Modeling of the Ni(II) removal from aqueous solutions by ion exchange resin: Comparison of various machine learning approaches

Abstract

This study aims to investigate the removal of Ni(II) ions from aqueous solutions using an ion exchange resin, focusing on various machine learning approaches to predict the process. The research highlights the efficiency of Amberlite IR120 Na as a strong acidic cation exchange resin, examining its adsorption capacity under varying conditions, including resin dose, initial Ni(II) concentration, solution pH, temperature, and contact time. The adsorption kinetics were accurately described by the pseudo-second-order kinetic model. Additionally, both surface adsorption and intra-particle diffusion played roles in the steps of the adsorption rate. The adsorption isotherm data fitted well with the Langmuir model, indicating a maximum adsorption capacity of 134.8 mg/g. Moreover, machine learning techniques were utilized to predict the resin’s performance, evaluating five diverse models: Support Vector Regression (SVR), Random Forest, Decision Tree, Multi-Layer Perceptron (MLP), and Polynomial Regression. The results showed that the SVR model performed better than the others, with a training R² of 0.990 and testing R² of 0.973, along with the lowest mean absolute error and mean squared error. These findings demonstrate the effectiveness of machine learning in accurately modeling the complex relationships within the adsorption process, thus offering valuable insights for optimizing heavy metal removal from wastewater.