Real-time coordination of multiple shuttle-conveyor-belts for inventory control of multi-quality stockpiles

Abstract

A hybrid model predictive control formulation with continuous and discrete variables that models multiple crushed-ore stockpiles per conveyor-belt is proposed for better conveying and stockpiling performance. The aim is to minimize the inventory level or holdup squared deviations from targets by varying the fill-time, idle-time, or run-length of the shuttle-conveyor-belts’ tripper-cars over multiple stockpiles’ positions (the discrete actuation or manipulated variable). In the problem, amounts of crushed-ore dropped by gravity from conveyor-belts fill up the upper-level of the stockpiles considering quality balances to specify blended ore by the mixtures of the material dropped by synchronized shuttle-conveyor-belts’ tripper-cars. The problem is solved as a mixed-integer quadratic programming (MIQP) that uses amount-of-quality balances as a linear (LP) approximation, tightening the MIQP-solutions to schedules of simultaneous blending of different quality shuttle-conveyor-belts’ raw materials. Results of the initialization and steady-state stages of the inventory-control and logistics-quality optimization highlight the modeling and solution strategies.