Publication date: 15th December 2025
Autonomous systems have achieved remarkable progress in domains such as self-driving vehicles, game-playing computers, and robotics, largely powered by reinforcement learning. However, their potential in electrochemical systems remains largely unexplored. In electrochemistry, pulsed operation often enhances conversion rates or power output, suggesting an opportunity for adaptive control strategies. This work presents the complete development process of a reinforcement learning-based AI controller designed to maximize the conversion rate of the glycerol oxidation reaction.[1] The process begins with a simple microkinetic model to design the initial controller architecture, followed by deployment and iterative refinement in real laboratory conditions. To accelerate algorithm refinement and reduce laboratory time, data collected during laboratory testing was used to construct a recurrent neural network-based digital twin. This virtual model enables rapid hypothesis testing through simulation of the electrochemical system, facilitating the evaluation of different improved AI controllers. Finally, the controller’s decision-making process is analysed and visualized by performing a time-series analysis using Markov chain modelling. Together, this work showcases a comprehensive AI-based approach that combines reinforcement learning, digital twinning, and time-series analysis to perform, simulate and analyse complex electrochemical experiments.
Funded by the Swiss National Science Foundation, Grant 222252
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