A green hydrogen production model from solar powered water electrolyze based on deep chaotic Levy gazelle optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10257229" target="_blank" >RIV/61989100:27240/24:10257229 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S221509862400260X?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S221509862400260X?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jestch.2024.101874" target="_blank" >10.1016/j.jestch.2024.101874</a>
Alternative languages
Result language
angličtina
Original language name
A green hydrogen production model from solar powered water electrolyze based on deep chaotic Levy gazelle optimization
Original language description
This paper presents a Deep Learning (DL) model designed for green hydrogen production using a solar-powered water electrolyzer. The model operates in four phases, beginning with the analysis of a solar radiation dataset and culminating in the prediction of green hydrogen production. A novel hybrid model, termed DeepGaz, is introduced to predict the solar energy required for hydrogen production. DeepGaz combines a new Chaotic-Le<acute accent>vy variant of the gazelle optimization algorithm (CGOA) with a recurrent neural network (RNN/LSTM) for hyperparameter optimization. To validate the performance of the proposed model, CGOA is first tested on the CEC2022 benchmark problems and compared with other advanced metaheuristic algorithms, with its accuracy further confirmed using Wilcoxon's rank-sum statistical analysis. Subsequently, DeepGaz is applied to a solarbased green hydrogen dataset, optimizing key parameters such as solar radiation, temperature, wind direction, and speed, collected from the HI-SEAS weather station in Hawaii over a four-month period. The results show that DeepGaz significantly improves the prediction process, achieving an average daily hydrogen production of 15.5199 kg/day during the four-month study. The model exhibits strong potential in predicting green hydrogen production, excelling in computational time, convergence stability, and overall solution accuracy.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Engineering Science and Technology, an International Journal
ISSN
2215-0986
e-ISSN
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Volume of the periodical
60
Issue of the periodical within the volume
Dec
Country of publishing house
US - UNITED STATES
Number of pages
28
Pages from-to
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UT code for WoS article
001356121800001
EID of the result in the Scopus database
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