Exploratory research on bubbles migration behavior and mass transfer capacity evaluation of proton exchange membrane water electrolyzer based on a volume of fluid-coupled electrochemical model

Zhou, Heng; Meng, Kai; Chen, Wenshang; Chen, Ben

doi:10.1016/j.enconman.2023.117217

Cited by 11 publications

(1 citation statement)

References 45 publications

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“…An electrolysis voltage of only 1.828 V is required to achieve 3 A cm –2 for the optimal MEA, closely matching the 1.82923 V predicted by the ML model. To the best of our knowledge, the performance of the MEA in this study surpasses that of other MEAs employing Nafion 115 as the proton exchange membrane in recent years ,,− (as shown in Table S4 and Figure S26). Therefore, we believe that ML methods can efficiently analyze data with complex features and the optimization algorithm can converge with a small number of attempts.…”

Section: Resultsmentioning

confidence: 52%

Data-Driven Optimization of High-Dimensional Variables in Proton Exchange Membrane Water Electrolysis Membrane Electrode Assembly Assisted by Machine Learning

Zhang,

Tan,

Yuan

et al. 2024

Ind. Eng. Chem. Res.

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The optimization of the membrane electrode assembly (MEA) is crucial for enhancing the performance of proton exchange membrane water electrolysis. Nevertheless, achieving global optimization of all manufacturing parameters of the MEA poses challenges due to their high-dimensional complexity and limited experimental data. In this study, machine learning (ML) techniques were introduced to tackle this intricate engineering challenge. 58 MEAs were fabricated and tested to construct a comprehensive database enriched with features and ample data. This was achieved through a data expansion method that involves altering the operating temperature of the electrolyzer. The XGBoost was employed to perform regression predictions on high-dimensional variables, achieving a remarkable coefficient of determination (R 2 ) value of 0.99926. The SHAP (SHapley Additive exPlanations) method and the genetic algorithm were applied for model interpretation and global optimization, respectively. By utilizing the insights provided by the SHAP method, we could narrow the decision variable dimensionality down to 5 key variables, achieving results that are comparable to full-variable optimization while notably reducing time costs by 67.9%. Guided by ML, the MEA with globally optimized variables achieved a voltage of only 1.828 V at 3 A cm −2 . The study presents an approach that integrates intelligent optimization techniques with data-driven methods for highdimensional variable optimization. This contribution provides valuable insights into energy conversion and storage technologies in the chemical industry.

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Section: Resultsmentioning

confidence: 52%

Data-Driven Optimization of High-Dimensional Variables in Proton Exchange Membrane Water Electrolysis Membrane Electrode Assembly Assisted by Machine Learning

Zhang,

Tan,

Yuan

et al. 2024

Ind. Eng. Chem. Res.

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High‐performance porous transport layers for proton exchange membrane water electrolyzers

Tao,

Wu,

et al. 2024

SusMat

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Hydrogen is a favored alternative to fossil fuels due to the advantages of cleanliness, zero emissions, and high calorific value. Large‐scale green hydrogen production can be achieved using proton exchange membrane water electrolyzers (PEMWEs) with utilization of renewable energy. The porous transport layer (PTL), positioned between the flow fields and catalyst layers (CLs) in PEMWEs, plays a critical role in facilitating water/gas transport, enabling electrical/thermal conduction, and mechanically supporting CLs and membranes. Superior corrosion resistance is essential as PTL operates in acidic media with oxygen saturation and high working potential. This paper covers the development of high‐performance titanium‐based PTLs for PEMWEs. The heat/electrical conduction and mass transport mechanisms of PTLs and how they affect the overall performances are reviewed. By carefully designing and controlling substrate microstructure, protective coating, and surface modification, the performance of PTL can be regulated and optimized. The two‐phase mass transport characteristics can be enhanced by fine‐tuning the microstructure and surface wettability of PTL. The addition of a microporous top‐layer can effectively improve PTL|CL contact and increase the availability of catalytic sites. The anticorrosion coatings, which are crucial for chemical stability and conductivity of the PTL, are compared and analyzed in terms of composition, fabrication, and performance.

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Numerical simulation and analysis of a two-phase flow model considering bubble coverage for alkaline electrolytic water

Li,

Liang,

Abdullah Rehan

et al. 2024

Applied Thermal Engineering

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Exploratory research on bubbles migration behavior and mass transfer capacity evaluation of proton exchange membrane water electrolyzer based on a volume of fluid-coupled electrochemical model

Cited by 11 publications

References 45 publications

Data-Driven Optimization of High-Dimensional Variables in Proton Exchange Membrane Water Electrolysis Membrane Electrode Assembly Assisted by Machine Learning

Data-Driven Optimization of High-Dimensional Variables in Proton Exchange Membrane Water Electrolysis Membrane Electrode Assembly Assisted by Machine Learning

High‐performance porous transport layers for proton exchange membrane water electrolyzers

Numerical simulation and analysis of a two-phase flow model considering bubble coverage for alkaline electrolytic water

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