Constrained model predictive control in ball mill grinding process

Stable control of grinding process is of great importance for improvements of operation efficiency, the recovery of the valuable minerals, and significant reductions of production costs in concentration plants. Decoupled multi-loop PID controllers are usually carried out to manage to eliminate the effects of interactions among the control loops, but they generally become sluggish due to imperfect process models and a close control of the process is usually impossible in real practice. Based on its inherent decoupling scheme, model predictive control (MPC) is employed to handle such highly interacting system. For high quality requirements, a three-input three-output model of the grinding process is constructed. Constrained dynamic matrix control (DMC) is applied in an iron ore concentration plant, and operation of the process close to their optimum operating conditions is achieved. Some practical problems about the application of MPC in grinding process are presented and discussed in detail.

Based on its inherent decoupling scheme, model predictive control (MPC) was employed to handle the interaction in grinding process. For high quality requirements, a three-input three-output model of grinding process was constructed. Constrained dynamic matrix control (DMC) was applied in an iron ore concentration plant, and operation of the process close to their optimum operating conditions was acquired.Figure optionsDownload as PowerPoint slide