Cover Image

Using artificial neural network to optimize hydrogen solubility and evaluation of environmental condition effects

Hydrogen is a clean energy and has many applications in petroleum refining, glass purification, pharmaceuticals, semiconductors, aerospace applications and cooling generators. Therefore, it is very important to store it in various ways. One of the new and cheap methods to store hydrogen is storing i...

Full description

Saved in:
Bibliographic Details
Main Authors: Cao, Yan, Ayed, Hamdi, Dahari, Mahidzal, Sene, Ndolane, Bouallegue, Belgacem
Format: Article
Published: Oxford Univ Press 2022
Subjects:
QC Physics
TJ Mechanical engineering and machinery
TP Chemical technology
Online Access:http://eprints.um.edu.my/33365/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.um.eprints.33365
record_format eprints
spelling my.um.eprints.333652022-08-05T01:47:08Z http://eprints.um.edu.my/33365/ Using artificial neural network to optimize hydrogen solubility and evaluation of environmental condition effects Cao, Yan Ayed, Hamdi Dahari, Mahidzal Sene, Ndolane Bouallegue, Belgacem QC Physics TJ Mechanical engineering and machinery TP Chemical technology Hydrogen is a clean energy and has many applications in petroleum refining, glass purification, pharmaceuticals, semiconductors, aerospace applications and cooling generators. Therefore, it is very important to store it in various ways. One of the new and cheap methods to store hydrogen is storing in the brine groundwater. In this method, the hydrogen gas is injected into the brine, in which storing capacity has a direct relationship with the pressure, temperature and salt concentration of the saltwater. In the present study, an artificial neural network (ANN) was used to estimate and optimize the hydrogen solubility (HS) in the saltwater with conventional best algorithms such as the feedback propagation, genetic algorithm (GA) and radial basis function. The optimization is implemented based on available experimental data bank based on the variation of the pressure, working temperature and salt concentration. The results and assessments of different optimization ANN algorithm show that the GA has the most usable and accurate estimation and prediction for HS in the saltwater. Also, the amounts of the relevancy coefficient (R-c) that correspond to the sensitivity of HS on the input parameters demonstrate that the salt concentration and pressure have the minimum and maximum R-c, respectively. That is, the least and most effect on the output values. Oxford Univ Press 2022-02-08 Article PeerReviewed Cao, Yan and Ayed, Hamdi and Dahari, Mahidzal and Sene, Ndolane and Bouallegue, Belgacem (2022) Using artificial neural network to optimize hydrogen solubility and evaluation of environmental condition effects. International Journal of Low-Carbon Technologies, 17. pp. 80-89. ISSN 1748-1317, DOI https://doi.org/10.1093/ijlct/ctab088 <https://doi.org/10.1093/ijlct/ctab088>. 10.1093/ijlct/ctab088
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QC Physics
TJ Mechanical engineering and machinery
TP Chemical technology
spellingShingle QC Physics
TJ Mechanical engineering and machinery
TP Chemical technology
Cao, Yan
Ayed, Hamdi
Dahari, Mahidzal
Sene, Ndolane
Bouallegue, Belgacem
Using artificial neural network to optimize hydrogen solubility and evaluation of environmental condition effects
description Hydrogen is a clean energy and has many applications in petroleum refining, glass purification, pharmaceuticals, semiconductors, aerospace applications and cooling generators. Therefore, it is very important to store it in various ways. One of the new and cheap methods to store hydrogen is storing in the brine groundwater. In this method, the hydrogen gas is injected into the brine, in which storing capacity has a direct relationship with the pressure, temperature and salt concentration of the saltwater. In the present study, an artificial neural network (ANN) was used to estimate and optimize the hydrogen solubility (HS) in the saltwater with conventional best algorithms such as the feedback propagation, genetic algorithm (GA) and radial basis function. The optimization is implemented based on available experimental data bank based on the variation of the pressure, working temperature and salt concentration. The results and assessments of different optimization ANN algorithm show that the GA has the most usable and accurate estimation and prediction for HS in the saltwater. Also, the amounts of the relevancy coefficient (R-c) that correspond to the sensitivity of HS on the input parameters demonstrate that the salt concentration and pressure have the minimum and maximum R-c, respectively. That is, the least and most effect on the output values.
format Article
author Cao, Yan
Ayed, Hamdi
Dahari, Mahidzal
Sene, Ndolane
Bouallegue, Belgacem
author_facet Cao, Yan
Ayed, Hamdi
Dahari, Mahidzal
Sene, Ndolane
Bouallegue, Belgacem
author_sort Cao, Yan
title Using artificial neural network to optimize hydrogen solubility and evaluation of environmental condition effects
title_short Using artificial neural network to optimize hydrogen solubility and evaluation of environmental condition effects
title_full Using artificial neural network to optimize hydrogen solubility and evaluation of environmental condition effects
title_fullStr Using artificial neural network to optimize hydrogen solubility and evaluation of environmental condition effects
title_full_unstemmed Using artificial neural network to optimize hydrogen solubility and evaluation of environmental condition effects
title_sort using artificial neural network to optimize hydrogen solubility and evaluation of environmental condition effects
publisher Oxford Univ Press
publishDate 2022
url http://eprints.um.edu.my/33365/
_version_ 1740826024999911424
score 13.244368

Similar Items