Yamashita André Shigueo, Zanin Antonio Carlos, Odloak Darci
Department of Chemical Engineering, University of São Paulo, Av. Prof. Luciano Gualberto, trv 3 380, 05424-970 São Paulo, Brazil.
Petrobras S.A., Center of Excellence for Technology Application in Industrial Automation, São Paulo, SP, Brazil.
ISA Trans. 2016 Jan;60:178-190. doi: 10.1016/j.isatra.2015.10.017. Epub 2015 Nov 6.
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.
本文考虑对工业原油蒸馏装置（CDU）的模型预测控制（MPC）参数进行调整。文中描述了一种实际场景，即CDU系统的输入具有优化目标，这些目标由控制结构的实时优化层提供。考虑了标称情况，即CDU模型和MPC模型相同。过程输出在区域内进行控制，而不是在固定设定点。然后，调整过程必须定义惩罚相对于控制区域的输出误差的权重、惩罚输入偏离其目标的权重以及惩罚输入变化的权重。提出了一种基于多目标优化的调整方法，并将其应用于CDU系统的MPC。通过仿真将采用所提方法调整后的控制器性能与同样基于多目标优化的现有方法的结果进行比较。仿真结果相似，但所提方法的计算量明显低于现有方法。调整工作量也比通常基于临时程序的传统实际方法低得多。
