Prediction of heavy metal adsorption by activated carbon using machine learning

Abstract

Heavy metal contamination in water is a major environmental challenge due to its toxicity, persistence, and potential for bioaccumulation in living organisms. Among the various treatment technologies, adsorption using activated carbon remains one of the most effective and economical methods for removing metal ions such as Cu(II), Zn(II), Ni(II), Pb(II), Cd(II), Cr(VI), and As(V). However, exploring and predicting adsorption performance through conventional experimentation is often time-consuming and resource-intensive. In this study, a machine learning based predictive framework was developed to estimate the amount of metal adsorbed at equilibrium (Qe, in mg g -1 ) on activated carbon. A large dataset comprising 1,528 experimental records was assembled, incorporating key adsorbent textural parameters (BET surface area, pore diameter, pore volume), operational variables (pH, temperature, initial concentration, dosage, contact time), and intrinsic ionic properties (hydrated radius, van der Waals radius, molar mass, electronegativity). Four supervised learning algorithms were implemented and evaluated: Random Forest Regressor (RF), Extra Trees regressor (ET), Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR). Among them, the Gradient Boosting regressor showed the best predictive performance on the test set (RMSE = 4.035, MAE = 2.205, R 2 = 0.9648). Beyond prediction, the proposed machine learning approach enables the identification of complex, nonlinear relationships governing adsorption behavior and highlights the relative importance of key physicochemical parameters. It therefore represents a relevant complementary tool to experimental studies for improving the design of water treatment systems.