Potential application of metal-organic frameworks (MOFs) for hydrogen storage: Simulation by artificial intelligent techniques
Cao Y. Dhahad H.A. Zare S.G. Farouk N. Anqi A.E. Issakhov A. Raise A.
22 October 2021Elsevier Ltd
International Journal of Hydrogen Energy
2021#46Issue 7336336 - 36347 pp.
Metal-organic frameworks are a new class of materials for hydrogen adsorption/storage applications. The hydrogen storage capacity of this structure is typically related to pressure, temperature, surface area, and adsorption enthalpy. Literature provides no reliable correlation for estimating the hydrogen uptake capacity of MOFs from these easy-measured variables. Therefore, this study introduces several straightforward and accurate artificial intelligence (AI) techniques to fill this gap, initially determining the appropriate topology of AI-based methods, then comparing their performances by statistical criteria, and introducing the most accurate. This study used artificial neural networks, hybrid neuro-fuzzy systems, and support vector machines as estimators. The general regression neural networks (GRNN) with a spread of 7.92 × 10−4 shows the highest correlation with the literature data and provides a relative absolute deviation of 5.34%, mean squared error of 0.059, and coefficient of determination of 0.9946.
Artificial intelligent methods , General regression neural networks , Hydrogen storage capacity , Metal-organic frameworks
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School of Mechatronic Engineering, Xian Technological University, Xian, 710021, China
Mechanical Engineering Department, University of Technology, Baghdad, Iraq
Industrial Engineering Department, Sadjad University of Technology, Mashhad, Iran
Department of Mechanical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Alkharj, 16273, Saudi Arabia
Mechanical Engineering Department, Faculty of Engineering, Red Sea University Port Sudan, Sudan
Department of Mechanical Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia
Department of Mathematical and Computer Modelling, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Department of Mathematics and Cybernetics, Kazakh British Technical University, Almaty, 050000, Kazakhstan
Department of Mechanical Engineering, Faculty of Engineering, Xian Technological University, Shaanxi, China
School of Mechatronic Engineering
Mechanical Engineering Department
Industrial Engineering Department
Department of Mechanical Engineering
Mechanical Engineering Department
Department of Mechanical Engineering
Department of Mathematical and Computer Modelling
Department of Mathematics and Cybernetics
Department of Mechanical Engineering
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