INTEGRATING REAL TIME OPTIMIZATION AND MODEL PREDICTIVE CONTROL OF A CRUDE DISTILLATION UNIT

Abstract This work reports the integration of Real Time Optimization and Model Predictive Control in the multi-layer control structure of an existing Crude Distillation Unit (CDU) of an oil refinery. The MPC considers output control zones and targets for the inputs or outputs. Both the infinite horizon and the finite output horizon controllers were tested. The plant results show that the infinite horizon controller tends to perform similarly or better then the finite horizon MPC when the CDU system needs to operate at quite different conditions. Although the dynamic layer based on the infinite horizon controller is nominally stable for any set of tuning parameters, in practice, it is observed that the interaction between the layers of the control structure associated to model uncertainty may result in oscillations in some variables that fail to converge to the optimum operation point. This problem can be solved with the retuning of the intermediary layer (target calculation layer), which indicates that the frequent tuning of the MPC is recommended and should be performed in conjunction with tuning of the intermediary layer.摘要 本研究将实时优化（Real Time Optimization）与模型预测控制（Model Predictive Control）集成至某现有炼油厂原油蒸馏装置（Crude Distillation Unit, CDU）的多层控制结构中，并对此展开报道。该模型预测控制（MPC）需兼顾输入或输出的控制区域与目标值。研究分别测试了无限时域控制器与有限输出时域控制器。装置运行结果显示，当原油蒸馏装置需在大幅偏离常规的工况下运行时，无限时域控制器的表现通常与有限时域模型预测控制相当，甚至更优。尽管基于无限时域控制器的动态层在理论上对任意整定参数均呈现名义稳定性，但实际运行中观察到，与模型不确定性相关的控制结构各层间的交互作用，可能引发部分变量振荡，使其无法收敛至最优运行点。该问题可通过对中间层（目标计算层）进行参数整定予以解决，这表明需对模型预测控制开展频繁整定，且该整定工作应与中间层的参数整定协同进行。