A critical review on applications of machine learning in wastewater treatment: insights and implications for distillery wastewater

Abstract

The critical requirement for treating distillery wastewater is globally recognized due to its significant environmental impact and regulatory requirements. This paper reviews the literature on wastewater treatment, focusing on the application of machine learning (ML) algorithms for analyzing large amounts of data and identifying complex patterns. The study uses the Scopus, ScienceDirect, and Web of Science data-bases for bibliometric analysis. ML has become increasingly attractive in engineering due to its ability to improve predictions for process output variables. It is used in chemistry and engineering to improve computational chemistry, plan materials synthesis, and model contami-nant remediation processes. The research proposes future research directions for distillery wastewater treatment using ML approaches. The aim of this review is to critically evaluate the application of ML models in wastewater treatment, drawing insights from existing studies and exploring their potential application to distillery wastewater. This study provides comparisons and offers recommendations for future research in this field.