Environmental-friendly recovery of non-metallic resources from waste printed circuit boards: A review

Precious metal recovery is the main economic power of electronic waste (e-waste) 1 recovery. The prediction of e-waste and its precious metal content is of great significance to the realisation of China’s circular economy. In this work, the generation mechanism of e-waste is firstly deduced to determine model characteristics suitable for its prediction. Fractional derivative grey models base on a weak singular power kernel function (PFGM(q,1)) and a non-singular exponential kernel function (EFGM(q,1)) are established. Compared with the PFGM(q,1), the EFGM(q,1) has the advantages of simpler solution, lower calculation complexity and wider scope of application. Next, some data are selected to verify the validity. The EFGM(q,1) is used to predict mobile phone, laptop, desktop and television waste, and the weight of printed circuit boards (PCBs) in the waste and content of precious metals in these PCBs are calculated. Finally, the trend of e-waste and its precious metal contents are analysed and discussed based on the calculation results.

Fractional grey model based on non-singular exponential kernel and its application in the prediction of electronic waste precious metal content.

Utilizing concrete pillars as an environmental mining practice in underground mines

Advances in physiochemical and biotechnological approaches for sustainable metal recovery from e-waste: A critical review

Heavy metals in soil-vegetable system around E-waste site and the health risk assessment

Recovering full metallic resources from waste printed circuit boards: A refined review

A novel approach for determining the accurate debromination time in the ball-milling process of nonmetallic particles from waste printed circuit boards by computation

Biocyanidation is an environment-friendly technology for recovering precious metals from waste printed circuit boards (WPCBs). Although the leaching mechanism has been studied a lot, Au-release behavior still remains unknown. In this paper, density functional theory (DFT) was employed to investigate the electronic structure of the complex existing in the Au cyanidation process. Extended charge decomposition analysis (ECDA) showed that the adsorption of CN– to Au and the adsorption of O to the formed [AuCN]⁻ caused net electron transfer of 0.356 and 0.574 au, respectively. Dissociation of O from [AuCNO]⁻ had a Gibbs free energy change of −154.229 kcal/mol. Electron localization function (ELF) and localized orbital locator (LOL) confirmed that CN– covalent bonding led to a transformation of the orbit localization region of the adsorbed Au atom. It might cause electrostatic repulsion from the nondirectly contacted Au atom. This speculation was demonstrated by the Mulliken overlap population analysis, which showed that CN– bonding caused antibonding interaction. The repulsive interaction would be an important factor triggering the release of the adsorbed Au atom. This work presented a new interpretation of Au cyanidation, providing important insights into Au-release behavior. It might help construct the leaching kinetics of multimetal resources to facilitate the recovery of precious metals from waste printed circuit boards.

Mechanism of Gold Cyanidation in Bioleaching of Precious Metals from Waste Printed Circuit Boards

Optimizing conditions of key factors influencing CN− producing ability of Pseudomonas biofilm to leach Ag from waste printed circuit boards

In recent years, the reverse logistics (RL) management of waste electrical and electronic equipment (WEEE) has aroused widespread interest due to its vital role in improving ecological and economic benefits. As accurate quantification and estimate of future WEEE are fundamental to adequate planning and efficient treatment, we propose the Multi-time Scale Attention Network (MULAN) model in this paper for WEEE RL return prediction. In MULAN, we take multiple windows of historical data to capture distinct characteristics presented at different time scales and use attention-based alignment to fuse information. We then propose smooth time-series embedding method based on neighborhood data aggregation and temporal feature embedding method from four kinds of time positions to tackle the challenges of dealing with high noise data and high sensitivity to temporal dependencies. We apply MULAN to a WEEE recycling enterprise and conduct extensive experiments to demonstrate MULAN has significantly improved prediction accuracy over other baseline prediction methods in most cases. 

Jiaqi Hu

Papers

Recovering full metallic resources from waste printed circuit boards: A refined review

A novel approach for determining the accurate debromination time in the ball-milling process of nonmetallic particles from waste printed circuit boards by computation

Mechanism of Gold Cyanidation in Bioleaching of Precious Metals from Waste Printed Circuit Boards

Optimizing conditions of key factors influencing CN− producing ability of Pseudomonas biofilm to leach Ag from waste printed circuit boards

Multi-time Scale Attention Network for WEEE reverse logistics return prediction