A machine learning framework for urban mining: A case study on recovery of copper from printed circuit boards

Plenty of research articles on developing methods to recover metals from secondary sources have been published. These methods are optimized for a specific source and have poor reproducibility when used for different sources. However, the composition of the source changes with time, manufacturer, and geography, making designing the recovery process a tedious endeavor. A modeling framework that captures the source variation and suggests the process parameters was developed and employed to design a process for copper recovery from the printed circuit board (PCB). Data collected from 23-research articles was visualized using four-dimensional plots. Plots show that the leaching time required for Cu recovery is inversely proportional to hydrogen peroxide concentration, acid concentration, and source % Cu. Recovery is amplified and faster when all these parameters are set to high value, which may not be feasible commercially. Five supervised machine-learning algorithms (support vector machine, random forest, gradient boost machine, XG Boost, and AdaBoost) were trained on 1200 data points as classification and regression problems and validated using a 10-fold cross-validation procedure. Models were tested on 120 data points and compared for predicting accuracy; the gradient boost machine model performs best with an MAE of 10.83% and an F1 score of 0.72. Feature importance analysis based on LIME and permutation importance is used to evaluate the contribution of each feature on recovery, and reduced parameter ranges for high recovery are obtained. Our modeling framework is generic, which can be used for designing any recovery process.