Back Propagation Neural Network (BPNN) Algorithm Model Application to Prediction and Optimization of Electrochemical Ammonia Removal
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摘要: 电化学方法已被证明是去除氨氮的一种有效方法,降低其电化学过程能耗是关键点。本研究利用反向传播神经网络(Back Propagation Neural Network,BPNN)建立氨氮去除效果预测模型和智能控制策略。模型由具有BPNN模型的预测模块和控制模块组成。首先,采用4层隐藏层(每60个神经元)和负反馈调节机制开发BPNN算法,优化模型并预测氨氮去除率。通过参数分析及响应面模型对比,所提出的BPNN模型具有更好的决定系数(0.958)。根据水质变化和确定的氨氮去除率目标可以通过BPNN模型获得电化学过程中电流调控策略。该智能控制策略减少了水质波动对氨氮去除的负面影响,并可降低能耗38%。本研究证明了人工智能和反向传播神经网络在电化学去除氨氮过程中的应用潜力,为实现电化学水处理过程自动调控提供可能。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.
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Key words:
- back propagation neural network /
- ammonia removal /
- electrochemical /
- intelligent control /
- prediction
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图 3 氨氮去除率随时间变化图
Figure 3. Variation of ammonia nitrogen removal rate with time
(a) different constant currents, initial pH=7, initial concentration of ammonia=150 mg/L, stirring rate=0 r/min; (b) different initial pH, constant current=10 mA , initial concentration of ammonia=150 mg/L, stirring rate=0 r/min; (c) different initial concentrations of ammonia, constant current=10 mA, initial pH=7, stirring rate=0 r/min; (d) different stirring rates, constant current=10 mA, initial pH=7, initial concentration of ammonia=150 mg/L. In the above experiments, the electrode area was constant at 4×6 cm2, the electrode spacing was constant at 2 cm, and the chloride ion concentration was constant at 1 g/L (calculated as NaCl)
图 4 (a)训练集和测试集数据的R2变化以及数据集不同数量的隐藏层神经元对应RMSE;(b)训练集和测试集数据的R2变化以及数据集不同数量的隐藏层神经元对应RMSE;(c)训练集和测试集RMSE图
Figure 4. (a) Changes in R2 of training set and test set data and RMSE values corresponding to different numbers of hidden layer neurons in the data set;( b) Changes in R2 of training set and test set data and data sets RMSE values corresponding to different numbers of hidden layer neurons; (c) Training set and test set RMSE.
图 5 BPNN模型预测值与实际值相关度对比图
Figure 5. Comparison of the correlation between the predicted value of the BPNN model and the actual value
(a) The comparison between the predicted value of voltage and the actual value; (b) The comparison between the predicted value of pH and the actual value; (c) The comparison between the predicted value of ammonia removal rate and the actual value.
图 8 a)BPNN模型原策略与智能控制策略氨氮去除率预测值变化趋势与电流变化图;b)原策略与智能控制策略能耗实时分析及氨氮去除率与系统总能耗对比图
Figure 8. (a) BPNN model post strategy and intelligent control strategy(i.e. New Strategy) ammonia removal rate predicted value change trend and current change diagram; (b) Real-time analysis of energy consumption of post strategy and intelligent control strategy and comparison of ammonia removal rate and total system energy consumption
表 1 反向传播神经网络(BPNN)模型的参数
Table 1. Parameters for Back Propagation Neural Network (BPNN) Models
BPNN parameter Number/Type Input layer neurons 6 Output layer neurons 1 Hidden layers 4 Hidden layer neurons 60 Activation function for hidden layer relu Activation function for output layer tanh -
[1] 熊小琴, 王岚, 史庆超, 等. 氨氮对鱼类的毒性效应研究进展[J]. 贵州农业科学, 2021, 49(7): 81-87. doi: 10.3969/j.issn.1001-3601.2021.07.014 [2] 吕妍. 氨氮等水质指标对水产养殖的影响及解决办法[J]. 黑龙江水产, 2021, 40(5): 53-56. doi: 10.3969/j.issn.1674-2419.2021.05.015 [3] 刘炎, 姜东升, 李雅洁, 等. 不同温度和pH下氨氮对河蚬和霍甫水丝蚓的急性毒性[J]. 环境科学研究, 2014, 27(9): 1067-1073. [4] 巩师俞. 沸石改性对水中氨氮及有机物的吸附试验研究[D]. 兰州交通大学, 2013. [5] 韩静. 高浓度氨氮废水的危害及主要治理技术[J]. 北方环境, 2011, 23(12): 120-122. [6] WANG C R, CHANG S, YE M, et al. Current Efficiency and Energy Consumption of Electrochemical Oxidation for Ammonia Removal from Coking Wastewater Using Boron-Doped Diamond Electrodes[J]. Applied Mechanics and Materials, 2013, 295-298: 1327-1332. doi: 10.4028/www.scientific.net/AMM.295-298.1327 [7] 白伟锋. 氮氧同位素在铁岭市河流氮来源解析中的应用研究[J]. 水利技术监督, 2019(6): 34-37. [8] 何潇, 罗建中, 蔡宗岳. 微污染水源水中氨氮的危害与现代处理技术[J]. 工业水处理, 2017, 37(4): 6-11. doi: 10.11894/1005-829x.2017.37(4).002 [9] 岳钧, 黄爽兵, 苏炤新, 等. 江汉平原中部浅层地下水中氨氮的有机质来源探究[J]. 地球与环境, 2020, 48(3): 332-340. [10] 李建霜. 沸石对生活污水氨氮处理的研究[D]. 重庆交通大学, 2014. [11] 曾青云, 薛丽燕, 曾繁钢, 等. 氨氮废水处理技术的研究现状[J]. 有色金属科学与工程, 2018, 9(4): 83-88. [12] 杨垒. 高效异养硝化细菌的脱氮特性及其处理高氨氮废水研究[D]. 西安建筑科技大学, 2016. [13] 杨延栋, 黄京, 韩晓宇, 等. 一体式厌氧氨氧化工艺处理高氨氮污泥消化液的启动[J]. 中国环境科学, 2015, 35(4): 1082-1087. [14] 金艳, 张永红, 宋兴福, 等. 耐盐菌MBR系统处理页岩气采出水性能及膜污染特性[J]. 华东理工大学学报(自然科学版), 2020, 46(6): 730-736. [15] 李天育, 陈钰, 张静, 等. 含氮废水的处理方法研究[J]. 广东化工, 2020, 47(24): 82-83. doi: 10.3969/j.issn.1007-1865.2020.24.037 [16] 房睿. 电吸附技术对水中盐类及氨氮去除的研究[J]. 水利科技与经济, 2021, 27(8): 24-27. doi: 10.3969/j.issn.1006-7175.2021.08.005 [17] 孟锋, 柴易达, 张洛红, 等. 硫酸亚铁结合电化学絮凝处理中水氨氮与总磷的研究[J]. 环境保护科学, 2020, 46(2): 39-43. [18] 王玉飞, 闫龙, 陈碧, 等. 电化学处理模拟氨氮废水研究[J]. 应用化工, 2015, 44(11): 1997-2000. [19] WANG H, WANG J, LU H, et al. Analysis of Coating Electrode Characteristics in the Process of Removing Pollutants from Wastewater[J]. Fresenius Environmental Bulletin, 2020, 29(2): 715-721. [20] 李进松, 万东锦. 电化学技术处理氨氮废水的研究进展[J]. 绿色科技, 2021, 23(10): 119-121+125. doi: 10.3969/j.issn.1674-9944.2021.10.042 [21] 滕洪辉, 李天育, 陈钰琦, 等. 含氮废水的电化学处理技术研究[J]. 吉林师范大学学报(自然科学版), 2021, 42(1): 82-87. [22] 胡元娟, 廖春华, 郑晓凤, 等. 电化学氧化法处理高氯盐氨氮废水[J]. 工业安全与环保, 2020, 46(12): 88-92. doi: 10.3969/j.issn.1001-425X.2020.12.020 [23] Lin K, Zhao Y, Kuo J, et al. Toward smarter management and recovery of municipal solid waste: A critical review on deep learning approaches[J]. Journal of Cleaner Production, 2022, 346: 130943. doi: 10.1016/j.jclepro.2022.130943 [24] XU A, CHANG H, XU Y, et al. Applying artificial neural networks (ANNs) to solve solid waste-related issues: a critical review[J]. Waste Management, 2021, 124: 385-402. doi: 10.1016/j.wasman.2021.02.029 [25] 董皓月, 范莎莎, 金春姬, 等. 人工神经网络优化电活化硫酸盐降解微囊藻毒素[J]. 环境化学, 2020, 39(12): 3390-3401. [26] 耿胜财. 基于改进BP神经网络的电解加工预测模型研究[D]. 沈阳理工大学, 2019. [27] 刘海军. 微波真空膨化浆果脆片的机理研究[D]. 东北农业大学, 2013. -