Oral:

Here, the implementation of the gradient-based Economic MPC (Model Predictive Control) in an industrial distillation system is studied. The approach is an alternative to overcome the conflict between MPC and RTO (Real Time Optimization) layers in the conventional control structure. The study is based on the rigorous dynamic simulation software (SimSci Dynsim®) that reproduces the real system very closely and is able to communicate with Matlab where the control/optimization algorithm is implemented. The gradient of the economic function, which is required to the on-line execution of the extended control strategy, is obtained through the sensitivity tool of the real-time optimization package (SimSci ROMeo®). It is shown that the proposed integration approach leads to convergence and stability to the closed-loop system. In order to study the pros and cons of the new strategy, a propylene distillation system is simulated with both, the proposed approach (one-layer MPC+RTO) and the conventional two-layers hierarchical structure of control and optimization. The results show that, from the performance, stability and disturbance rejection viewpoint, the proposed gradientbased extended control method for this particular system is equivalent or better than the conventional approach.