Multivariable identification of membrane fouling based on compacted cascade neural network

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.