Multivariable identification of membrane fouling based on compacted cascade neural network  

作　　者：Kun Ren Zheng Jiao Xiaolong Wu Honggui Han 

机构地区：[1]Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China [2]Engineering Research Center of Digital Community,Ministry of Education,Beijing 100124,China [3]Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing 100124,China

出　　处：《Chinese Journal of Chemical Engineering》2023年第1期37-45,共9页中国化学工程学报（英文版）

基　　金：supports by National Key Research and Development Project(2018YFC1900800-5);National Natural Science Foundation of China(61890930-5,62021003,61903010 and 62103012);Beijing Outstanding Young Scientist Program(BJJWZYJH01201910005020);Beijing Natural Science Foundation(KZ202110005009 and 4214068).

摘　　要：The membrane fouling phenomenon,reflected with various fouling characterization in the membrane bioreactor(MBR)process,is so complicated to distinguish.This paper proposes a multivariable identification model(MIM)based on a compacted cascade neural network to identify membrane fouling accurately.Firstly,a multivariable model is proposed to calculate multiple indicators of membrane fouling using a cascade neural network,which could avoid the interference of the overlap inputs.Secondly,an unsupervised pretraining algorithm was developed with periodic information of membrane fouling to obtain the compact structure of MIM.Thirdly,a hierarchical learning algorithm was proposed to update the parameters of MIM for improving the identification accuracy online.Finally,the proposed model was tested in real plants to evaluate its efficiency and effectiveness.Experimental results have verified the benefits of the proposed method.

关 键 词：Membrane fouling PERMEABILITY Cascade neural networks Model PREDICTION 

分 类 号：TQ051.893[化学工程] TP183[自动化与计算机技术—控制理论与控制工程]