Answers are grounded in this paper and cite their sources.
The processing capacity of high-throughput vision-based intelligent ore sorting systems is strictly constrained by the physical dead zones of mechanical actuators. Under random Poisson arrivals, particle clustering in space translates to severe time-domain congestion that degrades sorting performance. While algorithmic optimization cannot overcome this physical bound and homogeneous multi-stage configurations suffer from superimposed dead-zone penalties, this paper proposes a heterogeneous dual-stage sorting architecture and a corresponding adaptive collaborative scheduling strategy (ACSS). By cascading a primary high-torque actuator (HTA, modeled as M/D/1/0 loss queue) and a secondary high-dynamic actuator (HDA, modeled as M/G/1/0 loss queue), the architecture achieves spatiotemporal decoupling of congestion via multi-stage spatial stratification. The ACSS integrates dynamic time windows, state-holding logic and a predictive abort mechanism to proactively mitigate sorting losses arising from combined dead-zone effects across the two stages. Discrete-event simulations (DES) and hardware-in-the-loop (HIL) experiments demonstrate that, at 50 % target particle proportion, the system raises the 80 %-recovery threshold from 2 particles/s (single-stage HDA) to 4 particles/s, doubling the processing capacity. The proposed system exhibits an asymmetric U-shaped performance characteristic. Governed by the cascaded superposition of primary-stage saturation decline and secondary-stage throughput buildup, the system reaches its performance valley at a target particle proportion of 30 % and capitalizes on Poisson sparsity in low-grade environments (10 % target particle proportion), stretching the saturation limit to 6 particles/s. This spatiotemporal decoupling framework establishes a simulation-supported design point for potential underground pre-concentration applications, offering a promising control architecture for waste rejection in thin-vein precious metal extraction.
No takes yet. Share an insight, caveat, or question.
Li et al. (2026) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: