Real Time Optimization (RTO) with Model Predictive Control (MPC)

This paper studies a simplified methodology to integrate the real time optimization of a continuous system into the model predictive controller in the one layer strategy. The gradient of the economic objective function is included in the cost function of the controller. One of the control objectives is to zero the reduced gradient of the economic objective while maintaining the system outputs inside their zones. Optimal conditions of the process at steady state are searched through the use of a rigorous nonlinear process model, while the trajectory to be followed is predicted with the use of a linear dynamic model that can be obtained through a plant step test. Moreover, the reduced gradient of the economic objective is computed taking advantage of the predicted input and output trajectories. The main advantage of the proposed strategy is that the resulting control/optimization problem can be solved with a quadratic programming routine at each sampling step. Simulation results show that the approach proposed here is comparable to the strategy that solves the full economic optimization problem inside the MPC controller where the resulting control problem becomes a nonlinear programming with a high computer load.