---
_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. Energy : the international journal ; technologies, resources,
reserves, demands, impact, conservation, management, policy. 2024;306. doi:10.1016/j.energy.2024.132318'
apa: 'Klepp, G. H. (2024). Modelling activated carbon hydrogen storage tanks using
machine learning models. Energy : The International Journal ; Technologies,
Resources, Reserves, Demands, Impact, Conservation, Management, Policy, 306,
Article 132318. https://doi.org/10.1016/j.energy.2024.132318'
bjps: 'Klepp GH (2024) Modelling Activated Carbon Hydrogen Storage Tanks
Using Machine Learning Models. Energy : the international journal ; technologies,
resources, reserves, demands, impact, conservation, management, policy 306.'
chicago: 'Klepp, Georg Heinrich. “Modelling Activated Carbon Hydrogen Storage Tanks
Using Machine Learning Models.” Energy : The International Journal ; Technologies,
Resources, Reserves, Demands, Impact, Conservation, Management, Policy 306
(2024). https://doi.org/10.1016/j.energy.2024.132318.'
chicago-de: 'Klepp, Georg Heinrich. 2024. Modelling activated carbon hydrogen storage
tanks using machine learning models. Energy : the international journal ; technologies,
resources, reserves, demands, impact, conservation, management, policy 306.
doi:10.1016/j.energy.2024.132318,
.'
din1505-2-1: 'Klepp, Georg Heinrich:
Modelling activated carbon hydrogen storage tanks using machine learning models.
In: Energy : the international journal ; technologies, resources, reserves,
demands, impact, conservation, management, policy 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,” Energy : the international journal ; technologies, resources,
reserves, demands, impact, conservation, management, policy, vol. 306, Art.
no. 132318, 2024, doi: 10.1016/j.energy.2024.132318.'
mla: 'Klepp, Georg Heinrich. “Modelling Activated Carbon Hydrogen Storage Tanks
Using Machine Learning Models.” Energy : The International Journal ; Technologies,
Resources, Reserves, Demands, Impact, Conservation, Management, Policy, vol.
306, 132318, 2024, https://doi.org/10.1016/j.energy.2024.132318.'
short: 'G.H. Klepp, Energy : The International Journal ; Technologies, Resources,
Reserves, Demands, Impact, Conservation, Management, Policy 306 (2024).'
ufg: 'Klepp, Georg Heinrich: Modelling activated carbon hydrogen storage
tanks using machine learning models, in: Energy : the international journal ;
technologies, resources, reserves, demands, impact, conservation, management,
policy 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'
...
---
_id: '5435'
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.
author:
- first_name: Martin
full_name: Griese, Martin
id: '52308'
last_name: Griese
- first_name: Marc Philippe
full_name: Hoffrath, Marc Philippe
last_name: Hoffrath
- first_name: Timo
full_name: Broeker, Timo
id: '43927'
last_name: Broeker
- first_name: Thomas
full_name: Schulte, Thomas
id: '46242'
last_name: Schulte
- first_name: Jan
full_name: Schneider, Jan
id: '13209'
last_name: Schneider
orcid: 0000-0001-6401-8873
citation:
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. Energy : the international journal. 2019;181:77-90.
doi:10.1016/j.energy.2019.05.092'
apa: 'Griese, M., Hoffrath, M. P., Broeker, T., Schulte, T., & Schneider, J.
(2019). Hardware-in-the-Loop simulation of an optimized energy management incorporating
an experimental biocatalytic methanation reactor. Energy : The International
Journal, 181, 77–90. https://doi.org/10.1016/j.energy.2019.05.092'
bjps: 'Griese M et al. (2019) Hardware-in-the-Loop Simulation of an
Optimized Energy Management Incorporating an Experimental Biocatalytic Methanation
Reactor. Energy : the international journal 181, 77–90.'
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.” Energy : The
International Journal 181 (2019): 77–90. https://doi.org/10.1016/j.energy.2019.05.092.'
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. Energy :
the international journal 181: 77–90. doi:10.1016/j.energy.2019.05.092,
.'
din1505-2-1: 'Griese, Martin ; Hoffrath, Marc Philippe ; Broeker,
Timo ; Schulte, Thomas ;
Schneider, Jan: Hardware-in-the-Loop
simulation of an optimized energy management incorporating an experimental biocatalytic
methanation reactor. In: Energy : the international journal 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.'
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,” Energy : the international journal, vol. 181, pp.
77–90, 2019, doi: 10.1016/j.energy.2019.05.092.'
mla: 'Griese, Martin, et al. “Hardware-in-the-Loop Simulation of an Optimized Energy
Management Incorporating an Experimental Biocatalytic Methanation Reactor.” Energy :
The International Journal, vol. 181, 2019, pp. 77–90, https://doi.org/10.1016/j.energy.2019.05.092.'
short: 'M. Griese, M.P. Hoffrath, T. Broeker, T. Schulte, J. Schneider, Energy :
The International Journal 181 (2019) 77–90.'
ufg: 'Griese, Martin u. a.: Hardware-in-the-Loop simulation of an optimized
energy management incorporating an experimental biocatalytic methanation reactor,
in: Energy : the international journal 181 (2019), S. 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.'
conference:
end_date: 2018-06-21
location: Guimaraes, PORTUGAL
name: 31st International Conference on Efficiency, Cost, Optimization, Simulation,
and Environmental Impact of Energy Systems (ECOS)
start_date: 2018-06-17
date_created: 2021-04-08T07:42:48Z
date_updated: 2025-06-25T07:48:53Z
department:
- _id: DEP4023
- _id: DEP4018
doi: 10.1016/j.energy.2019.05.092
external_id:
isi:
- '000476965900009'
intvolume: ' 181'
isi: '1'
keyword:
- Biological methanation
- Energy management
- HIL simulation
- Optimization
- Scalable models
language:
- iso: eng
page: 77 - 90
publication: 'Energy : the international journal'
publication_identifier:
eissn:
- 1873-6785
issn:
- 0360-5442
publication_status: published
publisher: Elsevier
quality_controlled: '1'
status: public
title: Hardware-in-the-Loop simulation of an optimized energy management incorporating
an experimental biocatalytic methanation reactor
type: journal_article
user_id: '83781'
volume: 181
year: '2019'
...