Neural network-based model predictive control for waste heat recovery from PEM electrolysis with heat pumps

Hydrogen is expected to play a key role in future energy systems, with PEM electrolysis being particularly suitable for producing hydrogen from renewable sources. However, a significant amount of heat is released during operation. Utilizing this heat in heating networks or industrial processes could improve economic efficiency and help decarbonize the heating sector. Since PEM electrolyzers operate at relatively low temperatures, many applications need heat pumps to increase the temperature of the waste heat. Such a coupled system requires a controller that can handle the thermal management of the electrolysis stack with the heat pump. However, the rapid load changes of a PEM electrolyzer coupled with fluctuating renewable energy sources pose a challenge to the controller, as it must stabilize both the electrolyzer’s cooling cycle and the heat pump’s refrigeration cycle simultaneously. Therefore, this paper presents a model predictive controller (MPC) based on neural networks that efficiently controls waste heat recovery while meeting the temperature requirements of the electrolyzer and the heat sink. We investigated the performance of the control strategy in numerical case studies and compared it with conventional control using proportional-integral (PI) controllers. We could demonstrate that our method provides significant improvements in terms of minimizing temperature fluctuations and maximizing heat recovery efficiency. Under the influence of volatile power input, the MPC could increase the average efficiency of the heat pump by up to 7 %, reduce the use of auxiliary heating energy by up to 52 %, and reduce the average and maximum deviation from the temperature setpoints by up to 1.4 K and 10.4 K, respectively, compared to PI control.