This work introduces a multi-objective optimization strategy to handle conflicting set-point tracking and economic objectives in a two-layer hierarchical control framework. A dynamic multi-objective real-time optimizer (DMO), incorporated in the upper layer, handles multiple control objectives with set-point tracking being the higher priority objective and computes optimal plant trajectories. This plant-wide trajectory information is communicated to the lower-layer model predictive control (MPC) operating at a faster sampling rate. The conventional weight-based and lexicographical method for the DMO are discussed. A new algorithm is conceptualized based on the lexicographical method to handle prioritized objectives. The proposed algorithm modifies the higher priority tracking objective and establishes improved economic performance compared to the conventional techniques, with minimal effect on the conflicting tracking objective, through a systematic choice of the preferred Pareto solution. The proposed algorithm’s efficacy, within the hierarchical framework, is analyzed using two case studies: A polymerization reactor and a multi-unit reactor–separator system.

这项工作介绍了一种多目标优化策略，以在两层分层控制框架中处理冲突的设定点跟踪和经济目标。上层集成了一个动态多目标实时优化器（DMO），可将设定点跟踪作为优先级更高的目标，从而处理多个控制目标，并计算最佳工厂轨迹。该工厂范围内的轨迹信息被传递到以更快的采样率运行的下层模型预测控制（MPC）。讨论了DMO的常规基于权重和词典编排方法。一种新的算法是基于字典技术方法来处理优先目标的。与传统技术相比，该算法通过系统地选择首选的Pareto解决方案，修改了优先级更高的跟踪目标，并建立了比传统技术更高的经济性能，并且对冲突的跟踪目标影响最小。在两个层次的案例研究中，分析了所提出算法在分层框架内的功效：聚合反应器和多单元反应器-分离器系统。