Prediction of heavy metals removal by polymer inclusion membranes using machine learning techniques

Abstract

This study predicts heavy metals removal from aqueous solution by polymer inclusion membranes (PIMs) process using machine learning (ML) techniques such as multiple layer perceptron neural networks (MLPNN) and multiple linear regression (MLR) after data analysis. The removal efficiency (RE) of the PIMs process is predicted for cobalt (Co), cadmium (Cd) and chromium (Cr) by changing operating conditions including time, carrier type, carrier rate, film thickness, plasticizer type and plasticizer rate. The MLPNN model presents reliable results with lower mean square error (MSE) for an unseen testing dataset, whereas the MLR model shows higher MSE values. The coefficient of determination (R-2) of the MLPNN model for the testing dataset is 0.93, 0.90 and 0.86 for Co, Cd and Cr, respectively, whereas MLR shows poor results. Therefore, the MLPNN model can be a competitive, robust and fast alternate to optimize the PIMs process with minimum experimental work.