A Deep Learning Approach to Maximizing Electrostatic Sieve Efficiency in Regolith Beneficiation

This study investigates the optimization of an electrostatic sieve designed for lunar regolith beneficiation. Two parameters of the electrostatic sieve, 1) the voltage amplitude and 2) angle of inclination, were chosen as variables in the optimization process. Numerical simulations revealed that increasing voltage amplitude significantly enhances sieve performance over the sieve angle. However, optimal separation required careful voltage adjustment for specific sieve angles. A comprehensive dataset incorporating additional parameters was then created to train Machine Learning (ML) and Deep Learning (DL) models for further optimization. The ML/DL models were trained on a small subset of the original dataset to predict the yield. We showcase the benefits of leveraging DL techniques to improve the electrostatic sieve for regolith beneficiation via tailored evaluations. Our model, trained on lower-yield examples, accurately (92%) identifies parameter combinations that increase yields above 30%. It leads to a near-optimal yield with 10× reduction on runtime when compared with exhaustive simulations. This not only reduces the reliance on resource-intensive numerical simulations but also offers a rapid, validated approach to optimizing equipment for lunar mining operations.