In optical sorting of bulk material, the composition of particles may frequently change. State-of-the-art sorting approaches rely on tuning physical models of the particle motion. The aim of this work is to increase the prediction accuracy in complex fast-changing sorting scenarios with data-driven approaches. In this article, we propose two neural network (NN) experts for accurate prediction of a priori known particle types. To handle the large variety of particle types that can occur in real-world sorting scenarios, we introduce a simple but effective mixture of experts’ approach that combines NNs with hand-crafted motion models. Our new method not only improves the prediction accuracy for bulk material consisting of many particle classes, but also proves to be very adaptive and robust to new particle types.

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.

Pneumatic ejection has dominated sensor-based sorting for decades, yet diffuse air jets cause bycatch at high mass throughput, and compressed-air generation accounts for a large share of operating cost. We propose (i) a grid-based sorting architecture replacing the conventional single nozzle bar with a two-dimensional grid, and (ii) impact-based mechanical actuators within the grid. Together, these eliminate compressed air entirely while increasing spatio-temporal resolution by leveraging redundant particle–actuator assignments. To coordinate online ejection—deciding which actuators to trigger and when—we formulate sorting as a finite-horizon soc problem and introduce mtcr. mtcr integrates olf soc, mixed discrete–continuous-time modeling, situation-aware action-space reduction, and a greedy rollout heuristic, supported by analytically tractable evaluation of expected sorting costs. In high-mass-flow simulations with equal accept/reject ratios, mtcr maintains both tnr and tpr above 90 , demonstrating effective actuator coordination and bycatch reduction. Laboratory experiments on a 56-solenoid prototype show that the grid significantly exceeds a state-of-the-art pneumatic chute sorter in rejection performance at matched bycatch level, with actuation energy being at least 79 times lower. Grid-based mechanical sorting thus offers a promising route toward precise, energy-efficient separation for cost-sensitive industries such as recycling.