Sustainable Management of Wastewater Treatment Plants Using Artificial Intelligence Techniques

Abstract Modeling and simulation of wastewater treatment plants (WWTPs) suffer from a high degree of nonlinearity, regarding the multiple and complex physical, chemical, and biological processes. Hence, this chapter represents the state-of-the-art artificial intelligence (AI) techniques that have been widely used to determine the effects of operational conditions and environmental factors on the wastewater treatment systems’ management. Data are retrieved from the articles published in the SCOPUS database from 2010 to 2019, using the search keywords “Wastewater,” “Artificial,” “Neural,” “Network,” “Fuzzy,” and “Logic.” The survey analysis results (e.g., number of publications, funding sponsors, affiliations, subject area, and documents’ type) revealed that the AI models provided essential applications to operate and manage WWTPs and control the effluent quality. Moreover, the AI approaches were efficiently applied to evaluate the carbonaceous matter bioconversion, nitrification/denitrification process, and microbial activities in WWTPs. The chapter's findings are beneficial to operators and environmentalists, dealing with the simulation, prediction, knowledge management, and control aspects in WWTPs to maintain the effluent characteristics within the regulation-specified limits. Essential suggestions and findings are summarized in the recommendations and conclusions section.