Prediction analysis of a wastewater treatment system using a Bayesian network

Wastewater treatment is a complicated dynamic process, the effectiveness of which is affected by microbial, chemical, and physical factors. At present, predicting the effluent quality of wastewater treatment systems is difficult because of complex biological reaction mechanisms that vary with both time and the physical attributes of the system. Bayesian networks are useful for addressing uncertainties in artificial intelligence applications. Their powerful inferential capability and convenient decision support mechanisms provide flexibility and applicability for describing and analyzing factors affecting wastewater treatment systems. In this study, a Bayesian network-based approach for modeling and predicting a wastewater treatment system based on Modified Sequencing Batch Reactor (MSBR) was proposed. Using the presented approach, a Bayesian network model for MSBR can be constructed using experiential information and physical data relating to influent loads, operating conditions, and effluent concentrations. Additionally, MSBR prediction analysis, wherein effluent concentration can be predicted from influent loads and operational conditions, can be performed. This approach can be applied, with minimal modifications, to other types of wastewater treatment plants.