Asynchronous Reinforcement Learning for Robust Co-Optimization of Plant-Wide Industrial Processes Under Uncertain Environments
针对不确定环境下全厂工业流程的优化难题，提出鲁棒协同优化框架，将问题建模为无限时域决策过程，并设计异步强化学习算法求解，在煤泥浮选过程仿真中验证了其提升协调优化连续性和鲁棒性的效果。
Optimizing plant-wide industrial processes (PWIPs) under uncertain environments is challenging because uncertainties and abnormal sub-unit (SU) conditions may affect evaluation-index availability and plant-wide coordination. This article proposes a robust co-optimization (RCO) framework to support continued coordinated optimization after objective adjustment, without requiring full algorithm redesign or manual parameter reconfiguration. Specifically, the RCO problem is modeled as an infinite-horizon decision process with observable evaluation indices and measurable production conditions, and a multiobjective robust optimization (RO) model is established. A barrier function is then used to incorporate static constraints into the objective, converting the problem into an unconstrained single-objective form. To reduce conservatism, the uncertainty set is reconstructed by introducing auxiliary variables, and a tractable robust counterpart is derived through duality-based analysis. Based on this formulation, an asynchronous reinforcement learning (RL) algorithm is designed to solve the RCO problem. Simulation studies on a coal slurry flotation process under normal and abnormal SU conditions verify that the proposed framework improves the continuity, robustness, and recovery capability of plant-wide coordinated optimization under uncertain environments.