Markov decision process for current density optimization to improve hydrogen production by water electrolysis

Maximizing the hydrogen evolution reaction (HER) remains challenging due to its nonlinear kinetics and complex charge interactions within the electric double layer (EDL). This study introduces an adaptive current density control approach using a Markov Decision Process (MDP) to enhance HER performance in alkaline water electrolysis. The MDP algorithm dynamically adjusts current release timings from three capacitors connected to the cathode based on feedback from hydrogen concentration levels. Results show that this fluctuating control strategy is more effective than static or linearly increasing methods, as it helps minimize overpotential, reduce heat buildup, and prevent hydrogen bubble accumulation. The MDP-optimized system achieved 7460 ppm in 60 min, outperforms the control condition (5802 ppm) produced under uncontrolled conditions. This work highlights a novel application of reinforcement learning to actively regulate electrochemical parameters, offering a promising mechanism for improving electrolyzer efficiency.