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Abstract

Context:To increase the efficiencyof wastewater treatment, modeling and optimization of pollutant removal processes are the best solutions. The relationship between input and output parameters inwastewater treatment processes (WWTP) is a complicated one, and it is difficult for designing models using statistics. Artificial Intelligence (AI)models are generally more flexible when compared with statistical models while modeling complex datasets with nonlinearityand missing data.Objective:Studies on WWTP of AI-basedare increasing day by day. Therefore,it is crucialto systematically review the AI techniquesavailablewhich are implemented for WWTP. Such kind of review helpsforclassifyingthetechniques that are invented andhelpsto identify challenges as well asgaps for future studies.Lastly, cansort out the best AI technique to design predictive models for WWTP.Method:With the help of the most relevant digital libraries, the total number of papers collected is 1222 which are based on AI modeling on WWTP.Then thefiltration ofthe papersis mainlybased on the inclusion and exclusion criteria. Also, toidentify new relevant papers,snowballing is the other technique applied.Results:Finallyselected 76 primary papers toreach theresult were published between 2004 and 2020.Conclusion:ANN with MLP approach on BPalgorithm become a supervised neural network called BPNN is the most used AI modeling for WWTP and around 40% of the experimental research done with BPNN. Then there are some limitations on AI modeling of WWTP using photoreforming which is the current study of WWTP represents a promising pathforgeneratingrenewable and sustainable energy resources like chemicals and fuels.

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