{"date_created":"2024-07-31T14:23:52Z","date_updated":"2024-08-01T08:16:04Z","title":"Modelling activated carbon hydrogen storage tanks using machine learning models","language":[{"iso":"eng"}],"place":"Amsterdam","citation":{"havard":"G.H. Klepp, Modelling activated carbon hydrogen storage tanks using machine learning models, Energy : The International Journal ; Technologies, Resources, Reserves, Demands, Impact, Conservation, Management, Policy. 306 (2024).","din1505-2-1":"<span style=\"font-variant:small-caps;\">Klepp, Georg Heinrich</span>: Modelling activated carbon hydrogen storage tanks using machine learning models. In: <i>Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy</i> Bd. 306. Amsterdam, Elsevier BV (2024)","ama":"Klepp GH. Modelling activated carbon hydrogen storage tanks using machine learning models. <i>Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy</i>. 2024;306. doi:<a href=\"https://doi.org/10.1016/j.energy.2024.132318\">10.1016/j.energy.2024.132318</a>","ufg":"<b>Klepp, Georg Heinrich</b>: Modelling activated carbon hydrogen storage tanks using machine learning models, in: <i>Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy</i> 306 (2024).","van":"Klepp GH. Modelling activated carbon hydrogen storage tanks using machine learning models. Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy. 2024;306.","bjps":"<b>Klepp GH</b> (2024) Modelling Activated Carbon Hydrogen Storage Tanks Using Machine Learning Models. <i>Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy</i> <b>306</b>.","apa":"Klepp, G. H. (2024). Modelling activated carbon hydrogen storage tanks using machine learning models. <i>Energy : The International Journal ; Technologies, Resources, Reserves, Demands, Impact, Conservation, Management, Policy</i>, <i>306</i>, Article 132318. <a href=\"https://doi.org/10.1016/j.energy.2024.132318\">https://doi.org/10.1016/j.energy.2024.132318</a>","short":"G.H. Klepp, Energy : The International Journal ; Technologies, Resources, Reserves, Demands, Impact, Conservation, Management, Policy 306 (2024).","chicago":"Klepp, Georg Heinrich. “Modelling Activated Carbon Hydrogen Storage Tanks Using Machine Learning Models.” <i>Energy : The International Journal ; Technologies, Resources, Reserves, Demands, Impact, Conservation, Management, Policy</i> 306 (2024). <a href=\"https://doi.org/10.1016/j.energy.2024.132318\">https://doi.org/10.1016/j.energy.2024.132318</a>.","chicago-de":"Klepp, Georg Heinrich. 2024. Modelling activated carbon hydrogen storage tanks using machine learning models. <i>Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy</i> 306. doi:<a href=\"https://doi.org/10.1016/j.energy.2024.132318\">10.1016/j.energy.2024.132318</a>, .","mla":"Klepp, Georg Heinrich. “Modelling Activated Carbon Hydrogen Storage Tanks Using Machine Learning Models.” <i>Energy : The International Journal ; Technologies, Resources, Reserves, Demands, Impact, Conservation, Management, Policy</i>, vol. 306, 132318, 2024, <a href=\"https://doi.org/10.1016/j.energy.2024.132318\">https://doi.org/10.1016/j.energy.2024.132318</a>.","ieee":"G. H. Klepp, “Modelling activated carbon hydrogen storage tanks using machine learning models,” <i>Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy</i>, vol. 306, Art. no. 132318, 2024, doi: <a href=\"https://doi.org/10.1016/j.energy.2024.132318\">10.1016/j.energy.2024.132318</a>."},"type":"scientific_journal_article","keyword":["Hydrogen storage","Adsorption","Activated carbon","Machine learning","Simulation","Computational fluid dynamics"],"status":"public","publication_status":"published","intvolume":"       306","year":"2024","_id":"11808","user_id":"83781","department":[{"_id":"DEP6017"}],"doi":"10.1016/j.energy.2024.132318","publisher":"Elsevier BV","author":[{"full_name":"Klepp, Georg Heinrich","id":"49011","first_name":"Georg Heinrich","last_name":"Klepp"}],"publication_identifier":{"eissn":["1873-6785"],"issn":["0360-5442"]},"publication":"Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy","abstract":[{"lang":"eng","text":"The application of hydrogen for energy storage and as a vehicle fuel necessitates efficient and effective storage technologies. In addition to traditional cryogenic and high-pressure tanks, an alternative approach involves utilizing porous materials such as activated carbons within the storage tank. The adsorption behaviour of hydrogen in porous structures is described using the Dubinin-Astakhov isotherm. To model the flow of hydrogen within the tank, we rely on the equations of mass conservation, the Navier-Stokes equations, and the equation of energy conservation, which are implemented in a computational fluid dynamics code and additional terms account for the amount of hydrogen involved in sorption and the corresponding heat release. While physical models are valuable, data-driven models often offer computational advantages. Based on the data from the physical adsorption model, a data-driven model is derived using various machine learning techniques. This model is then incorporated as source terms in the governing conservation equations, resulting in a novel hybrid formulation which is computationally more efficient. Consequently, a new method is presented to compute the temperature and concentration distribution during the charging and discharging of hydrogen tanks and identifying any limiting phenomena more easily."}],"article_number":"132318","volume":306}