Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes
arXiv:2605.21211v1 Announce Type: cross Abstract: In this work we present an efficient and practically implementable approach for the application of reinforcement learning (RL)-based control in chemical process systems. This is an area that has yet to widely adopt RL-based control largely due to inherent challenges in trusting RL algorithms and the time-consuming process of training reliable agents. To address these challenges, we leverage a class of RL algorithms termed Y-wise Affine Neural Network (YANN)- RL, which we have developed in our prior work (Braniff and Tian, 2025a). By strategical
The paper leverages recent advancements in Y-wise Affine Neural Network (YANN)-RL, building on prior work from 2025 by the same authors, indicating a maturation of this specific RL approach for industrial application.
This development addresses a critical barrier to wider industrial adoption of RL—trust and training reliability—by showcasing a practical, efficient, and potentially more robust RL method for complex chemical processes.
Traditional control systems for chemical processes may be augmented, or in some cases replaced, by RL-based methods, increasing automation and optimization potential in a sector historically resistant to direct RL application.
- · Chemical process industries
- · Applied AI/ML researchers
- · Industrial automation software vendors
- · YANN-RL developers
- · Legacy process control systems providers (if not adaptive)
- · Chemical plants without AI integration strategies
Increased efficiency and reduced operational costs in chemical manufacturing due to optimized RL-based control.
Expansion of RL applications into other complex industrial processes, driving demand for specialized AI hardware and talent.
Ethical and safety frameworks for autonomous AI control systems become paramount as 'black box' issues in RL are mitigated by more explainable models.
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Read at arXiv cs.LG