[{"year":"2024","author":[{"last_name":"Klepp","first_name":"Georg Heinrich","full_name":"Klepp, Georg Heinrich","id":"49011"}],"status":"public","title":"Modelling activated carbon hydrogen storage tanks using machine learning models","volume":306,"citation":{"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>, .","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)","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>.","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>","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).","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>.","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>.","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.","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>"},"keyword":["Hydrogen storage","Adsorption","Activated carbon","Machine learning","Simulation","Computational fluid dynamics"],"language":[{"iso":"eng"}],"publication_identifier":{"eissn":["1873-6785"],"issn":["0360-5442"]},"publication":"Energy : the international journal ; technologies, resources, reserves, demands, impact, conservation, management, policy","date_created":"2024-07-31T14:23:52Z","date_updated":"2024-08-01T08:16:04Z","department":[{"_id":"DEP6017"}],"abstract":[{"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.","lang":"eng"}],"publisher":"Elsevier BV","publication_status":"published","place":"Amsterdam","_id":"11808","doi":"10.1016/j.energy.2024.132318","article_number":"132318","type":"scientific_journal_article","user_id":"83781","intvolume":"       306"},{"type":"journal_article","intvolume":"        11","user_id":"74004","_id":"1722","doi":"10.3390/w11040730","article_number":"730","place":"Basel","publication_status":"published","abstract":[{"text":"Granular activated carbon (GAC) adsorption, as well as ozonation in combination with biodegradation was investigated in order to remove refractory organics from biologically pre-treated process waters (PW) produced by the hydrothermal carbonization (HTC) of spent grains and fine mulch. Kinetic tests revealed that the organics in spent grains PW had much lower molecular weights than organics in fine mulch PW. Moreover, isotherms showed that they were more strongly adsorbable. This was confirmed in GAC column experiments, where the breakthrough curves could be predicted fairly well by a dynamic adsorption model. On the other hand, ozonation had a stronger effect on fine mulch PW with respect to an enhancement of the aerobic degradability. Thus, the type of input material determines the properties of soluble reaction products from the carbonization process that must be accounted for when selecting the most suitable post-treatment method for HTC PW. However, adsorption on granular activated carbon should always be the final stage.","lang":"eng"}],"issue":"4","department":[{"_id":"DEP3000"},{"_id":"DEP8020"}],"publisher":"MDPI","publication":"Water","publication_identifier":{"issn":["2073-4441"]},"date_created":"2019-07-30T09:44:40Z","date_updated":"2023-03-15T13:49:37Z","oa":"1","keyword":["HTC process water","post-treatment","refractory organics","activated carbon adsorption","ozonation"],"language":[{"iso":"eng"}],"title":"Options for Removing Refractory Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization","volume":11,"citation":{"ieee":"J. H. O. Fettig, U. Austermann-Haun, J.-F. Meier, A. Busch, and E. Gilbert, “Options for Removing Refractory Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization,” <i>Water</i>, vol. 11, no. 4, 2019.","ama":"Fettig JHO, Austermann-Haun U, Meier J-F, Busch A, Gilbert E. Options for Removing Refractory Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization. <i>Water</i>. 2019;11(4). doi:<a href=\"https://doi.org/10.3390/w11040730\">10.3390/w11040730</a>","chicago-de":"Fettig, Joachim Hans Otto, Ute Austermann-Haun, Jan-Felix Meier, Anna Busch und Eva Gilbert. 2019. Options for Removing Refractory Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization. <i>Water</i> 11, Nr. 4. doi:<a href=\"https://doi.org/10.3390/w11040730,\">10.3390/w11040730,</a> .","din1505-2-1":"<span style=\"font-variant:small-caps;\">Fettig, Joachim Hans Otto</span> ; <span style=\"font-variant:small-caps;\">Austermann-Haun, Ute</span> ; <span style=\"font-variant:small-caps;\">Meier, Jan-Felix</span> ; <span style=\"font-variant:small-caps;\">Busch, Anna</span> ; <span style=\"font-variant:small-caps;\">Gilbert, Eva</span>: Options for Removing Refractory Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization. In: <i>Water</i> Bd. 11. Basel, MDPI (2019), Nr. 4","short":"J.H.O. Fettig, U. Austermann-Haun, J.-F. Meier, A. Busch, E. Gilbert, Water 11 (2019).","chicago":"Fettig, Joachim Hans Otto, Ute Austermann-Haun, Jan-Felix Meier, Anna Busch, and Eva Gilbert. “Options for Removing Refractory Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization.” <i>Water</i> 11, no. 4 (2019). <a href=\"https://doi.org/10.3390/w11040730\">https://doi.org/10.3390/w11040730</a>.","apa":"Fettig, J. H. O., Austermann-Haun, U., Meier, J.-F., Busch, A., &#38; Gilbert, E. (2019). Options for Removing Refractory Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization. <i>Water</i>, <i>11</i>(4). <a href=\"https://doi.org/10.3390/w11040730\">https://doi.org/10.3390/w11040730</a>","ufg":"<b>Fettig, Joachim Hans Otto et. al. (2019)</b>: Options for Removing Refractory Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization, in: <i>Water</i> <i>11</i> (<i>4</i>).","bjps":"<b>Fettig JHO <i>et al.</i></b> (2019) Options for Removing Refractory Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization. <i>Water</i> <b>11</b>.","mla":"Fettig, Joachim Hans Otto, et al. “Options for Removing Refractory Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization.” <i>Water</i>, vol. 11, no. 4, 730, MDPI, 2019, doi:<a href=\"https://doi.org/10.3390/w11040730\">10.3390/w11040730</a>.","havard":"J.H.O. Fettig, U. Austermann-Haun, J.-F. Meier, A. Busch, E. Gilbert, Options for Removing Refractory Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization, Water. 11 (2019).","van":"Fettig JHO, Austermann-Haun U, Meier J-F, Busch A, Gilbert E. Options for Removing Refractory Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization. Water. 2019;11(4)."},"author":[{"first_name":"Joachim Hans Otto","id":"21018","last_name":"Fettig","full_name":"Fettig, Joachim Hans Otto"},{"first_name":"Ute","id":"40803","last_name":"Austermann-Haun","full_name":"Austermann-Haun, Ute"},{"full_name":"Meier, Jan-Felix","first_name":"Jan-Felix","last_name":"Meier"},{"last_name":"Busch","full_name":"Busch, Anna","first_name":"Anna","id":"58686"},{"last_name":"Gilbert","first_name":"Eva","full_name":"Gilbert, Eva"}],"year":2019,"main_file_link":[{"open_access":"1","url":"https://www.mdpi.com/2073-4441/11/4/730"}],"status":"public"}]