Effluent quality prediction in papermaking wastewater treatment processes using dynamic Bayesian networks

Effective online modeling of papermaking wastewater treatment processes (WWTPs) is an important means to ensure wastewater recycling and harmless discharge. A composite model integrating variable importance in projection with dynamic Bayesian networks (VIP-DBN) is proposed to improve the modeling ability and reliability in WWTPs. First, a variable selection method is applied to simplify the network structure and reduce modeling costs. Then the augmented matrices technique is embedded into the Bayesian networks to cope with the dynamic characteristics, nonlinearity, and uncertainty simultaneously. The modeling performance of VIP-DBN is evaluated through two case studies, a simulated WWTP based on benchmark simulation model no. 1 (BSM1) and a real papermaking WWTPs, in which the VIP-DBN model shows better modeling performance than its rivals. Specifically, compared with partial least squares, artificial neural networks, and Bayesian networks, the determination coefficient value of VIP-DBN is increased by 34.36%, 20.55%, and 3.30%, respectively, for the prediction of effluent nitrate in BSM1. The VIP-DBN can be used to deal with complex WWTPs in industrial applications, which provides a better practical method for soft sensor modeling and a guarantee for the effective decisionmaking of wastewater treatment processes for papermaking enterprises. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The VIP-DBN composite model proposed in this study showed better modeling performance in papermaking wastewater treatment processes compared to its competitors, which provides a better practical method for soft sensor modeling in dealing with complex WWTPs and guarantees effective decision-making for wastewater treatment processes in papermaking enterprises.