Over-complete deep recurrent neutral network based on wastewater treatment process soft sensor application

Abstract

The wastewater treatment process (WWTP) is a complex biochemical reaction process in which sensor data has strong nonlinear, non-Gaussian and time correlation characteristics. The traditional methods ignore to consider the aforementioned three characteristics simultaneously, which may have insufficient feature extraction of WWTP. In this work, an Over-Complete Deep Recurrent Neural Network (ODRNN) method is proposed to solve the above issues. The ODRNN combines the over-complete independent component analysis (OICA) and binary particle swarm optimization (BPSO) to efficiently extract the non-Gaussian information, and then the extracted information is fed into DRNN to obtain the time correlation characteristics. In this way, the method can not only capture the non-linear and non-Gaussian information but also extract temporal correlation of WWTP data. Simulation results on BSM1 showed that the ODRNN based soft sensor method has higher accuracy and robustness than other state-of-the-art methods.