Data-driven approach for Cu recovery from hazardous e-waste

Abstract

The Cu recovery from e-waste is beneficial since it contributes the most economic value to electronic scraps (∼322,000 tons Cu available with PCB). Data Science and Artificial Intelligence (AI) tools, experimental data-driven Adaptive Neuro-Fuzzy Inference System (ANFIS), and Response Surface Methodology (RSM) were adopted for the prediction of Cu recovery from e-waste. These models were developed using four variables (H2SO4, H2O2, Solid/Liquid ratio, and reaction time) and validated by validation experiments. The developed RSM-based model predicts the Cu recovery with an average error of ∼10.51%, whereas the ANFIS-based model predicts the Cu recovery with an error of ∼6.03%. Higher values of R2 (0.99), F (641.59), and a lower value of P (< 0.05) were observed in the ANOVA study indicating better suitability of the model. Based on findings from the comparison, the relevant data-driven ANFIS model is used for further analysis. The 3D interactive plots have been developed to find a suitable range of input parameters to improve the recovery of Cu from e-waste. The results and findings reported in this article will be valuable to Cu recovery, e-waste management, and hazardous waste management. The proposed ANFIS model is highly efficient for effective and accurate automation at the industrial scale. Environmental implicationScientific communities must pay attention to the challenges raised due to surplus e-waste. E-waste is reported to be the most dangerous anthropogenic material due to hazardous chemicals and toxic metals. This article explains how e-waste can be converted into economically beneficial products. Since Cu contributes the most economic value to e-waste, its recovery can minimize its hazardous effect and promote a circular economy. Modern data-driven tools are highly efficient for accurately predicting Cu recovery. In this article, RSM and ANFIS-based prediction models have been developed with high accuracy. It was observed that ANFIS is more efficient than RSM and better for the automation of copper recovery. The results and findings reported in this article will be valuable to Cu recovery, e-waste management, and hazardous management of e-waste. The proposed data-driven approach helps to minimize the load of electronic waste and reduces a significant proportion of hazardous effects of e-waste on the environment.