Model predictive static programming control applied to mineral processing plants

Abstract

In a mineral processing plant, the separation of valuable material from ore has multiple stages. Usually, the ore is crushed or ground into smaller parts through multiple crushers or grinding mills. This is called the communition process. This process is typically the first stage for extracting valuable material and is important for further down-stream processes. The output of the communintion stage is usually regulated to achieve a stable throughput and a specific ore particle size. After the ore is crushed and ground to a specified size, the valuable material in the ore needs to be separated from the undesired materials.

The properties of the desired material influence the method used for separation. These methods include froth flotation, gravitational separation, magnetic separation and electrostatic separation. The separation process can include multiple process streams to get a high grade of the desired minerals out of the ore. In froth flotation, the main objective is to extract the desired material from the ore to obtain a large mineral recovery. Because the flotation process relies on the flotation of particles, particle size is extremely important.

The use of control systems in mineral processing plants has been adopted to improve throughput, optimize power usage, ensure safe process operation and to running at a stable operating condition. The control of these plants makes use of different advanced process control strategies which include but are not limited to cascaded control, where multiple layers of control systems are applied, and model predictive control. These different control strategies can range from regulatory control to supervisory control. Because of the large number of inputs to these plants, efficient controllers are necessary to obtain desired results.

The use of Nonlinear Model Predictive Control (NMPC) is an attractive option for most mineral processing plants because of the constraint management capabilities of the controller. Unfortunately, the NMPC method has a large computational load which requires sufficient resources to make it a viable option. Another model predictive control method known as Model Predictive Static Programming (MPSP) has shown promise to improve the computational time of a standard NMPC controller. The MPSP control philosophy generates a static optimization problem which is less computationally difficult to solve compared to the dynamic optimization problem that is generated through NMPC. In this dissertation, the control of a single-stage grinding mill circuit and a four-cell flotation circuit with an MPSP controller to reduce the computational load is proposed. The computational efficiency and the output performance of MPSP controllers are compared to NMPC controllers as a motivation for the use thereof. The comparison is done by simulating two mineral processing stages, namely the communition phase and the separation phase. The simulations considered different configurations for both the MPSP and NMPC controllers.

The comparison of the controllers in the simulations shows that the MPSP controller obtained similar or improved plant results while also having a reduced computational time compared to the NMPC controller. The MPSP controller also displays scalability improvements compared to the NMPC controllers which can be beneficial for supervisory control of large-scale processing plants.