Model-predictive control fundamentals

Abstract

Model-predictive control (MPC) improves the capability of process units by stabilizing operation, increasing throughput, improving fractionator performance, decreasing product quality giveaway, and reducing utility consumption. MPC provides real-time information to higher-level applications, such as planning models and process optimizers. MPC input comes from the distributed control system (DCS), advanced regulatory controllers (ARCs), and laboratory data. A well-implemented MPC controller responds once per minute – or in some cases more frequently – to changes in feedstock, ambient temperature, and so on, by moving several variables simultaneously. For major process units, returns on investment for MPC can exceed $0.50 per barrel, not including collateral benefits, such as improving the efficiency of operators and engineers, and improving process safety.