Trustworthy and Explainable Deep Reinforcement Learning for Safe and Energy-Efficient Process Control: A Use Case in Industrial Compressed Air Systems

TL;DR

This paper introduces a trustworthy, explainable deep reinforcement learning framework for industrial compressed air systems, achieving energy savings and safe operation without explicit physics models.

Contribution

It develops a multi-level explainability pipeline and demonstrates energy efficiency and safety improvements in real industrial settings.

Findings

Energy savings of approximately 4%

Policy is physically plausible and anticipates demand

Pressure and forecast info dominate decision-making

Abstract

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.