Machine-learning-assisted prediction of maximum metal recovery from spent zinc-manganese batteries

Abstract

Spent zinc-manganese batteries contain heavy toxic metals that pose a serious threat to the environment. Recovering these metals is vital not only for industrial use but also for saving the environment. Recycling metal from spent batteries is a complex task. In this study, machine-learning-based predictive models are developed for predicting metal recovery from spent zinc-manganese batteries by studying the energy substrates concentration, pH control of bioleaching media, incubating temperature and pulp density. The main objective of this study is to make a detailed comparison among five machine learning models, namely, linear regression, random forest regression, AdaBoost regression, gradient boosting regression and XG boost regression. All the machine learning models are tuned for optimal hyperparameters. The results from each of the machine learning models are compared using several statistical metrics such as R-2, mean squared error (MSE), mean absolute error (MAE), maximum error and median error. The XG Boost regression model is observed to be the most effective among the tested algorithms.