4 results
Search Results
Now showing 1 - 4 of 4
Item Metadata only Data-driven optimization of proton exchange membrane water electrolyzers using an integrated artificial neural network–genetic algorithm framework(2026-03-24) Boekfah A.; Seanglumlert C.; Rumnum S.; Rattanaphan S.; Punurai W.; Suvanjumrat C.; Boekfah A.; Mahidol UniversityProton exchange membrane water electrolyzers (PEMWEs) represent a pivotal technology for clean hydrogen generation, directly converting electrical energy into chemical energy through water splitting. Despite their intrinsic advantages—such as low... indicators: cell voltage (V), hydrogen generation rate ((Formula presented) ), and specific electrical energy consumption (E). The ANN were trained using four key inputs—water temperature ((Formula presented) ), flow rate ((Formula presented) ), currentItem Metadata only An integrated multiphysics modeling and ANN–GA optimization framework for high-performance proton exchange membrane water electrolyzer stacks(2026-05-01) Suvanjumrat C.; Rumnum S.; Seanglumlert C.; Rattanaphan S.; Priyadumkol J.; Promtong M.; Punurai W.; Boekfah A.; Suvanjumrat C.; Mahidol UniversityEnhancing the performance of proton exchange membrane water electrolyzer (PEMWE) stacks necessitates an integrated modeling and optimization framework capable of simultaneously delivering high predictive fidelity, optimization robustness, and design... curves across a broad range of operating conditions and parameter combinations. To facilitate efficient and reliable optimization, a unified artificial neural network–genetic algorithm (ANN–GA) framework is established, with water flow rate (QH2Item Metadata only Modeling and optimization of PEM water electrolysis via an ANN–GA hybrid approach(2025-10-13) Boekfah A.; Rumnum S.; Suvanjumrat C.; Boekfah A.; Mahidol UniversityAdvancing water electrolysis technologies is essential for scaling up green hydrogen production. Among these, proton exchange membrane water electrolyzer (PEMWE) stands out for its high efficiency and environmental compatibility. For optimal... performance, PEMWE systems must deliver high hydrogen production rates (QH2) while maintaining low electrical energy consumption (E). However, accurately predicting and optimizing these outcomes remains a complex challenge dueItem Metadata only Data-driven prediction and multi-objective optimization of pemfc performance using an ANN–GA hybrid model(2026-03-01) Boekfah A.; Seanglumlert C.; Rumnum S.; Rattanaphan S.; Punurai W.; Suvanjumrat C.; Boekfah A.; Mahidol UniversityProton exchange membrane fuel cells (PEMFCs) are regarded as a key clean energy technology for transportation, portable power devices, and stationary power generation due to their high efficiency, low operating temperature, and zero-emission... cell voltage (V) and power density (I). The model accounts for key operating and design parameters, including hydrogen flow rate (QH2), anode relative humidity (RHa), anode back pressure (Pa), cell operating