Simulation and optimization of the impacts of metal-organic frameworks on the hydrogen adsorption using computational fluid dynamics and artificial neural networks

Abstract 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).