Double deep Q network intelligent adaptive control for highly efficient dynamic magnetic field assisted water electrolysis.

Green water electrolysis holds promise for clean energy production. One low-cost variant, Dynamic Magnetic Field (DMF) assisted water electrolysis, shows high hydrogen evolution rates (HER). However, its efficiency diminishes at high magnet rotational speeds, posing a challenge to its widespread adoption. This study aims to enhance the efficiency of DMF-assisted water electrolysis by providing adaptive control for DMF, particularly at high magnet rotational speeds. We employ a Double Deep Q Learning (DDQN) based artificial intelligence (AI) system to design and implement this adaptive control. We utilized a Double Deep Q Learning (DDQN) based artificial intelligence (AI) system to design an adaptive control mechanism for DMF. The DDQN agent learned parameter tuning to adjust the rotational speed of the motor, with a magnet bar attached, for optimal performance. The optimum control behavior was achieved after 52 training episodes, and minor adjustments were made to ensure practical applicability. Our study reveals that the DDQN-based AI control is effective in continuously tuning the rotational speed of the motor with a magnet bar attached, triggering a bifurcation in the electrolyte. This adaptive control mechanism significantly improves and maintains the efficiency of DMF-assisted water electrolysis. The DDQN based AI control is effective to alter and maintain efficiency of DMF assisted water electrolysis. [Display omitted] • The hydrogen production of DDQN controlled DMF is superior to fixed RPM DMF. • DDQN AI provide realtime optimization of DMF electrolysis efficiency. • DDQN ensures correct and applicable control behavior after 52 episodes. • DDQN AI control optimizes DMF electrolysis to sustain the efficiency. • RPM tuning enhances performance by triggers electrolyte bifurcation. [ABSTRACT FROM AUTHOR]