[{"author":[{"first_name":"Georg Heinrich","id":"49011","full_name":"Klepp, Georg Heinrich","last_name":"Klepp"}],"date_created":"2024-07-31T14:23:52Z","keyword":["Hydrogen storage","Adsorption","Activated carbon","Machine learning","Simulation","Computational fluid dynamics"],"publication_status":"published","volume":306,"year":"2024","language":[{"iso":"eng"}],"publisher":"Elsevier BV","date_updated":"2024-08-01T08:16:04Z","publication":"Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy","publication_identifier":{"eissn":["1873-6785"],"issn":["0360-5442"]},"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."}],"title":"Modelling activated carbon hydrogen storage tanks using machine learning models","doi":"10.1016/j.energy.2024.132318","citation":{"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).","short":"G.H. Klepp, Energy : The International Journal ; Technologies, Resources, Reserves, Demands, Impact, Conservation, Management, Policy 306 (2024).","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>.","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>","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>.","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>","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.","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)","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>.","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>, ."},"status":"public","department":[{"_id":"DEP6017"}],"intvolume":"       306","place":"Amsterdam","user_id":"83781","type":"scientific_journal_article","_id":"11808","article_number":"132318"},{"_id":"5435","type":"journal_article","conference":{"end_date":"2018-06-21","start_date":"2018-06-17","name":"31st International Conference on Efficiency, Cost, Optimization, Simulation, and Environmental Impact of Energy Systems (ECOS)","location":"Guimaraes, PORTUGAL"},"citation":{"chicago-de":"Griese, Martin, Marc Philippe Hoffrath, Timo Broeker, Thomas Schulte und Jan Schneider. 2019. Hardware-in-the-Loop simulation of an optimized energy management incorporating an experimental biocatalytic methanation reactor. <i>Energy : the international journal</i> 181: 77–90. doi:<a href=\"https://doi.org/10.1016/j.energy.2019.05.092\">10.1016/j.energy.2019.05.092</a>, .","chicago":"Griese, Martin, Marc Philippe Hoffrath, Timo Broeker, Thomas Schulte, and Jan Schneider. “Hardware-in-the-Loop Simulation of an Optimized Energy Management Incorporating an Experimental Biocatalytic Methanation Reactor.” <i>Energy : The International Journal</i> 181 (2019): 77–90. <a href=\"https://doi.org/10.1016/j.energy.2019.05.092\">https://doi.org/10.1016/j.energy.2019.05.092</a>.","bjps":"<b>Griese M <i>et al.</i></b> (2019) Hardware-in-the-Loop Simulation of an Optimized Energy Management Incorporating an Experimental Biocatalytic Methanation Reactor. <i>Energy : the international journal</i> <b>181</b>, 77–90.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Griese, Martin</span> ; <span style=\"font-variant:small-caps;\">Hoffrath, Marc Philippe</span> ; <span style=\"font-variant:small-caps;\">Broeker, Timo</span> ; <span style=\"font-variant:small-caps;\">Schulte, Thomas</span> ; <span style=\"font-variant:small-caps;\">Schneider, Jan</span>: Hardware-in-the-Loop simulation of an optimized energy management incorporating an experimental biocatalytic methanation reactor. In: <i>Energy : the international journal</i> Bd. 181, Elsevier (2019), S. 77–90","havard":"M. Griese, M.P. Hoffrath, T. Broeker, T. Schulte, J. Schneider, Hardware-in-the-Loop simulation of an optimized energy management incorporating an experimental biocatalytic methanation reactor, Energy : The International Journal. 181 (2019) 77–90.","van":"Griese M, Hoffrath MP, Broeker T, Schulte T, Schneider J. Hardware-in-the-Loop simulation of an optimized energy management incorporating an experimental biocatalytic methanation reactor. Energy : the international journal. 2019;181:77–90.","ama":"Griese M, Hoffrath MP, Broeker T, Schulte T, Schneider J. Hardware-in-the-Loop simulation of an optimized energy management incorporating an experimental biocatalytic methanation reactor. <i>Energy : the international journal</i>. 2019;181:77-90. doi:<a href=\"https://doi.org/10.1016/j.energy.2019.05.092\">10.1016/j.energy.2019.05.092</a>","ieee":"M. Griese, M. P. Hoffrath, T. Broeker, T. Schulte, and J. Schneider, “Hardware-in-the-Loop simulation of an optimized energy management incorporating an experimental biocatalytic methanation reactor,” <i>Energy : the international journal</i>, vol. 181, pp. 77–90, 2019, doi: <a href=\"https://doi.org/10.1016/j.energy.2019.05.092\">10.1016/j.energy.2019.05.092</a>.","apa":"Griese, M., Hoffrath, M. P., Broeker, T., Schulte, T., &#38; Schneider, J. (2019). Hardware-in-the-Loop simulation of an optimized energy management incorporating an experimental biocatalytic methanation reactor. <i>Energy : The International Journal</i>, <i>181</i>, 77–90. <a href=\"https://doi.org/10.1016/j.energy.2019.05.092\">https://doi.org/10.1016/j.energy.2019.05.092</a>","mla":"Griese, Martin, et al. “Hardware-in-the-Loop Simulation of an Optimized Energy Management Incorporating an Experimental Biocatalytic Methanation Reactor.” <i>Energy : The International Journal</i>, vol. 181, 2019, pp. 77–90, <a href=\"https://doi.org/10.1016/j.energy.2019.05.092\">https://doi.org/10.1016/j.energy.2019.05.092</a>.","ufg":"<b>Griese, Martin u. a.</b>: Hardware-in-the-Loop simulation of an optimized energy management incorporating an experimental biocatalytic methanation reactor, in: <i>Energy : the international journal</i> 181 (2019),  S. 77–90.","short":"M. Griese, M.P. Hoffrath, T. Broeker, T. Schulte, J. Schneider, Energy : The International Journal 181 (2019) 77–90."},"doi":"10.1016/j.energy.2019.05.092","external_id":{"isi":["000476965900009"]},"title":"Hardware-in-the-Loop simulation of an optimized energy management incorporating an experimental biocatalytic methanation reactor","page":"77 - 90","intvolume":"       181","department":[{"_id":"DEP4023"},{"_id":"DEP4018"}],"status":"public","isi":"1","user_id":"83781","publisher":"Elsevier","date_updated":"2025-06-25T07:48:53Z","publication":"Energy : the international journal","publication_identifier":{"eissn":["1873-6785"],"issn":["0360-5442"]},"abstract":[{"lang":"eng","text":"Towards renewable energy systems, the coupling of multiple sectors is important and incorporates novel technologies where currently no models exist that correctly represent all transient effects. Therefore, we present a method that incorporates Hardware-in-the-Loop simulations where virtual components as models are coupled to real and experimental facilities in real time. By including experimental components, a higher validity can be obtained and the practical applicability of renewable energy scenario can be discussed more profoundly. In this paper, the considered energy system consists of an experimental biocatalytic methanation reactor, a real photovoltaic park, a regenerative fuel cell and short-term storage units to supply a residential district. A representative control sequence of the methanator is obtained by modeling the scenario as an optimal control problem. A first HIL simulation highlights that modifications of the instrumentation are required for a grid injection of the generated methane. The scientific approach can be applied to any energy system where some of the considered components are available as experimental or real facilities. Non-exisiting components are simply replaced by models. The presented approach helps to determine which parts or process parameters are crucial for the planed operation before the overall energy system is realized on a larger scale. (C) 2019 Elsevier Ltd. All rights reserved."}],"date_created":"2021-04-08T07:42:48Z","author":[{"last_name":"Griese","full_name":"Griese, Martin","id":"52308","first_name":"Martin"},{"first_name":"Marc Philippe","full_name":"Hoffrath, Marc Philippe","last_name":"Hoffrath"},{"last_name":"Broeker","id":"43927","full_name":"Broeker, Timo","first_name":"Timo"},{"first_name":"Thomas","id":"46242","full_name":"Schulte, Thomas","last_name":"Schulte"},{"last_name":"Schneider","first_name":"Jan","id":"13209","orcid":"0000-0001-6401-8873","full_name":"Schneider, Jan"}],"volume":181,"publication_status":"published","keyword":["Biological methanation","Energy management","HIL simulation","Optimization","Scalable models"],"year":"2019","quality_controlled":"1","language":[{"iso":"eng"}]}]