Automating a shuttle-conveyor for multi-stockpile level control

We describe the application of an online dynamic and discrete scheduling optimization, also known as real-time hybrid model predictive control, applied to a shuttle-conveyor / tripper car intermittently delivering crushed-ore containing copper, iron, etc., to several stockpiles. Each stockpile continuously feeds an apron feeder located in a tunnel underneath the stockpile where multiple of these are combined to a belt feeder to charge a grinding mill which ultimately produces concentrates of minerals. Crushed-ore from a mine several kilometres upstream is transported by both conveyor belt and automatic trains. The shuttle-conveyor is the discrete actuation or manipulated variable depositing the solids onto each stockpile where we vary the time over each stockpile to automatically control to a setpoint or target their levels sensed by industrial radar. In the field, the motion of the shuttle-conveyor is known as sweeping as it moves from one stockpile (or tunnel) to the next in a fixed sequence. The purpose of our application is to improve the stockpile level control performance by automatically adjusting the up-time, fill-time or run-length of the shuttle-conveyor tripper car over each stockpile based on real-time level measurement feedback and the application which is referred to as smart sweeping.