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February 5, 2026SHILAP Revista de lepidopterologíaOpen Access

Trustworthy and explainable deep reinforcement learning for safe and energy-efficient process control: A use case in industrial compressed air systems

Vincent Bezold Patrick Wagner Jakob Hofmann Marco Huber Alexander Sauer

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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.

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Implication

Observational analysis reveals energy-efficient process control in industrial compressed air systems, suggesting improved operational safety.

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Cite This Study

Bezold et al. (2026) studied this question.

synapsesocial.com/papers/69a767f1badf0bb9e87e2f93 https://doi.org/10.1016/j.egyai.2026.100685

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