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

The Cu recovery from e-waste is beneficial since it contributes the most economic value to electronic scraps (similar to 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 similar to 10.51%, whereas the ANFIS-based model predicts the Cu recovery with an error of similar to 6.03%. Higher values of R-2 (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.