Linear and Nonlinear Modeling for Predicting Nickel Removal from Aqueous Solutions

Abstract

Mathematical models are widely used to predict removal rates of heavy metals from aqueous solutions. In this study, partial least squares (PLS), wavelet neural network (WNN), and support vector regression (SVR) were used to predict the amount of nickel (Ni) removal by dried sunflower stalks from a synthetic wastewater, based on experimental data sets from a laboratory batch mode. Effect of pH, initial concentration of the adsorbate, contact time, and dose of the adsorbent was considered in the adsorption process. Results showed that the coefficient of determination (R2 or q2) for the relationship between the model-predicted and experimental data of the final concentration of Ni at calibration stage was 0.87, 0.98, and 0.99 and for cross-validation was 0.73, 0.8, and 0.91 for PLS, WNN, and SVR models, respectively. It was concluded that the SVR model performed relatively better than the other models due to its capability in capturing the nonlinear relationships between the variables. Grid search was a fast and effective method that optimized the hyperparameters in SVR modeling. The SVR and WNN models were also used to investigate the effect of different variables on Ni removal efficiency. The results showed that initial concentration of Ni and pH of the solution were more important in the adsorption process, relative to contact time and dose of the adsorbent.