---
_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: '1722'
abstract:
- lang: eng
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.
article_number: '730'
author:
- first_name: Joachim Hans Otto
full_name: Fettig, Joachim Hans Otto
id: '21018'
last_name: Fettig
- first_name: Ute
full_name: Austermann-Haun, Ute
id: '40803'
last_name: Austermann-Haun
- first_name: Jan-Felix
full_name: Meier, Jan-Felix
last_name: Meier
- first_name: Anna
full_name: Busch, Anna
id: '58686'
last_name: Busch
- first_name: Eva
full_name: Gilbert, Eva
last_name: Gilbert
citation:
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.
Water. 2019;11(4). doi:10.3390/w11040730
apa: Fettig, J. H. O., Austermann-Haun, U., Meier, J.-F., Busch, A., & Gilbert,
E. (2019). Options for Removing Refractory Organic Substances in Pre-Treated Process
Water from Hydrothermal Carbonization. Water, 11(4). https://doi.org/10.3390/w11040730
bjps: Fettig JHO et al. (2019) Options for Removing Refractory Organic
Substances in Pre-Treated Process Water from Hydrothermal Carbonization. Water
11.
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.” Water 11, no. 4 (2019).
https://doi.org/10.3390/w11040730.
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. Water 11,
Nr. 4. doi:10.3390/w11040730,
.
din1505-2-1: 'Fettig, Joachim Hans Otto
; Austermann-Haun, Ute ; Meier,
Jan-Felix ; Busch, Anna ;
Gilbert, Eva: Options for Removing
Refractory Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization.
In: Water Bd. 11. Basel, MDPI (2019), Nr. 4'
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).
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,” Water, vol. 11, no. 4, 2019.
mla: Fettig, Joachim Hans Otto, et al. “Options for Removing Refractory Organic
Substances in Pre-Treated Process Water from Hydrothermal Carbonization.” Water,
vol. 11, no. 4, 730, MDPI, 2019, doi:10.3390/w11040730.
short: J.H.O. Fettig, U. Austermann-Haun, J.-F. Meier, A. Busch, E. Gilbert, Water
11 (2019).
ufg: 'Fettig, Joachim Hans Otto et. al. (2019): Options for Removing Refractory
Organic Substances in Pre-Treated Process Water from Hydrothermal Carbonization,
in: Water 11 (4).'
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).
date_created: 2019-07-30T09:44:40Z
date_updated: 2023-03-15T13:49:37Z
department:
- _id: DEP3000
- _id: DEP8020
doi: 10.3390/w11040730
intvolume: ' 11'
issue: '4'
keyword:
- HTC process water
- post-treatment
- refractory organics
- activated carbon adsorption
- ozonation
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.mdpi.com/2073-4441/11/4/730
oa: '1'
place: Basel
publication: Water
publication_identifier:
issn:
- 2073-4441
publication_status: published
publisher: MDPI
status: public
title: Options for Removing Refractory Organic Substances in Pre-Treated Process Water
from Hydrothermal Carbonization
type: journal_article
user_id: '74004'
volume: 11
year: 2019
...