Monitoring of wastewater treatment process based on multi-stage variational autoencoder

Abstract

The wastewater treatment process (WWTP) is a physical and biochemical reaction process with multi-stage, non-linear and non-Gaussian characteristics. The multi-stage means that the data has diversity in different substages. In the process monitoring of the WWTP, Affinity Propagation (AP) algorithm is first used to divide the data of the WWTP into stages to process the stage characteristics of data. Next, the capability of Variational Auto-Encoder (VAE) monitoring model to restrict the Gaussian distribution in hidden layers is utilized to address the coexistence of non-Gaussian and nonlinearity. Finally, to illustrate the superiority and feasibility, the proposed model is conducted on the Benchmark Simulation Model 1 (BSM1). The experimental result illustrates that the multistage variational autoencoder (M-VAE) can extract the characteristics of data more comprehensively, with an improvement of average monitoring accuracy by 37.8%, 2.7%, 12.7% and 0.52% in 10/8 process faults compared to the state-of-the-art fault monitoring methods Auto-encoder (AE), VAE, Deep Recurrent Neural Network (DRNN), deep recurrent network with high-order statistic information (HSI-DRNN) respectively.