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In the development of decarbonization technologies and renewable energy, water electrolysis has emerged as a key technology. The efficiency of hydrogen production and its applications are significantly affected by power stability. Enhancing power stability not only improves hydrogen production efficiency and reduces maintenance costs but also ensures long-term reliable system operation. This study proposes a thermal control method that stabilizes hydrogen output by precisely adjusting the temperature of the electrolysis stack, thereby improving hydrogen production efficiency. Fluctuations in the electrolysis stack temperature can lead to instability in the hydrogen output and energy utilization, negatively affecting overall hydrogen production. To address this issue, this study introduces an innovative system architecture and a novel thermal control strategy combining fuzzy logic control with a long short-term memory neural network. This method predicts and adjusts the flow rate of chilled water to maintain the electrolysis stack temperature within a range of ±1 °C while sustaining a constant power output of 10 kW. This approach is crucial for ensuring system stability and maximizing hydrogen production efficiency. Long-term experiments have validated the effectiveness and reliability of this method, demonstrating that this thermal control strategy not only stabilizes the hydrogen production process but also increases the volume of hydrogen generated. [ABSTRACT FROM AUTHOR]

In the development of decarbonization technologies and renewable energy, water electrolysis has emerged as a key technology. The efficiency of hydrogen production and its applications are significantly affected by power stability. Enhancing power stability not only improves hydrogen production efficiency and reduces maintenance costs but also ensures long-term reliable system operation. This study proposes a thermal control method that stabilizes hydrogen output by precisely adjusting the temperature of the electrolysis stack, thereby improving hydrogen production efficiency. Fluctuations in the electrolysis stack temperature can lead to instability in the hydrogen output and energy utilization, negatively affecting overall hydrogen production. To address this issue, this study introduces an innovative system architecture and a novel thermal control strategy combining fuzzy logic control with a long short-term memory neural network. This method predicts and adjusts the flow rate of chilled water to maintain the electrolysis stack temperature within a range of ±1 °C while sustaining a constant power output of 10 kW. This approach is crucial for ensuring system stability and maximizing hydrogen production efficiency. Long-term experiments have validated the effectiveness and reliability of this method, demonstrating that this thermal control strategy not only stabilizes the hydrogen production process but also increases the volume of hydrogen generated.

Generation of hydrogen using electrolysis process with integrated renewable energy sources is highly important especially in environmental aspects. In this paper, we demonstrate that the enhancement of electrolysis performance of alkaline electrolysis stacks by diminishing the distance between electrodes, while changing the properties of the Membrane Electrode Assembly (MEA). Prior to that, the performances of mono-polar and bi-polar configurations of alkaline electrolysis stack are compared through 3-D modelling and experimental fabrication. At first, two different single cell alkaline electrolysers are designed using SolidWorks as a design software and the designed cell has been fabricated using an in-house 3D printer, to avoid post machining processes. Thereafter, the best performing cell is selected by considering the performance of both designs through different experiments. Finally, the performance of the selected cell is enhanced by changing the distance between electrodes and properties of MEA. Thus, the best performing cell has been selected for the fabrication process of mono-polar and bi-polar electrolysis stacks. At last, both mono-polar and bi-polar configurations of alkaline electrolysis stacks are designed and implemented to compare the electrolysis performance of both configurations by maintaining minimum electrode distance. The results imply that the electrolysis performance of the cell can be enhanced by reducing the distance between electrodes, and the designed bi-polar stack has a better performance in terms of the efficiency, power and flow rates than the mono-polar equivalent. • Alkaline electrolysis configuration (i.e. mono-polar and bi-polar) modelling for performance analysis. • Membrane electrode assembly (MEA) configuration effect on electrolysis performance. • Improvement of electrolysis performance with bi-polar stack compared to mono-polar equivalent. [ABSTRACT FROM AUTHOR]

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