Machine learning toward improving the performance of membrane-based wastewater treatment: A review

Abstract

Machine learning (ML) is a data-driven approach that can be applied to design, analyze, predict, and optimize a process based on existing data. Recently, ML has found its application in improving membrane separation performance for wastewater treatment. Models have been developed to predict the performance of membranes to separate contaminants from wastewater, design optimum conditions for membrane fabrication for greater membrane separation performance and predict backwashing membranes and membrane fouling. This review summarizes the progress of ML-based membrane separation modeling and explores the direction of the future development of ML in membrane separation-based wastewater treatment. The strengths and drawbacks of the ML algorithms extensively used in membrane separation-based wastewater treatment are summarized. Artificial neural network (ANN) was the most used algorithm for modeling membrane separation-based wastewater treatment. Future research is recommended to focus on the development of integrated ML algorithms and on combining ML algorithms with other modeling approaches (e.g., process-based models and statistical models). This will serve to achieve higher accuracy and better performance of the ML application.