Tuning the Model Predictive Control of a Crude Distillation Unit. (January 2016) - Yamashita, André Shigueo; Zanin, Antonio Carlos; Odloak, Darci
Abstract: Tuning the parameters of the Model Predictive Control (MPC) of an industrial Crude Distillation Unit (CDU) is considered here. A realistic scenario is depicted where the inputs of the CDU system have optimizing targets, which are provided by the Real Time Optimization layer of the control structure. It is considered the nominal case, in which both the CDU model and the MPC model are the same. The process outputs are controlled inside zones instead of at fixed set points. Then, the tuning procedure has to define the weights that penalize the output error with respect to the control zone, the weights that penalize the deviation of the inputs from their targets, as well as the weights that penalize the input moves. A tuning approach based on multi-objective optimization is proposed and applied to the MPC of the CDU system. The performance of the controller tuned with the proposed approach is compared through simulation with the results of an existing approach also based on multi-objective optimization. The simulation results are similar, but the proposed approach has a computational load significantly lower than the existing method. The tuning effort is also much lower than in the conventional practical approaches that are usually based on ad-hoc procedures. Highlights: A new tuning of MPC with input targets methodology is established. Input and output goals are stated as reference trajectories. The technique is illustrated by tuning the MPC of a Crude Distillation Unit. The flexible framework makes it possible to define different operating scenarios.