|Table of Contents|

VIP optimal neural network inverse system soft sensing method in bio-fermentation process


Research Field:
Publishing date:


VIP optimal neural network inverse system soft sensing method in bio-fermentation process
Yu Shuang1Liu Guohai2Mei Congli2Cheng Jinxiang2
1.Department of Mechanical and Electrical Engineering,Suzhou Institute of Industrial Technology, Suzhou 215104,China; 2.School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China
variable importance in the project neural network inverse system soft sensing fermentation process inherent sensing variables selection online measuring
To solve the online measuring of biochemical variables in the fermentation process,a neural network inverse soft sensing method which is optimized using the variable importance in the project(VIP)is proposed.According to the inverse system theory,a soft sensing model of biochemical variables is constructed.Due to the complexity of the fermentation process,the soft sensing model is not unique and not exact.This paper proposes that secondary variables should be optimized using the VIP method.The variables which have greater contribution to key variables are selected as the secondary variables of the soft sensing model.This paper collectes the fermentation process data offline and trains neural network approximating complex soft sensing model.The optimal neural network inverse system soft sensing model is obtained.It can estimate the mycelium concentration and substrate concentration online.Numerical simulations based on the Pensim data platform show that the optimal soft sensing model has higher estimation accuracy and stronger generation ability.


[1] 潘丰,李海波,顾蕊.基于递归补偿模糊神经网络的发酵过程建模[J].南京理工大学学报,2005,10(29):108-111.
Pan Feng,Li Haibo,Gu Rui.Fermentation process modeling based on recurrent compensatory neuro-fuzzy network[J].Journal of Nanjing University of Science and Technology,2005,10(29),108-111.
[2]Tan Baohua,Quan Shuhai,Zhang Liyan,et al.Study on membrane water content soft sensing method based on internal resistance in PEMFC[J].International Journal of Digital Content Technology and Its Applications,2012,6(11):205-212.
[3]Sheng Biqi,Zhang Rongbiao,Pan Tianhong,et al.Dynamic canonical correlation analysis for onlinequality estimation in an industrial sputtering process[J].International Journal of Digital Content Technology and Its Applications,2012,6(7):93-101.
[4]Zhang Yonghong,Xia Zhining,Qin Litang,et al.Prediction of blood-brain partitioning:A model based on molecular electronegativity distance vector descriptors[J].Journal of Molecular Graphics and Modelling,2010,29(2):214-220.
[5]Wang Shengwei,Wang Jinbo.Law-based sensor fault diagnosis and validation for building air-conditioning systems[J].HVAC and R Research,1999,5(4):353-380.
[6]Yan Xuefeng.Modified nonlinear generalized ridge regression and its application to develop naphtha cut point soft sensor[J].Computers and Chemical Engineering,2008,32(3):608-621.
[7]Cheruy A.Software sensors in bioprocess engineering[J].Journal of Biotechnology,1997,52(3):193-199.
Sun Yukun,Zhang Yao,Huang Yonghong,et al.Application of dynamic recursive fuzzy neural network based on immune genetic algorithm to fermentation process[J].Information and Control,2011,40(1):110-114.
Tian Xuemin,Wang Qiang,Deng Xiaogang.Soft sensing based on wavelet neural networks with momentum[J].Journal of Chemical Industry and Engineering,2011,62(8):2238-2242.
[10]Dai Xianzhong,Wang Wancheng,Ding Yuhan,et al.“Assumed Inherent Sensor” inversion based ANN dynamic soft-sensing method and its application in erythromycin fermentation process[J].Computers and Chemical Engineering,2006,30(8):1203-1225.
[11]Birol G,Undey C,Çinar A.A modular simulation package for fed-batch fermentation:Penicillin production[J].Computers and Chemical Engineering,2002,26(11):1553-1565.
[12]Chen Weiliang,Zhang Kaifeng,Lu Chao,et al.Soft-sensing of crucial biochemical variables in penicillin fermentation[A].Proceedings of the 29th Chinese Control Conference[C].Washington D C,USA:IEEE Computer Society,2010:1391-1397.


Last Update: 2015-08-31