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  • ISSN 1006-3080
  • CN 31-1691/TQ

偏最小二乘法预测头孢菌素C工业发酵过程中发酵液流变特性

杨倚铭 陈 震 田锡炜 储 炬

杨倚铭, 陈 震, 田锡炜, 储 炬. 偏最小二乘法预测头孢菌素C工业发酵过程中发酵液流变特性[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cki.1006-3080.20211208001
引用本文: 杨倚铭, 陈 震, 田锡炜, 储 炬. 偏最小二乘法预测头孢菌素C工业发酵过程中发酵液流变特性[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cki.1006-3080.20211208001
YANG Yiming, CHEN Zhen, TIAN Xiwei, CHU Ju. Fermentation Broth Rheology Prediction of Industrial Cephalosporin C Process Based on Partial Least Squares Regression[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cki.1006-3080.20211208001
Citation: YANG Yiming, CHEN Zhen, TIAN Xiwei, CHU Ju. Fermentation Broth Rheology Prediction of Industrial Cephalosporin C Process Based on Partial Least Squares Regression[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cki.1006-3080.20211208001

偏最小二乘法预测头孢菌素C工业发酵过程中发酵液流变特性

doi: 10.14135/j.cki.1006-3080.20211208001
详细信息
    作者简介:

    杨倚铭(1982—),男,浙江义乌人,博士,主要研究方向:发酵工艺优化。E-mail:327914243@qq.com

    通讯作者:

    储 炬,E-mail:juchu@ecust.edu.cn

  • 中图分类号: Q815

Fermentation Broth Rheology Prediction of Industrial Cephalosporin C Process Based on Partial Least Squares Regression

  • 摘要: 本研究目的是量化发酵液中各种因素对流变学的影响。在头孢菌素C工业发酵过程中,发酵液表现出典型的非牛顿流体性质,并可用幂律模型充分描述。传统经验型模型并不能很好地预测头孢菌素C工业发酵过程的流变特性,原因在于模型过于简化,忽略了潜在因素如底物浓度、进料方式、培养基组成等对流变特性的影响,但更多变量的引入会显著增加模型复杂度,且这些参数的动态变化存在相关性,为了解决这个问题,采用了偏最小二乘法进行建模。标准偏回归系数可以量化不同因素对流变参数的作用,其中节孢子比率和菌丝平均分支长度对流变学特性的影响最大。使用偏最小二乘回归(PLSR)模型,可以很好地预测头孢菌素C发酵液的流动行为指数n和稠度指数K,R2分别为0.94和0.91,具有较好的实用性。

     

  • 图  1  头孢菌素C发酵过程中菌体形态变化(×1000)

    Figure  1.  Morphological changes during CPC fermentation process

    图  2  影响流变特性的潜在参数的时序变化

    Figure  2.  Time course of multiple variables changes during fermentation

    图  3  顶头孢霉发酵液样品的拟合结果

    Figure  3.  Viscosity plotted against shear rate for a random chosen fermentation sample

    图  4  顶头孢霉发酵液流动指数和稠度指数随着发酵周期的变化趋势

    Figure  4.  Changes in n and K versus fermentation time

    图  5  流变参数nK的观测值和预测值线性回归分析

    Figure  5.  Predicted values of the rheological parameters plotted against the observed values

    表  1  不同变量组合的PLS建模后的残差均方和比较

    Table  1.   The prediction power analysis of PLS models taking different independent variables combinations


    Variable(Variable number,Extraction group fraction)
    MRSS
    $ \sum _{i=1}^{j}{({n}_{pi}-{n}_{oi})}^{2} $/j$ \sum _{i=1}^{j}{({K}_{pi}-{K}_{oi})}^{2} $/j
    Morphological parameters(3,3)0.13401.8171
    Biomass, Morphological parameters(4,4)0.06771.2906
    Biomass, Morphological parameters, Medium phase, Feeding, Period(7,4)0.00290.6133
    Biomass, Morphological parameters, Medium phase, Sugar concentration, Oil concentration, Period(9, 6)0.03320.9320
    下载: 导出CSV

    表  2  偏最小二乘回归结果

    Table  2.   Coefficients results from PLSR model

    Standard partial regression coefficient
    VariablenK
    Intercept00
    Period0.529750.97171
    Average branch length of hypha1.837141.21371
    Biomass0.207120.41248
    Arthrospore ratio2.719913.47677
    Medium phase
    ()()
    0.692480.36789
    Arthrospore number0.148710.22127
    Feeding0.09037- 0.11853
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-08
  • 网络出版日期:  2022-04-12

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