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

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 nonlinear 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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The wastewater treatment process is a complex biochemical reaction process with strong nonlinear, non-Gaussian, and time correlation characteristics. An Over-Complete Deep Recurrent Neural Network (ODRNN) method is proposed to address these issues by efficiently extracting non-Gaussian information and temporal correlation features. Simulation results demonstrate that the ODRNN based soft sensor method outperforms other state-of-the-art methods in terms of accuracy and robustness.