---
_id: '11808'
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'
author:
- first_name: Georg Heinrich
  full_name: Klepp, Georg Heinrich
  id: '49011'
  last_name: Klepp
citation:
  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>'
  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>'
  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>,
    .'
  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)'
  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).'
  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>.'
  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>.'
  short: 'G.H. Klepp, Energy : The International Journal ; Technologies, Resources,
    Reserves, Demands, Impact, Conservation, Management, Policy 306 (2024).'
  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.'
date_created: 2024-07-31T14:23:52Z
date_updated: 2024-08-01T08:16:04Z
department:
- _id: DEP6017
doi: 10.1016/j.energy.2024.132318
intvolume: '       306'
keyword:
- Hydrogen storage
- Adsorption
- Activated carbon
- Machine learning
- Simulation
- Computational fluid dynamics
language:
- iso: eng
place: Amsterdam
publication: 'Energy : the international journal ; technologies, resources, reserves,
  demands, impact, conservation, management, policy'
publication_identifier:
  eissn:
  - 1873-6785
  issn:
  - 0360-5442
publication_status: published
publisher: Elsevier BV
status: public
title: Modelling activated carbon hydrogen storage tanks using machine learning models
type: scientific_journal_article
user_id: '83781'
volume: 306
year: '2024'
...