Trustworthy and explainable deep reinforcement learning for safe and energy-efficient process control: A use case in industrial compressed air systems | Synapse
March 3, 2026Open Access
Trustworthy and explainable deep reinforcement learning for safe and energy-efficient process control: A use case in industrial compressed air systems
Key Points
Energy efficiency was enhanced by implementing a deep reinforcement learning algorithm in compressed air systems, leading to more sustainable operations.
Optimal performance improvements resulted in up to 30% energy savings per cycle, indicating the potential for widespread application.
Assessment used a simulation-based model to evaluate process control strategies under various operational scenarios in industrial environments.
Highlights the necessity for safe and explainable AI systems in industrial settings, aiming to enhance reliability and user trust.