Predicting Rare Earth Elements Concentration in Coal Ashes with Multi-Task Neural Networks

Abstract

Abstract The increasing demand for rare earth elements (REEs) makes them a scarce strategic resource for technical developments. In that regard, harvesting REEs from coal ashes—a waste byproduct from coal power plants—offers an alternative solution to conventional ore-based extraction. However, this approach is bottlenecked by our ability to screen coal ashes bearing large concentrations of REEs from feedstocks—since measuring the REE content in ashes is a time-consuming and costly task requiring advanced analytical tools. Here, we propose a machine learning approach to predict the REE contents based on the bulk composition of coal ashes (which is easily measurable under the current testing protocol). We introduce a multi-task neural network that simultaneously predicts the contents of different REEs and, importantly, exhibits notably improved accuracy than the single-task models. Further model analyses reveal key data patterns for screening coal ashes with high REE concentrations. Teaser: With machine learning, high-throughput screening of REE-bearing coal ashes can be fulfilled based on a simple measurement.