Abstract Distributed real-time optimization (RTO) enables optimal operation of large-scale process systems with common resources shared across several clusters. Typically in distributed RTO, the different subsystems are optimized locally, and a centralized master problem is used to coordinate the different subsystems in order to reach system-wide optimal operation. This is especially beneficial in industrial symbiosis, where only limited information can be shared between the different clusters. However, one of the main challenges with this approach is the need to solve numerical optimization problems online for each subsystem. With the recent surge of interest in feedback optimizing control, where the optimization problem is converted into a feedback control problem, this paper proposes a distributed feedback-based RTO (DFRTO) framework for optimal resource sharing in an industrial symbiotic setting. In this approach, a master coordinator updates the shadow price for the shared resource, and the different subsystems locally optimize their operation using feedback control for the given shadow price. The proposed framework is shown to converge to a stationary point of the system-wide optimization problem, and is demonstrated using an industrial symbiotic offshore oil and gas production system with shared resources.

中文翻译：

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一种基于分布式反馈的在线流程优化框架，用于优化资源共享

摘要 分布式实时优化 (RTO) 使具有跨多个集群共享公共资源的大规模过程系统的优化运行成为可能。通常在分布式 RTO 中，不同的子系统在本地进行优化，并使用集中式主问题来协调不同的子系统，以达到系统范围的优化运行。这在工业共生中尤其有益，在这种情况下，不同集群之间只能共享有限的信息。然而，这种方法的主要挑战之一是需要为每个子系统在线解决数值优化问题。随着最近对反馈优化控制的兴趣激增，其中优化问题被转换为反馈控制问题，本文提出了一种基于分布式反馈的 RTO (DFRTO) 框架，用于在工业共生环境中实现最佳资源共享。在这种方法中，主协调器更新共享资源的影子价格，不同子系统使用给定影子价格的反馈控制在本地优化其操作。所提出的框架被证明收敛到系统范围优化问题的静止点，并使用具有共享资源的工业共生海上石油和天然气生产系统进行了演示。