Sustainable recovery of Cu, Ag and Au from the waste printed circuit boards and process optimisation by machine learning

Abstract

The sustainable supply of metals, especially precious metals, is critical for the manufacturing of the electronic chips used in the printed circuit boards of mobile phones. At the same time, the large volume of waste printed circuit boards (WPCBs) of mobile phones is a serious environmental issue that requires developing sustainable processes for the recovery of metals and to handle the waste in a resourceful manner. To address the two challenges of sustainable material supplies for chip manufacturing and waste management of WPCBs of mobile phones, we present a machine learning (ML) powered process optimization framework for the sustainable recovery of Cu, Ag and Au from the WPCBs. The process employs NH₄Cl and low-temperature roasting for the recovery of metals for designed experimental conditions. The input-output data obtained from the experiments is deployed to make approximations of the metal recovery profiles for Cu, Ag and Au by Gaussian Process (GP) models. The GP models trained for the three metals are embedded in the objective function of an optimisation problem for determining the optimised experimental conditions that maximise the recovery of the metals from the WPCBs. The verification of optimized experimental conditions, obtained after solving the optimization problem, in made in the lab that confirms 99 %, 90 % and 80 % respectively recovery of Cu, Ag and Au from the WPCBs. This demonstrates the effectiveness of the developed ML powered analysis workflow that improves the material utilisation efficiency and supports sustainable AI by considering material requirements for chip manufacturing and waste management.