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

CFD-based irreversibility analysis of avant-garde semi-O/O-shape grooving fashions of solar pond heat trade-off unit

Yan Cao, Hamdi Ayed, Mahidzal Dahari, Ndolane Sene and Belgacem Bouallegue

International Journal of Low-Carbon Technologies, 2022, vol. 17, 328-43

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 () that correspond to the sensitivity of HS on the input parameters demonstrate that the salt concentration and pressure have the minimum and maximum , respectively. That is, the least and most effect on the output values.

Keywords: ANN prediction; modeling of hydrogen solubility; relevancy coefficient; genetic algorithm; feedback propagation method (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/ijlct/ctab088 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:17:y:2022:i::p:328-43.

Access Statistics for this article

International Journal of Low-Carbon Technologies is currently edited by Saffa B. Riffat

More articles in International Journal of Low-Carbon Technologies from Oxford University Press
Bibliographic data for series maintained by Oxford University Press ().