Multivariable identification of membrane fouling based on compacted cascade neural network

Abstract

The system consists of three modules: data acquisition, feature extraction, and online prediction. The data acquisition module obtains the value of process variables from the sensor and transfers the acquired variables to the database via PLC. The feature extraction module is designed to filter the collected process data. In this module, multiple related variables are selected as the preselected variables. Then the PLS method is used to reduce the dimension of the preselected variables to obtain the feature variables with a high correlation with the predicted variables. Finally, the online prediction module based on a cascade neural network predicts membrane fouling, membrane integrity, and membrane life for clean decisions. • A multivariable identification model is construted. • Cascade neural network is proposed to predict multiple variables to avoid the interference of overlap inputs. • Unsupervised pretraining algorithm is used to initialize the structure of cascade neural network. • Because the parameters are affected by multiple errors, the hierarchical learning algorithm is used to improve the identification accuracy. 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.