Advanced Process Control in Minerals Processing: Modular First-Principles Models Simplify Engineering

摘要

Advanced process control techniques are most frequently used in the chemical and petrochemical industries. However, the minerals processing industry also poses interesting challenges that these techniques can successfully tackle. We will first describe the formulation of model predictive control (MPC) strategies using modular first-principles models. In real-life applications, a state estimation technique such as moving-horizon estimation (MHE) is frequently required. Combining MPC and MHE offers the possibility of sharing a common model. We describe two different modeling frameworks - linear mixed-logical dynamical (MLD) models and nonlinear Modelica models. Both offer the advantage that process models can be assembled from basic units, thus making the resulting control strategies easy to understand and to modify. The second part of the paper is dedicated to applications of this approach to the minerals processing industry.