Stochastic Optimal Control of an Optical Sorter With Material Recirculation

The optical bulk material sorting is a key technology on our way toward a circular economy and efficient recycling. However, controlling the sorting accuracy has so far been severely limited, as the achievable accuracy of conventional sorters is strongly determined by the mass flow and the mixing ratio of the incoming particle stream. To enable closed-loop control, in the previous work, we introduced a modification to the sorter design, in which controlled fractions of the already sorted mass flows are returned to the inlet of the sorter. In this article, we now propose two open-loop and two closed-loop feedback (CLF) stochastic model predictive controllers (MPCs) for the control of sorting systems with recirculation operating under dynamically changing conditions. In addition, we propose to integrate a desired minimum accuracy as a chance constraint into our controllers’ stochastic formulation. Our evaluations using a coupled discrete element-computational fluid dynamics (DEM-CFD) simulation show that our controllers considerably improve on the system without recirculation and outperform the previously known controllers. Furthermore, we found that they are able to maintain a predefined minimum quality even in highly dynamic scenarios, making the approach highly valuable for tasks where achieving a certain quality at any point in time is crucial.