Biochar performance evaluation for heavy metals removal from industrial wastewater based on machine learning: Application for environmental protection

Abstract

Industrial wastewaters contaminated with heavy and toxic metals cause serious risks to human health and other forms of life. The performance of biochar for the elimination of heavy metals has been acclaimed. It is highly advantageous to develop efficient computational methods to predict its biosorption performance. In this research, the performance of four types of machine learning methods including adaptive neuro fuzzy inference system (ANFIS), coupled simulated annealing-least squares support vector machine (CSA-LSSVM), particle swarm optimization-ANFIS (PSO-ANFIS) and genetic programming (GP) was evaluated. The modeling was conducted on 44 types of biochar reported in 353 datasets from heavy metal adsorption experiments. All four models have demonstrated good predictive performance, especially by LSSVM, GP and PSO-ANFIS procedures. The correlation coefficient (R2) values of test dataset for ANFIS, CSA-LSSVM, PSO-ANFIS, and GP were 0.9428, 0.9832, 0.9712 and 0.9750. The values of mean squared error (MSE) and average absolute relative deviation (AARD) were 0.0020 and 0.36 for CSA-LSSVM model which has the superior capability than other models. The sensitivity analysis showed that the key parameters in heavy metal removal by biochar were the concentration ratio of heavy metals/biochar and total carbon content in biochar. A MATLAB code was developed to estimate the biosorption efficiency. Novel equation based genetic programming assists researchers to predict sorption yield of heavy metals by reducing the costs and time. Analyzing the results of this research can increase the understanding of researchers towards the effective remediation of hazardous chemicals in water resources.