Forecasting Material Recovery in Electronics Recycling Plants with Circular Manufacturing Practices

Abstract

The exponential growth of electronic waste globally presents significant environmental and economic challenges, necessitating a transition from linear consumption models to circular manufacturing practices. A critical bottleneck in this transition is the unpredictable nature of material recovery yields in electronics recycling plants, which often rely on deterministic processing models that fail to account for the highly variable degradation states of end-of-life electronics. This paper explores a novel approach to predicting material recovery by integrating reliability testing methodologies directly into the sorting and processing workflows of recycling facilities. By subjecting sample batches of electronic waste to accelerated stress tests, including thermal cycling, mechanical vibration, and chemical degradation assessments, operators can gather empirical data on the physical and chemical state of the materials. This data serves as the foundation for predictive models that estimate the optimal recovery pathways and expected yields for precious metals, rare earth elements, and base polymers. Through a comprehensive analysis of plant operational data, this study demonstrates that incorporating reliability testing metrics significantly enhances the accuracy of recovery predictions, thereby optimizing energy consumption, reducing chemical waste, and improving the overall economic viability of circular manufacturing in the electronics sector.