Predicting rare earth elements concentration in coal ashes with multi-task neural networks

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, easily measurable under the routine testing protocol. We introduce a multi-task neural network that simultaneously predicts the contents of different REEs. Compared to the single-task model, this model exhibits notably improved accuracy and reduced sensitivity to noise. Further model analyses reveal key data patterns for screening coal ashes with high REE concentrations. Additionally, we showcase the utilization of transfer learning to improve the adaptability of our model to coal ashes from a distinct source. Our multi-task neural network approach simultaneously predicts the concentration of all types of rare earth elements (REEs) in coal ashes, with an improved accuracy and robustness as compared to conventional single-task neural networks.