Pourrahmani Hossein, Mohammadi Mohammad Hadi, Pourhasani Bahar, Gharehghani Ayat, Moghimi Mahdi, Van Herle Jan
Group of Energy Materials, École Polytechnique Fédérale de Lausanne, Sion, 1951, Switzerland.
School of Mechanical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
Sci Rep. 2023 Oct 21;13(1):18032. doi: 10.1038/s41598-023-45391-x.
One of the barriers to further commercialization of the proton exchange membrane fuel cell (PEMFC) is hydrogen storage. Conventional methods are based on pressurizing the hydrogen up to 700 bar. The focus of this study is to characterize the hydrogen storage capacity of hydrogen tanks filled with MOF-5 at low pressures. Thus, Computational Fluid Dynamic (CFD) was used in a transient condition to analyze the hydrogen storage. Benefiting from the CFD model, three input parameters of the MOF-5, namely, density, specific heat, and conductivity, were utilized to develop an artificial neural network (ANN) model to find the highest mass of adsorption at the lowest required pressure. The optimum possible MOF among 729220 different possibilities, which enables the adsorption of 0.0099 kg at 139 bar, was found using a newly defined parameter called Pressure Adsorption Parameter (PAP).
质子交换膜燃料电池（PEMFC）进一步商业化的障碍之一是氢存储。传统方法是将氢气加压至700巴。本研究的重点是表征在低压下填充有MOF-5的氢气罐的储氢能力。因此，在瞬态条件下使用计算流体动力学（CFD）来分析氢存储。受益于CFD模型，利用MOF-5的三个输入参数，即密度、比热容和电导率，开发了一个人工神经网络（ANN）模型，以在最低所需压力下找到最高吸附质量。使用新定义的压力吸附参数（PAP），在729220种不同可能性中找到了最佳的MOF，它能够在139巴下吸附0.0099千克。
