Fault diagnosis of an industrial CGO coker model predictive control system

We report a successful fault diagnosis and trouble shooting process of an industrial model predictive control system. The approach is completely data driven. Routine closed-loop operating data is the only information required for applying such a diagnosis. The source of the problem has been attributed to inappropriate selection of the disturbance variables for the MPC controller. The problem is not unusual in industrial model predictive control systems. It is therefore recommended to carry out such analysis to other industrial MPC control systems as well.