Removal of Ni(II) ions from wastewater by ion exchange resin: Process optimization using response surface methodology and ensemble machine learning techniques

Abstract

This study investigates the removal of nickel ions from aqueous solutions using Amberlite IR120 Na resin. Response surface methodology (RSM) and Ensemble machine learning models were employed to predict and evaluate the adsorption capacity of the resin. The effects of the initial concentration of nickel ions (10–90 mg/L), resin dose (0.1–0.7 g/L), initial pH (3−9), and temperature (10–40 ºC) were analyzed as independent variables. In order to assess the efficiency of Ensemble models, five methods, including random forest, extra tree, AdaBoost, gradient boosting, and XGboost models, were employed. The results demonstrated that the XGboost model predicts the adsorption capacity of the resin for Ni(II) with very high accuracy in the training phase (R2 = 1) and the test phase (R2 =0.96). The maximum adsorption capacity of the resin in the optimization step was achieved for CCD (initial Ni(II) concentration of 45.69 mg/L, resin dose of 0.1 g/L, pH of 6.64, and temperature of 39.68 ºC) and Bayesian optimization (initial Ni(II) concentration of 54.78 mg/L, resin dose of 0.11 g/L, pH of 4.72, and temperature of 39.02 ºC) techniques by 142.67 mg/g and 139.60 mg/g, respectively. Moreover, the Bayesian optimization method resulted in a lower error (0.63 mg/g) than the CCD model (1.53 mg/g). Based on statistical measures, it can be concluded that XGBoost outperforms CCD.