Model Update Based on Transient Measurements for Model Predictive Control and Hybrid Real-Time Optimization

The process model has the most relevant role in model predictive control (MPC) design since it is responsible for capturing system dynamics and behavior for control action calculation. Besides that, in real-time optimization (RTO), an accurate model allows the estimation of the optimum values that will lead the plant to optimal operation. Related to linear models, the linearization point sometimes is not capable of tracking the process trajectory in different regions, jeopardizing the entire representation. Regarding these issues, it is proposed in this paper to employ an augmented unscented Kalman filter to update the linear process model used in the MPC and the steady-state nonlinear model used in the hybrid RTO, at each sampling time, to capture the true process behavior and the updated economic cost. The cost function, solved in the RTO layer, is handled in the MPC layer as a process output, a new variable combined by the measurements, that must be driven to the provided optimum value, respecting constraints. The extended MPC approach is capable of handle zone tracking and set-point/target tracking. The Williams-Otto reactor scheme was employed to corroborate the proposed approach since it presents structural and parametric discrepancies between the model and the plant. The presented results showed that the approach was able to track the true value of the optimal cost operation, respecting the soft-constraints (or range) for the process outputs without exceeding manipulating efforts.