用可解释强化学习优化工业空压系统，节能4%且保障安全
Trustworthy and Explainable Deep Reinforcement Learning for Safe and Energy-Efficient Process Control: A Use Case in Industrial Compressed Air Systems
- 结合扰动测试与SHAP分析，实现多层级决策可解释性
- 相比传统控制器降低4%能耗，避免过度加压并预判需求
- 适合工业能源系统部署，特别关注安全与透明性
本文提出一种可信的强化学习方法，用于工业压缩空气系统的控制。构建了一个在真实边界条件下实现安全高效运行的框架，并设计了多层级可解释性管道，整合输入扰动测试、基于梯度的敏感性分析和SHAP特征归因。在多个压缩机配置上的实证评估表明，所学策略具有物理合理性，能预判未来需求并始终遵守系统边界。相比现有工业控制器，该方法减少了不必要的过压现象，实现了约4%的节能效果，且无需依赖显式物理模型。结果进一步显示，系统压力和预测信息主导策略决策，而压缩机级输入作用较小。整体上，效率提升、预测行为与透明验证的结合，支持强化学习在工业能源系统中的可信部署。
原文摘要 · Abstract (English)
This paper presents a trustworthy reinforcement learning approach for the control of industrial compressed air systems. We develop a framework that enables safe and energy-efficient operation under realistic boundary conditions and introduce a multi-level explainability pipeline combining input perturbation tests, gradient-based sensitivity analysis, and SHAP (SHapley Additive exPlanations) feature attribution. An empirical evaluation across multiple compressor configurations shows that the learned policy is physically plausible, anticipates future demand, and consistently respects system boundaries. Compared to the installed industrial controller, the proposed approach reduces unnecessary overpressure and achieves energy savings of approximately 4\,\% without relying on explicit physics models. The results further indicate that system pressure and forecast information dominate policy decisions, while compressor-level inputs play a secondary role. Overall, the combination of efficiency gains, predictive behavior, and transparent validation supports the trustworthy deployment of reinforcement learning in industrial energy systems.
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