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.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | Deanship of Scientific Research at the King Khalid University [R.G.P.2/140/42], Universiti Malaya [GPF054A-2020] |
| Uncontrolled Keywords: | ANN prediction; Modeling of hydrogen solubility; Relevancy coefficient; Genetic algorithm; Feedback propagation method |
| Subjects: | Q Science > QC Physics T Technology > TJ Mechanical engineering and machinery T Technology > TP Chemical technology |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 05 Aug 2022 01:47 |
| Last Modified: | 05 Aug 2022 01:47 |
| URI: | http://eprints.um.edu.my/id/eprint/33365 |
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