etiology leading to disability in hemophilia patients which is caused by the recurrent bleeding in the joints. Over expression of pro-inflammatory cytokines has been putatively recognized as one of the mechanism. In the progression of HA, proinflammatory cytokines serve as signaling mediators, among which TNFα is one of the most important. As a receptor of TNFα, sTNFR (soluble tumor necrosis factor receptor) specifically binds to TNFα and antagonizes its proinflammtory effect. To investigate the therapeutic effect of local long-term TNFα expression on HA, hemophilia B mice were intraarticularly injected with rAAV5-TNFR:Fc. In the present study, plasmid pAAV-TNFR:Fc was constructed and used for the package of rAAV5-TNFR:Fc. Transgene expression mediated by infection in vitro and in vivo were confirmed by western blot. Then hemophilia B mice divided into prophylaxis group and treatment group were intraarticularly injected with rAAV5-TNFR:Fc, and 6 weeks after HA was induced, joints tissues were collected for RNA extract to measure the mRNA expression level of TNFα、IL-1β、IL-6 and IL-10. The pathology changes of joints were also graded by histology and the scorings of synovitis arthritis, macrophage infiltration and neovascularization were obtained. The results suggested that, 6 weeks after joints hemarthrosis induction, sTNFR expression in joints persisted. The pathological sequela in two groups were reduced in different degrees after rAAV5-TNFR:Fc local delivery and delivery as a prophylaxis showed better outcome than as a treatment. It was concluded that recombinant adeno-associated virus mediated soluble tumor necrosis factor receptor could be a therapeutic approach for HA treatment.
Hemophilia is an X chromosome-linked bleeding disorder disease caused by a lack of coagulation factor. Hemophilia patients suffer from spontaneous bleedings that may occur in different organs and tissues. Hemophilic arthropathy (HA) is the primary etiology leading to disability in hemophilia patients which is caused by the recurrent bleeding in the joints. Over expression of pro-inflammatory cytokines has been putatively recognized as one of the mechanism. In the progression of HA, proinflammatory cytokines serve as signaling mediators, among which TNFα is one of the most important. As a receptor of TNFα, sTNFR (soluble tumor necrosis factor receptor) specifically binds to TNFα and antagonizes its proinflammtory effect. To investigate the therapeutic effect of local long-term TNFα expression on HA, hemophilia B mice were intraarticularly injected with rAAV5-TNFR:Fc. In the present study, plasmid pAAV-TNFR:Fc was constructed and used for the package of rAAV5-TNFR:Fc. Transgene expression mediated by infection in vitro and in vivo were confirmed by western blot. Then hemophilia B mice divided into prophylaxis group and treatment group were intraarticularly injected with rAAV5-TNFR:Fc, and 6 weeks after HA was induced, joints tissues were collected for RNA extract to measure the mRNA expression level of TNFα、IL-1β、IL-6 and IL-10. The pathology changes of joints were also graded by histology and the scorings of synovitis arthritis, macrophage infiltration and neovascularization were obtained. The results suggested that, 6 weeks after joints hemarthrosis induction, sTNFR expression in joints persisted. The pathological sequela in two groups were reduced in different degrees after rAAV5-TNFR:Fc local delivery and delivery as a prophylaxis showed better outcome than as a treatment. It was concluded that recombinant adeno-associated virus mediated soluble tumor necrosis factor receptor could be a therapeutic approach for HA treatment.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210517005
Abstract:
In order to improve the detection accuracy of Android malware, a static detection method of Android malware based on LSTM-SVM (long short-term memory network-support vector machine) model is proposed. Firstly, by means of the APK (Android Package) file of decompilation Android software, three types of information, including permission, component and intent, are extracted from the AndroidManifest.xml file to form the XML features. Then, the API features are formed by analyzing the API (Application Programming Interface) called situation. By considering the timing and feature dimension of malware operation, LSTM anomaly detection model is constructed based on XML feature, meanwhile, SVM anomaly detection model is constructed based on API feature. The obtained models are parallelly undergone to obtain the final detection result via the probability difference fusion algorithm. Finally, the experimental results on CICAndMal2017 data set show that the detection accuracy of this proposed method can reach more than 98%.
In order to improve the detection accuracy of Android malware, a static detection method of Android malware based on LSTM-SVM (long short-term memory network-support vector machine) model is proposed. Firstly, by means of the APK (Android Package) file of decompilation Android software, three types of information, including permission, component and intent, are extracted from the AndroidManifest.xml file to form the XML features. Then, the API features are formed by analyzing the API (Application Programming Interface) called situation. By considering the timing and feature dimension of malware operation, LSTM anomaly detection model is constructed based on XML feature, meanwhile, SVM anomaly detection model is constructed based on API feature. The obtained models are parallelly undergone to obtain the final detection result via the probability difference fusion algorithm. Finally, the experimental results on CICAndMal2017 data set show that the detection accuracy of this proposed method can reach more than 98%.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210427004
Abstract:
Since convergence and diversity are taken into consideration cooperatively in the whole iteration process, the traditional multi-objective optimization algorithms will generate a large number of dominated solutions in the early stage of search, which will inevitably result in the waste of computing resources, even the non-convergence of solving procedure. Aiming at the above shortcoming, this paper proposes a stepwise multi-objective optimization algorithm based on statistical information feedback (SFI-SMOEA), which is composed of three steps, i.e., the optimal value exploration for each objective, rough search for Pareto front, local optimization stage with group division. According to the feature of each step, the corresponding task and strategy is assigned to promote convergence and diversity. In the 2nd and 3rd steps, the solutions are divided into different groups according to the objective function, the information of different regional solutions is statistically analyzed, and then, the statistical feedback information (SFI) of different regional solutions is utilized to guide the parent-selection process. Thus, the distribution and convergence of the solutions can be controlled more precisely. Finally, the proposed algorithm is tested on DTLZ and WFG series issues, and compared with other multi-objective optimization algorithms, from which it is illustrated that the proposed algorithm has the advantages of solving complex and difficult convergence problems.
Since convergence and diversity are taken into consideration cooperatively in the whole iteration process, the traditional multi-objective optimization algorithms will generate a large number of dominated solutions in the early stage of search, which will inevitably result in the waste of computing resources, even the non-convergence of solving procedure. Aiming at the above shortcoming, this paper proposes a stepwise multi-objective optimization algorithm based on statistical information feedback (SFI-SMOEA), which is composed of three steps, i.e., the optimal value exploration for each objective, rough search for Pareto front, local optimization stage with group division. According to the feature of each step, the corresponding task and strategy is assigned to promote convergence and diversity. In the 2nd and 3rd steps, the solutions are divided into different groups according to the objective function, the information of different regional solutions is statistically analyzed, and then, the statistical feedback information (SFI) of different regional solutions is utilized to guide the parent-selection process. Thus, the distribution and convergence of the solutions can be controlled more precisely. Finally, the proposed algorithm is tested on DTLZ and WFG series issues, and compared with other multi-objective optimization algorithms, from which it is illustrated that the proposed algorithm has the advantages of solving complex and difficult convergence problems.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210401002
Abstract:
The feature point matching can generate a corresponding relationship between the input images. It is a basic and important module in visual odometry and has a wide application in different computer vision fields. Random sample consensus (RANSAC) is a widely used image matching algorithm, but it has the disadvantages of low recall rate and long time-consuming. By considering the grid motion statistics method and the sequence selection strategy, this paper proposes an improved RANSAC algorithm. Firstly, the quality of the initial feature matching is sorted, based which the input image is divided into a certain number of grids and the matching statistics in the grid is performed according to the motion smoothness theory. Then the grids with higher scores are selected to estimate the local homography matrix, respectively. Moreover, the local homography matrices are aggregated to further eliminate the influence of noise and obtain the optimal model. In addition, the sequential selection strategy is used to obtain the homography matrix, which further improves the efficiency of the proposed algorithm. Finally, the simulation results show that the proposed image matching algorithm based on grid acceleration and sequential selection strategy has better performance.
The feature point matching can generate a corresponding relationship between the input images. It is a basic and important module in visual odometry and has a wide application in different computer vision fields. Random sample consensus (RANSAC) is a widely used image matching algorithm, but it has the disadvantages of low recall rate and long time-consuming. By considering the grid motion statistics method and the sequence selection strategy, this paper proposes an improved RANSAC algorithm. Firstly, the quality of the initial feature matching is sorted, based which the input image is divided into a certain number of grids and the matching statistics in the grid is performed according to the motion smoothness theory. Then the grids with higher scores are selected to estimate the local homography matrix, respectively. Moreover, the local homography matrices are aggregated to further eliminate the influence of noise and obtain the optimal model. In addition, the sequential selection strategy is used to obtain the homography matrix, which further improves the efficiency of the proposed algorithm. Finally, the simulation results show that the proposed image matching algorithm based on grid acceleration and sequential selection strategy has better performance.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220531001
Abstract:
A series of α-diimine nickel (Ⅱ) catalysts [(2,6-C6H3R2)-N=C(R1)-C(R1)=N-(2,6-C6H3R2)]NiBr2 ( C1 : R1 = H, R2 = CH3, C2 : R1 = H, R2 = iPr, C3 : R1 = CH3, R2 = CH3, C4 : R1 = CH3, R2 = iPr, C5 : R1 = acenaphthyl, R2 = iPr), in the presence of ethylaluminum sesquichloride (EASC) as cocatalyst, were applied in the copolymerization of ethylene and vinyl trimethoxysilane (VTMoS). The effects of polymerization parameters, in which VTMoS concentration, catalyst dosage, molar ration of Al/Ni (nAl/nNi), ethylene pressure, reaction time and solvents were varied on the copolymerization were investigated with C4 as a model catalyst. The optimized catalytic reaction conditions, in which ([VTMoS] = 0.5 mol/L, 5 μmol catalyst, nAl/nNi = 400, 1 MPa pressure, 25 ℃, for 0.5 h, toluene as solvent were obtained. The influence of the catalyst structures on the copolymerization were explored under the optimized conditions. High temperature 1H, 13C-NMR, Fourier infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) were used to characterize the microstructure of the copolymers. Inductively coupled plasma (ICP) was applied to determine the silicon content of the copolymers. Equilibrium swelling method was used to study the crosslinking degree of copolymers. The results show that the catalytic activity of C4 reaches to 106 g/(molNi·h) in toluene and ethylene pressure of 2~3 MPa. With the increase of steric hindrance of α-diimine skeleton structure, the crosslinking degree of copolymer increases and the crosslinking network become more uniform. The contents of silicon in copolymer are in the range of 0.8% ~ 2.1%.
A series of α-diimine nickel (Ⅱ) catalysts [(2,6-C6H3R2)-N=C(R1)-C(R1)=N-(2,6-C6H3R2)]NiBr2 ( C1 : R1 = H, R2 = CH3, C2 : R1 = H, R2 = iPr, C3 : R1 = CH3, R2 = CH3, C4 : R1 = CH3, R2 = iPr, C5 : R1 = acenaphthyl, R2 = iPr), in the presence of ethylaluminum sesquichloride (EASC) as cocatalyst, were applied in the copolymerization of ethylene and vinyl trimethoxysilane (VTMoS). The effects of polymerization parameters, in which VTMoS concentration, catalyst dosage, molar ration of Al/Ni (nAl/nNi), ethylene pressure, reaction time and solvents were varied on the copolymerization were investigated with C4 as a model catalyst. The optimized catalytic reaction conditions, in which ([VTMoS] = 0.5 mol/L, 5 μmol catalyst, nAl/nNi = 400, 1 MPa pressure, 25 ℃, for 0.5 h, toluene as solvent were obtained. The influence of the catalyst structures on the copolymerization were explored under the optimized conditions. High temperature 1H, 13C-NMR, Fourier infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) were used to characterize the microstructure of the copolymers. Inductively coupled plasma (ICP) was applied to determine the silicon content of the copolymers. Equilibrium swelling method was used to study the crosslinking degree of copolymers. The results show that the catalytic activity of C4 reaches to 106 g/(molNi·h) in toluene and ethylene pressure of 2~3 MPa. With the increase of steric hindrance of α-diimine skeleton structure, the crosslinking degree of copolymer increases and the crosslinking network become more uniform. The contents of silicon in copolymer are in the range of 0.8% ~ 2.1%.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220524001
Abstract:
In this study, five carbon sources, including methanol, sodium acetate, glycol, glycerol, and glucose were used to investigate the denitrification performance of different electron acceptors in sequencing batch reactors (SBRs). The results showed that the removal ratios and specific denitrification rates of different carbon sources all showed upward trends with the increased C/N ratios. The optimal carbon to nitrogen ratio (C/N) of five carbon resources for NO3−−N and NO2−−N were 5, 5, 7, 7, and 8, and 3, 3, 3.5, 4, and 4, respectively. Under the optimal C/N ratio, the specific denitrification rates decreased with the sequence of sodium acetate, methanol, glycol, glycerol and glucose for NO3−−N electron acceptor, and methanol, sodium acetate, glycol, glucose and glycerol for NO2−−N electron acceptor. After calculation, the cost for treating the same concentrations of NO3−−N increased with the sequence of methanol, glucose, sodium acetate, glycol, and glycerol; while the cost for treating the same concentrations of NO2−−N increased with the sequence of methanol, glucose, glycol, sodium acetate, and glycerol. Compared to NO3−−N, NO2−−N saved 40%-50% carbon sources cost at the same concentration. Glucose was recommended as the external carbon source with low ammonia nitrogen loading rates, and sodium acetate was recommended with high ammonia nitrogen loading rates, while glycol could be served as the substitute for sodium acetate.
In this study, five carbon sources, including methanol, sodium acetate, glycol, glycerol, and glucose were used to investigate the denitrification performance of different electron acceptors in sequencing batch reactors (SBRs). The results showed that the removal ratios and specific denitrification rates of different carbon sources all showed upward trends with the increased C/N ratios. The optimal carbon to nitrogen ratio (C/N) of five carbon resources for NO3−−N and NO2−−N were 5, 5, 7, 7, and 8, and 3, 3, 3.5, 4, and 4, respectively. Under the optimal C/N ratio, the specific denitrification rates decreased with the sequence of sodium acetate, methanol, glycol, glycerol and glucose for NO3−−N electron acceptor, and methanol, sodium acetate, glycol, glucose and glycerol for NO2−−N electron acceptor. After calculation, the cost for treating the same concentrations of NO3−−N increased with the sequence of methanol, glucose, sodium acetate, glycol, and glycerol; while the cost for treating the same concentrations of NO2−−N increased with the sequence of methanol, glucose, glycol, sodium acetate, and glycerol. Compared to NO3−−N, NO2−−N saved 40%-50% carbon sources cost at the same concentration. Glucose was recommended as the external carbon source with low ammonia nitrogen loading rates, and sodium acetate was recommended with high ammonia nitrogen loading rates, while glycol could be served as the substitute for sodium acetate.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220407001
Abstract:
Aspergillus represents a potential host for expression of recombinant proteins after bacteria, yeast, plant and animal cells. Due to the late start of Aspergillus expression system research, the technology is still immature. And the number of alternative expression vectors and available promoters are limited, which limits the application of Aspergillus expression system. On our previous work, we found a peroxiredoxin from Aspergillus nidulans (AnPrxA) could be expressed at a high level and its expression level responded to H2O2 in the medium. Based on the low price and self-decomposition properties of H2O2, we explored a novel and efficient A. nidulans expression system using A. nidulans as the expression host, AnPrxA promoter as the expression promoter, and H2O2 as the expression inducer. The specific research is as follows. Four promoters of different lengths of the 5’untranslated regions (5’-UTR) of A. nidulans (PPRX500, PPRX1000, PPRX1500 and PPRX2033) were amplified and cloned in vectors, and the ability of the four promoters to drive GFP expression was evaluated. The results depicted that all four expression systems were sensitive to H2O2, and PPRX500 was sufficient for GFP expression, but the induction ratio of PPRX2033 is higher. The optimal induction time of H2O2 was determined to be 8 hours. The optimal induction concentration range was 0.5-2.0 mmol/L. Comparison of the conditions with and without the inducer revealed that Pprx2033 mediated a 30-fold induction ratio of GFP at the transcriptional level and a 3-fold induction ratio at the protein expression level. In the induction phase of this expression system, the residual concentration could no longer be detected after 2 hours of adding 2 mmol/L H2O2 in the medium, and could no longer be detected after 1 hour of adding 0.5 mmol/L H2O2.
Aspergillus represents a potential host for expression of recombinant proteins after bacteria, yeast, plant and animal cells. Due to the late start of Aspergillus expression system research, the technology is still immature. And the number of alternative expression vectors and available promoters are limited, which limits the application of Aspergillus expression system. On our previous work, we found a peroxiredoxin from Aspergillus nidulans (AnPrxA) could be expressed at a high level and its expression level responded to H2O2 in the medium. Based on the low price and self-decomposition properties of H2O2, we explored a novel and efficient A. nidulans expression system using A. nidulans as the expression host, AnPrxA promoter as the expression promoter, and H2O2 as the expression inducer. The specific research is as follows. Four promoters of different lengths of the 5’untranslated regions (5’-UTR) of A. nidulans (PPRX500, PPRX1000, PPRX1500 and PPRX2033) were amplified and cloned in vectors, and the ability of the four promoters to drive GFP expression was evaluated. The results depicted that all four expression systems were sensitive to H2O2, and PPRX500 was sufficient for GFP expression, but the induction ratio of PPRX2033 is higher. The optimal induction time of H2O2 was determined to be 8 hours. The optimal induction concentration range was 0.5-2.0 mmol/L. Comparison of the conditions with and without the inducer revealed that Pprx2033 mediated a 30-fold induction ratio of GFP at the transcriptional level and a 3-fold induction ratio at the protein expression level. In the induction phase of this expression system, the residual concentration could no longer be detected after 2 hours of adding 2 mmol/L H2O2 in the medium, and could no longer be detected after 1 hour of adding 0.5 mmol/L H2O2.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220508002
Abstract:
Three petroleum-based pitches with different softening points were used to treat natural graphite by liquid phase coating method. The particle size, structure, morphology and electrochemical properties of the samples were analyzed. The results show that the high softening point pitch can form a thicker coating layer on the natural graphite surface, and the graphite surface becomes more rounded and the defects are reduced. The pitch coated natural graphite with softening point 250 ℃ shows the best electrochemical performance, the first Coulomb efficiency increases from 84.7% to 88.04%, and the capacity retention rate increases from 63.14% to 81.19% after 250 cycles. The low softening point pitch has a large irreversible capacity loss due to the large number of micropores formed by the light component release coating during carbonization, while the high softening point pitch is beneficial to the formation of complete coating, which can improve the cycling and rate performance of natural graphite best.
Three petroleum-based pitches with different softening points were used to treat natural graphite by liquid phase coating method. The particle size, structure, morphology and electrochemical properties of the samples were analyzed. The results show that the high softening point pitch can form a thicker coating layer on the natural graphite surface, and the graphite surface becomes more rounded and the defects are reduced. The pitch coated natural graphite with softening point 250 ℃ shows the best electrochemical performance, the first Coulomb efficiency increases from 84.7% to 88.04%, and the capacity retention rate increases from 63.14% to 81.19% after 250 cycles. The low softening point pitch has a large irreversible capacity loss due to the large number of micropores formed by the light component release coating during carbonization, while the high softening point pitch is beneficial to the formation of complete coating, which can improve the cycling and rate performance of natural graphite best.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220616001
Abstract:
Schiff base is produced by reversible condensation of primary amine and active carbonyl, such as aldehyde and ketone, which is one of the earliest reactions used in self-healing materials. Inspired by the mechanism of the Schiff base reaction, introduction of self-healing capabilities has been regarded as a promising approach to extend the service life of conductive hydrogel. It is desired to develop self-healing conductive hydrogels with enhanced mechanical properties and satisfactory healing efficiency at the same time by integrating dynamic Schiff base and hydrogen bonding. Poly(acrylamide-N-2-amino ethylacrylamide hydrochloride-N-acryloyloxy succinimide)-BORAX/LiCl self-healing conductive hydrogel was synthesized from acrylamide (AM), N-2-aminoethyl acrylamide hydrochloride (AEAM) and N-acryloxysuccinimide (ASI) by free-radical polymerization in a mixed solution of the BORAX and lithium chloride, The 2-hydroxy-1-(4-(2-hydroxyethoxy)phenyl)-2-methyl-1-propanon (I2959) was used as photoinitiator, and was illuminated by UV light at a wavelength of 284 nm. The conductive hydrogel has two types of dynamic healing mechanisms base on dynamic schiff base and hydrogen bonding, endowing the hydrogel with excellent mechanical stability and good strength. The structure of N-2-aminoethyl acrylamide hydrochloride and N-acryloxysuccinimide was confirmed by 1H NMR, and the formation of schiff base in hydrogels was confirmed by Fourier infrared spectroscopy. The high elongation at break and self-healing efficiency of hydrogel were tested by Universal Tension Machine (the maximum elongation up to 1280.2% and strength of 300.3 kPa, strain healing efficiency of 56.1% within 48 h). The electrochemical workstation confirmed that the conductive hydrogel had an excellent electrical conductivity of 3.11 S/m.
Schiff base is produced by reversible condensation of primary amine and active carbonyl, such as aldehyde and ketone, which is one of the earliest reactions used in self-healing materials. Inspired by the mechanism of the Schiff base reaction, introduction of self-healing capabilities has been regarded as a promising approach to extend the service life of conductive hydrogel. It is desired to develop self-healing conductive hydrogels with enhanced mechanical properties and satisfactory healing efficiency at the same time by integrating dynamic Schiff base and hydrogen bonding. Poly(acrylamide-N-2-amino ethylacrylamide hydrochloride-N-acryloyloxy succinimide)-BORAX/LiCl self-healing conductive hydrogel was synthesized from acrylamide (AM), N-2-aminoethyl acrylamide hydrochloride (AEAM) and N-acryloxysuccinimide (ASI) by free-radical polymerization in a mixed solution of the BORAX and lithium chloride, The 2-hydroxy-1-(4-(2-hydroxyethoxy)phenyl)-2-methyl-1-propanon (I2959) was used as photoinitiator, and was illuminated by UV light at a wavelength of 284 nm. The conductive hydrogel has two types of dynamic healing mechanisms base on dynamic schiff base and hydrogen bonding, endowing the hydrogel with excellent mechanical stability and good strength. The structure of N-2-aminoethyl acrylamide hydrochloride and N-acryloxysuccinimide was confirmed by 1H NMR, and the formation of schiff base in hydrogels was confirmed by Fourier infrared spectroscopy. The high elongation at break and self-healing efficiency of hydrogel were tested by Universal Tension Machine (the maximum elongation up to 1280.2% and strength of 300.3 kPa, strain healing efficiency of 56.1% within 48 h). The electrochemical workstation confirmed that the conductive hydrogel had an excellent electrical conductivity of 3.11 S/m.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220704002
Abstract:
The stainless steel mesh (SSM) is laser etched with a 1064 nm near-infrared laser, and the stainless steel screen with the best surface roughness is obtained by adjusting the laser parameters. Compared with the traditional chemical etching consumption of chemical reagents and environmental pollution problems, the laser etching process is simple, with the advantages of process controllability, fast and efficient, safety and environmental protection. Using the laser-etched SSM as the matrix, chitosan (CS) as the hydrophilic component, acrylamide (AM) as a monomer, and then different contents of silica (SiO2) dispersed into the system, the photoinitiator was added, N,N'-methylenebisacrylamide (MBA) as the crosslinker, the underwater superoleophobic oil-water separation membrane (PAM/CS/SiO2@SSM) was prepared by ultraviolet light initiated radical polymerization. Laser scanning confocal microscopy (CLSM), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), and other test methods were used for the characterization of PAM/CS/SiO2@SSM separation mesh, and the effects of different CS components and different SiO2 additions on the surface roughness of composite mesh were studied. Gravity-driven method was used to separate the oil-water mixture, and the oil-water separation efficiency of the composite mesh was explored. The experimental results show that the laser etch mesh has the advantages of simple process, fast etching and controllable parameters; the underwater oil contact angle of the prepared PAM/CS/SiO2@SSM separation mesh can reach 155°, indicating the excellent hydrophilicity and oleophobicity; The separation efficiency of the composite mesh for three different oil-water mixtures including liquid paraffin, n-hexane, and petroleum ether can be reached 99.1%, 98.8% and 98.4%, respectively, indicating a high oil-water separation efficiency.
The stainless steel mesh (SSM) is laser etched with a 1064 nm near-infrared laser, and the stainless steel screen with the best surface roughness is obtained by adjusting the laser parameters. Compared with the traditional chemical etching consumption of chemical reagents and environmental pollution problems, the laser etching process is simple, with the advantages of process controllability, fast and efficient, safety and environmental protection. Using the laser-etched SSM as the matrix, chitosan (CS) as the hydrophilic component, acrylamide (AM) as a monomer, and then different contents of silica (SiO2) dispersed into the system, the photoinitiator was added, N,N'-methylenebisacrylamide (MBA) as the crosslinker, the underwater superoleophobic oil-water separation membrane (PAM/CS/SiO2@SSM) was prepared by ultraviolet light initiated radical polymerization. Laser scanning confocal microscopy (CLSM), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), and other test methods were used for the characterization of PAM/CS/SiO2@SSM separation mesh, and the effects of different CS components and different SiO2 additions on the surface roughness of composite mesh were studied. Gravity-driven method was used to separate the oil-water mixture, and the oil-water separation efficiency of the composite mesh was explored. The experimental results show that the laser etch mesh has the advantages of simple process, fast etching and controllable parameters; the underwater oil contact angle of the prepared PAM/CS/SiO2@SSM separation mesh can reach 155°, indicating the excellent hydrophilicity and oleophobicity; The separation efficiency of the composite mesh for three different oil-water mixtures including liquid paraffin, n-hexane, and petroleum ether can be reached 99.1%, 98.8% and 98.4%, respectively, indicating a high oil-water separation efficiency.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220601001
Abstract:
Because of the advantage of saving water resources, the indirect air-cooling tower has been widely used in thermal power generating units in north China. Indirect air-cooling tower is an important cold side equipment of air-cooling generator unit. Its heat transfer performance directly affects the operation of the whole generator unit. Therefore, it is important to study the factors affecting the heat transfer performance of indirect air-cooling tower, especially the ambient crosswind, which has the greatest influence on its performance. In this research, by using user-defined function (UDF) to calculate the real-time coupling of ambient wind temperature and cooling water temperature, the dual-indirect air-cooling tower system of a Ningxia power plant was simulated in Fluent software. Then the freezing risk of the system was researched under strong wind conditions in winter. The results show that the coupling calculation model has superior accuracy and the error is less than 5% compared with the measured data. Ambient crosswind will increase the unevenness of outlet water temperature of each sector, which is mainly manifested as the sharp decrease of outlet water temperature in windward sectors. Reducing the shutters’ opening of the windward sectors can increase the cooling water outlet temperature, but at the same time, the water temperature of the outlet in the leeward sectors will drop sharply. When the shutters’ opening of windward and leeward sectors are reduced in the meantime, the outlet water temperature of each sector rises and distributes more evenly, and the freezing risk of the whole tower is generally reduced.
Because of the advantage of saving water resources, the indirect air-cooling tower has been widely used in thermal power generating units in north China. Indirect air-cooling tower is an important cold side equipment of air-cooling generator unit. Its heat transfer performance directly affects the operation of the whole generator unit. Therefore, it is important to study the factors affecting the heat transfer performance of indirect air-cooling tower, especially the ambient crosswind, which has the greatest influence on its performance. In this research, by using user-defined function (UDF) to calculate the real-time coupling of ambient wind temperature and cooling water temperature, the dual-indirect air-cooling tower system of a Ningxia power plant was simulated in Fluent software. Then the freezing risk of the system was researched under strong wind conditions in winter. The results show that the coupling calculation model has superior accuracy and the error is less than 5% compared with the measured data. Ambient crosswind will increase the unevenness of outlet water temperature of each sector, which is mainly manifested as the sharp decrease of outlet water temperature in windward sectors. Reducing the shutters’ opening of the windward sectors can increase the cooling water outlet temperature, but at the same time, the water temperature of the outlet in the leeward sectors will drop sharply. When the shutters’ opening of windward and leeward sectors are reduced in the meantime, the outlet water temperature of each sector rises and distributes more evenly, and the freezing risk of the whole tower is generally reduced.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211230004
Abstract:
As a general technique for separation and purification, crystallization was used in a wide range. In the crystallization process, the change of growth conditions, such as temperature and supersaturation, may lead to changes in the size and morphology of the crystallized product, which not only affects the crystal quality, but also affects the subsequent process operations. In this work, cellular automata are used to simulate the batch crystallization process of the solution at meso-scale. It is built in a two-dimensional space, each cell is represented by a square, and neighbors adopt Moore's Law, that is, eight cells around the central cell are its neighbors. The evolution rule is based on the crystal classical diffusion theory, and the state of each cell is described by two parameters: concentration and crystal state (crystal or solution). The change of concentration is determined by Fick's law. For simplicity, the concentration of cell is the average of its neighbors. Whether the solution is converted into crystals is realized by the Monte Carlo method. By simulation, the size distribution and morphology information of the crystal at mesoscale are obtained. At the same time, the crystallization process is visualized, which can give an intuitive understanding of the crystal morphology distribution and each crystal morphology during the crystallization. Compared with the morphology distribution obtained by high-resolution finite volume method, the results of the two methods have similar accuracy, which proves that this method is feasible. This work provides a theoretical reference for identification, modeling and crystallization analysis, which can be used in the industrial practice of specific crystals, and provides a new research idea for controlling crystal morphology in the future.
As a general technique for separation and purification, crystallization was used in a wide range. In the crystallization process, the change of growth conditions, such as temperature and supersaturation, may lead to changes in the size and morphology of the crystallized product, which not only affects the crystal quality, but also affects the subsequent process operations. In this work, cellular automata are used to simulate the batch crystallization process of the solution at meso-scale. It is built in a two-dimensional space, each cell is represented by a square, and neighbors adopt Moore's Law, that is, eight cells around the central cell are its neighbors. The evolution rule is based on the crystal classical diffusion theory, and the state of each cell is described by two parameters: concentration and crystal state (crystal or solution). The change of concentration is determined by Fick's law. For simplicity, the concentration of cell is the average of its neighbors. Whether the solution is converted into crystals is realized by the Monte Carlo method. By simulation, the size distribution and morphology information of the crystal at mesoscale are obtained. At the same time, the crystallization process is visualized, which can give an intuitive understanding of the crystal morphology distribution and each crystal morphology during the crystallization. Compared with the morphology distribution obtained by high-resolution finite volume method, the results of the two methods have similar accuracy, which proves that this method is feasible. This work provides a theoretical reference for identification, modeling and crystallization analysis, which can be used in the industrial practice of specific crystals, and provides a new research idea for controlling crystal morphology in the future.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220607001
Abstract:
To develop a high-efficiency recombinant expression and purification strategy for lysyl endopeptidase (Lys-C), and present a new choice for breaking the bottleneck of Lys-C generated by natural strains, which has a poor yield and high cost. The artificial pro-peptide (MGSK) and the 6×His tag were fused to the N-terminus and C-terminus of the Lys-C mature peptide gene sequence from Lysobacter enzymogenes, respectively, and then the codon-optimized sequence was inserted into plasmid pET-28a. The Lys-C was efficiently expressed in the recombinant strain JM109DE3_PT7- LysC and controlled by the IPTG-inducible promoter PT7. The inclusion bodies were collected by high-density fermentation in a bioreactor, after which they were solubilized to obtain the Lys-C denaturing solution and DTT was removed by Sephadex G25 chromatography. Then the pre-pro peptide (pre-N-pro) was added to Lys-C refolding solution to assist in the folding of the mature protein. Further, the large volume Lys-C refolding solution was concentrated using tangential flow filtration, followed by a multi-step purification process of Ni NTA-Sepharose affinity chromatography, ultrafiltration, and Sephacryl S-100 chromatography to obtain high-purity recombinant Lys-C. Finally, the activity of recombinant Lys-C was measured by chromogenic reaction and digestion of insulin aspart precursors. The recombinant Lys-C fermentation yield was achieved at 2.4 g/L, and the final yield reached 48 mg/L after renaturation and purification. The Lys-C enzyme activity was improved by addition of 80 mg/L of pre-N-pro and reached up to 13.8 AU/L, which increased by 4.8-fold. The specific enzyme activity of Lys-C achieved 10.2 AU/mg after multi-step purification. And the digestion efficiency of Lys-C to insulin precursors reached 93.5%. In this study, recombinant Lys-C with high yield and good activity was obtained and allowed for accurate digestion of the insulin precursors, and this provides a reference for Lys-C recombinant expression and potential industrial application.
To develop a high-efficiency recombinant expression and purification strategy for lysyl endopeptidase (Lys-C), and present a new choice for breaking the bottleneck of Lys-C generated by natural strains, which has a poor yield and high cost. The artificial pro-peptide (MGSK) and the 6×His tag were fused to the N-terminus and C-terminus of the Lys-C mature peptide gene sequence from Lysobacter enzymogenes, respectively, and then the codon-optimized sequence was inserted into plasmid pET-28a. The Lys-C was efficiently expressed in the recombinant strain JM109DE3_PT7- LysC and controlled by the IPTG-inducible promoter PT7. The inclusion bodies were collected by high-density fermentation in a bioreactor, after which they were solubilized to obtain the Lys-C denaturing solution and DTT was removed by Sephadex G25 chromatography. Then the pre-pro peptide (pre-N-pro) was added to Lys-C refolding solution to assist in the folding of the mature protein. Further, the large volume Lys-C refolding solution was concentrated using tangential flow filtration, followed by a multi-step purification process of Ni NTA-Sepharose affinity chromatography, ultrafiltration, and Sephacryl S-100 chromatography to obtain high-purity recombinant Lys-C. Finally, the activity of recombinant Lys-C was measured by chromogenic reaction and digestion of insulin aspart precursors. The recombinant Lys-C fermentation yield was achieved at 2.4 g/L, and the final yield reached 48 mg/L after renaturation and purification. The Lys-C enzyme activity was improved by addition of 80 mg/L of pre-N-pro and reached up to 13.8 AU/L, which increased by 4.8-fold. The specific enzyme activity of Lys-C achieved 10.2 AU/mg after multi-step purification. And the digestion efficiency of Lys-C to insulin precursors reached 93.5%. In this study, recombinant Lys-C with high yield and good activity was obtained and allowed for accurate digestion of the insulin precursors, and this provides a reference for Lys-C recombinant expression and potential industrial application.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220517001
Abstract:
A new type of thrust foil bearing is proposed. The advantage of this bearing is that it can change the dynamic characteristics such as the axial stiffness of the foil bearing by the combination of bump foil and flat foil. Taking the new type of thrust foil bearing as the reserch object, the deformation of the top foil and bump foil of the thrust foil bearing is analyzed by elastic mechanics theory, and the non-isothermal Reynolds equation is obtained by combining the viscosity-temperature equation, the three-dimensional energy equation of the gas film and the isothermal Reynolds equation. The non-isothermal Reynolds equation is processed by the small perturbation method to obtain the steady control equation and differential control equation of the gas film pressure. Using the finite difference method and the point-by-point relaxation iteration method to solve it numerically, the air film pressure distribution, air film thickness distribution, foil deformation distribution, air film temperature distribution, axial stiffness, and axial damping of the thrust foil bearing are obtained. The influences of some structural parameters and operating parameters of the thrust foil bearing on the relevant dynamic characteristic parameters are studied, and the research results can provide a theoretical reference for the new type-thrust foil bearing in the design stage.
A new type of thrust foil bearing is proposed. The advantage of this bearing is that it can change the dynamic characteristics such as the axial stiffness of the foil bearing by the combination of bump foil and flat foil. Taking the new type of thrust foil bearing as the reserch object, the deformation of the top foil and bump foil of the thrust foil bearing is analyzed by elastic mechanics theory, and the non-isothermal Reynolds equation is obtained by combining the viscosity-temperature equation, the three-dimensional energy equation of the gas film and the isothermal Reynolds equation. The non-isothermal Reynolds equation is processed by the small perturbation method to obtain the steady control equation and differential control equation of the gas film pressure. Using the finite difference method and the point-by-point relaxation iteration method to solve it numerically, the air film pressure distribution, air film thickness distribution, foil deformation distribution, air film temperature distribution, axial stiffness, and axial damping of the thrust foil bearing are obtained. The influences of some structural parameters and operating parameters of the thrust foil bearing on the relevant dynamic characteristic parameters are studied, and the research results can provide a theoretical reference for the new type-thrust foil bearing in the design stage.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220606001
Abstract:
Aiming at the problem of catalyst and adsorbent failure caused by the leakage of heat taking medium in the tower top low-temperature heat recovery process of aromatics combined unit, a heat taking scheme of phase change heat exchange of intermediate working medium is proposed to ensure the intrinsic safety of the process. Aspen HYSYS and EDR are used to establish the steady-state numerical model for the whole process simulation of the aromatic low-temperature heat recovery phase change heat exchange system under the field working condition of 100kW. The reliability of the steady-state numerical model is verified by the combination of experiment and numerical simulation. The influence of process and structural parameters on the thermal characteristics of the system is studied. The results show that with the increase of the inlet temperature of demineralized water, the heat load, circulating work quality and liquid level height of the system gradually decrease, The working temperature increases gradually; With the increase of overhead gas flow, the system heat load and working temperature first increase and then decrease, the circulating work quality and liquid level height first decrease and then increase, and the lower the inlet temperature of demineralized water, the greater the critical flow value; With the increase of demineralized water flow, the system heat load, circulating work quality and liquid level height gradually increase, and the working temperature gradually decreases. In case of large temperature difference and large flow of demineralized water, in order to avoid the restriction of system heat transfer performance by driving force, it is necessary to ensure that the installation height is not less than the liquid level difference; The system heat load increases gradually with the increase of the rising pipe diameter, and basically unchanged after the pipe diameter is greater than Φ159mm; In the case of "cut-off", the system heat load decreases gradually with the increase of installation height. In fact, the installation height does not need to be increased too much; The heat exchange of the system with horizontal (reboiler) and horizontal (condenser) combination is about 3% greater than that of horizontal and vertical combination.
Aiming at the problem of catalyst and adsorbent failure caused by the leakage of heat taking medium in the tower top low-temperature heat recovery process of aromatics combined unit, a heat taking scheme of phase change heat exchange of intermediate working medium is proposed to ensure the intrinsic safety of the process. Aspen HYSYS and EDR are used to establish the steady-state numerical model for the whole process simulation of the aromatic low-temperature heat recovery phase change heat exchange system under the field working condition of 100kW. The reliability of the steady-state numerical model is verified by the combination of experiment and numerical simulation. The influence of process and structural parameters on the thermal characteristics of the system is studied. The results show that with the increase of the inlet temperature of demineralized water, the heat load, circulating work quality and liquid level height of the system gradually decrease, The working temperature increases gradually; With the increase of overhead gas flow, the system heat load and working temperature first increase and then decrease, the circulating work quality and liquid level height first decrease and then increase, and the lower the inlet temperature of demineralized water, the greater the critical flow value; With the increase of demineralized water flow, the system heat load, circulating work quality and liquid level height gradually increase, and the working temperature gradually decreases. In case of large temperature difference and large flow of demineralized water, in order to avoid the restriction of system heat transfer performance by driving force, it is necessary to ensure that the installation height is not less than the liquid level difference; The system heat load increases gradually with the increase of the rising pipe diameter, and basically unchanged after the pipe diameter is greater than Φ159mm; In the case of "cut-off", the system heat load decreases gradually with the increase of installation height. In fact, the installation height does not need to be increased too much; The heat exchange of the system with horizontal (reboiler) and horizontal (condenser) combination is about 3% greater than that of horizontal and vertical combination.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220620001
Abstract:
For coal gasification technology, a general flame control technology is needed to ensure the stable combustion in the gasification furnace. And in the combustion process, the flame will produce huge amounts of charged particles, so that the flame has electrical properties, and can be affected by the electric field. In this study, a laminar flame experimental system under direct current electric field was designed and developed. A direct current electric field was applied to a methane-oxygen non-premixed laminar flame. Then the electrode spacing of DC electric field and the combustion equivalent ratio of the laminar flame would be changed. The high-speed camera was use to record the rule of flame pulsation affected by the DC electric field. The effect of direct current electric field on flame stability was explored, and the feasibility of using electric field to confine the laminar flame was verified. The results show that, when high voltage direct current is applied to the flame, the laminar diffusion flame pulsation will be influenced by the effect of ion wind. The amplitude of flame pulsation will gradually diminish until the flame is in a steady state, and the electric field intensity which can stabilize the flame is associated initial pulse amplitude. The higher the initial amplitude is, the greater the voltage will be demanded. At the same time, the change of electrode spacing will also affect the electric field intensity required for flame stability. When the electrode spacing changes greatly, for the flame with the same equivalent ratio, the larger the distance is, the higher the stable voltage will be required.
For coal gasification technology, a general flame control technology is needed to ensure the stable combustion in the gasification furnace. And in the combustion process, the flame will produce huge amounts of charged particles, so that the flame has electrical properties, and can be affected by the electric field. In this study, a laminar flame experimental system under direct current electric field was designed and developed. A direct current electric field was applied to a methane-oxygen non-premixed laminar flame. Then the electrode spacing of DC electric field and the combustion equivalent ratio of the laminar flame would be changed. The high-speed camera was use to record the rule of flame pulsation affected by the DC electric field. The effect of direct current electric field on flame stability was explored, and the feasibility of using electric field to confine the laminar flame was verified. The results show that, when high voltage direct current is applied to the flame, the laminar diffusion flame pulsation will be influenced by the effect of ion wind. The amplitude of flame pulsation will gradually diminish until the flame is in a steady state, and the electric field intensity which can stabilize the flame is associated initial pulse amplitude. The higher the initial amplitude is, the greater the voltage will be demanded. At the same time, the change of electrode spacing will also affect the electric field intensity required for flame stability. When the electrode spacing changes greatly, for the flame with the same equivalent ratio, the larger the distance is, the higher the stable voltage will be required.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220418002
Abstract:
In order to improve the success rate of new drugs, major pharmaceutical companies are committed to developing new strategies and technologies for Computer Aided Drug Design (CADD), Scaffold hopping is one of the most commonly used strategies for discovering lead compounds. However, most of the lead compounds generated by existing scaffold hopping strategies are derivatives or analogs of reported lead compounds that lack novelty. To improve structure novelty, we proposed a geometry-based scaffold hopping strategy, which takes the idea for the first time to keep the relative distances and angles unchanged between pharmacophores and skeleton. Thus, a linker library for scaffold hopping based on geometric features was constructed. The original compounds of the linker library were derived from Cambridge Structural Database (CSD), and which were broken into fragments by Retrosynthetic Combinatorial Analysis Procedure (RECAP). After a series of screening and processing, we designed a linker library containing 1587020 fragments with 2~4 connection points. There was a case about Skepinone-L, a selective inhibitor of MAPK11, which verified the usefulness of the linker library. Based on the distances and angles between the center to connected points of the skeleton, the linker library could recommend fragments with novel scaffolds to maintain the relative position of pharmacophores, which is beneficial to guide scaffold hopping and accelerate the process of drug discovery.
In order to improve the success rate of new drugs, major pharmaceutical companies are committed to developing new strategies and technologies for Computer Aided Drug Design (CADD), Scaffold hopping is one of the most commonly used strategies for discovering lead compounds. However, most of the lead compounds generated by existing scaffold hopping strategies are derivatives or analogs of reported lead compounds that lack novelty. To improve structure novelty, we proposed a geometry-based scaffold hopping strategy, which takes the idea for the first time to keep the relative distances and angles unchanged between pharmacophores and skeleton. Thus, a linker library for scaffold hopping based on geometric features was constructed. The original compounds of the linker library were derived from Cambridge Structural Database (CSD), and which were broken into fragments by Retrosynthetic Combinatorial Analysis Procedure (RECAP). After a series of screening and processing, we designed a linker library containing 1587020 fragments with 2~4 connection points. There was a case about Skepinone-L, a selective inhibitor of MAPK11, which verified the usefulness of the linker library. Based on the distances and angles between the center to connected points of the skeleton, the linker library could recommend fragments with novel scaffolds to maintain the relative position of pharmacophores, which is beneficial to guide scaffold hopping and accelerate the process of drug discovery.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220331001
Abstract:
The application of thermal barrier coatings (TBCs) is greatly limited by calciummagnesium-alumina-silicate (CMAS) attack. The surface microstructure of TBCs is demonstrated its fundamental effect on the wetting behavior of molten CMAS, thus further influencing CMAS resistance of TBCs. In this study, hierarchical microstructure was innovatively fabricated by suspension plasma spray technology (SPS) on air plasma spraying (APS) coating surface, where cauliflower-like microstructure formed by the stacking of numerous micron and nanometer particles were densely distributed. And the wetting behavior of the melt on the coating surface was investigated and compared. Results indicate that SPS coatings showed a superior excellence of repelling the molten CMAS wetting compared with conventional APS coating, resulting from its micro-nano surface microstructure. After kept at 1300 oC for 5 min, the contact angle of melt on SPS coating was 115.1°, which was more than twice that on APS coating (52.1°), and that on SPS coating was 3.2 times larger than that on APS coating after 10 min. The effectiveness of SPS coating in repelling the melt was illustrated by theoretical analysis to be attributed to its micro-nano multi-scale microstructure. Also, air stored in the porous microstructure of SPS coating plays a vital role in lifting CMAS droplet during the wetting process.
The application of thermal barrier coatings (TBCs) is greatly limited by calciummagnesium-alumina-silicate (CMAS) attack. The surface microstructure of TBCs is demonstrated its fundamental effect on the wetting behavior of molten CMAS, thus further influencing CMAS resistance of TBCs. In this study, hierarchical microstructure was innovatively fabricated by suspension plasma spray technology (SPS) on air plasma spraying (APS) coating surface, where cauliflower-like microstructure formed by the stacking of numerous micron and nanometer particles were densely distributed. And the wetting behavior of the melt on the coating surface was investigated and compared. Results indicate that SPS coatings showed a superior excellence of repelling the molten CMAS wetting compared with conventional APS coating, resulting from its micro-nano surface microstructure. After kept at 1300 oC for 5 min, the contact angle of melt on SPS coating was 115.1°, which was more than twice that on APS coating (52.1°), and that on SPS coating was 3.2 times larger than that on APS coating after 10 min. The effectiveness of SPS coating in repelling the melt was illustrated by theoretical analysis to be attributed to its micro-nano multi-scale microstructure. Also, air stored in the porous microstructure of SPS coating plays a vital role in lifting CMAS droplet during the wetting process.
, Available online
, doi: 10.14135/j.cnki.1006-3080.2022043002
Abstract:
In order to construct ferritin (FTH1) based dual-targeting nanoparticles targeting both epidermal growth factor receptor (EGFR) and interleukin-4 receptor (IL-4R), an anti-EGFR single-chain antibody (anti-EGFR scFv) and an IL-4R-targeting AP1 peptide were genetically engineered to the N-terminal of FTH1, respectively. Then anti-EGFR scFv-FTH1/AP1-FTH1 dual-targeting nanoparticles were successfully prepared by protein expression and in vitro mixed refolding technologies. The results showed that they could be correctly assembled into hollow cage-like nanoparticles with a particle size of (13.2 ± 1.3) nm. In an asthma mouse model, these nanoparticles could effectively inhibit inflammatory cell infiltration, goblet cell hyperplasia, mucus secretion. They also show reduced airway hyperresponsiveness, which was better than that of single-targeted anti-EGFR scFv-FTH1/FTH1 nanoparticles. All of these provide a theoretical and practical basis for ferritin-based dual-targeted nanoparticles in treatment of diseases.
In order to construct ferritin (FTH1) based dual-targeting nanoparticles targeting both epidermal growth factor receptor (EGFR) and interleukin-4 receptor (IL-4R), an anti-EGFR single-chain antibody (anti-EGFR scFv) and an IL-4R-targeting AP1 peptide were genetically engineered to the N-terminal of FTH1, respectively. Then anti-EGFR scFv-FTH1/AP1-FTH1 dual-targeting nanoparticles were successfully prepared by protein expression and in vitro mixed refolding technologies. The results showed that they could be correctly assembled into hollow cage-like nanoparticles with a particle size of (13.2 ± 1.3) nm. In an asthma mouse model, these nanoparticles could effectively inhibit inflammatory cell infiltration, goblet cell hyperplasia, mucus secretion. They also show reduced airway hyperresponsiveness, which was better than that of single-targeted anti-EGFR scFv-FTH1/FTH1 nanoparticles. All of these provide a theoretical and practical basis for ferritin-based dual-targeted nanoparticles in treatment of diseases.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220518001
Abstract:
We investigated and determinated the concentration of sugar and caffeine in bubble tea from popular brands on the market, combined with people's daily consumption habits, the median concentration of sugar and caffeine in bubble tea was used as the standard to gavage mice. Then the C57 BL/6J male mice fed with a normal diet or a high-sugar and high-fat diet were used to simulate the humans drinking a cup of bubble tea every day. The physiological metabolism of mice was evaluated by body weight, blood glucose, blood lipids, inflammatory factors, and histological examination to study the effects of the sustainable intake of sucrose and caffeine. The results showed that: In the bubble tea selected, the concentration of sugar ranged from 25 to 135 g/L, and the concentration of caffeine ranged from 190 to 853 mg/L. When the normal diet supplemented with sucrose plus caffeine group is compared with the normal diet group, the concentration of fasting blood glucose (FBG) increased from 4.27±0.06 mmol/L to 5.43±0.39 mmol/L, the concentration of serum tumor necrosis factor-α increased from 89.48±2.85 ng/L to 112.94±1.76 ng/L, the concentration of serum interleukin-6 increased from 49.31±3.23 ng/L to 65.44±4.70 ng/L, the weight gain decreased from 3.33±0.64 g to 2.45±0.51 g, and the concentration of serum triglyceride decreased from 0.88±0.29 mmol/L to 0.74±0.04 mmol/L. When the high-sugar and high-fat diet supplemented with sucrose plus caffeine is compared with the high-sugar and high-fat diet group, the weight gain decreased from 8.38±1.42 g to 3.67±1.23 g, and the concentration of serum triglyceride decreased from 0.86±0.07 mmol/L to 0.71±0.06 mmol/L. Long-term intake of sucrose plus caffeine mix solution brought a decrease in the body weight and serum triglyceride, but it brought an increase in the concentration of the FBG and pro-inflammatory factors.
We investigated and determinated the concentration of sugar and caffeine in bubble tea from popular brands on the market, combined with people's daily consumption habits, the median concentration of sugar and caffeine in bubble tea was used as the standard to gavage mice. Then the C57 BL/6J male mice fed with a normal diet or a high-sugar and high-fat diet were used to simulate the humans drinking a cup of bubble tea every day. The physiological metabolism of mice was evaluated by body weight, blood glucose, blood lipids, inflammatory factors, and histological examination to study the effects of the sustainable intake of sucrose and caffeine. The results showed that: In the bubble tea selected, the concentration of sugar ranged from 25 to 135 g/L, and the concentration of caffeine ranged from 190 to 853 mg/L. When the normal diet supplemented with sucrose plus caffeine group is compared with the normal diet group, the concentration of fasting blood glucose (FBG) increased from 4.27±0.06 mmol/L to 5.43±0.39 mmol/L, the concentration of serum tumor necrosis factor-α increased from 89.48±2.85 ng/L to 112.94±1.76 ng/L, the concentration of serum interleukin-6 increased from 49.31±3.23 ng/L to 65.44±4.70 ng/L, the weight gain decreased from 3.33±0.64 g to 2.45±0.51 g, and the concentration of serum triglyceride decreased from 0.88±0.29 mmol/L to 0.74±0.04 mmol/L. When the high-sugar and high-fat diet supplemented with sucrose plus caffeine is compared with the high-sugar and high-fat diet group, the weight gain decreased from 8.38±1.42 g to 3.67±1.23 g, and the concentration of serum triglyceride decreased from 0.86±0.07 mmol/L to 0.71±0.06 mmol/L. Long-term intake of sucrose plus caffeine mix solution brought a decrease in the body weight and serum triglyceride, but it brought an increase in the concentration of the FBG and pro-inflammatory factors.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210920001
Abstract:
In the process of the slurry breakup, the throat of the liquid bridge keeps shrinking. When the minimum characteristic diameter of the liquid bridge is close to the size of the solid particle, the slurry will exhibit the complex variation characteristics in the process of time, which is significantly different from pure liquid. Therefore, the study of the micro breakup characteristics of the slurry is helpful to reveal the atomization mechanism and improve the simulation model of slurry. Here Shenhua coal and Huadian coal are used as the raw materials to prepare coal water slurry with a mass concentration range of 58% -62 %(mass fraction). The influence of the physical and chemical parameters of coal water slurry on its microscopic breakup process has been studied by the rotary rheometer, the static surface tension meter, the dynamic surface tension meter, the high-speed camera, the image processing software, and so on. Coal water slurry is a shear thinning non-Newtonian fluid. So in this paper the Herschel-Bulkley model is used to establish the rheological relationship of coal water slurry. Unlike the static surface tension, the dynamic surface tension of coal water slurry decreases with the increase of the characteristic bubble time. After increasing, the minimum surface tension appears around about 200 ms. Finally based on the rheological properties and the dynamic surface tension of coal water slurry, the relationship between the change of the characteristic diameter of coal water slurry micro-breakup and the time of breakup is obtained.
In the process of the slurry breakup, the throat of the liquid bridge keeps shrinking. When the minimum characteristic diameter of the liquid bridge is close to the size of the solid particle, the slurry will exhibit the complex variation characteristics in the process of time, which is significantly different from pure liquid. Therefore, the study of the micro breakup characteristics of the slurry is helpful to reveal the atomization mechanism and improve the simulation model of slurry. Here Shenhua coal and Huadian coal are used as the raw materials to prepare coal water slurry with a mass concentration range of 58% -62 %(mass fraction). The influence of the physical and chemical parameters of coal water slurry on its microscopic breakup process has been studied by the rotary rheometer, the static surface tension meter, the dynamic surface tension meter, the high-speed camera, the image processing software, and so on. Coal water slurry is a shear thinning non-Newtonian fluid. So in this paper the Herschel-Bulkley model is used to establish the rheological relationship of coal water slurry. Unlike the static surface tension, the dynamic surface tension of coal water slurry decreases with the increase of the characteristic bubble time. After increasing, the minimum surface tension appears around about 200 ms. Finally based on the rheological properties and the dynamic surface tension of coal water slurry, the relationship between the change of the characteristic diameter of coal water slurry micro-breakup and the time of breakup is obtained.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210813001
Abstract:
Dipropargyl ether of bisphenols and dichlorosilane were polymerized by Grignard process to obtain silicon-containing propargyl ether of bisphenol A resin (PSPE-A) and silicon-containing propargyl ether of diphenyl ether resin (PSPE-O). The silicon-containing propargyl ether of bisphenol resins (PSPE) were further end capped with diethynyl benzene to obtain the ethynylphenyl-terminated PSPE-A resin (DPSPE-A) and PSPE-O resin (DPSPE-O). The four resins were characterized by proton nuclear magnetic resonance and size exclusion chromatograph. The cure reactions, thermal stability and mechanical properties of the resins were studied as well as the mechanical properties of the quartz fiber cloth (QF) and carbon fiber cloth (T300CF) reinforced resin composites. The results show that synthesized PSPE resins present the high curing temperature. The curing temperature and apparent activation energy of the PSPE resins can be distinctly declined by introducing diethynyl benzene as end capping agent. The viscosity at 110 ℃ of PSPE-A and PSPE-O are stable and lower than 300 mPa s. The viscosity at 110 ℃ of PSPE-O is much lower than that of PSPE-A. The temperature of 5% weight loss (Td5) and residual yield at 800 ℃ (Yr800 ℃) in N2 of the cured ethynylphenyl-terminated PSPE resin can increase to 486.3 ℃ and 75.1%. The glass transition temperature (Tg) of the cured DPSPE is higher than 400 ℃. However, the flexural strength and impact strength of the cured PSPE resin decrease to 27.8 MPa and 3.27 kJ/m2 after end capped with ethynylphenyl groups. The flexural strength and interlaminar shear strength (ILSS) of the QF/PSPE-A and T300CF/PSPE-A composites are 461.3 MPa and 35.6 MPa, 655.2 MPa and 39.3 MPa, respectively.
Dipropargyl ether of bisphenols and dichlorosilane were polymerized by Grignard process to obtain silicon-containing propargyl ether of bisphenol A resin (PSPE-A) and silicon-containing propargyl ether of diphenyl ether resin (PSPE-O). The silicon-containing propargyl ether of bisphenol resins (PSPE) were further end capped with diethynyl benzene to obtain the ethynylphenyl-terminated PSPE-A resin (DPSPE-A) and PSPE-O resin (DPSPE-O). The four resins were characterized by proton nuclear magnetic resonance and size exclusion chromatograph. The cure reactions, thermal stability and mechanical properties of the resins were studied as well as the mechanical properties of the quartz fiber cloth (QF) and carbon fiber cloth (T300CF) reinforced resin composites. The results show that synthesized PSPE resins present the high curing temperature. The curing temperature and apparent activation energy of the PSPE resins can be distinctly declined by introducing diethynyl benzene as end capping agent. The viscosity at 110 ℃ of PSPE-A and PSPE-O are stable and lower than 300 mPa s. The viscosity at 110 ℃ of PSPE-O is much lower than that of PSPE-A. The temperature of 5% weight loss (Td5) and residual yield at 800 ℃ (Yr800 ℃) in N2 of the cured ethynylphenyl-terminated PSPE resin can increase to 486.3 ℃ and 75.1%. The glass transition temperature (Tg) of the cured DPSPE is higher than 400 ℃. However, the flexural strength and impact strength of the cured PSPE resin decrease to 27.8 MPa and 3.27 kJ/m2 after end capped with ethynylphenyl groups. The flexural strength and interlaminar shear strength (ILSS) of the QF/PSPE-A and T300CF/PSPE-A composites are 461.3 MPa and 35.6 MPa, 655.2 MPa and 39.3 MPa, respectively.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220606002
Abstract:
Industrial control system (ICS) is an important part of a nation's critical infrastructure. With the continuous integration of information technology and industrial control network, critical infrastructure control system has become part of the Internet and is more vulnerable to various cyber-attacks. The abnormity or collapse of ICS may bring economic losses, environmental damage and even loss of human life. It is very important to strengthen the network security protection for ICS. Intrusion detection system is a very effective information security mechanism that can monitor network traffic, detect and prevent cyber-attacks. As the rapid development of industrial Internet, there has generated massive industrial network traffic data, which has resulted in low detection accuracy and low time efficiency of traditional intrusion detection algorithms. This paper presents an industrial control intrusion detection model that combines improved sparrow search algorithm (ISSA) and light gradient boosting machine (LightGBM). The method can process massive industrial data at high speed without sacrificing detection performance. The paper adopted the improved ISSA algorithm to solve the problem that the adjustment of LightGBM hyperparameters is difficult. First, a discrete decoding strategy is introduced to avoid decimals in the shaping hyperparameters; Secondly, the generation method of the initial population is optimized and the population diversity is improved; Finally, the position update function of SSA algorithm is improved which makes the algorithm have better global search ability. Applying ISSA to the LightGBM intrusion detection model for parameter optimization, the research results on massive standard industrial control network dataset show that this method has higher detection accuracy and time efficiency, and is especially suitable for processing massive industrial data.
Industrial control system (ICS) is an important part of a nation's critical infrastructure. With the continuous integration of information technology and industrial control network, critical infrastructure control system has become part of the Internet and is more vulnerable to various cyber-attacks. The abnormity or collapse of ICS may bring economic losses, environmental damage and even loss of human life. It is very important to strengthen the network security protection for ICS. Intrusion detection system is a very effective information security mechanism that can monitor network traffic, detect and prevent cyber-attacks. As the rapid development of industrial Internet, there has generated massive industrial network traffic data, which has resulted in low detection accuracy and low time efficiency of traditional intrusion detection algorithms. This paper presents an industrial control intrusion detection model that combines improved sparrow search algorithm (ISSA) and light gradient boosting machine (LightGBM). The method can process massive industrial data at high speed without sacrificing detection performance. The paper adopted the improved ISSA algorithm to solve the problem that the adjustment of LightGBM hyperparameters is difficult. First, a discrete decoding strategy is introduced to avoid decimals in the shaping hyperparameters; Secondly, the generation method of the initial population is optimized and the population diversity is improved; Finally, the position update function of SSA algorithm is improved which makes the algorithm have better global search ability. Applying ISSA to the LightGBM intrusion detection model for parameter optimization, the research results on massive standard industrial control network dataset show that this method has higher detection accuracy and time efficiency, and is especially suitable for processing massive industrial data.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210810001
Abstract:
A metal-free catalytic “one-pot” strategy for the facile synthesis of biologically relevant molecular architectures pyrrolo[1,2-a] (iso)quinolines has been developed. Based on our previous studies on the synthesis of indolizines and imidazopyridines, the process is promoted by amine and N-heterocyclic carbene (NHC) relay catalysis via Michael addition-[3+2] cycloaddition of azaarenes and α,β-unsaturated aldehydes. The reactions between azaarenes (1 equiv) and α,β-unsaturated aldehydes (2 equiv) are catalyzed by the amine catalyst (20 mol%) and NHC catalyst (20 mol%) relay catalysis in the presence of 4-dimethylaminopyridine (DMAP) as base (3 equiv) and (diacetoxyiodo)benzene (PIDA) as oxidant (4 equiv) in toluene at room temperature. The first Michael addition step is catalyzed by amine catalyst for 24 h at room temperature to give the Michael addition product. Without any further work-up, then NHC catalyst, DMAP and PIDA are added into the solution for additional 18 h at room temperature. In this step, the cycloaddition and aromatization happen to give pyrrolo[1,2-a] quinoline products. The “one-pot” reactions perform smoothly for various substituted and more conjugated quinolines, including tethered neutral, electron-withdrawing and electron-donating groups at 4, 6, 7-position of quinolines. Ethyl 2-(benzo[f]quinolin-3-yl)acetate also works well to give the quinoline product in 75% yield. The alternation of the α,β-unsaturated aldehyde structures with electron-donating and -withdrawing groups on the phenyl ring work well to give the corresponding quinoline products in moderate to good yields. Moreover, isoquinoline can be tolerated in this reaction to give the corresponding isoquinoline product in good yield (72%). And hetero quinoline-ethyl 2-(quinoxalin-2-yl)acetate works well to afford ethyl 1-formyl-2-phenylpyrrolo[1,2-a]quinoxaline-3-carboxylate in 56% yield.
A metal-free catalytic “one-pot” strategy for the facile synthesis of biologically relevant molecular architectures pyrrolo[1,2-a] (iso)quinolines has been developed. Based on our previous studies on the synthesis of indolizines and imidazopyridines, the process is promoted by amine and N-heterocyclic carbene (NHC) relay catalysis via Michael addition-[3+2] cycloaddition of azaarenes and α,β-unsaturated aldehydes. The reactions between azaarenes (1 equiv) and α,β-unsaturated aldehydes (2 equiv) are catalyzed by the amine catalyst (20 mol%) and NHC catalyst (20 mol%) relay catalysis in the presence of 4-dimethylaminopyridine (DMAP) as base (3 equiv) and (diacetoxyiodo)benzene (PIDA) as oxidant (4 equiv) in toluene at room temperature. The first Michael addition step is catalyzed by amine catalyst for 24 h at room temperature to give the Michael addition product. Without any further work-up, then NHC catalyst, DMAP and PIDA are added into the solution for additional 18 h at room temperature. In this step, the cycloaddition and aromatization happen to give pyrrolo[1,2-a] quinoline products. The “one-pot” reactions perform smoothly for various substituted and more conjugated quinolines, including tethered neutral, electron-withdrawing and electron-donating groups at 4, 6, 7-position of quinolines. Ethyl 2-(benzo[f]quinolin-3-yl)acetate also works well to give the quinoline product in 75% yield. The alternation of the α,β-unsaturated aldehyde structures with electron-donating and -withdrawing groups on the phenyl ring work well to give the corresponding quinoline products in moderate to good yields. Moreover, isoquinoline can be tolerated in this reaction to give the corresponding isoquinoline product in good yield (72%). And hetero quinoline-ethyl 2-(quinoxalin-2-yl)acetate works well to afford ethyl 1-formyl-2-phenylpyrrolo[1,2-a]quinoxaline-3-carboxylate in 56% yield.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220430001
Abstract:
The high temperature flame in the reformer of non-catalytic partial oxidation (NC-POX) of methane under pure oxygen atmosphere will affect the safe and stable operation of the actual industrial plant. Adding diluent to dilute pure oxygen in the reaction system can improve the temperature distribution and flame structure of the reformer. An O2/CH4 ratio of approximately 0.69 was typically used when NC-POX reformers were used industrially, numerical modelling was conducted using ANSYS Fluent 16.1 commercial simulation software, with a Realizable k–ε turbulence model considered for Reynolds-averaged Navier-Stokes (RANS) model, the Eddy Dissipation Concept (EDC) model coupled with GRI3.0 chemical reaction mechanism was suitable for simulating turbulent combustion and chemical reaction processes in the reformer. This study investigates the effect of three diluent (N2, CO2, H2O) on the distribution of temperature, radicals (OH、CH2O) and flame reaction zone. Results indicate that the effects of N2 and H2O dilution on the temperature, radicals and flame reaction zone distribution are basically indistinguishable, and the effect of CO2 dilution is the most significant. The increasing of CO2 content decreases the peak temperature by about 50K and peak values of OH and CH2O radicals decreased by about 33% and 24.5%, respectively. In addition, three effects of CO2 dilution on the temperature and radicals distribution in the reformer are in the order of dilution effect>thermal effect>chemical effect. Therefore, using the diluent CO2 to improve the temperature distribution of the reformer system not only realizes the effective adjustment of the H2/CO ratio of the syngas, but also provides support for the flame control in a high-temperature reducing atmosphere.
The high temperature flame in the reformer of non-catalytic partial oxidation (NC-POX) of methane under pure oxygen atmosphere will affect the safe and stable operation of the actual industrial plant. Adding diluent to dilute pure oxygen in the reaction system can improve the temperature distribution and flame structure of the reformer. An O2/CH4 ratio of approximately 0.69 was typically used when NC-POX reformers were used industrially, numerical modelling was conducted using ANSYS Fluent 16.1 commercial simulation software, with a Realizable k–ε turbulence model considered for Reynolds-averaged Navier-Stokes (RANS) model, the Eddy Dissipation Concept (EDC) model coupled with GRI3.0 chemical reaction mechanism was suitable for simulating turbulent combustion and chemical reaction processes in the reformer. This study investigates the effect of three diluent (N2, CO2, H2O) on the distribution of temperature, radicals (OH、CH2O) and flame reaction zone. Results indicate that the effects of N2 and H2O dilution on the temperature, radicals and flame reaction zone distribution are basically indistinguishable, and the effect of CO2 dilution is the most significant. The increasing of CO2 content decreases the peak temperature by about 50K and peak values of OH and CH2O radicals decreased by about 33% and 24.5%, respectively. In addition, three effects of CO2 dilution on the temperature and radicals distribution in the reformer are in the order of dilution effect>thermal effect>chemical effect. Therefore, using the diluent CO2 to improve the temperature distribution of the reformer system not only realizes the effective adjustment of the H2/CO ratio of the syngas, but also provides support for the flame control in a high-temperature reducing atmosphere.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220312002
Abstract:
Water jet surface strengthening is an effective way to improve the fatigue life of aeroengine blades. In view of the fact that the water jet surface strengthening equipment and process system do not have the ability to monitor, detect, predict and make decisions on the strengthening process, the digital twin technology is introduced into the water jet surface strengthening process. Firstly, according to the principle of water jet strengthening technology, the framework of digital twin system for water jet surface strengthening was defined; Secondly, the design of the digital twin system was completed at the five levels of physical level, model level, data level, application level and data transmission level, which realizes the virtual mapping of the strengthening equipment and process in the physical world, as well as the connection and interaction between all levels. The application case shows that the developed digital twin system has the ability to monitor the condition of the strengthening equipment in real time, detect the surface integrity of the strengthened blade, predict the strengthening effect under the specific strengthening process, and provide decision support for the selection and optimization of the strengthening process.
Water jet surface strengthening is an effective way to improve the fatigue life of aeroengine blades. In view of the fact that the water jet surface strengthening equipment and process system do not have the ability to monitor, detect, predict and make decisions on the strengthening process, the digital twin technology is introduced into the water jet surface strengthening process. Firstly, according to the principle of water jet strengthening technology, the framework of digital twin system for water jet surface strengthening was defined; Secondly, the design of the digital twin system was completed at the five levels of physical level, model level, data level, application level and data transmission level, which realizes the virtual mapping of the strengthening equipment and process in the physical world, as well as the connection and interaction between all levels. The application case shows that the developed digital twin system has the ability to monitor the condition of the strengthening equipment in real time, detect the surface integrity of the strengthened blade, predict the strengthening effect under the specific strengthening process, and provide decision support for the selection and optimization of the strengthening process.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220314003
Abstract:
The mixer is an important part of the purification system of multi-nozzle opposed gasification technology, however, the adhesion of fly ash particles on the wall of the mixer can cause clogging, resulting in lower syngas purification efficiency and even affecting the gasification efficiency of the gasifier and leading to economic losses. In this paper, the deposition characteristics of gasification fine ash particles in the mixer are numerically simulated by using the particle deposition critical velocity model, and the influence of different particle sizes, gas flow rate and mixer structure on particle deposition is investigated. The results show that, at the same inlet gas velocity, the deposition rate of gasified fine ash on the inner wall surface of the mixer generally tends to decrease with the increase of particle size, especially for 5-13 μm particles; the deposition of 1 μm particles on the surface of wall4 is the largest per unit area; the deposition rate of different particle sizes varies with the gas flow rate, but generally shows a decreasing trend; the change of the distance H from the outlet face of the water pipe to the end face of the indented casing mouth has no significant effect on the deposition rate of particles in the mixer; mixer C is a structure that can effectively avoid clogging near the indented face.
The mixer is an important part of the purification system of multi-nozzle opposed gasification technology, however, the adhesion of fly ash particles on the wall of the mixer can cause clogging, resulting in lower syngas purification efficiency and even affecting the gasification efficiency of the gasifier and leading to economic losses. In this paper, the deposition characteristics of gasification fine ash particles in the mixer are numerically simulated by using the particle deposition critical velocity model, and the influence of different particle sizes, gas flow rate and mixer structure on particle deposition is investigated. The results show that, at the same inlet gas velocity, the deposition rate of gasified fine ash on the inner wall surface of the mixer generally tends to decrease with the increase of particle size, especially for 5-13 μm particles; the deposition of 1 μm particles on the surface of wall4 is the largest per unit area; the deposition rate of different particle sizes varies with the gas flow rate, but generally shows a decreasing trend; the change of the distance H from the outlet face of the water pipe to the end face of the indented casing mouth has no significant effect on the deposition rate of particles in the mixer; mixer C is a structure that can effectively avoid clogging near the indented face.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220517002
Abstract:
The α-Al2O3 powder was synthesized by calcining NH4Al(SO4)2·12H2O with different content glucose added. The effects of glucose content and sample pre-pressing on the synthesis process and temperature of α-Al2O3 powder were investigated. The results showed that the existing of glucose could change the phase transition process and decrease the synthesizing temperature obviously. With 30wt% glucose added, α-Al2O3 powder could be synthesized at 950 ℃, which was about 200 ℃ lower than that of no glucose added. With 75wt% glucose added, the synthesizing temperature of α-Al2O3 could be further decreased to 900 ℃. Two effects of glucose are believed to be the main reasons for the decrease of α-Al2O3 synthesizing temperature. One is accelerating the thermal decomposition of NH4Al(SO4)2·12H2O, the other is to make NH4Al(SO4)2·12H2O disperse evenly and amorphous. The results also showed that pre-pressing could further decrease the synthesizing temperature of α-Al2O3 by 25-50 ℃, which might cause by the destruction of agglomeration and the further homogenization of powder dispersion.
The α-Al2O3 powder was synthesized by calcining NH4Al(SO4)2·12H2O with different content glucose added. The effects of glucose content and sample pre-pressing on the synthesis process and temperature of α-Al2O3 powder were investigated. The results showed that the existing of glucose could change the phase transition process and decrease the synthesizing temperature obviously. With 30wt% glucose added, α-Al2O3 powder could be synthesized at 950 ℃, which was about 200 ℃ lower than that of no glucose added. With 75wt% glucose added, the synthesizing temperature of α-Al2O3 could be further decreased to 900 ℃. Two effects of glucose are believed to be the main reasons for the decrease of α-Al2O3 synthesizing temperature. One is accelerating the thermal decomposition of NH4Al(SO4)2·12H2O, the other is to make NH4Al(SO4)2·12H2O disperse evenly and amorphous. The results also showed that pre-pressing could further decrease the synthesizing temperature of α-Al2O3 by 25-50 ℃, which might cause by the destruction of agglomeration and the further homogenization of powder dispersion.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220530002
Abstract:
Three kinds of maleimide-based enediynes with different numbers of hydroxyl groups at the alkyne termini were synthesized by Sonogashira coupling reaction between diiodomaleimide and different kinds of terminal alkynes. Through the maleimide assisted rearrangement and cycloaromatization (MARACA) mechanism, these enediyne molecules can be converted to enyne-allene structures at physiological temperatures, leading to the formation of highly reactive diradical intermediates through Myer-Saito cyclization reactions. Electron paramagnetic resonance experiments confirmed the generation of free radical intermediates in the system by using a free radical trapping agent N-tert-Butyl-α-phenylnitrone. DNA gel electrophoresis experiments confirmed that these enediynes could generate highly reactive free radicals and further abstract hydrogen atoms from the DNA backbone at physiological temperature, resulted in the fragmentation of plasmid DNA strands and cleavage of supercoiled DNA into cyclic form. The DNA cleavage process is concentration-dependent, and different enediyne structures exhibit different cleaving effects under the same concentration gradient. Protein gel electrophoresis experiments showed that the ability of the enediyne compounds to degrade proteins is in line with their ability for generating free radicals. For the highly reactive enediyne structure with an oxygen atom at the propargyl position, the structural integrity of the protein backbone can be destroyed at a rather low concentration (5 mmol/L) of enediyne compounds, resulted in protein denaturing. Altogether, this work provides preliminary results for future research on maleimide-based enediyne for the damaging of a variety of biological macromolecules such as viral proteins, tumor cell DNA, and provides new ideas for the potential clinical applications of these highly intriguing synthetic enediyne compounds.
Three kinds of maleimide-based enediynes with different numbers of hydroxyl groups at the alkyne termini were synthesized by Sonogashira coupling reaction between diiodomaleimide and different kinds of terminal alkynes. Through the maleimide assisted rearrangement and cycloaromatization (MARACA) mechanism, these enediyne molecules can be converted to enyne-allene structures at physiological temperatures, leading to the formation of highly reactive diradical intermediates through Myer-Saito cyclization reactions. Electron paramagnetic resonance experiments confirmed the generation of free radical intermediates in the system by using a free radical trapping agent N-tert-Butyl-α-phenylnitrone. DNA gel electrophoresis experiments confirmed that these enediynes could generate highly reactive free radicals and further abstract hydrogen atoms from the DNA backbone at physiological temperature, resulted in the fragmentation of plasmid DNA strands and cleavage of supercoiled DNA into cyclic form. The DNA cleavage process is concentration-dependent, and different enediyne structures exhibit different cleaving effects under the same concentration gradient. Protein gel electrophoresis experiments showed that the ability of the enediyne compounds to degrade proteins is in line with their ability for generating free radicals. For the highly reactive enediyne structure with an oxygen atom at the propargyl position, the structural integrity of the protein backbone can be destroyed at a rather low concentration (5 mmol/L) of enediyne compounds, resulted in protein denaturing. Altogether, this work provides preliminary results for future research on maleimide-based enediyne for the damaging of a variety of biological macromolecules such as viral proteins, tumor cell DNA, and provides new ideas for the potential clinical applications of these highly intriguing synthetic enediyne compounds.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210729001
Abstract:
Based on the reaction of synthetic ammonia, a three-dimensional multi-component reaction-diffusion model of catalyst particles was established and verified by COMSOL software. The results of model verification show that there is little difference between the multi-component diffusion model and the one-component diffusion model. The inner surface utilization of ammonia catalysts with different shapes was close to each other. In industrial reactors, the calculation of internal diffusion efficiency factor of ammonia synthesis catalyst could be carried out according to the uniform surface area of spheres. Based on the different positions of the reactor, the simulation results of the synthesis ammonia A301 catalyst show that: Temperature, particle size and reaction process are important factors affecting the efficiency of diffusion of catalyst. The diffusion effects of different reaction stages are quite different. At the initial stage of reaction, when the reacting rate and the internal diffusion retardation is large, the efficiency factor of internal diffusion can be improved obviously by reducing the particle size of catalyst. The results show that the efficiency factor is almost linearly negatively correlated to the particle size of catalyst. In the middle and late stage, when the reaction is close to equilibrium, the utilization rate of the inner surface of the catalyst can remain above 0.9, and the internal diffusion is not sensitive to the changes of temperature and particle size at this time. In this case, the catalyst with appropriate large particle size can be selected in reactor in order to reduce the pressure drop of the catalyst bed.
Based on the reaction of synthetic ammonia, a three-dimensional multi-component reaction-diffusion model of catalyst particles was established and verified by COMSOL software. The results of model verification show that there is little difference between the multi-component diffusion model and the one-component diffusion model. The inner surface utilization of ammonia catalysts with different shapes was close to each other. In industrial reactors, the calculation of internal diffusion efficiency factor of ammonia synthesis catalyst could be carried out according to the uniform surface area of spheres. Based on the different positions of the reactor, the simulation results of the synthesis ammonia A301 catalyst show that: Temperature, particle size and reaction process are important factors affecting the efficiency of diffusion of catalyst. The diffusion effects of different reaction stages are quite different. At the initial stage of reaction, when the reacting rate and the internal diffusion retardation is large, the efficiency factor of internal diffusion can be improved obviously by reducing the particle size of catalyst. The results show that the efficiency factor is almost linearly negatively correlated to the particle size of catalyst. In the middle and late stage, when the reaction is close to equilibrium, the utilization rate of the inner surface of the catalyst can remain above 0.9, and the internal diffusion is not sensitive to the changes of temperature and particle size at this time. In this case, the catalyst with appropriate large particle size can be selected in reactor in order to reduce the pressure drop of the catalyst bed.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220316001
Abstract:
Recently, the microchannel has attracted wide attention due to its safety, reliability and high mixing performance. Most of the current researches focus on Newtonian fluids, but the liquids used in practical applications are often elastic and viscous. Therefore, the planar laser-induced fluorescence technique (PLIF) was used to visualize viscoelastic fluid (polyethylene oxide solution) in cross-shaped channels, focusing on the influence of Reynolds number, channel sizes and polymer solution concentration on flow regimes, oscillatory characteristics and mixing effect. It is shown that for Newtonian fluid (pure water), the separation flow, steady engulfment flow, vortex shedding oscillations and unsteady engulfment flow were found with the increase of Reynolds number (20<Re<500) in all cross-shaped channels (500 μm, 6 mm and 1 cm). For viscoelastic fluid, as the polymer solution concentration increases (0.01%≤c≤0.3%), the fluid elastic effect is enhanced, where an inertioelastic unsteady oscillation occurs at low concentrations which enhances mixing, and an elastic-dominated unsteady oscillation emerges at higher concentrations within the microchannel.The elastic-dominated unsteady oscillation has periodic characteristics at low Wi and Re. As the Wi and Re increases, oscillation period decreases and the flow tends to be irregular. For the 6 mm and 1 cm cross-shaped channels, the critical Reynolds number of vortex shedding oscillations and the unsteady engulfment flow increases with the increase of concentrations, and the unsteady engulfment flow disappears at higher concentrations. In this paper, complex flowcharacteristics and mixing mechanisms of viscoelastic fluid were revealed within the cross-shaped channels of different scales, which provides a new way of strengthening mixing.
Recently, the microchannel has attracted wide attention due to its safety, reliability and high mixing performance. Most of the current researches focus on Newtonian fluids, but the liquids used in practical applications are often elastic and viscous. Therefore, the planar laser-induced fluorescence technique (PLIF) was used to visualize viscoelastic fluid (polyethylene oxide solution) in cross-shaped channels, focusing on the influence of Reynolds number, channel sizes and polymer solution concentration on flow regimes, oscillatory characteristics and mixing effect. It is shown that for Newtonian fluid (pure water), the separation flow, steady engulfment flow, vortex shedding oscillations and unsteady engulfment flow were found with the increase of Reynolds number (20<Re<500) in all cross-shaped channels (500 μm, 6 mm and 1 cm). For viscoelastic fluid, as the polymer solution concentration increases (0.01%≤c≤0.3%), the fluid elastic effect is enhanced, where an inertioelastic unsteady oscillation occurs at low concentrations which enhances mixing, and an elastic-dominated unsteady oscillation emerges at higher concentrations within the microchannel.The elastic-dominated unsteady oscillation has periodic characteristics at low Wi and Re. As the Wi and Re increases, oscillation period decreases and the flow tends to be irregular. For the 6 mm and 1 cm cross-shaped channels, the critical Reynolds number of vortex shedding oscillations and the unsteady engulfment flow increases with the increase of concentrations, and the unsteady engulfment flow disappears at higher concentrations. In this paper, complex flowcharacteristics and mixing mechanisms of viscoelastic fluid were revealed within the cross-shaped channels of different scales, which provides a new way of strengthening mixing.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220424001
Abstract:
Taking advantage of the self-assembly of Pluronic block copolymer F127 and the hydrolysis and condensation of (3-Mercaptopropyl) trimethoxysilane (MPTMS) in alkaline environment, a highly stable silica-based hybrid micelles were firstly synthesized. Furthermore, based on the coordination interaction between palladium species and thiols on the surface of hybrid micelles, the palladium clusters-loaded silica-based hybrid micelles (Pd@FOMs) were prepared via the “in-situ confined growth” strategy. The hydrodynamic diameter and morphology of Pd@FOMs were characterized by dynamic light scattering (DLS) and transmission electron microscopy (TEM). The structure of Pd@FOMs was analyzed by X-ray diffraction (XRD), infrared spectroscopy (FT-IR) and Raman spectroscopy. 3,3′,5,5′-tetramethylbenzidine (TMB) was used to verify the peroxidase-like (POD-like) activity of Pd@FOMs. In the presence of hydrogen peroxide, Pd@FOMs could oxidize TMB to TMB oxide with characteristic absorption at 652 nm. The Michaelis constant (Km) of Pd@FOMs was calculated to be 113.91 mmol/L. Besides, the photothermal performance of Pd@FOMs was explored. Under 808 nm laser irradiation (1.0 W/cm2) for 5 min, Pd@FOMs with the concentration of Pd at 100 mg/L induced temperature rise from 18.1 ℃ to 52.9 ℃. In addition, Pd@FOMs had good photothermal stability with a high photothermal conversion efficiency of 65.76%. Cell experiments showed that Pd@FOMs could be endocytosed by SMMC-7721 tumor cells effectively and had good biocompatibility and excellent photothermal toxicity. Thus, Pd@FOMs are expected to be applied as imaging-guided photothermal therapeutic agent for diagnosis and treatment of tumors.
Taking advantage of the self-assembly of Pluronic block copolymer F127 and the hydrolysis and condensation of (3-Mercaptopropyl) trimethoxysilane (MPTMS) in alkaline environment, a highly stable silica-based hybrid micelles were firstly synthesized. Furthermore, based on the coordination interaction between palladium species and thiols on the surface of hybrid micelles, the palladium clusters-loaded silica-based hybrid micelles (Pd@FOMs) were prepared via the “in-situ confined growth” strategy. The hydrodynamic diameter and morphology of Pd@FOMs were characterized by dynamic light scattering (DLS) and transmission electron microscopy (TEM). The structure of Pd@FOMs was analyzed by X-ray diffraction (XRD), infrared spectroscopy (FT-IR) and Raman spectroscopy. 3,3′,5,5′-tetramethylbenzidine (TMB) was used to verify the peroxidase-like (POD-like) activity of Pd@FOMs. In the presence of hydrogen peroxide, Pd@FOMs could oxidize TMB to TMB oxide with characteristic absorption at 652 nm. The Michaelis constant (Km) of Pd@FOMs was calculated to be 113.91 mmol/L. Besides, the photothermal performance of Pd@FOMs was explored. Under 808 nm laser irradiation (1.0 W/cm2) for 5 min, Pd@FOMs with the concentration of Pd at 100 mg/L induced temperature rise from 18.1 ℃ to 52.9 ℃. In addition, Pd@FOMs had good photothermal stability with a high photothermal conversion efficiency of 65.76%. Cell experiments showed that Pd@FOMs could be endocytosed by SMMC-7721 tumor cells effectively and had good biocompatibility and excellent photothermal toxicity. Thus, Pd@FOMs are expected to be applied as imaging-guided photothermal therapeutic agent for diagnosis and treatment of tumors.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220418003
Abstract:
Taking the water supply of multi-story buildings and high-rise buildings in typical old residential areas in Shanghai as the research object, this paper analyzes and compares the water flow, energy consumption and efficiency data of secondary water supply pumps under different working conditions. The results show that due to the cistern+variable frequency pump mode , the water supply flow in the main period of the day is much lower than the rated flow, so under the condition of meeting the same water supply target, the operating efficiency of the pump is about 50% of that in the cistern+main frequency pump+water tank mode. However, some water tanks are too close to the top floor residents to meet the water supply pressure demand only by gravity flow. The cistern+main frequency pump+water tank mode mode needs to be re pressurized, and the re pressurization energy consumption of the roof water pump is about 200% of the energy consumption of the variable frequency pump in the pump station. At this time, the energy consumption of the cistern+variable frequency pump mode is lower.
Taking the water supply of multi-story buildings and high-rise buildings in typical old residential areas in Shanghai as the research object, this paper analyzes and compares the water flow, energy consumption and efficiency data of secondary water supply pumps under different working conditions. The results show that due to the cistern+variable frequency pump mode , the water supply flow in the main period of the day is much lower than the rated flow, so under the condition of meeting the same water supply target, the operating efficiency of the pump is about 50% of that in the cistern+main frequency pump+water tank mode. However, some water tanks are too close to the top floor residents to meet the water supply pressure demand only by gravity flow. The cistern+main frequency pump+water tank mode mode needs to be re pressurized, and the re pressurization energy consumption of the roof water pump is about 200% of the energy consumption of the variable frequency pump in the pump station. At this time, the energy consumption of the cistern+variable frequency pump mode is lower.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220208001
Abstract:
FCC slurry oil, rich in polycyclic aromatic hydrocarbons, is a relatively ideal raw material of high value-added petroleum products such as carbon fiber, needle coke and carbon black. However, these uses are limited by the inside solid particles. The removal of particles in the slurry oil is essential for its efficient utilization. The removal of solid particles by settlement is an effective way and very important for the future use. The viscosity of the slurry, density difference between the slurry and particle, and particle size are the key factors which affect the sedimentation of solid particles in the slurry oil. In present work, the effect of the addition of petroleum ether and sedimentation temperature were tested to change the viscosity of the slurry and density difference. Mclaughlin method have been applied to calculate the settling velocity in the slurry oil in the settling experiments. The change of characteristics of slurry oil and the sedimentation laws of particles of different size was analyzed to get settling velocity model. The results showed that the error between experiment and model method was within 10%. The results showed that, the sedimentation temperature and the addition amount of petroleum ether obviously affected the viscosity of the slurry oil and then the settlement behavior. As the viscosity decreases, particle settling speed increases. In addition, the sedimentation laws of particles of different size were dramatically different. The sedimentation rate of the particles with the diameter larger than 40 μm were significantly affected by the process conditions, while the smaller particles with the diameter below 20μm were less affected. It can provide support for improving the separation efficiency of solid particles in FCC slurry settlement process.
FCC slurry oil, rich in polycyclic aromatic hydrocarbons, is a relatively ideal raw material of high value-added petroleum products such as carbon fiber, needle coke and carbon black. However, these uses are limited by the inside solid particles. The removal of particles in the slurry oil is essential for its efficient utilization. The removal of solid particles by settlement is an effective way and very important for the future use. The viscosity of the slurry, density difference between the slurry and particle, and particle size are the key factors which affect the sedimentation of solid particles in the slurry oil. In present work, the effect of the addition of petroleum ether and sedimentation temperature were tested to change the viscosity of the slurry and density difference. Mclaughlin method have been applied to calculate the settling velocity in the slurry oil in the settling experiments. The change of characteristics of slurry oil and the sedimentation laws of particles of different size was analyzed to get settling velocity model. The results showed that the error between experiment and model method was within 10%. The results showed that, the sedimentation temperature and the addition amount of petroleum ether obviously affected the viscosity of the slurry oil and then the settlement behavior. As the viscosity decreases, particle settling speed increases. In addition, the sedimentation laws of particles of different size were dramatically different. The sedimentation rate of the particles with the diameter larger than 40 μm were significantly affected by the process conditions, while the smaller particles with the diameter below 20μm were less affected. It can provide support for improving the separation efficiency of solid particles in FCC slurry settlement process.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220314002
Abstract:
The cracks appear on slab edge which is a significant quality problem faced in rolling process. In this paper, an energy analysis model for judging the growth and closure of edge crack in vertical rolling process is established. According to the proposed Γ-cubic function dog-bone model, the corresponding expressions of plastic flow velocity field, strain rate field, as well as total power functional with crack size parameters are derived. The calculation of critical crack point is achieved after dog-bone deformation parameters and rolling force are solved numerically by using the principle of minimum energy. Then, the established calculation method is verified by specific examples. Furthermore, the effects of crack size on rolling parameters and mechanical properties are studied. The results show that the growth of crack is related to the deformation degree of the slab edge. For the premise of width reduction requirement, increasing the radius of vertical rollers is advantage to control the edge crack and improve the crack closure rate. The research and analysis of this paper can be helpful for the promise of product quality and the optimization of rolling process.
The cracks appear on slab edge which is a significant quality problem faced in rolling process. In this paper, an energy analysis model for judging the growth and closure of edge crack in vertical rolling process is established. According to the proposed Γ-cubic function dog-bone model, the corresponding expressions of plastic flow velocity field, strain rate field, as well as total power functional with crack size parameters are derived. The calculation of critical crack point is achieved after dog-bone deformation parameters and rolling force are solved numerically by using the principle of minimum energy. Then, the established calculation method is verified by specific examples. Furthermore, the effects of crack size on rolling parameters and mechanical properties are studied. The results show that the growth of crack is related to the deformation degree of the slab edge. For the premise of width reduction requirement, increasing the radius of vertical rollers is advantage to control the edge crack and improve the crack closure rate. The research and analysis of this paper can be helpful for the promise of product quality and the optimization of rolling process.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220124002
Abstract:
Cylinders are common equipment in the laboratory, which has the characteristics of large number, high danger concealment and great accident harm. Therefore, the supervision of cylinders has always been a pain point of laboratory safety management. Video monitoring is an effective measures of laboratory safety management, but the monitoring videos need to be watched by specially assigned staff, and the ability of monitoring personnel is different, so it can not be guaranteed that they can identify the dangerous information from the video pictures. Therefore, this paper proposes an image caption method combining object detection and text recognition for the first time, which is used to identify the potential hazard information in the cylinder scene and warn the monitoring personnel in the form of text. This method firstly extracts the features of the scene object and the text on the cylinder body, then maps the features into the multi-modal embedding space, then uses Transformer structure to generate caption results, and finally determines whether the scene is dangerous according to the description statement. The experimental results show that the description statements generated by this method can effectively identify the dangerous substances and causes in the laboratory cylinder scene.
Cylinders are common equipment in the laboratory, which has the characteristics of large number, high danger concealment and great accident harm. Therefore, the supervision of cylinders has always been a pain point of laboratory safety management. Video monitoring is an effective measures of laboratory safety management, but the monitoring videos need to be watched by specially assigned staff, and the ability of monitoring personnel is different, so it can not be guaranteed that they can identify the dangerous information from the video pictures. Therefore, this paper proposes an image caption method combining object detection and text recognition for the first time, which is used to identify the potential hazard information in the cylinder scene and warn the monitoring personnel in the form of text. This method firstly extracts the features of the scene object and the text on the cylinder body, then maps the features into the multi-modal embedding space, then uses Transformer structure to generate caption results, and finally determines whether the scene is dangerous according to the description statement. The experimental results show that the description statements generated by this method can effectively identify the dangerous substances and causes in the laboratory cylinder scene.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220403001
Abstract:
Fatty acid potassium is an important surfactant for daily chemical products and cosmetics industries with high foaming, soft texture and other advantages. In recent years, as a "green" soft matter, fatty acid potassium gel has been applied in drug carriers, nanomaterials, phase change materials and other aspects, which has attracted the attention of many scholars. However, there are many unclear areas, such as the crystalline structure, formation mechanism and thermal stability of fatty acid potassium gel, which need to be further explored. This work has investigated the crystalline structure, rheological property and thermal stability of potassium laurate (LK) and potassium palmitate (PK) gels. At room temperature, polarized optical micrographs reveal that there are strip-like crystallites with cross stacking in 30%LK gel and linear crystalline fiber bundles in 30%PK gel. LK gel forms at 30% LK concentration and PK gel forms at 6% PK concentration, which was mainly attributed to the stronger hydrophobic effect of PK long alkyl chain compared to LK short alkyl chain. X-ray diffraction results show that LK molecules of different concentrations keep the hydrated lamellar crystallites, while PK hydrated lamellar crystallites change into anhydrous lamellar crystallites with increasing PK concentration. Upon heating, LK hydrated lamellar crystallites transform into hexagonal liquid crystallites and spherical micelles, and PK anhydrous lamellar crystallites change into lamellar liquid crystallites. PK gel has basically stable storage and loss modulus at 20-60℃, which are 10 times of those for LK gel. These results indicate that PK gel formed by long alkyl chain fatty acid potassium has stronger crystalline network structure and higher thermal stability than LK gel.
Fatty acid potassium is an important surfactant for daily chemical products and cosmetics industries with high foaming, soft texture and other advantages. In recent years, as a "green" soft matter, fatty acid potassium gel has been applied in drug carriers, nanomaterials, phase change materials and other aspects, which has attracted the attention of many scholars. However, there are many unclear areas, such as the crystalline structure, formation mechanism and thermal stability of fatty acid potassium gel, which need to be further explored. This work has investigated the crystalline structure, rheological property and thermal stability of potassium laurate (LK) and potassium palmitate (PK) gels. At room temperature, polarized optical micrographs reveal that there are strip-like crystallites with cross stacking in 30%LK gel and linear crystalline fiber bundles in 30%PK gel. LK gel forms at 30% LK concentration and PK gel forms at 6% PK concentration, which was mainly attributed to the stronger hydrophobic effect of PK long alkyl chain compared to LK short alkyl chain. X-ray diffraction results show that LK molecules of different concentrations keep the hydrated lamellar crystallites, while PK hydrated lamellar crystallites change into anhydrous lamellar crystallites with increasing PK concentration. Upon heating, LK hydrated lamellar crystallites transform into hexagonal liquid crystallites and spherical micelles, and PK anhydrous lamellar crystallites change into lamellar liquid crystallites. PK gel has basically stable storage and loss modulus at 20-60℃, which are 10 times of those for LK gel. These results indicate that PK gel formed by long alkyl chain fatty acid potassium has stronger crystalline network structure and higher thermal stability than LK gel.
Effect of Hydrogen Bond on the Performance of Organic Gel Based on Sterically-Hindered Diarylethenes
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220426001
Abstract:
Photo-responsive organic functional gels have drawn increasing attention for its various application. Based on sterically-hindered diarylethenes, we synthesize two photoresponsive organic gels ap-BAM and ap-YJ, reversibly exhibiting effective manipulation on fluorescence and gel properties with light irradiation. The two organic gel materials both exhibit high conversion ratio and outstanding fatigue resistance in solution state. Meanwhile, ap-BAM with hydrogen bonding groups can be formed into gel state at room temperature while the reference system of ap-YJ only can be formed into gel under low temperature (−10 °C). In addition, due to the aggregation-induced emission (AIE) property of the sterically hindered diarylethene core, the open form of the gel exhibits relatively strong fluorescence. Upon irradiated by UV light, two photoresponsive gels transfer from gel to solution state along with the quenched fluorescence. This can be attributed to increase of the rigidity of the closed form and steric hindrance of the methyl group on active carbon, which destroy the intermolecular interaction. The microscopic morphology of the gel is observed with scanning electron microscopy (SEM). A better dense gel with three-dimensional porous network can be found in ap-BAM gel when compared with the only dense film in the reference ap-YJ gel, indicating that the effect of fixed solvent of ap-BAM gel with hydrogen-bonded units is stronger than the reference ap-YJ gel. Thus, we fabricate two novel photoresponsive organic gel materials, exploring the preparation of novel stimuli-responsive soft materials.
Photo-responsive organic functional gels have drawn increasing attention for its various application. Based on sterically-hindered diarylethenes, we synthesize two photoresponsive organic gels ap-BAM and ap-YJ, reversibly exhibiting effective manipulation on fluorescence and gel properties with light irradiation. The two organic gel materials both exhibit high conversion ratio and outstanding fatigue resistance in solution state. Meanwhile, ap-BAM with hydrogen bonding groups can be formed into gel state at room temperature while the reference system of ap-YJ only can be formed into gel under low temperature (−10 °C). In addition, due to the aggregation-induced emission (AIE) property of the sterically hindered diarylethene core, the open form of the gel exhibits relatively strong fluorescence. Upon irradiated by UV light, two photoresponsive gels transfer from gel to solution state along with the quenched fluorescence. This can be attributed to increase of the rigidity of the closed form and steric hindrance of the methyl group on active carbon, which destroy the intermolecular interaction. The microscopic morphology of the gel is observed with scanning electron microscopy (SEM). A better dense gel with three-dimensional porous network can be found in ap-BAM gel when compared with the only dense film in the reference ap-YJ gel, indicating that the effect of fixed solvent of ap-BAM gel with hydrogen-bonded units is stronger than the reference ap-YJ gel. Thus, we fabricate two novel photoresponsive organic gel materials, exploring the preparation of novel stimuli-responsive soft materials.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220223002
Abstract:
The large amount of accident information in the accident case database provides rich and valuable experience for the design of safety related system, including the time, place, cause, process and so on. These information plays an important role in hazard identification, but they are usually distributed in various paragraphs of accident documents, which makes manual extraction inefficient and costly. This paper presents a text classification method of accident cases based on Bert pre training model, which can be divided into four categories: ACCIDENT、CAUSE、CONSEQUENCE、RESPONSE. In addition, the accident case text data set is collected and produced for training the model. Experiments show that this method can realize the automatic classification of accident case text, and the classification accuracy is 73.44%, the recall is 69.13%, and the F1 value is 0.71. In this paper, multiple groups of different experimental parameters were set up, and the influence of parameter Settings on classification effect was fully explored through experiments to find the best parameter Settings. This classification method helps to better mine the semantic information in the accident case text and provides powerful technical support for the subsequent establishment of expert knowledge base and efficient accident retrieval platform.
The large amount of accident information in the accident case database provides rich and valuable experience for the design of safety related system, including the time, place, cause, process and so on. These information plays an important role in hazard identification, but they are usually distributed in various paragraphs of accident documents, which makes manual extraction inefficient and costly. This paper presents a text classification method of accident cases based on Bert pre training model, which can be divided into four categories: ACCIDENT、CAUSE、CONSEQUENCE、RESPONSE. In addition, the accident case text data set is collected and produced for training the model. Experiments show that this method can realize the automatic classification of accident case text, and the classification accuracy is 73.44%, the recall is 69.13%, and the F1 value is 0.71. In this paper, multiple groups of different experimental parameters were set up, and the influence of parameter Settings on classification effect was fully explored through experiments to find the best parameter Settings. This classification method helps to better mine the semantic information in the accident case text and provides powerful technical support for the subsequent establishment of expert knowledge base and efficient accident retrieval platform.
, Available online
, doi: 10.14135/j.cnki.1006.3080.20220126001
Abstract:
In order to study the effect of tipping paper air permeability on the medium cigarettes, the mainstream smoke components were analyzed and the combustion temperature parameters with cigarettes was also tested with FLIR. The results showed that: With the increasing of tipping paper air permeability the MS smoke components TPM (total particulate matter）, water, tar, nicotine and CO) was decreased and the puff was increased; The linear relationship between air permeability and per mouth of TPM, water, tar and CO were better and per mouth of nicotine was not obvious; With the increasing of air permeability on tipping paper the sensory quality of smoke characteristics was decreased and the taste characteristics firstly increased and then decreased. In terms of aroma characteristics, with the increasing of air permeability the aroma and harmonization were decreased obviously and the offensive taste firstly increased and then decreased. With the increasing of tipping paper air permeability the highest burning temperature, average value of highest burning temperature, average temperature, average peak value of temperature decreased and the average temperature of smoldering was not obvious; The ventilation of cigarette filter was changed with the air permeability of tipping paper, which not only dilution the mainstream smoke, but also reduction the combustion temperature of tobacco. In order to ensure the overall aroma characteristics and smoke characteristics, it can be appropriately improved the air permeability of tipping paper to reduce part of the offensive taste and irritancy, so as to improve smoking amenity.
In order to study the effect of tipping paper air permeability on the medium cigarettes, the mainstream smoke components were analyzed and the combustion temperature parameters with cigarettes was also tested with FLIR. The results showed that: With the increasing of tipping paper air permeability the MS smoke components TPM (total particulate matter）, water, tar, nicotine and CO) was decreased and the puff was increased; The linear relationship between air permeability and per mouth of TPM, water, tar and CO were better and per mouth of nicotine was not obvious; With the increasing of air permeability on tipping paper the sensory quality of smoke characteristics was decreased and the taste characteristics firstly increased and then decreased. In terms of aroma characteristics, with the increasing of air permeability the aroma and harmonization were decreased obviously and the offensive taste firstly increased and then decreased. With the increasing of tipping paper air permeability the highest burning temperature, average value of highest burning temperature, average temperature, average peak value of temperature decreased and the average temperature of smoldering was not obvious; The ventilation of cigarette filter was changed with the air permeability of tipping paper, which not only dilution the mainstream smoke, but also reduction the combustion temperature of tobacco. In order to ensure the overall aroma characteristics and smoke characteristics, it can be appropriately improved the air permeability of tipping paper to reduce part of the offensive taste and irritancy, so as to improve smoking amenity.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220112005
Abstract:
The combination of Terahertz (THz) frequency band and non-orthogonal Multiple Access (NOMA) technology has outstanding advantages in realizing large-scale connection and ultra-high speed communication. However, there are few researches on resource allocation of downlink THZ-NOMA system, and the existing resource allocation schemes have disadvantages of high algorithm complexity and low system performance. In order to obtain a resource allocation scheme that is balanced in both complexity and performance, this paper studies the resource allocation problem of downlink THz-NOMA system with the optimization goal of maximizing energy efficiency. Firstly, in order to reduce the complexity of matching between users and subchannels, the subchannel allocation problem is equivalent to the two-side Match (TSM) problem, and a low-complexity TSM subchannel allocation algorithm is proposed. Secondly, in view of the non-convexity of inter-channel power allocation problem, this paper transforms the non-convex function into the difference of two convex functions, and proposes an inter-channel power allocation algorithm based on difference convex programming. The solution of inter-channel power allocation problem is obtained by solving the convex subproblem iteratively. For user power allocation in subchannels, a closed form solution of optimal user power allocation in subchannels is derived. Simulation results show that the proposed subchannel matching algorithm is less complex than the switching matching algorithm, and the proposed power allocation algorithm can achieve higher system energy efficiency than the traditional power allocation algorithm.
The combination of Terahertz (THz) frequency band and non-orthogonal Multiple Access (NOMA) technology has outstanding advantages in realizing large-scale connection and ultra-high speed communication. However, there are few researches on resource allocation of downlink THZ-NOMA system, and the existing resource allocation schemes have disadvantages of high algorithm complexity and low system performance. In order to obtain a resource allocation scheme that is balanced in both complexity and performance, this paper studies the resource allocation problem of downlink THz-NOMA system with the optimization goal of maximizing energy efficiency. Firstly, in order to reduce the complexity of matching between users and subchannels, the subchannel allocation problem is equivalent to the two-side Match (TSM) problem, and a low-complexity TSM subchannel allocation algorithm is proposed. Secondly, in view of the non-convexity of inter-channel power allocation problem, this paper transforms the non-convex function into the difference of two convex functions, and proposes an inter-channel power allocation algorithm based on difference convex programming. The solution of inter-channel power allocation problem is obtained by solving the convex subproblem iteratively. For user power allocation in subchannels, a closed form solution of optimal user power allocation in subchannels is derived. Simulation results show that the proposed subchannel matching algorithm is less complex than the switching matching algorithm, and the proposed power allocation algorithm can achieve higher system energy efficiency than the traditional power allocation algorithm.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220328001
Abstract:
In this study, the optimum temperature, optimum pH and pH stability of lipase MAS1 for the catalysis of the long-chain substrate 4-nitrophenol myristate (pNP-C14) were determined. Two single point mutations G145W and T141L were initially screened out according to the decrease of binding free energy by molecular dynamics simulation (Molecular Dynamics, MD). Experiments showed that the Km value of the mutant G145W with a significant decrease in binding free energy was reduced by 11% compared with the wild-type MAS1, and the kcat /Km value was 1.29 times that of the wild-type MAS1. Compared with the wild-type MAS1, the Km value of the mutant T141L with a smaller decrease in binding free energy was increased by 22%, and the kcat /Km value was 0.88 times that of the wild-type MAS1. Compared with the wild type, the affinity and catalytic efficiency of G145W for long-chain substrates were improved, while those of T141L were lower than those of the wild type. It shows that the absolute zero value of the binding energy difference is not an accurate mutation screening criterion. In-depth analysis was performed by Molecular Mechanics / Generalized Born Surface Area (MM/GBSA) residue disassembly. It was concluded that residues T38, F39, L149, F153, V202 and V233 contributed more to the binding stability of the lipase active pocket and long-chain substrates than other residues; residues T38, G40, N41, N45, and T237 have important contributions to the improvement of lipase affinity for long-chain substrates and are predicted to be hotspot residues whose reduced binding free energy can serve as a reference standard for mutation screening.
In this study, the optimum temperature, optimum pH and pH stability of lipase MAS1 for the catalysis of the long-chain substrate 4-nitrophenol myristate (pNP-C14) were determined. Two single point mutations G145W and T141L were initially screened out according to the decrease of binding free energy by molecular dynamics simulation (Molecular Dynamics, MD). Experiments showed that the Km value of the mutant G145W with a significant decrease in binding free energy was reduced by 11% compared with the wild-type MAS1, and the kcat /Km value was 1.29 times that of the wild-type MAS1. Compared with the wild-type MAS1, the Km value of the mutant T141L with a smaller decrease in binding free energy was increased by 22%, and the kcat /Km value was 0.88 times that of the wild-type MAS1. Compared with the wild type, the affinity and catalytic efficiency of G145W for long-chain substrates were improved, while those of T141L were lower than those of the wild type. It shows that the absolute zero value of the binding energy difference is not an accurate mutation screening criterion. In-depth analysis was performed by Molecular Mechanics / Generalized Born Surface Area (MM/GBSA) residue disassembly. It was concluded that residues T38, F39, L149, F153, V202 and V233 contributed more to the binding stability of the lipase active pocket and long-chain substrates than other residues; residues T38, G40, N41, N45, and T237 have important contributions to the improvement of lipase affinity for long-chain substrates and are predicted to be hotspot residues whose reduced binding free energy can serve as a reference standard for mutation screening.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220112002
Abstract:
In order to achieve the collaborative optimization of the hydrogen distribution network and the desulfurization process, it is necessary to accurately model the desulfurization process. The simplification of the desulfurization process model will affect the accuracy of the results, while a strict process model based on process thermodynamics is disadvantaged by its inherent high complicacy. To solve this dilemma, this paper proposes a new strategy for coupling optimization of the hydrogen distribution network and the hydrogen sulfide removal process in the refinery. Surrogate models are developed as approximations to the rigorous desulfurization processes. The surrogate model is then integrated into the mathematical programming model for hydrogen network optimization. The proposed method is applied to a case study taken from a refinery in Western China. The result indicates that the proposed model can effectively reduce the H2S content in the system. Moreover, a comparison between the proposed method and the literary work based on simplified desulfurization models proves that the model presented in this paper is more competitive in obtaining practical optimization results.
In order to achieve the collaborative optimization of the hydrogen distribution network and the desulfurization process, it is necessary to accurately model the desulfurization process. The simplification of the desulfurization process model will affect the accuracy of the results, while a strict process model based on process thermodynamics is disadvantaged by its inherent high complicacy. To solve this dilemma, this paper proposes a new strategy for coupling optimization of the hydrogen distribution network and the hydrogen sulfide removal process in the refinery. Surrogate models are developed as approximations to the rigorous desulfurization processes. The surrogate model is then integrated into the mathematical programming model for hydrogen network optimization. The proposed method is applied to a case study taken from a refinery in Western China. The result indicates that the proposed model can effectively reduce the H2S content in the system. Moreover, a comparison between the proposed method and the literary work based on simplified desulfurization models proves that the model presented in this paper is more competitive in obtaining practical optimization results.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220303001
Abstract:
In recent years, the detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI by deep learning has become an atractive research method for many scholars. In order to get good model performance on small samples of MRI, this paper proposes a cascade structure consisting of CNN and LSTM for AD diagnosis. Deep features in the image are extracted by the CNN, and then inputted to the LSTM for the classification task. Knowledge distillation is used to obtain compressed lightweight model, and inter-teacher-student attention mechanism is employed to improve the accuracy of model classification. Experiments show that the diagnostic model can achieve good performance on the ADNI dataset.
In recent years, the detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI by deep learning has become an atractive research method for many scholars. In order to get good model performance on small samples of MRI, this paper proposes a cascade structure consisting of CNN and LSTM for AD diagnosis. Deep features in the image are extracted by the CNN, and then inputted to the LSTM for the classification task. Knowledge distillation is used to obtain compressed lightweight model, and inter-teacher-student attention mechanism is employed to improve the accuracy of model classification. Experiments show that the diagnostic model can achieve good performance on the ADNI dataset.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220107002
Abstract:
As one of inherited autosomal-dominant neurodegenerative diseases, Huntington's disease (HD) is dominantly caused by an abnormally expanded CAG repeat near the N terminus of the huntingtin gene (HTT) which leads to the production of mutant huntingtin protein (mHTT). The predominant toxic gain-of-function mechanism of mHTT results in loss of striatal neurons which decrease the level of dopamine (DA) in the striatum, and then disrupts the balance of neurotransmitters between striatum and substantia nigra. 3-Nitropropionic acid （3-NP）is a mitochondrial toxic reagent which can damage the striatum and induce HD-like symptoms. In this study, a rat model of Huntington's disease (HD) was established by intraperitoneal injection of 3-NP to investigate the effect and mechanism of tetrabenazine (TBZ) on HD-like symptoms. TBZ alleviates 3-NP-induced weight loss, dyskinesia, the expression of Htt and neuron loss. Meanwhile, it was found that TBZ regulated the content of DA and HVA in striatum by ELISA, which might be one of the reasons for the improvement of motor ability. Further mechanism study showed that TBZ inhibited the expression of tyrosine hydroxylase (TH), reduced the expression of vesicular monoamine transporter 2 (VMAT2) and enhanced synaptophysin, which could reduce the synthesis of DA and transportation, protected the synapses in the striatum. Our data suggested that TBZ reduced the release and synthesis of dopamine (DA) in the striatum by simultaneously regulating synaptic vesicular transport and TH expression. These effects further protected neurons from damage, thereby alleviating HD-like symptoms. This study provides new evidence for the mechanism of TBZ in the treatment of HD.
As one of inherited autosomal-dominant neurodegenerative diseases, Huntington's disease (HD) is dominantly caused by an abnormally expanded CAG repeat near the N terminus of the huntingtin gene (HTT) which leads to the production of mutant huntingtin protein (mHTT). The predominant toxic gain-of-function mechanism of mHTT results in loss of striatal neurons which decrease the level of dopamine (DA) in the striatum, and then disrupts the balance of neurotransmitters between striatum and substantia nigra. 3-Nitropropionic acid （3-NP）is a mitochondrial toxic reagent which can damage the striatum and induce HD-like symptoms. In this study, a rat model of Huntington's disease (HD) was established by intraperitoneal injection of 3-NP to investigate the effect and mechanism of tetrabenazine (TBZ) on HD-like symptoms. TBZ alleviates 3-NP-induced weight loss, dyskinesia, the expression of Htt and neuron loss. Meanwhile, it was found that TBZ regulated the content of DA and HVA in striatum by ELISA, which might be one of the reasons for the improvement of motor ability. Further mechanism study showed that TBZ inhibited the expression of tyrosine hydroxylase (TH), reduced the expression of vesicular monoamine transporter 2 (VMAT2) and enhanced synaptophysin, which could reduce the synthesis of DA and transportation, protected the synapses in the striatum. Our data suggested that TBZ reduced the release and synthesis of dopamine (DA) in the striatum by simultaneously regulating synaptic vesicular transport and TH expression. These effects further protected neurons from damage, thereby alleviating HD-like symptoms. This study provides new evidence for the mechanism of TBZ in the treatment of HD.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220104001
Abstract:
In traditional Chinese medicine, doctors usually apply different pulse-taking depths according to the patient's physique, and then make a diagnosis based on the observed pulse waves. The most common pulse-taking instrument to implement this diagnostic technique is to mechanically extract the pulse by applying fixed pressures for all patients. However, this instrument does not take into account the patients’ individual characteristics such as gender, age, and skin thickness. In this study, we propose a new method to find the suitable pulse-taking depth that incorporates the individual characteristics of patients. Firstly, the equivalent stiffness of the skin is extracted from the contact pressure and sensor displacement. Then, the stiffness information is used to identify the suitable pulse-taking depth. To verify our method, we measured pulse waves from 6 subjects at three positions of cun, guan and ci. The pulse feature approximate entropy and peak-to-peak value were extracted of each measurement and the appropriate pulse depth was determined by the new method. The results show that the pulse-taking depths at different positions of different individuals determined by the new method can fall in the region with large peak-to-peak value and low approximate entropy, which means that the pulse wave is strong and stable. Thus, the proposed new method can be used to find the suitable pulse-taking depth to overcome the influence of individual characteristics and position of pulse-taking. This method is expected to provide new ideas for in-depth study of the objective of pulse diagnosis in traditional Chinese medicine and the development of new pulse-taking instruments.
In traditional Chinese medicine, doctors usually apply different pulse-taking depths according to the patient's physique, and then make a diagnosis based on the observed pulse waves. The most common pulse-taking instrument to implement this diagnostic technique is to mechanically extract the pulse by applying fixed pressures for all patients. However, this instrument does not take into account the patients’ individual characteristics such as gender, age, and skin thickness. In this study, we propose a new method to find the suitable pulse-taking depth that incorporates the individual characteristics of patients. Firstly, the equivalent stiffness of the skin is extracted from the contact pressure and sensor displacement. Then, the stiffness information is used to identify the suitable pulse-taking depth. To verify our method, we measured pulse waves from 6 subjects at three positions of cun, guan and ci. The pulse feature approximate entropy and peak-to-peak value were extracted of each measurement and the appropriate pulse depth was determined by the new method. The results show that the pulse-taking depths at different positions of different individuals determined by the new method can fall in the region with large peak-to-peak value and low approximate entropy, which means that the pulse wave is strong and stable. Thus, the proposed new method can be used to find the suitable pulse-taking depth to overcome the influence of individual characteristics and position of pulse-taking. This method is expected to provide new ideas for in-depth study of the objective of pulse diagnosis in traditional Chinese medicine and the development of new pulse-taking instruments.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220223001
Abstract:
In this paper, Panax notoginseng (P. notoginseng) suspension cells were used for the production of chlorogenic acids (CGAs), of which bioactivities were evaluated. Firstly, CGAs in P. notoginseng suspension cells were identified by liquid chromatography-tandem mass spectrometry. Secondly, the suspension culture system and elicitation mode were optimized. At last, the antioxidant and α-glucosidase inhibitory activity of CGAs from P. notoginseng cells were determined. The main results were as follows: 1) Four CGAs were inferred in P. notoginseng cells. 2) The optimal condition was obtained (B5 + 4.0 mg/L NAA + 0.2 mg/L 6-BA + 40 g/L sucrose + 2 g/L PVP, pH 7.5 and 15% inoculation size). 3) By co-culture with 30 μmol/L methyl jasmonate for 3 days, the CGAs yield could reach up to 684.07 mg/L, which was 1.44 times that of control. 4) CGAs from P. notoginseng cells showed an excellent antioxidant capacity in radical-scavenging test and, moreover, certain inhibition on α-glucosidase activity. Thus, our results indicated that the production of CGAs by P. notoginseng cells have potential applications in healthy food and daily cosmetics.
In this paper, Panax notoginseng (P. notoginseng) suspension cells were used for the production of chlorogenic acids (CGAs), of which bioactivities were evaluated. Firstly, CGAs in P. notoginseng suspension cells were identified by liquid chromatography-tandem mass spectrometry. Secondly, the suspension culture system and elicitation mode were optimized. At last, the antioxidant and α-glucosidase inhibitory activity of CGAs from P. notoginseng cells were determined. The main results were as follows: 1) Four CGAs were inferred in P. notoginseng cells. 2) The optimal condition was obtained (B5 + 4.0 mg/L NAA + 0.2 mg/L 6-BA + 40 g/L sucrose + 2 g/L PVP, pH 7.5 and 15% inoculation size). 3) By co-culture with 30 μmol/L methyl jasmonate for 3 days, the CGAs yield could reach up to 684.07 mg/L, which was 1.44 times that of control. 4) CGAs from P. notoginseng cells showed an excellent antioxidant capacity in radical-scavenging test and, moreover, certain inhibition on α-glucosidase activity. Thus, our results indicated that the production of CGAs by P. notoginseng cells have potential applications in healthy food and daily cosmetics.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220105005
Abstract:
The low voltage ride through performance of doubly fed fan depends not only on the control strategy, but also on the selection of control parameters.Because the control parameter optimization algorithm takes too long to achieve the corresponding effect in real-time control, a method based on off-line parameter optimization, model training and on-line fault identification is proposed in this paper.Firstly, a large number of different types of fault data are obtained through the established DFIG grid connection model, and the control parameters are optimized offline according to the fault type to form the corresponding low-voltage ride through mode, and then the different fault data are classified to form the training samples of neural network.At the moment of power grid fault, the fault data can be directly used to quickly judge the fault type through the trained distributed deep neural network, and select the appropriate control strategy according to the fault type.The feasibility of this method and its advantages in control effect and speed are verified by the fault identification and parameter optimization method of doubly fed fan model.
The low voltage ride through performance of doubly fed fan depends not only on the control strategy, but also on the selection of control parameters.Because the control parameter optimization algorithm takes too long to achieve the corresponding effect in real-time control, a method based on off-line parameter optimization, model training and on-line fault identification is proposed in this paper.Firstly, a large number of different types of fault data are obtained through the established DFIG grid connection model, and the control parameters are optimized offline according to the fault type to form the corresponding low-voltage ride through mode, and then the different fault data are classified to form the training samples of neural network.At the moment of power grid fault, the fault data can be directly used to quickly judge the fault type through the trained distributed deep neural network, and select the appropriate control strategy according to the fault type.The feasibility of this method and its advantages in control effect and speed are verified by the fault identification and parameter optimization method of doubly fed fan model.
, Available online
, doi: 10.14135/j.cnki.1006-3080-20220123002
Abstract:
The chlorination of sucrose-6-acetates (S-6-A) to produce sucralose-6-acetate (TGS-6-A) is a key step in the synthesis of sucralose. In this work, the thermodynamic calculation of the chlorination reaction was carried out using the group contribution method. The results show that the reaction is exothermic and irreversible in the temperature range of 372 K to 389 K. The thermodynamic calculations were performed on the hydrolysis reaction of TGS-6-A and the equilibrium constants at different temperatures were obtained, which agree well with the experimental value, confirming the reliability of the calculation methods used in this work. Then the effect of temperature on reaction rate was studied by batch experiments and a chain reaction kinetic model of chlorination reaction was established. The activation energy of the main reactions was calculated as 103.87 kJ·mol−1 and 153.87 kJ·mol−1, respectively, and the activation energy of the side reaction was 87.09 kJ·mol−1. The kinetic experiment results show that the main reaction is more affected by reaction temperature, increasing the temperature and controlling the reaction time can effectively increase the yield of the target product TGS-6-A.
The chlorination of sucrose-6-acetates (S-6-A) to produce sucralose-6-acetate (TGS-6-A) is a key step in the synthesis of sucralose. In this work, the thermodynamic calculation of the chlorination reaction was carried out using the group contribution method. The results show that the reaction is exothermic and irreversible in the temperature range of 372 K to 389 K. The thermodynamic calculations were performed on the hydrolysis reaction of TGS-6-A and the equilibrium constants at different temperatures were obtained, which agree well with the experimental value, confirming the reliability of the calculation methods used in this work. Then the effect of temperature on reaction rate was studied by batch experiments and a chain reaction kinetic model of chlorination reaction was established. The activation energy of the main reactions was calculated as 103.87 kJ·mol−1 and 153.87 kJ·mol−1, respectively, and the activation energy of the side reaction was 87.09 kJ·mol−1. The kinetic experiment results show that the main reaction is more affected by reaction temperature, increasing the temperature and controlling the reaction time can effectively increase the yield of the target product TGS-6-A.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220106006
Abstract:
Saccharomyces cerevisiae is one of the cell factories in biomanufacturing because of numerous advantages towards industrial fermentations, which include robust growth in low pH, lower temperatures, high tolerance to shear stress, lack of phage contamination, and ease of separation. However, the Crabtree effect of S. cerevisiae made ethanol and glycerol be accumulated due to carbon overflow. For the production of intermediate derivatives of the TCA cycle, such as itaconic acid, the Crabtree effect must be overcome by using a suitable strategy. In this paper, the role of NOX and AOX1 on the Crabtree effect in batch fermentation of S. cerevisiae was investigated by expressing the NADH oxidase NOX and the alternative oxidase AOX1 with plasmids having different copy numbers. It was revealed that both strains expressing NOX and AOX1 in high copy vectors caused significant metabolic changes. The high copy expressing nox strains were able to oxidize cytoplasmic NADH, glycerol secretion in the medium was reduced by 43.94%, and IA concentration was not changed. In contrast, strains with high copy expression of aox1 had cytoplasmic residual AOX1, which oxidized cytoplasmic NADH and reduced glycerol accumulation. Further location of AOX1 to the mitochondria of S. cerevisiae with the mitochondrial location signals AAC2 and BCS1p reduced the effect of AOX1 on glycerol synthesis, and IA production was enhanced to 116.98 mg/L. However, none of the strains expressing AOX1, AAC2-AOX1 and BCS1p-AOX1 significantly alleviated the accumulation of ethanol in batch fermentation. This study helps to improve the production of TCA cycle derivatives from glucose by engineered S. cerevisiae, provides a reference for the production of TCA cycle derivatives from S. cerevisiae in the batch culture at high original glucose concentrations.
Saccharomyces cerevisiae is one of the cell factories in biomanufacturing because of numerous advantages towards industrial fermentations, which include robust growth in low pH, lower temperatures, high tolerance to shear stress, lack of phage contamination, and ease of separation. However, the Crabtree effect of S. cerevisiae made ethanol and glycerol be accumulated due to carbon overflow. For the production of intermediate derivatives of the TCA cycle, such as itaconic acid, the Crabtree effect must be overcome by using a suitable strategy. In this paper, the role of NOX and AOX1 on the Crabtree effect in batch fermentation of S. cerevisiae was investigated by expressing the NADH oxidase NOX and the alternative oxidase AOX1 with plasmids having different copy numbers. It was revealed that both strains expressing NOX and AOX1 in high copy vectors caused significant metabolic changes. The high copy expressing nox strains were able to oxidize cytoplasmic NADH, glycerol secretion in the medium was reduced by 43.94%, and IA concentration was not changed. In contrast, strains with high copy expression of aox1 had cytoplasmic residual AOX1, which oxidized cytoplasmic NADH and reduced glycerol accumulation. Further location of AOX1 to the mitochondria of S. cerevisiae with the mitochondrial location signals AAC2 and BCS1p reduced the effect of AOX1 on glycerol synthesis, and IA production was enhanced to 116.98 mg/L. However, none of the strains expressing AOX1, AAC2-AOX1 and BCS1p-AOX1 significantly alleviated the accumulation of ethanol in batch fermentation. This study helps to improve the production of TCA cycle derivatives from glucose by engineered S. cerevisiae, provides a reference for the production of TCA cycle derivatives from S. cerevisiae in the batch culture at high original glucose concentrations.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220302001
Abstract:
Due to inherent factors such as infrastructure construction, wireless sensor networks must consider the problem of limited network resources and uneven resource consumption. In this paper, based on swarm intelligence fuzzy control, fuzzy control is introduced into swarm intelligence artificial bee swarm routing protocol to solve the optimization problem of multipath routing planning in software-defined sensor networks. Based on the SDN-WISE software defined network architecture and swarm intelligence algorithm, and the optimal link was searched by generating artificial bees to simulate the process of honey gathering. Artificial bees adjust different data transmission links, judge regional state through fuzzy logic, and evaluate the data link with the highest value by generating fitness function, generating an optimized routing solution. The experimental results show that, compared with the classical routing algorithms is adopted in this method to optimize the routing problem solving process in the framework of loosely coupled software-defined network by integrating the agent adaptive ability of artificial bees and the fault-tolerant logic of fuzzy control. The experimental results show that, It has obvious advantages in residual energy management, network utilization, transmission delay and packet delivery rate.
Due to inherent factors such as infrastructure construction, wireless sensor networks must consider the problem of limited network resources and uneven resource consumption. In this paper, based on swarm intelligence fuzzy control, fuzzy control is introduced into swarm intelligence artificial bee swarm routing protocol to solve the optimization problem of multipath routing planning in software-defined sensor networks. Based on the SDN-WISE software defined network architecture and swarm intelligence algorithm, and the optimal link was searched by generating artificial bees to simulate the process of honey gathering. Artificial bees adjust different data transmission links, judge regional state through fuzzy logic, and evaluate the data link with the highest value by generating fitness function, generating an optimized routing solution. The experimental results show that, compared with the classical routing algorithms is adopted in this method to optimize the routing problem solving process in the framework of loosely coupled software-defined network by integrating the agent adaptive ability of artificial bees and the fault-tolerant logic of fuzzy control. The experimental results show that, It has obvious advantages in residual energy management, network utilization, transmission delay and packet delivery rate.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20200212001
Abstract:
This paper considers a hybrid fuzzy neural networks (FNN) for time-series prediction based on error distribution analysis. Firstly, a new hybrid FNN (HFNN) structure is established, where the last two layers is replaced by a combination of a full connection layer and nonlinear activation function. Thus, more parameters can be updated in training process to guarantee the prediction accuracy. Secondly, a novel attention loss function is proposed to make a sample with a certain error distribution get more gains in training process. Based on rule analysis with probability density function, it is seen that the proposed method can provide a more uniform and stable predicted output. The prediction errors of HFNN converge to a compact set. Finally, two benchmark problems are applied to demonstrate the hybrid model performance on time series prediction. The comparisons with other prediction models have verified the efficiency and accuracy of the proposed HFNN model.
This paper considers a hybrid fuzzy neural networks (FNN) for time-series prediction based on error distribution analysis. Firstly, a new hybrid FNN (HFNN) structure is established, where the last two layers is replaced by a combination of a full connection layer and nonlinear activation function. Thus, more parameters can be updated in training process to guarantee the prediction accuracy. Secondly, a novel attention loss function is proposed to make a sample with a certain error distribution get more gains in training process. Based on rule analysis with probability density function, it is seen that the proposed method can provide a more uniform and stable predicted output. The prediction errors of HFNN converge to a compact set. Finally, two benchmark problems are applied to demonstrate the hybrid model performance on time series prediction. The comparisons with other prediction models have verified the efficiency and accuracy of the proposed HFNN model.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220107003
Abstract:
Heat integration across different units is an effective measure to improve energy utilization in chemical plants. In order to obtain the actual energy-saving potential across the units and improve the fluctuation resistance of the retrofitting schemes, a retrofitting method based on the actual cold and heat composite curves is put forward. Based on the industrial data of benzene production and C8 units in a petrochemical enterprise, the heat exchanger networks of the two units are simulated in Aspen HYSYS and the pinch analysis is done with Aspen Energy Analyzer. Considering the unreasonable heat transfer of benzene production unit and the safety constraints, retrofitting schemes for the heat exchanger network of benzene production unit are proposed to reduce the steam consumption. Since the energy-saving potential of the C8 unit is limited and the unreasonable heat transfer is distributed in different heat exchangers, it is not cost-effective to retrofit the heat exchanger network of the C8 unit. The energy-saving potential of heat integration between the two units is analyzed by constructing the actual cold and hot composite curves. The advantage of using the actual cold and hot composite curves to guide the heat integration across the two units is that it uses the actual residual energy, which is more practical than the theoretical situation such as the grand composite curve. Besides, limitations and complexity of implementation are also considered to make retrofitting schemes. As a result, two heat integration retrofit schemes across the two units are proposed, compared, and discussed. The results show that the scheme with more energy saving needs more investment costs and has a slightly longer payback period, but has more economic benefits in the long term.
Heat integration across different units is an effective measure to improve energy utilization in chemical plants. In order to obtain the actual energy-saving potential across the units and improve the fluctuation resistance of the retrofitting schemes, a retrofitting method based on the actual cold and heat composite curves is put forward. Based on the industrial data of benzene production and C8 units in a petrochemical enterprise, the heat exchanger networks of the two units are simulated in Aspen HYSYS and the pinch analysis is done with Aspen Energy Analyzer. Considering the unreasonable heat transfer of benzene production unit and the safety constraints, retrofitting schemes for the heat exchanger network of benzene production unit are proposed to reduce the steam consumption. Since the energy-saving potential of the C8 unit is limited and the unreasonable heat transfer is distributed in different heat exchangers, it is not cost-effective to retrofit the heat exchanger network of the C8 unit. The energy-saving potential of heat integration between the two units is analyzed by constructing the actual cold and hot composite curves. The advantage of using the actual cold and hot composite curves to guide the heat integration across the two units is that it uses the actual residual energy, which is more practical than the theoretical situation such as the grand composite curve. Besides, limitations and complexity of implementation are also considered to make retrofitting schemes. As a result, two heat integration retrofit schemes across the two units are proposed, compared, and discussed. The results show that the scheme with more energy saving needs more investment costs and has a slightly longer payback period, but has more economic benefits in the long term.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220103001
Abstract:
The order of the stability and binding constant of curcumin encapsulated by three Tween aggregates is: Tween-85 vesicles > Tween-60 vesicles > Tween-80 micelles. By UV and fluorescence measurements, it is found that curcumin is encapsulated in the hydrophobic region of the alkyl chains of Tween aggregates with the hydrophobic interaction as the main driving force. The 1H NMR data confirm that the encapsulation position and force of curcumin are closely related to the alkyl chain structure of Tween surfactants. Compared with Tween-60 vesicles with a particle size of ~92 nm, the micelles formed by Tween-80 containing double bonds in the alkyl chain have loosely arranged hydrophobic region. Therefore, curcumin encapsulated by Tween-80 micelles exhibits lower stability, binding constant, and UV absorption and fluorescence emission intensities. Tween-85 with three unsaturated alkyl chains can generate vesicles with a particle size of ~150 nm, and its bilayer has the highest hydrophobicity which has the best encapsulation effect on curcumin.
The order of the stability and binding constant of curcumin encapsulated by three Tween aggregates is: Tween-85 vesicles > Tween-60 vesicles > Tween-80 micelles. By UV and fluorescence measurements, it is found that curcumin is encapsulated in the hydrophobic region of the alkyl chains of Tween aggregates with the hydrophobic interaction as the main driving force. The 1H NMR data confirm that the encapsulation position and force of curcumin are closely related to the alkyl chain structure of Tween surfactants. Compared with Tween-60 vesicles with a particle size of ~92 nm, the micelles formed by Tween-80 containing double bonds in the alkyl chain have loosely arranged hydrophobic region. Therefore, curcumin encapsulated by Tween-80 micelles exhibits lower stability, binding constant, and UV absorption and fluorescence emission intensities. Tween-85 with three unsaturated alkyl chains can generate vesicles with a particle size of ~150 nm, and its bilayer has the highest hydrophobicity which has the best encapsulation effect on curcumin.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220107004
Abstract:
Heat exchanger network, HEN, is one of the most important parts in chemical production process. HEN optimization become an effective tool to save energy and keep sustainable development. There are many methods to optimize HEN. In principle, mathematical programming seems a comprehensive solution, while the pinch point method is still a handy tool, due to its simplicity and clear physical meaning. Special arrangement is required when phase change is considered in the system. The aim of this paper is to investigate six streams distributed in adjacent sections in an ethylene cracking process from systematic perspective, as no heat recovery is involved, and only utility is matched to meet their temperature requirement in process. Problem table is used to determine the pinch point for the design of HEN. Due to the complex phase change of the mixture in the heat exchange system, the phase change section is converted into one or more streams with constant heat capacity flow rate according to its thermal load and material characteristics, so as to determine the temperature interval. In traditional pinch point method, △Tmin is found as 11℃, and it is determined to be 9℃ with the consideration of carbon emission. The pinch point is determined by problem table at 88.3℃ for the hot stream and 79.3℃ for the cold stream. Under this condition, the minimum thermal utility required by the heat exchanger network is 12727.27 kW, and the required minimum cold utility is 38719.59kW. The total annual cost is reduced by 2620585.49 USD/a, and the carbon emission is reduced by 61453.50t/a. According to the design principles of pinch technology and stream matching criteria, the energy-efficient heat exchanger network structure has been obtained.
Heat exchanger network, HEN, is one of the most important parts in chemical production process. HEN optimization become an effective tool to save energy and keep sustainable development. There are many methods to optimize HEN. In principle, mathematical programming seems a comprehensive solution, while the pinch point method is still a handy tool, due to its simplicity and clear physical meaning. Special arrangement is required when phase change is considered in the system. The aim of this paper is to investigate six streams distributed in adjacent sections in an ethylene cracking process from systematic perspective, as no heat recovery is involved, and only utility is matched to meet their temperature requirement in process. Problem table is used to determine the pinch point for the design of HEN. Due to the complex phase change of the mixture in the heat exchange system, the phase change section is converted into one or more streams with constant heat capacity flow rate according to its thermal load and material characteristics, so as to determine the temperature interval. In traditional pinch point method, △Tmin is found as 11℃, and it is determined to be 9℃ with the consideration of carbon emission. The pinch point is determined by problem table at 88.3℃ for the hot stream and 79.3℃ for the cold stream. Under this condition, the minimum thermal utility required by the heat exchanger network is 12727.27 kW, and the required minimum cold utility is 38719.59kW. The total annual cost is reduced by 2620585.49 USD/a, and the carbon emission is reduced by 61453.50t/a. According to the design principles of pinch technology and stream matching criteria, the energy-efficient heat exchanger network structure has been obtained.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211024001
Abstract:
High temperature and high dielectric constant polymer nanocomposites have attracted widespread attention in pulse power system. such as mobile electronics, electric vehicles and electronic equipment ,which due to their processing flexibility, light weight, and low cost. Herein, a new type of thermosetting benzoxazole high-temperature resistant resin NPBO was synthesized by chemical methods. The chemical structure and thermal curing behavior of NPBO were studied by H-NMR , EI-MS spectra and DSC, it proved excellent thermal properties. At the same time, the stepwise reaction and chemical grafting were used to prepare polyurethane-coated barium titanate core-shell hybrid nanoparticles (PU@BT), and then the PU @BT and NPBO resins were compounded according to different components to prepare PU@BT/NPBO nanocomposites. Use scanning electron microscope (SEM) and transmission electron microscope (TEM) to observe the morphology of PU@BT. and particles are evenly coated and showed a good dispersion performance. Finally, the dielectric properties of the composite materials are measured by a broadband dielectric spectrometer. It is found that as the volume fraction of PU@BT increases from 0 to 10%, the dielectric constant of the composite material increases significantly. At 1 kHz, the dielectric constant of NPBO is 3.3, and when 10% PU@BT is added, the dielectric constant of the composite is 7.3, which is an increase of 1.21 times. The composite material provides a theoretical basis for its application in the field of dielectrics.
High temperature and high dielectric constant polymer nanocomposites have attracted widespread attention in pulse power system. such as mobile electronics, electric vehicles and electronic equipment ,which due to their processing flexibility, light weight, and low cost. Herein, a new type of thermosetting benzoxazole high-temperature resistant resin NPBO was synthesized by chemical methods. The chemical structure and thermal curing behavior of NPBO were studied by H-NMR , EI-MS spectra and DSC, it proved excellent thermal properties. At the same time, the stepwise reaction and chemical grafting were used to prepare polyurethane-coated barium titanate core-shell hybrid nanoparticles (PU@BT), and then the PU @BT and NPBO resins were compounded according to different components to prepare PU@BT/NPBO nanocomposites. Use scanning electron microscope (SEM) and transmission electron microscope (TEM) to observe the morphology of PU@BT. and particles are evenly coated and showed a good dispersion performance. Finally, the dielectric properties of the composite materials are measured by a broadband dielectric spectrometer. It is found that as the volume fraction of PU@BT increases from 0 to 10%, the dielectric constant of the composite material increases significantly. At 1 kHz, the dielectric constant of NPBO is 3.3, and when 10% PU@BT is added, the dielectric constant of the composite is 7.3, which is an increase of 1.21 times. The composite material provides a theoretical basis for its application in the field of dielectrics.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220221003
Abstract:
PBT polyether polyurethanes with different active hydrogen components were prepared by a two-step method using 3, 3-diazymoxy-tetrahydrofuran copolymer (PBT) as the soft segment of polyether polyurethanes, toluene diisocyanate (TDI) as the curing agent, diethylene glycol (DEG) as the chain extender and trimethylol propane (TMP) as the crosslinking agent. The curing reaction kinetics and mechanical properties of PBT/TDI, PBT/TDI/DEG, PBT/TDI/TMP and PBT/TDI/DEG/TMP systems were studied by Fourier Transform infrared spectroscopy (FT-IR), differential scanning calorimeter (DSC), electronic universal testing machine and swelling ratio test. The results show that the curing reactions of PBT / TDI, PBT / TDI / DEG, PBT / TDI / TMP / and PBT / TDI / DEG / TMP systems are second-order reactions, and the activation energies of these systems are 135.98, 165.57, 164.93 and 164.29 kJ / mol respectively. The addition of DEG can significantly increase the elongation at break of the adhesive matrix, but the tensile strength decreases; The addition of TMP can improve the tensile strength of the adhesive matrix and reduce the elongation at break; when DEG and TMP exist simultaneously, the tensile strength of the adhesive matrix increased and the elongation at break decreased. DEG and TMP can both improve the crosslinking density of the curing systems.
PBT polyether polyurethanes with different active hydrogen components were prepared by a two-step method using 3, 3-diazymoxy-tetrahydrofuran copolymer (PBT) as the soft segment of polyether polyurethanes, toluene diisocyanate (TDI) as the curing agent, diethylene glycol (DEG) as the chain extender and trimethylol propane (TMP) as the crosslinking agent. The curing reaction kinetics and mechanical properties of PBT/TDI, PBT/TDI/DEG, PBT/TDI/TMP and PBT/TDI/DEG/TMP systems were studied by Fourier Transform infrared spectroscopy (FT-IR), differential scanning calorimeter (DSC), electronic universal testing machine and swelling ratio test. The results show that the curing reactions of PBT / TDI, PBT / TDI / DEG, PBT / TDI / TMP / and PBT / TDI / DEG / TMP systems are second-order reactions, and the activation energies of these systems are 135.98, 165.57, 164.93 and 164.29 kJ / mol respectively. The addition of DEG can significantly increase the elongation at break of the adhesive matrix, but the tensile strength decreases; The addition of TMP can improve the tensile strength of the adhesive matrix and reduce the elongation at break; when DEG and TMP exist simultaneously, the tensile strength of the adhesive matrix increased and the elongation at break decreased. DEG and TMP can both improve the crosslinking density of the curing systems.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220123003
Abstract:
The electrochemical method has been proved to be an effective method to remove ammonia, but the research on the energy consumption control has been neglected. This research uses artificial intelligence and back propagation neural network to establish the ammonia removal rate prediction model and intelligent control strategy. The model consists of a prediction module and a control module with a back propagation neural network (BPNN) algorithm model. First, 4 hidden layers (per 60 neurons) and a negative feedback adjustment mechanism are used to develop the BPNN algorithm to optimize the model and predict the ammonia removal rate. Through parameter analysis and comparison of response surface models, the BPNN model proposed in this paper has better coefficient of determination and lower mean square error. According to the water quality changes and the determined target of ammonia removal rate, the current control strategy in the electrochemical can be obtained through the BPNN model. Finally, the proposed intelligent control strategy is applied to the electrochemical system for ammonia removal, reducing the negative impact of water quality changes, and can also reduce energy consumption by 38% compared with the original strategy. This work proves the application potential of artificial intelligence and back propagation neural network in the electrochemical of ammonia removal, and provides the possibility to automate the water treatment process.
The electrochemical method has been proved to be an effective method to remove ammonia, but the research on the energy consumption control has been neglected. This research uses artificial intelligence and back propagation neural network to establish the ammonia removal rate prediction model and intelligent control strategy. The model consists of a prediction module and a control module with a back propagation neural network (BPNN) algorithm model. First, 4 hidden layers (per 60 neurons) and a negative feedback adjustment mechanism are used to develop the BPNN algorithm to optimize the model and predict the ammonia removal rate. Through parameter analysis and comparison of response surface models, the BPNN model proposed in this paper has better coefficient of determination and lower mean square error. According to the water quality changes and the determined target of ammonia removal rate, the current control strategy in the electrochemical can be obtained through the BPNN model. Finally, the proposed intelligent control strategy is applied to the electrochemical system for ammonia removal, reducing the negative impact of water quality changes, and can also reduce energy consumption by 38% compared with the original strategy. This work proves the application potential of artificial intelligence and back propagation neural network in the electrochemical of ammonia removal, and provides the possibility to automate the water treatment process.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220124003
Abstract:
Antifungal effects of 7 different active components from plant essential oils (including cinnamaldehyde, citral, carvacrol, linalool, thymol, menthol, perillyl alcohol) against Fusarium graminearum (F.g.) were compared by the method of inhibiting mycelial growth in vitro. Citral, carvacrol, and thymol were selected due to their lower EC50 values and formed a compound with each other, respectively. The compound composed of carvacrol and thymol was considered the most excellent paring with the best inhibitory effect against F.g. in vitro. Additionally, the mass ratio of carvacrol and thymol in the compound was optimized and finalized the formulation of natural compound fungicide. Results showed that when the mass ratio of carvacrol and thymol was 1∶2, the fungicide had the best antifungal effect against F.g. and the synergistic index (S.I.) is 1.45, which showed a synergistic effect. The possible antifungal mechanisms of carvacrol and thymol compound were also analyzed. The prepared natural compound fungicide could change the permeability of F.g.’s cell membrane, reflected by the change in conductivity. Furthermore, the effects of the prepared natural compounded fungicide on wheat coleoptiles against F.g. were studied. It could significantly inhibit the growth of lesions on wheat coleoptiles. When fungicide, which concentration was 200 μg·mL−1, was administered to wheat coleoptiles infected by F.g., the control rate of the protective group and the curative group were 79.08 % and 84.54 %, respectively. This research provided theoretical guidance for developing natural compound fungicides with precise efficacy. The possibility of further application of active components of plant essential oils has also been discussed.
Antifungal effects of 7 different active components from plant essential oils (including cinnamaldehyde, citral, carvacrol, linalool, thymol, menthol, perillyl alcohol) against Fusarium graminearum (F.g.) were compared by the method of inhibiting mycelial growth in vitro. Citral, carvacrol, and thymol were selected due to their lower EC50 values and formed a compound with each other, respectively. The compound composed of carvacrol and thymol was considered the most excellent paring with the best inhibitory effect against F.g. in vitro. Additionally, the mass ratio of carvacrol and thymol in the compound was optimized and finalized the formulation of natural compound fungicide. Results showed that when the mass ratio of carvacrol and thymol was 1∶2, the fungicide had the best antifungal effect against F.g. and the synergistic index (S.I.) is 1.45, which showed a synergistic effect. The possible antifungal mechanisms of carvacrol and thymol compound were also analyzed. The prepared natural compound fungicide could change the permeability of F.g.’s cell membrane, reflected by the change in conductivity. Furthermore, the effects of the prepared natural compounded fungicide on wheat coleoptiles against F.g. were studied. It could significantly inhibit the growth of lesions on wheat coleoptiles. When fungicide, which concentration was 200 μg·mL−1, was administered to wheat coleoptiles infected by F.g., the control rate of the protective group and the curative group were 79.08 % and 84.54 %, respectively. This research provided theoretical guidance for developing natural compound fungicides with precise efficacy. The possibility of further application of active components of plant essential oils has also been discussed.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220117001
Abstract:
An improved attention algorithm based on the MaskRCNN network to improve the effect of the semantic segmentation of the test paper, because separating the printed and handwritten regions is a key step to achieve the semantic segmentation of the test paper. The algorithm embeds the Subspace Multiscale Feature Fusion (SMFF) module into the feature pyramid structure of the MaskRCNN network, which calculates attention features based on the subspace, and reduces the spatial and channel redundancy in the feature map. Fusion can effectively extract features of text regions of different sizes and enhance the correlation between features. The experimental results show that the average accuracy of the MaskRCNN network model based on the SMFF module is 15.8% and 10.2% higher than that of the original MaskRCNN network model in the target detection and semantic segmentation tasks of the test paper image dataset, which has a large performance improvement than the MaskRCNN based on the commonly used attention module.
An improved attention algorithm based on the MaskRCNN network to improve the effect of the semantic segmentation of the test paper, because separating the printed and handwritten regions is a key step to achieve the semantic segmentation of the test paper. The algorithm embeds the Subspace Multiscale Feature Fusion (SMFF) module into the feature pyramid structure of the MaskRCNN network, which calculates attention features based on the subspace, and reduces the spatial and channel redundancy in the feature map. Fusion can effectively extract features of text regions of different sizes and enhance the correlation between features. The experimental results show that the average accuracy of the MaskRCNN network model based on the SMFF module is 15.8% and 10.2% higher than that of the original MaskRCNN network model in the target detection and semantic segmentation tasks of the test paper image dataset, which has a large performance improvement than the MaskRCNN based on the commonly used attention module.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220211001
Abstract:
Due to multi-path effect and electromagnetic interference, RSS-based indoor positioning systems show poor accuracy. In this paper, an indoor positioning scheme of a heterogeneous wireless sensor network (WSN) based on the RSS and inertial measurement is proposed, which uses the distributed consensus cubature information filters with credibility evaluation to estimate target positions collaboratively. An event triggering mechanism is introduced, where the sensor nodes are awaken to serve only if their RSS constraints are satisfied. The simulation and experiment results of a mobile car show that the positioning accuracy and robustness of the proposed method improve significantly. Moreover, the distributed positioning sheme and event-triggering mechanism help to reduce network energy consumption effectively.
Due to multi-path effect and electromagnetic interference, RSS-based indoor positioning systems show poor accuracy. In this paper, an indoor positioning scheme of a heterogeneous wireless sensor network (WSN) based on the RSS and inertial measurement is proposed, which uses the distributed consensus cubature information filters with credibility evaluation to estimate target positions collaboratively. An event triggering mechanism is introduced, where the sensor nodes are awaken to serve only if their RSS constraints are satisfied. The simulation and experiment results of a mobile car show that the positioning accuracy and robustness of the proposed method improve significantly. Moreover, the distributed positioning sheme and event-triggering mechanism help to reduce network energy consumption effectively.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211118002
Abstract:
Traditional linear multivariate Granger causality test introduces conditional variables to determine whether the causal relationships exist between every two variables or not. However, the traditional way of selecting conditional variables is manual, which lacks of reasonable rules. To deal with the problem, an improved nonlinear multivariate Granger causality test method with selecting conditional variables is proposed in this paper. The proposed method in this paper combines traditional Granger test and multivariable Granger test. This method uses nonlinear Granger causality test to construct a preliminary structure by analyzing the potential relationships between variables to determine which variables are suitable as conditional variables, then nonlinear multivariable Granger causality test can be further used on these preprocessed conditional variables; Two kinds of topological structures are introduced to avoid the repeated inspection of some real relationships that do not produce pseudo causality problems. In our method, support vector regression is used as the way to cope with the nonlinearity. The experimental results on numerical simulation and wastewater treatment benchmark simulation model show that the influence of irrelevant variables is reduced by the proposed method in this paper via selecting condition variables and the causal relationship between variables could be analyzed more accurately. Moreover, the proposed method can adapt to nonlinear conditions and has better performance in terms of computational intensity.
Traditional linear multivariate Granger causality test introduces conditional variables to determine whether the causal relationships exist between every two variables or not. However, the traditional way of selecting conditional variables is manual, which lacks of reasonable rules. To deal with the problem, an improved nonlinear multivariate Granger causality test method with selecting conditional variables is proposed in this paper. The proposed method in this paper combines traditional Granger test and multivariable Granger test. This method uses nonlinear Granger causality test to construct a preliminary structure by analyzing the potential relationships between variables to determine which variables are suitable as conditional variables, then nonlinear multivariable Granger causality test can be further used on these preprocessed conditional variables; Two kinds of topological structures are introduced to avoid the repeated inspection of some real relationships that do not produce pseudo causality problems. In our method, support vector regression is used as the way to cope with the nonlinearity. The experimental results on numerical simulation and wastewater treatment benchmark simulation model show that the influence of irrelevant variables is reduced by the proposed method in this paper via selecting condition variables and the causal relationship between variables could be analyzed more accurately. Moreover, the proposed method can adapt to nonlinear conditions and has better performance in terms of computational intensity.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220115004
Abstract:
MiRNA is a single-stranded and small non-coding RNA, which is closely related to human diseases. Predicting miRNA-disease associations can help understand the pathogenesis of diseases at the molecular level, so as to provide basis for studying the prognosis, diagnosis, evaluation and treatment of diseases. In miRNA-disease association prediction, most methods used miRNA functional similarity and disease semantic similarity as input, they ignored the miRNA sequence similarity, disease functional similarity and hamming similarity. And in the feature extraction process, they not considered the information complementarity between the linear features and nonlinear features, which would affect the quality of feature extraction of miRNA and disease. Therefore, we propose a novel miRNA-disease association prediction model GCNMSF. First, we introduce the miRNA sequence similarity, disease semantic similarity and hamming similarity, and use similarity kernel fusion method to integrate multi-source similarities of miRNA and disease respectively. Then, we use the graph convolutional network to learn nonlinear features. And the convolutional attention block is embedded into GCN to optimize feature distribution. At the same time, the non-negative matrix factorization method is introduced to learn linear features of miRNA and disease to enrich the feature space which can improve the ability of predicting miRNA-disease associations. Finally, we fused the linear and nonlinear features of miRNA and disease to predict miRNA-disease associations. We use five-fold cross validation to evaluate GCNMSF and the experimental results show that our model is better than the existing methods. In addition, we conduct ablation experiment and case studies to evaluate the effectiveness and applicability of the model. The results of ablation experiment verify the fusion of multi-source similarity information and the combination of linear and nonlinear features are helpful for miRNA-disease association prediction. The case studies of lung and breast cancers further confirmed that GCNMSF can not only predict the potential miRNA-disease associations, but also discover the miRNA-disease associations of unknown diseases.
MiRNA is a single-stranded and small non-coding RNA, which is closely related to human diseases. Predicting miRNA-disease associations can help understand the pathogenesis of diseases at the molecular level, so as to provide basis for studying the prognosis, diagnosis, evaluation and treatment of diseases. In miRNA-disease association prediction, most methods used miRNA functional similarity and disease semantic similarity as input, they ignored the miRNA sequence similarity, disease functional similarity and hamming similarity. And in the feature extraction process, they not considered the information complementarity between the linear features and nonlinear features, which would affect the quality of feature extraction of miRNA and disease. Therefore, we propose a novel miRNA-disease association prediction model GCNMSF. First, we introduce the miRNA sequence similarity, disease semantic similarity and hamming similarity, and use similarity kernel fusion method to integrate multi-source similarities of miRNA and disease respectively. Then, we use the graph convolutional network to learn nonlinear features. And the convolutional attention block is embedded into GCN to optimize feature distribution. At the same time, the non-negative matrix factorization method is introduced to learn linear features of miRNA and disease to enrich the feature space which can improve the ability of predicting miRNA-disease associations. Finally, we fused the linear and nonlinear features of miRNA and disease to predict miRNA-disease associations. We use five-fold cross validation to evaluate GCNMSF and the experimental results show that our model is better than the existing methods. In addition, we conduct ablation experiment and case studies to evaluate the effectiveness and applicability of the model. The results of ablation experiment verify the fusion of multi-source similarity information and the combination of linear and nonlinear features are helpful for miRNA-disease association prediction. The case studies of lung and breast cancers further confirmed that GCNMSF can not only predict the potential miRNA-disease associations, but also discover the miRNA-disease associations of unknown diseases.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211221001
Abstract:
One of the important way of 3D model making is 3D reconstruction. At present, 3D scene reconstruction with moving object interference is a research hotspot. To solve this problem, this paper proposes a 3D reconstruction framework named ORBTSDF-SCNet. This framework combines SLAM (Simultaneous Localization And Mapping), TSDF(Truncated Signed Distance Function) and SCNet(Sample Consistency Networks) technology to complete 3D scene reconstruction with moving object interference. In this framework, firstly, aiming at the fact that SLAM system can only output point cloud and can not directly generate 3D model, this paper proposes a 3D reconstruction method ORBTSDF.In this method, depth camera or binocular camera obtains RGBD image of the moving objects and scene, the tracking thread of ORB_SLAM2 is applied to obtains pose information in real time, the surface reconstruction algorithm TSDF is adopted to realize 3D model reconstruction combined with depth image.At the same time, in order to eliminate the interference of moving objects in 3D scene reconstruction, such as image smear, low accuracy or reconstruction failure etc., a deep learning instance segmentation network SCNet is used to detect and segment moving objects. By combining with some optimization strategies, the error of detection and instance segmentation , the alignment error of depth map and RGB map are reduced. When the instance of the moving object is removed, the RGBD image is transmitted back to the part of ORBTSDF to form a 3D scene reconstruction without moving objects. Comparative experiments on ICL-NUM and TUM datasets shows the effectiveness of the proposed method.
One of the important way of 3D model making is 3D reconstruction. At present, 3D scene reconstruction with moving object interference is a research hotspot. To solve this problem, this paper proposes a 3D reconstruction framework named ORBTSDF-SCNet. This framework combines SLAM (Simultaneous Localization And Mapping), TSDF(Truncated Signed Distance Function) and SCNet(Sample Consistency Networks) technology to complete 3D scene reconstruction with moving object interference. In this framework, firstly, aiming at the fact that SLAM system can only output point cloud and can not directly generate 3D model, this paper proposes a 3D reconstruction method ORBTSDF.In this method, depth camera or binocular camera obtains RGBD image of the moving objects and scene, the tracking thread of ORB_SLAM2 is applied to obtains pose information in real time, the surface reconstruction algorithm TSDF is adopted to realize 3D model reconstruction combined with depth image.At the same time, in order to eliminate the interference of moving objects in 3D scene reconstruction, such as image smear, low accuracy or reconstruction failure etc., a deep learning instance segmentation network SCNet is used to detect and segment moving objects. By combining with some optimization strategies, the error of detection and instance segmentation , the alignment error of depth map and RGB map are reduced. When the instance of the moving object is removed, the RGBD image is transmitted back to the part of ORBTSDF to form a 3D scene reconstruction without moving objects. Comparative experiments on ICL-NUM and TUM datasets shows the effectiveness of the proposed method.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220120001
Abstract:
In order to ensure the safe and stable operation of the production process and avoid losses due to failures, timely detection of abnormal conditions and accurate diagnosis of abnormal conditions are of very important research significance. Aiming at the complexity of the chemical process, this paper proposes a parallel long and short-term memory network and convolutional neural network (PLSTM-CNN) model for fault detection in the chemical production process. This model effectively combines the LSTM's ability to extract global features from time series data and the CNN model's ability to extract local features, reducing the loss of feature information and achieving a higher fault detection rate. The one-dimensional dense convolutional neural network is used as the main body of CNN, combined with the LSTM network's sensitivity to sequence information changes, to avoid model overfitting while building a deeper network. The maximum mutual information coefficient (MMIC) data preprocessing method is adopted to improve the local correlation of the data and improve the efficiency of the PLSTM-CNN model in detecting faults under different initial conditions. Taking the Eastman Process of Tennessee (TE) process in Tennessee as the research object, the PLSTM-CNN model is significantly better than the traditional recurrent neural network in indicators such as the average failure detection rate and the false negative rate.
In order to ensure the safe and stable operation of the production process and avoid losses due to failures, timely detection of abnormal conditions and accurate diagnosis of abnormal conditions are of very important research significance. Aiming at the complexity of the chemical process, this paper proposes a parallel long and short-term memory network and convolutional neural network (PLSTM-CNN) model for fault detection in the chemical production process. This model effectively combines the LSTM's ability to extract global features from time series data and the CNN model's ability to extract local features, reducing the loss of feature information and achieving a higher fault detection rate. The one-dimensional dense convolutional neural network is used as the main body of CNN, combined with the LSTM network's sensitivity to sequence information changes, to avoid model overfitting while building a deeper network. The maximum mutual information coefficient (MMIC) data preprocessing method is adopted to improve the local correlation of the data and improve the efficiency of the PLSTM-CNN model in detecting faults under different initial conditions. Taking the Eastman Process of Tennessee (TE) process in Tennessee as the research object, the PLSTM-CNN model is significantly better than the traditional recurrent neural network in indicators such as the average failure detection rate and the false negative rate.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210928002
Abstract:
Aimed at solving the problems of complex background and variable lighting in laboratory scene understanding, based on the complementary characteristics of RGB image information and depth image information in scene understanding, a perceptual attention and lightweight spatial fusion network model is proposed. In the perceptual attention module of this model, the RGB image and the depth image in the network are used to implement the multi-level assistance to the RGB information by the depth information in a weighted mode. In the lightweight spatial pyramid pooling module, increasing the level of the joint atrous space convolution not only effectively aggregates multi-scale features, but also reduces the parameter amount of the traditional spatial pyramid pooling module by around 89%, enabling the RGB image information and depth image information to fuse more adequately. The model performs better on the two public datasets of indoor scenes than among the classic algorithm. The analysis of each module through ablation experiments verifies that the mean intersections over union of the algorithm proposed in this paper increase by 4.3% and 3.5% respectively. Finally, a test based on the dataset of biological laboratory on the more complex scenes is carried out, which shows that the model can effectively realize the scene understanding of biological laboratory.
Aimed at solving the problems of complex background and variable lighting in laboratory scene understanding, based on the complementary characteristics of RGB image information and depth image information in scene understanding, a perceptual attention and lightweight spatial fusion network model is proposed. In the perceptual attention module of this model, the RGB image and the depth image in the network are used to implement the multi-level assistance to the RGB information by the depth information in a weighted mode. In the lightweight spatial pyramid pooling module, increasing the level of the joint atrous space convolution not only effectively aggregates multi-scale features, but also reduces the parameter amount of the traditional spatial pyramid pooling module by around 89%, enabling the RGB image information and depth image information to fuse more adequately. The model performs better on the two public datasets of indoor scenes than among the classic algorithm. The analysis of each module through ablation experiments verifies that the mean intersections over union of the algorithm proposed in this paper increase by 4.3% and 3.5% respectively. Finally, a test based on the dataset of biological laboratory on the more complex scenes is carried out, which shows that the model can effectively realize the scene understanding of biological laboratory.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210922003
Abstract:
Mental workload can be employed as an indicator of the brain effort. It reflects people’s capability of processing information when performing a task. Recently, mental workload assessment has been widely studied in various tasks, such as simulated flight tasks, cognitive tasks and so on. The traditional methods of evaluating mental workload include subjective scale method, task performance method and physiological signal parameters method. NIRS has many advantages over other physiological techniques as it has better spatial resolution than EEG and better temporal resolution than fMRI. Besides, it is portable and lightweight with simple data acquisition and less exposed to electrical artifacts. In this paper, near-infrared spectrum signals (NIRS) are selected to build a mental workload assessment model. In recent years, deep learning with convolutional neural networks has revolutionized signal processing through end-to-end learning due to its efficiency and convenience. In order to eliminate the redundant information and extract features from the multi-channel near-infrared spectrum signals (NIRS), a novel mental workload recognition model was created based on the hybrid autoencoders. First, the original signals were sent to the stack autoencoder for channel dimensionality reduction, then these processed signals were fed to the convolutional autoencoder to extract the abstract features. Then we employed three base classifiers, i.e., the Support Vector Machine (SVM), K Nearest Neighbors (KNN), Random Forest (RF), for building models. Finally, the integration strategies of soft voting and hard voting were applied to improve the assessment accuracy for mental workload. The results show that proper way of compressing signal channels helps to improve the recognition accuracy of the model. The best accuracy of our proposed model for classifying three levels of mental workload can reach 95.12%, which is significantly improved compared to similar studies.
Mental workload can be employed as an indicator of the brain effort. It reflects people’s capability of processing information when performing a task. Recently, mental workload assessment has been widely studied in various tasks, such as simulated flight tasks, cognitive tasks and so on. The traditional methods of evaluating mental workload include subjective scale method, task performance method and physiological signal parameters method. NIRS has many advantages over other physiological techniques as it has better spatial resolution than EEG and better temporal resolution than fMRI. Besides, it is portable and lightweight with simple data acquisition and less exposed to electrical artifacts. In this paper, near-infrared spectrum signals (NIRS) are selected to build a mental workload assessment model. In recent years, deep learning with convolutional neural networks has revolutionized signal processing through end-to-end learning due to its efficiency and convenience. In order to eliminate the redundant information and extract features from the multi-channel near-infrared spectrum signals (NIRS), a novel mental workload recognition model was created based on the hybrid autoencoders. First, the original signals were sent to the stack autoencoder for channel dimensionality reduction, then these processed signals were fed to the convolutional autoencoder to extract the abstract features. Then we employed three base classifiers, i.e., the Support Vector Machine (SVM), K Nearest Neighbors (KNN), Random Forest (RF), for building models. Finally, the integration strategies of soft voting and hard voting were applied to improve the assessment accuracy for mental workload. The results show that proper way of compressing signal channels helps to improve the recognition accuracy of the model. The best accuracy of our proposed model for classifying three levels of mental workload can reach 95.12%, which is significantly improved compared to similar studies.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210916002
Abstract:
Optical chemical structure recognition from scientific publications is an essential part of rediscovering a chemical structure. Rule-based approaches and emerging deep learning methods both face certain problems, such as a low recognition rate. In this paper, we propose DeepOCSR, a deep learning method for optical chemical structure recognition. Based on the encoder–decoder architecture, this method introduces Transformer and ResNeSt models for converting chemical structure images from publications into SMILES sequences. To train and verify our method, two novel chemical structure datasets were constructed, one of which contained common substituents in the chemical literature. Our proposed method has been extensively tested against existing publicly available deep-learning approaches. The experimental results show that our method outperforms the compared approaches in several pivotal evaluation metrics, including similarity and validity, proving the effectiveness of our method.
Optical chemical structure recognition from scientific publications is an essential part of rediscovering a chemical structure. Rule-based approaches and emerging deep learning methods both face certain problems, such as a low recognition rate. In this paper, we propose DeepOCSR, a deep learning method for optical chemical structure recognition. Based on the encoder–decoder architecture, this method introduces Transformer and ResNeSt models for converting chemical structure images from publications into SMILES sequences. To train and verify our method, two novel chemical structure datasets were constructed, one of which contained common substituents in the chemical literature. Our proposed method has been extensively tested against existing publicly available deep-learning approaches. The experimental results show that our method outperforms the compared approaches in several pivotal evaluation metrics, including similarity and validity, proving the effectiveness of our method.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211014002
Abstract:
Pitch-based spherical activated carbon (PSAC) is widely used in medical treatment, environmental protection and other fields because of its advantages of high specific surface area, high mechanical strength, high packing intensity and low fluid resistance. A series of SnOx-CeOx/PSAC catalysts were prepared by impregnation method using PSAC prepared from high softening point petroleum pitch as support. And their catalytic performance were evaluated by the low-temperature selective catalytic reduction (SCR) of NO with NH3. The obtained samples were mainly characterized by nitrogen adsorption/desorption, X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The results show that SnOx-CeOx/PSAC catalyst exhibits higher SCR activity in comparison with CeOx/PSAC catalyst, and the trend of NO conversion firstly increases and then decreases with the increasing of metal loading. The Sn(5%)Ce(13%)/PSAC catalyst exhibits the highest NO removal activity, where the highest NO conversion can reach about 98% in the temperature range of 100~300 oC. The reason is mainly attributed to the improved dispersion of cerium oxide on the surface of PSAC by addition of SnOx, and the formation of solid solution between SnOx and CeOx with the fluorite-type structure, which may be caused by the incorporation of Sn4+ into the crystal lattice of CeO2. Furthermore, there are a certain amount of Ce3+, and higher percentage of surface chemisorbed oxygen on the catalyst surface because of the synergistic effect between tin and cerium oxides. These factors result in the excellent NH3-SCR performance of the Sn(5%)Ce(13%)/PSAC catalyst. Compared with CeOx/PSAC catalyst, SnOx-CeOx/PSAC catalyst exhibits a higher resistance to SO2 poisoning. NO conversion of Sn(5%)Ce(13%)/PSAC catalyst is still about 80% at 260 oC after the introduction of SO2 in the feed gas for 420 min.
Pitch-based spherical activated carbon (PSAC) is widely used in medical treatment, environmental protection and other fields because of its advantages of high specific surface area, high mechanical strength, high packing intensity and low fluid resistance. A series of SnOx-CeOx/PSAC catalysts were prepared by impregnation method using PSAC prepared from high softening point petroleum pitch as support. And their catalytic performance were evaluated by the low-temperature selective catalytic reduction (SCR) of NO with NH3. The obtained samples were mainly characterized by nitrogen adsorption/desorption, X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The results show that SnOx-CeOx/PSAC catalyst exhibits higher SCR activity in comparison with CeOx/PSAC catalyst, and the trend of NO conversion firstly increases and then decreases with the increasing of metal loading. The Sn(5%)Ce(13%)/PSAC catalyst exhibits the highest NO removal activity, where the highest NO conversion can reach about 98% in the temperature range of 100~300 oC. The reason is mainly attributed to the improved dispersion of cerium oxide on the surface of PSAC by addition of SnOx, and the formation of solid solution between SnOx and CeOx with the fluorite-type structure, which may be caused by the incorporation of Sn4+ into the crystal lattice of CeO2. Furthermore, there are a certain amount of Ce3+, and higher percentage of surface chemisorbed oxygen on the catalyst surface because of the synergistic effect between tin and cerium oxides. These factors result in the excellent NH3-SCR performance of the Sn(5%)Ce(13%)/PSAC catalyst. Compared with CeOx/PSAC catalyst, SnOx-CeOx/PSAC catalyst exhibits a higher resistance to SO2 poisoning. NO conversion of Sn(5%)Ce(13%)/PSAC catalyst is still about 80% at 260 oC after the introduction of SO2 in the feed gas for 420 min.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211111001
Abstract:
Solid dispersions (SDs) is one of the main technologies to improve the dissolution of poorly soluble drugs in drug research and development. However, supersaturated high-energy amorphous state drug in SDs are often associated with a tendency to recrystallized during long term storage. The carrier of SDs plays a key role in maintaining the amorphous state of the drug. Traditionally, the screening of the carrier in the development process is a time-consuming process. The effect of polymer carriers on the long-term physical stability of the amorphous state of Erlotinib (ERL) in SDs were studied. ERL SDs were prepared with different ratios of HPMC, HPMCAS, PVP, PVP/VA, Eudragit, and Soluplus by solvent evaporation method. Through the Flory-Huggins interaction parameter χ and anti-solvent microscopic observations, the compatibility of the polymer with ERL and polymer's influence on the crystallization of ERL were predicted. Focused Beam Reflectance Measurement (FBRM) system was used to analyzed morphologically the regulating effect of the polymer on the crystallization process. Then the amorphous state formed by different proportions of SDs were characterized by Powder X-Ray Diffraction (PXRD), Differential Scanning Calorimetry (DSC) and Fourier Transform Infrared Spectroscopy(FT-IR). The physical stability of amorphous state of SDs in accelerated test condition were determined by PXRD. The results showed that HPMC is a suitable carrier for the preparation of ERL amorphous SDs. The combination of the interaction parameter χ, anti-solvent microscopic observation and FBRM analysis is an effective way to select suitable carrier for amorphous SDs. A full understanding of the impact of polymers on amorphous SDs is of positive significance for the rapid development of poorly soluble drugs.
Solid dispersions (SDs) is one of the main technologies to improve the dissolution of poorly soluble drugs in drug research and development. However, supersaturated high-energy amorphous state drug in SDs are often associated with a tendency to recrystallized during long term storage. The carrier of SDs plays a key role in maintaining the amorphous state of the drug. Traditionally, the screening of the carrier in the development process is a time-consuming process. The effect of polymer carriers on the long-term physical stability of the amorphous state of Erlotinib (ERL) in SDs were studied. ERL SDs were prepared with different ratios of HPMC, HPMCAS, PVP, PVP/VA, Eudragit, and Soluplus by solvent evaporation method. Through the Flory-Huggins interaction parameter χ and anti-solvent microscopic observations, the compatibility of the polymer with ERL and polymer's influence on the crystallization of ERL were predicted. Focused Beam Reflectance Measurement (FBRM) system was used to analyzed morphologically the regulating effect of the polymer on the crystallization process. Then the amorphous state formed by different proportions of SDs were characterized by Powder X-Ray Diffraction (PXRD), Differential Scanning Calorimetry (DSC) and Fourier Transform Infrared Spectroscopy(FT-IR). The physical stability of amorphous state of SDs in accelerated test condition were determined by PXRD. The results showed that HPMC is a suitable carrier for the preparation of ERL amorphous SDs. The combination of the interaction parameter χ, anti-solvent microscopic observation and FBRM analysis is an effective way to select suitable carrier for amorphous SDs. A full understanding of the impact of polymers on amorphous SDs is of positive significance for the rapid development of poorly soluble drugs.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211029001
Abstract:
Based on the method of Grand Canonical Monte Carlo and molecular dynamic simulation, the adsorption, diffusion and permeation behavior of H2O in PBT polyether polyurethane (PUPBT) elastomer were simulated. The results show that the heat of adsorption of H2O on PUPBT at 298, 318, 338 and 358k is 41.15, 40.23, 36.84 and 34.16 kJ/mol respectively in the fugacity range of 0~1000 kPa. The adsorption equilibrium has been reached when the temperature is 298 K. With the increase of temperature, the adsorption capacity of PUPBT toward H2O is declined. The adsorption of H2O on PUPBT is not a uniform adsorption, H2O molecules Adsorbed to the lower potential energy region near the center of the holes in the polymer. The results of diffusion simulation show that under the environmental conditions of 298, 318, 338 and 358 K when the pressure is 101 kPa, the free volume fraction of H2O/PUPBT was 14.37%, 15.55%, 17.00% and 17.85%, respectively, when the diffusion coefficients of H2O into PUPBT are 1.488×10−6、1.999×10−6、3.086×10−6 and 3.462×10-6 cm2/s. And the diffusion of H2O into PUPBT is not a uniform diffusion, but a jump-motion diffusion in free volumes. The solubility coefficient of H2O molecules in PUPBT is the major factor that affects the permeability coefficient of the system. With the increase of temperature, the permeability coefficient of H2O into PUPBT decreases gradually.
Based on the method of Grand Canonical Monte Carlo and molecular dynamic simulation, the adsorption, diffusion and permeation behavior of H2O in PBT polyether polyurethane (PUPBT) elastomer were simulated. The results show that the heat of adsorption of H2O on PUPBT at 298, 318, 338 and 358k is 41.15, 40.23, 36.84 and 34.16 kJ/mol respectively in the fugacity range of 0~1000 kPa. The adsorption equilibrium has been reached when the temperature is 298 K. With the increase of temperature, the adsorption capacity of PUPBT toward H2O is declined. The adsorption of H2O on PUPBT is not a uniform adsorption, H2O molecules Adsorbed to the lower potential energy region near the center of the holes in the polymer. The results of diffusion simulation show that under the environmental conditions of 298, 318, 338 and 358 K when the pressure is 101 kPa, the free volume fraction of H2O/PUPBT was 14.37%, 15.55%, 17.00% and 17.85%, respectively, when the diffusion coefficients of H2O into PUPBT are 1.488×10−6、1.999×10−6、3.086×10−6 and 3.462×10-6 cm2/s. And the diffusion of H2O into PUPBT is not a uniform diffusion, but a jump-motion diffusion in free volumes. The solubility coefficient of H2O molecules in PUPBT is the major factor that affects the permeability coefficient of the system. With the increase of temperature, the permeability coefficient of H2O into PUPBT decreases gradually.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211130001
Abstract:
A gas-liquid-solid three-phase ebullated-bed reactor with an inner diameter of 286 mm and a height of 7.2 m was used to conduct intermittent liquid phase and continuous gas phase operations. The three-phase system was composed of water, air, and Al2O3 spherical particles. The macroscopic liquid circulation velocity was measured at a solid holdup of 12% ~ 30% and the superficial gas velocity of 0.086 ~ 0.216 m/s. In this study, the tracer method was used to determine the concentration curves of multiple tracers at the inlet and outlet of the reactor. The axial dispersion coefficient of the liquid phase was solved by MATLAB software. Substituting it into the definition of Einstein's diffusion coefficient to get the liquid circulation velocity. The experimental results show that at a certain solid holdup, as the superficial gas velocity increases, small bubbles gradually gather into large bubbles. The rising velocity of the bubbles continues to increase, and the liquid circulation velocity also increases accordingly. Increasing the superficial gas velocity can significantly increase the liquid circulation velocity. At a constant superficial gas velocity, as the solid holdup increases, the large bubble holdup increases, and the bubble rise velocity also increases. As a result, the liquid circulation velocity also increases. However, because the increase of solid holdup will hinder the circulation of liquid to a certain extent, as the solid holdup increases, the increase of the liquid circulation velocity continues to decrease. It shows that there may be an optimal value for the solid holdup.
A gas-liquid-solid three-phase ebullated-bed reactor with an inner diameter of 286 mm and a height of 7.2 m was used to conduct intermittent liquid phase and continuous gas phase operations. The three-phase system was composed of water, air, and Al2O3 spherical particles. The macroscopic liquid circulation velocity was measured at a solid holdup of 12% ~ 30% and the superficial gas velocity of 0.086 ~ 0.216 m/s. In this study, the tracer method was used to determine the concentration curves of multiple tracers at the inlet and outlet of the reactor. The axial dispersion coefficient of the liquid phase was solved by MATLAB software. Substituting it into the definition of Einstein's diffusion coefficient to get the liquid circulation velocity. The experimental results show that at a certain solid holdup, as the superficial gas velocity increases, small bubbles gradually gather into large bubbles. The rising velocity of the bubbles continues to increase, and the liquid circulation velocity also increases accordingly. Increasing the superficial gas velocity can significantly increase the liquid circulation velocity. At a constant superficial gas velocity, as the solid holdup increases, the large bubble holdup increases, and the bubble rise velocity also increases. As a result, the liquid circulation velocity also increases. However, because the increase of solid holdup will hinder the circulation of liquid to a certain extent, as the solid holdup increases, the increase of the liquid circulation velocity continues to decrease. It shows that there may be an optimal value for the solid holdup.
, Available online
, doi: 10.14135/j.cki.1006-3080.20211208001
Abstract:
The aim of this study was to quantify the effects of multiple factors on fermentation broth rheology. Industrial fed-batch fermentations of Acremonium chrysogenum were conducted, and rheology properties of samples were adequately described by power law model. Nonlinear modeling taking only fungal morphology and cell concentration into consideration led to poor correlation and little prediction function. One of the reasons probably was that the model was oversimplified and some inconspicuous but significant factors were omitted. Consequently, extra elements such as substrate concentration, feed mode, media composition were taken into account, following tremendously increased sample library and existence of variables multicollinearity. Two major morphologies of A. chrysogenum were observed in fermentation broth, i.e.freely dispersed arthrospores and filamentous mycelium. It was found that the number of arthrospores was the major factor contributing to rheology properties, based on the standard partial regression coefficients. Using the partial least squares regression (PLSR) model, good prediction of flow index(n) and consistency index(K) can be made from linear recombination of variables, with R2=0.94, R2=0.91 respectively.
The aim of this study was to quantify the effects of multiple factors on fermentation broth rheology. Industrial fed-batch fermentations of Acremonium chrysogenum were conducted, and rheology properties of samples were adequately described by power law model. Nonlinear modeling taking only fungal morphology and cell concentration into consideration led to poor correlation and little prediction function. One of the reasons probably was that the model was oversimplified and some inconspicuous but significant factors were omitted. Consequently, extra elements such as substrate concentration, feed mode, media composition were taken into account, following tremendously increased sample library and existence of variables multicollinearity. Two major morphologies of A. chrysogenum were observed in fermentation broth, i.e.freely dispersed arthrospores and filamentous mycelium. It was found that the number of arthrospores was the major factor contributing to rheology properties, based on the standard partial regression coefficients. Using the partial least squares regression (PLSR) model, good prediction of flow index(n) and consistency index(K) can be made from linear recombination of variables, with R2=0.94, R2=0.91 respectively.
, Available online
, doi: 10.14135/j.cnki.1006-3080.^20211209002
Abstract:
Changes in the choroid are closely related to many ophthalmic diseases. Doctors often need to manually split the choroid layer in the optical tomography image (OCT) during diagnosis, and then quantify the health of the choroid, but manual segmentation is time-consuming and laborious. The difficulty of automatic segmentation of choroid lies in the blurred boundary of the OCT images, for it is difficult to capture the context information, and secondly, the choroidal structure is similar to the retina structure, which is easy to confuse. In order to solve this difficulty, the residual codec model of fusion coordinate parallel attention module and dense atrous convolution module is proposed. A bridge structure is designed, which combines attention mechanism and atrous convolution to suppress shallow noise while increasing the model's receptive fields. In order to make the model pay attention to the choroidal structure information, a hybrid loss function with structural similarity is introduced. The experimental results show that the model can effectively improve the segmentation accuracy of the choroid, and the Dice coefficient and Jaccard similarity reached 97.63 percent and 95.28 percent on the OCT images data set.
Changes in the choroid are closely related to many ophthalmic diseases. Doctors often need to manually split the choroid layer in the optical tomography image (OCT) during diagnosis, and then quantify the health of the choroid, but manual segmentation is time-consuming and laborious. The difficulty of automatic segmentation of choroid lies in the blurred boundary of the OCT images, for it is difficult to capture the context information, and secondly, the choroidal structure is similar to the retina structure, which is easy to confuse. In order to solve this difficulty, the residual codec model of fusion coordinate parallel attention module and dense atrous convolution module is proposed. A bridge structure is designed, which combines attention mechanism and atrous convolution to suppress shallow noise while increasing the model's receptive fields. In order to make the model pay attention to the choroidal structure information, a hybrid loss function with structural similarity is introduced. The experimental results show that the model can effectively improve the segmentation accuracy of the choroid, and the Dice coefficient and Jaccard similarity reached 97.63 percent and 95.28 percent on the OCT images data set.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211231001
Abstract:
Process monitoring is a crucial part of ensuring the safety and quality of industrial production. A sparse D-vine Copula-based (SDVC) process monitoring method is proposed for the problem of nonlinearity and non-Gaussian properties of high-dimensional data in industrial processes. Firstly, considering that the traditional Vine Copula structure optimization method tends to cause estimation errors to accumulate in the Vine structure and the computational burden grows sharply with the increase of data dimensionality. The prior probability of bivariate Copula is modified so that the bivariate Copula in high-level structure tree is more inclined to be optimized to independent states, and the sparse optimization of the high-level tree structure is achieved. Secondly, the vine structure node order determination method is improved. It is expanded sequentially according to the sum of correlations among nodes, making it more applicable to D-vine modeling of horizontal structure. Finally, the high density region (HDR) and density quantile theory are introduced to determine the control boundary and construct generalized local probability (GLP) index to realize real-time monitoring of industrial processes. The superior performance of the proposed method was verified through the Tennessee-Eastman (TE) and acetic acid dehydration industrial processes.
Process monitoring is a crucial part of ensuring the safety and quality of industrial production. A sparse D-vine Copula-based (SDVC) process monitoring method is proposed for the problem of nonlinearity and non-Gaussian properties of high-dimensional data in industrial processes. Firstly, considering that the traditional Vine Copula structure optimization method tends to cause estimation errors to accumulate in the Vine structure and the computational burden grows sharply with the increase of data dimensionality. The prior probability of bivariate Copula is modified so that the bivariate Copula in high-level structure tree is more inclined to be optimized to independent states, and the sparse optimization of the high-level tree structure is achieved. Secondly, the vine structure node order determination method is improved. It is expanded sequentially according to the sum of correlations among nodes, making it more applicable to D-vine modeling of horizontal structure. Finally, the high density region (HDR) and density quantile theory are introduced to determine the control boundary and construct generalized local probability (GLP) index to realize real-time monitoring of industrial processes. The superior performance of the proposed method was verified through the Tennessee-Eastman (TE) and acetic acid dehydration industrial processes.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210831004
Abstract:
Cyber-physical systems (CPS) are tight integration of embedded computers and physical devices, which has a wide of applications in many areas such as process industry, smart energy, medical care, and national defense. However, it is a challenging task to design CPS software that meets both functional and performance requirements, since various physical devices and software in CSPs are interconnected and complex in structures and behaviors. The CPS that controls the operation of physical devices is usually running in dynamical environment. The environmental parameters will affect the structures and behaviors of CPS. This paper proposes a data-based adaptive software structure model design method. In this method, the software architecture model of CPS is constructed by the hierarchical combination of unit modules. The multi-level formal models for CPS software are based on formalisms of Petri net and temporal logic, in order to precisely specify CPS software architecture model, properties, and refine the relation between different levels. The adaptive evolution of CPS is realized by taking advantage of formal semantics, aspect-oriented method, and data analysis algorithms, which abstracts the function of environmental factors into aspect model and obtains a comprehensive CPS model and basic model. The formal method based on Petri nets and temporal logic provides mathematical expression and analysis means for CPS model. Theoretical analysis and experiments show that the designed method is feasible and efficient.
Cyber-physical systems (CPS) are tight integration of embedded computers and physical devices, which has a wide of applications in many areas such as process industry, smart energy, medical care, and national defense. However, it is a challenging task to design CPS software that meets both functional and performance requirements, since various physical devices and software in CSPs are interconnected and complex in structures and behaviors. The CPS that controls the operation of physical devices is usually running in dynamical environment. The environmental parameters will affect the structures and behaviors of CPS. This paper proposes a data-based adaptive software structure model design method. In this method, the software architecture model of CPS is constructed by the hierarchical combination of unit modules. The multi-level formal models for CPS software are based on formalisms of Petri net and temporal logic, in order to precisely specify CPS software architecture model, properties, and refine the relation between different levels. The adaptive evolution of CPS is realized by taking advantage of formal semantics, aspect-oriented method, and data analysis algorithms, which abstracts the function of environmental factors into aspect model and obtains a comprehensive CPS model and basic model. The formal method based on Petri nets and temporal logic provides mathematical expression and analysis means for CPS model. Theoretical analysis and experiments show that the designed method is feasible and efficient.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211202003
Abstract:
Electroencephalogram (EEG) functional connectivity microstates represent quasi-stable global neuronal activity and are considered the building blocks of brain dynamics. Therefore, microstate sequence analysis is a promising method to understand the brain dynamics behind various emotional states. Recent studies have shown that the sequence of EEG microstates is non-Markov and non-stationary, which also explains the importance of temporal dynamics between different emotional states. However, the microstate features based on probability statistics can not well represent the dynamic characteristics of EEG signals. These findings inspire us to use recurrence analysis to model time series of microstates to capture non-obvious correlations in time series. In conclusion, we propose an emotion decoding model based on recurrence analysis of EEG functional connectivity microstate sequences. Firstly, the functional connection microstate pattern of each frame signal is established by using the correlation of time-domain signals between each channel, and the typical microstate patterns are obtained by clustering. Then, the original EEG signals were mapped to microstate time series according to typical microstate patterns, and the time series were analyzed recursively to construct recurrence plots to characterize the EEG dynamic characteristics. Finally, Convolutional Neural Networks (CNNs) are used to predict the regression of emotions based on the valence or arousal value. On open dataset DEAP, the regression effect of the Mean Square Error (MSE) of the model in the two dimensions of valence and arousal is 3.45±1.42 and 2.79±1.48, respectively, which is better than the MSE of 3.87±1.67 and 3.25±1.71 based on the traditional statistical characteristics of microstate features.
Electroencephalogram (EEG) functional connectivity microstates represent quasi-stable global neuronal activity and are considered the building blocks of brain dynamics. Therefore, microstate sequence analysis is a promising method to understand the brain dynamics behind various emotional states. Recent studies have shown that the sequence of EEG microstates is non-Markov and non-stationary, which also explains the importance of temporal dynamics between different emotional states. However, the microstate features based on probability statistics can not well represent the dynamic characteristics of EEG signals. These findings inspire us to use recurrence analysis to model time series of microstates to capture non-obvious correlations in time series. In conclusion, we propose an emotion decoding model based on recurrence analysis of EEG functional connectivity microstate sequences. Firstly, the functional connection microstate pattern of each frame signal is established by using the correlation of time-domain signals between each channel, and the typical microstate patterns are obtained by clustering. Then, the original EEG signals were mapped to microstate time series according to typical microstate patterns, and the time series were analyzed recursively to construct recurrence plots to characterize the EEG dynamic characteristics. Finally, Convolutional Neural Networks (CNNs) are used to predict the regression of emotions based on the valence or arousal value. On open dataset DEAP, the regression effect of the Mean Square Error (MSE) of the model in the two dimensions of valence and arousal is 3.45±1.42 and 2.79±1.48, respectively, which is better than the MSE of 3.87±1.67 and 3.25±1.71 based on the traditional statistical characteristics of microstate features.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211215002
Abstract:
An adaptive weighted concept drift detection method based on McDiarmid boundary (WMDDM) is proposed to solve the problems of high detection delay, missed detection and false alarm in the active detection method of concept drift. WMDDM algorithm has a weight adjustment mechanism. The adaptive attenuation algorithm is introduced as a weight function to give the old data lower weights and dynamically adjust according to the changes in the data stream in order to adapt to the concept drift faster. The warning level and drift level of the weighted classification accuracy are obtained by McDiarmid's inequality. When it is detected that the weighted classification accuracy rate drops outside the drift level, the detection result is fed back to the classifier. When it is detected that the weighted classification accuracy rate drops beyond the warning level, the detector adapts to the change of the data flow through the triggered weight adjustment mechanism. The experiment uses 4 artificial data sets (two mutation drift data sets, two gradual drift data sets) and 1 real data set, which are mainly compared with Fast Hoeffding Drift Detection Method (FHDDM), Drift Detection Method based on the Hoeffding’s inequality (HDDM) and other algorithms. Experimental results show that the WMDDM algorithm has the lowest false alarm rate and missed detection rate, and the average detection delay and accuracy rate rank the top 2 among the six algorithms. Finally, WMDDM algorithm is used to classify real data sets and compared with FHDDM algorithm. The results show that WMDDM algorithm has a higher classification accuracy rate than FHDDM. Therefore, the WMDDM algorithm is suitable for abrupt and gradual conceptual drift, and has strong robustness.
An adaptive weighted concept drift detection method based on McDiarmid boundary (WMDDM) is proposed to solve the problems of high detection delay, missed detection and false alarm in the active detection method of concept drift. WMDDM algorithm has a weight adjustment mechanism. The adaptive attenuation algorithm is introduced as a weight function to give the old data lower weights and dynamically adjust according to the changes in the data stream in order to adapt to the concept drift faster. The warning level and drift level of the weighted classification accuracy are obtained by McDiarmid's inequality. When it is detected that the weighted classification accuracy rate drops outside the drift level, the detection result is fed back to the classifier. When it is detected that the weighted classification accuracy rate drops beyond the warning level, the detector adapts to the change of the data flow through the triggered weight adjustment mechanism. The experiment uses 4 artificial data sets (two mutation drift data sets, two gradual drift data sets) and 1 real data set, which are mainly compared with Fast Hoeffding Drift Detection Method (FHDDM), Drift Detection Method based on the Hoeffding’s inequality (HDDM) and other algorithms. Experimental results show that the WMDDM algorithm has the lowest false alarm rate and missed detection rate, and the average detection delay and accuracy rate rank the top 2 among the six algorithms. Finally, WMDDM algorithm is used to classify real data sets and compared with FHDDM algorithm. The results show that WMDDM algorithm has a higher classification accuracy rate than FHDDM. Therefore, the WMDDM algorithm is suitable for abrupt and gradual conceptual drift, and has strong robustness.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211227001
Abstract:
Optimization of heat exchanger network is an effective way of energy recovery. However, the model with large optimization space of heat exchanger network is often a complex mixed-integer nonlinear programming (MINLP) model with nonlinear and non-convexity constraints, and difficult to get a feasible solution. In this paper, based on the stage-wise superstructure, a new type of heat exchanger network contains flow split, reflux and non-isothermal mixing was built, and while increasing the optimization space of heat exchange network, linear constraints were set to greatly improve the solvability of MINLP model. Two cases in literature were used to verify the contribution of flow split, reflux, isothermal mixing and non-isothermal mixing to the optimization of heat exchanger network, and the effectiveness and applicability of the model.
Optimization of heat exchanger network is an effective way of energy recovery. However, the model with large optimization space of heat exchanger network is often a complex mixed-integer nonlinear programming (MINLP) model with nonlinear and non-convexity constraints, and difficult to get a feasible solution. In this paper, based on the stage-wise superstructure, a new type of heat exchanger network contains flow split, reflux and non-isothermal mixing was built, and while increasing the optimization space of heat exchange network, linear constraints were set to greatly improve the solvability of MINLP model. Two cases in literature were used to verify the contribution of flow split, reflux, isothermal mixing and non-isothermal mixing to the optimization of heat exchanger network, and the effectiveness and applicability of the model.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211030001
Abstract:
With the development of the logistics industry, cold chain logistics have been studied by more and more scholarsas an important branch of the logistics industry. Because the waste of resources in cold chain logistics and distribution is a problem that cannot be underestimated, we use the optimization algorithm to solve the multi-objective optimization model to provide an effective distribution plan for solving the problem of resource waste in this paper. We establish a multi-objective cold chain logistics optimization model with minimizing distribution costs and maximizing customer satisfaction as the objective function in this paper. Customer satisfaction is reflected by the relationship between the delivery vehicle’s arrival time at the customer’s point and the customer’s specific time window; delivery costs are composed of transportation costs, cargo damage costs, cooling costs, and time penalty costs. We adopt the improved five-elements cycle optimization (FECO), which is the five-elements cycle optimization algorithm of dual-mode updating individuals (FECO-DMUI) for multi-objective cold chain logistics optimization model in this paper. The chain logistics optimization model is solved by FECO-DMUI algorithm and compared with FECO algorithm, NSGA-II, whale optimization algorithm and gray wolf optimization algorithm. The effectiveness of the model and algorithm is verified through specific examples, and the FECO-DMUI algorithm can be used to obtain the optimal solution set for path optimization more efficiently in the multi-objective cold chain distribution problem.
With the development of the logistics industry, cold chain logistics have been studied by more and more scholarsas an important branch of the logistics industry. Because the waste of resources in cold chain logistics and distribution is a problem that cannot be underestimated, we use the optimization algorithm to solve the multi-objective optimization model to provide an effective distribution plan for solving the problem of resource waste in this paper. We establish a multi-objective cold chain logistics optimization model with minimizing distribution costs and maximizing customer satisfaction as the objective function in this paper. Customer satisfaction is reflected by the relationship between the delivery vehicle’s arrival time at the customer’s point and the customer’s specific time window; delivery costs are composed of transportation costs, cargo damage costs, cooling costs, and time penalty costs. We adopt the improved five-elements cycle optimization (FECO), which is the five-elements cycle optimization algorithm of dual-mode updating individuals (FECO-DMUI) for multi-objective cold chain logistics optimization model in this paper. The chain logistics optimization model is solved by FECO-DMUI algorithm and compared with FECO algorithm, NSGA-II, whale optimization algorithm and gray wolf optimization algorithm. The effectiveness of the model and algorithm is verified through specific examples, and the FECO-DMUI algorithm can be used to obtain the optimal solution set for path optimization more efficiently in the multi-objective cold chain distribution problem.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211102001
Abstract:
In multivariate time series prediction, it is difficult to capture short-term mutation during long time series, which leads to significant prediction errors. A short-term information enhancement model called clockwork triggered long short term memory (CWTLSTM) neural network is proposed in this paper. The new model groups neurons in the network and assigns different activation frequencies to each group. The neurons in each group can be activated only when the time step is equal to an integer multiple of their specified period. According to the number of the group period, the network is divided into backbone network chain and short-term input enhancement chain. When the short-term input enhancement chain is activated on the time step close to the output position, the input information at that point will be transmitted to the backbone network chain uniaxially, and the weight of short-term input data will be enhanced. So the model can quickly respond to the data fluctuation caused by short-term mutation information, on the basis of storing long-term information. The prediction performance of CWTLSTM was verified by air pollution data set and cement cooler data set, compared with LSTM, XGboost and CWRNN models. The results show that the proposed model has good performance in reducing forecasting error and forecasting future trend. In the experiment, the parameter sensitivity of the model to the periodic allocation strategy is also analyzed, which verifies the role of CWTLSTM in short-term information enhancement to a certain extent.
In multivariate time series prediction, it is difficult to capture short-term mutation during long time series, which leads to significant prediction errors. A short-term information enhancement model called clockwork triggered long short term memory (CWTLSTM) neural network is proposed in this paper. The new model groups neurons in the network and assigns different activation frequencies to each group. The neurons in each group can be activated only when the time step is equal to an integer multiple of their specified period. According to the number of the group period, the network is divided into backbone network chain and short-term input enhancement chain. When the short-term input enhancement chain is activated on the time step close to the output position, the input information at that point will be transmitted to the backbone network chain uniaxially, and the weight of short-term input data will be enhanced. So the model can quickly respond to the data fluctuation caused by short-term mutation information, on the basis of storing long-term information. The prediction performance of CWTLSTM was verified by air pollution data set and cement cooler data set, compared with LSTM, XGboost and CWRNN models. The results show that the proposed model has good performance in reducing forecasting error and forecasting future trend. In the experiment, the parameter sensitivity of the model to the periodic allocation strategy is also analyzed, which verifies the role of CWTLSTM in short-term information enhancement to a certain extent.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211129001
Abstract:
Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often lack of complete facts. In this paper, we study the problem of predicting missing links by learning embeddings of entities and relations in graph knowledge. We introduce a mirror space translation method to learning the symmetric/antisymmetric patterns. Relations are still modelled as translations in our new space, while entities are modelled as points that have mirror points. Within this space, translation-based models gain the ability to model symmetry/antisymmetry relations. Our proposed model MTransE applies the concept of mirrored space to TransE, with experiments on four well-known datasets, shows the performance over other baseline models.
Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often lack of complete facts. In this paper, we study the problem of predicting missing links by learning embeddings of entities and relations in graph knowledge. We introduce a mirror space translation method to learning the symmetric/antisymmetric patterns. Relations are still modelled as translations in our new space, while entities are modelled as points that have mirror points. Within this space, translation-based models gain the ability to model symmetry/antisymmetry relations. Our proposed model MTransE applies the concept of mirrored space to TransE, with experiments on four well-known datasets, shows the performance over other baseline models.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20220122003
Abstract:
Over recent years, immunotherapy has developed rapidly, which has changed the way of cancer treatment. However, most patients cannot benefit from immunotherapy, which may be due to insufficient reprogramming of the immunosuppressive tumor microenvironment (TME) and thus limited reinvigoration of antitumor immunity. In TME, adenosine, a metabolite of ATP, is an effective immunoregulatory factor. Extracellular 5'-nucleotidase (CD73) is the rate-limiting molecule in the process of adenosine production. Overexpression of CD73 on tumor cells and immune cells leads to a higher concentration of adenosine in the TME. The high concentration of adenosine suppresses the anti-tumor immune response, promotes tumor cells proliferate, metastasis and angiogenesis. Therefore, anti-CD73 therapy is expected to become a promising strategy for cancer immunotherapy. Several anti-CD73 mAbs (MEDI9447, BMS986179, SRF373/NZV930, CPI-006/CPX-006, IPH5301, TJ004309) and small molecule CD73 inhibitors ((LY3475070, AB680, CB-708) are being investigated in early phase clinical trials. But so far, there is no CD73 targeted product for the treatment of cancer on the market. In this study, firstly we performed the screening of our compound library containing 876 listed drugs to identify candidate inhibitors targeting CD73. Preliminary experimental results showed that Cisatracurium besylate (51w89) could inhibit the enzyme activity of recombinant CD73 with an IC50 value of 13.30 μmol/L. To verify the interactions between 51w89 and CD73 and evaluate their binding affinities, we performed surface plasmon resonance (SPR) experiments using a Biacore T200 (GE Healthcare). The binding affinity (KD value) of 51w89 binding to CD73 was 20.45 μmol/L. Encouraged by these results at the molecular level, we next evaluated the inhibitory effect of 51w89 against CD73 in MDA-MB-231 cells. The results show that the IC50 of 51w89 against CD73 in MDA-MB-231 cells was 17.70 μmol/L, which was close to the IC50 value at the molecular level. Subsequently, we proved the role of CD73 in the migration of MDA-MB-231 cells through scratch tests, transwell tests and siRNA tests, and found that 51w89 could inhibit the migration of MDA-MB-231 cells. Moreover, we found that 51w89 could limit the inhibitory effect of AMP on CD8+ T cells. Taken together, we speculated that 51w89 could be used as a potent small-molecule inhibitor of CD73 for subsequent antitumor research.
, Available online
, doi: 10.14135/J.cnki.1006-3080.20211018003
Abstract:
Single-walled carbon nanotubes (SWCNTs) can combine with proteins in organisms, creating potential biosafety risks. In this paper, SWCNTs and two separated single-chiral single-walled carbon nanotubes ((6,5)-SWCNT, (8,3)-SWCNT) were combined with Bovine hemoglobin (BHb) respectively, and SWCNTs interaction with BHb were analyzed by fluorescence spectroscopy. The results showed that the fluorescence quenching of BHb by SWCNTs was resulted from the combination of dynamic quenching and static quenching. The fluorescence quenching of BHb by single chiral (6,5)-SWCNT and (8,3)-SWCNT was static quenching. The order of the binding constants of BHb and different SWCNTs was as follows: SWCNTs>(6,5)-SWCNT>(8,3)-SWCNT. Van der Waals force, hydrogen bond and hydrophobic interaction were the main forces in the interaction. The result of this article will assist the revealing of the potential biosafety risks of SWCNTs.
Single-walled carbon nanotubes (SWCNTs) can combine with proteins in organisms, creating potential biosafety risks. In this paper, SWCNTs and two separated single-chiral single-walled carbon nanotubes ((6,5)-SWCNT, (8,3)-SWCNT) were combined with Bovine hemoglobin (BHb) respectively, and SWCNTs interaction with BHb were analyzed by fluorescence spectroscopy. The results showed that the fluorescence quenching of BHb by SWCNTs was resulted from the combination of dynamic quenching and static quenching. The fluorescence quenching of BHb by single chiral (6,5)-SWCNT and (8,3)-SWCNT was static quenching. The order of the binding constants of BHb and different SWCNTs was as follows: SWCNTs>(6,5)-SWCNT>(8,3)-SWCNT. Van der Waals force, hydrogen bond and hydrophobic interaction were the main forces in the interaction. The result of this article will assist the revealing of the potential biosafety risks of SWCNTs.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211116001
Abstract:
Gasification temperature is the most important operating parameter of entrained flow gasifier. However, the coal gasifier unit lacks long-term and reliable gasification temperature measurement. In order to monitor the operation state of entrained flow gasifier in real time and ensure the safe and stable operation of gasification system, measurable data such as gasifier cooling system and reaction system are collected. The outlet temperature of gasifier was predicted by using theoretical calculation model and BP neural based on genetic algorithm model(GABP). The results of prediction were compared with industrial measurement data. The results show that the outlet temperature of gasifier can be obtained by theoretical calculation of quench system, but the accuracy and stability of prediction results are poor due to low sensitivity of measurement parameters. GABP neural network model can greatly improve the prediction performance. Base on the gasification chamber parameters, the prediction error is large due to the fluctuation of coal water slurry flow rate and the lack of coal property data. Taking quench system parameters as the input of GABP neural network can greatly improve the prediction accuracy, and the absolute value of the prediction error is less than 15 K. Both of the train set and verification set have excellent prediction results, the average absolute errors of GABP model with quench system parameters as input are about 5 K. GABP model has good performances in the face of complex working conditions. Carry out predictions under different conditions, the results under steady and variable coal load have good prediction precision and stability, meet the demand of online monitoring of gasifier temperature.
Gasification temperature is the most important operating parameter of entrained flow gasifier. However, the coal gasifier unit lacks long-term and reliable gasification temperature measurement. In order to monitor the operation state of entrained flow gasifier in real time and ensure the safe and stable operation of gasification system, measurable data such as gasifier cooling system and reaction system are collected. The outlet temperature of gasifier was predicted by using theoretical calculation model and BP neural based on genetic algorithm model(GABP). The results of prediction were compared with industrial measurement data. The results show that the outlet temperature of gasifier can be obtained by theoretical calculation of quench system, but the accuracy and stability of prediction results are poor due to low sensitivity of measurement parameters. GABP neural network model can greatly improve the prediction performance. Base on the gasification chamber parameters, the prediction error is large due to the fluctuation of coal water slurry flow rate and the lack of coal property data. Taking quench system parameters as the input of GABP neural network can greatly improve the prediction accuracy, and the absolute value of the prediction error is less than 15 K. Both of the train set and verification set have excellent prediction results, the average absolute errors of GABP model with quench system parameters as input are about 5 K. GABP model has good performances in the face of complex working conditions. Carry out predictions under different conditions, the results under steady and variable coal load have good prediction precision and stability, meet the demand of online monitoring of gasifier temperature.
Research on Chinese Named Entity Recognition Based on Hierarchical Adjustment of Lexicon Information
, Available online
, doi: 10.14135/j.cnki.1006-3080.20211105003
Abstract:
In the task of Chinese named entity recognition, word information fusion vocabulary information can enrich text features, but a word may correspond to multiple candidate words, which is prone to vocabulary conflict. The fusion of irrelevant vocabulary information will affect the recognition effect of the model. In this paper, a Chinese named entity recognition method based on hierarchical adjustment of dictionary information is proposed. All potential words are layered according to the word length, and the weight of low-level words is adjusted through high-level word feedback to retain more useful information, so as to alleviate the problem of semantic deviation and reduce the impact of word conflict. Then, the word information is spliced into the word information to enhance the text feature representation. Experiments are carried out on resume and Weibo data sets. The experimental results show that this method has better effect than the traditional method.
In the task of Chinese named entity recognition, word information fusion vocabulary information can enrich text features, but a word may correspond to multiple candidate words, which is prone to vocabulary conflict. The fusion of irrelevant vocabulary information will affect the recognition effect of the model. In this paper, a Chinese named entity recognition method based on hierarchical adjustment of dictionary information is proposed. All potential words are layered according to the word length, and the weight of low-level words is adjusted through high-level word feedback to retain more useful information, so as to alleviate the problem of semantic deviation and reduce the impact of word conflict. Then, the word information is spliced into the word information to enhance the text feature representation. Experiments are carried out on resume and Weibo data sets. The experimental results show that this method has better effect than the traditional method.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210909003
Abstract:
Recently, the method of combining BERT(Bidirectional Encoder Representations from Transformers) and neural network model has been widely used in the field of Chinese medical named entity recognition. However, BERT was segmented at the granularity of characters in Chinese, and Chinese word segmentation was not considered. And neural network models were often locally unstable, and even small disturbances may mislead them, resulting in poor model robustness. In order to solve these two problems, a Chinese medical named entity recognition model based on RoBERTa(A Robustly Optimized BERT Pre-training Approach) and adversarial training, namely AT-RBC (Adversarial Training with RoBERTa-wwm-ext-large+BiLSTM+CRF), was proposed. Firstly, use RoBERTa-wwm-ext-large(A Robustly Optimized BERT Pre-training Approach-whole word masking-extended data-large) pre-trained model to obtain initial vector representation of input text. Secondly, some perturbations were added to the initial vector representation to generate adversarial samples. Finally, the initial vector representation and adversarial samples were sequentially inputted to bidirectional long short-term memory network and conditional random field to obtain the final prediction. Experiments on the CCKS 2019 data set show that the F1 score of the improved model reaches 88.96%, achieving good results. Experiments were also conducted on the Resume data set, and the F1 value reaches 97.14%, which proved the effectiveness of the improved model.
Recently, the method of combining BERT(Bidirectional Encoder Representations from Transformers) and neural network model has been widely used in the field of Chinese medical named entity recognition. However, BERT was segmented at the granularity of characters in Chinese, and Chinese word segmentation was not considered. And neural network models were often locally unstable, and even small disturbances may mislead them, resulting in poor model robustness. In order to solve these two problems, a Chinese medical named entity recognition model based on RoBERTa(A Robustly Optimized BERT Pre-training Approach) and adversarial training, namely AT-RBC (Adversarial Training with RoBERTa-wwm-ext-large+BiLSTM+CRF), was proposed. Firstly, use RoBERTa-wwm-ext-large(A Robustly Optimized BERT Pre-training Approach-whole word masking-extended data-large) pre-trained model to obtain initial vector representation of input text. Secondly, some perturbations were added to the initial vector representation to generate adversarial samples. Finally, the initial vector representation and adversarial samples were sequentially inputted to bidirectional long short-term memory network and conditional random field to obtain the final prediction. Experiments on the CCKS 2019 data set show that the F1 score of the improved model reaches 88.96%, achieving good results. Experiments were also conducted on the Resume data set, and the F1 value reaches 97.14%, which proved the effectiveness of the improved model.
, Available online
, doi: 10.14135/j.cnki.1006-3080.20210921001
Abstract:
Microservice architecture builds applications as independent components and runs each application process as a service. The decoupling and independent development of microservices make the flexibility and speed of software update possible. Meanwhile, it also brings many problems, such as service decomposition, transmission delay, and reliability. This paper uses PrT net (Predicated Petri net) to model the microservice composition by event bus to establish the dependency among microservices, transmission latency, and reliability of microservice composition. The event listening mechanism is a delegated event handling mechanism. When a specified event occurs in the event source, it will notify the specified event listener to perform the corresponding operation. For event-based communication, when the event occurs, the microservice will publish the event. Then, we propose a BP (primary and backup) replication allocation strategy meeting the sub-deadline through microservice instances of the primary and backup replica to improve the overall reliability of microservice composition. In this paper, the PB replica deployment strategy is analyzed from two cases: single task and multi task PB replica. By deploying the primary and backup replica of the task in different containers or host resources, the goal of improving the reliability of cloud applications has been achieved. The related properties of constructed models are established by using the related theories of PrT net. Through semantic and syntax analysis, the correctness of the PrT net modeling is analyzed. Finally, several experiments are carried out to verify the effectiveness of the modeling and analysis method. Experimental results show that the proposed microservice reliability strategy is effective by taking the guarantee ratio as the reliability parameter.
Microservice architecture builds applications as independent components and runs each application process as a service. The decoupling and independent development of microservices make the flexibility and speed of software update possible. Meanwhile, it also brings many problems, such as service decomposition, transmission delay, and reliability. This paper uses PrT net (Predicated Petri net) to model the microservice composition by event bus to establish the dependency among microservices, transmission latency, and reliability of microservice composition. The event listening mechanism is a delegated event handling mechanism. When a specified event occurs in the event source, it will notify the specified event listener to perform the corresponding operation. For event-based communication, when the event occurs, the microservice will publish the event. Then, we propose a BP (primary and backup) replication allocation strategy meeting the sub-deadline through microservice instances of the primary and backup replica to improve the overall reliability of microservice composition. In this paper, the PB replica deployment strategy is analyzed from two cases: single task and multi task PB replica. By deploying the primary and backup replica of the task in different containers or host resources, the goal of improving the reliability of cloud applications has been achieved. The related properties of constructed models are established by using the related theories of PrT net. Through semantic and syntax analysis, the correctness of the PrT net modeling is analyzed. Finally, several experiments are carried out to verify the effectiveness of the modeling and analysis method. Experimental results show that the proposed microservice reliability strategy is effective by taking the guarantee ratio as the reliability parameter.
