A green hydrogen production model from solar powered water electrolyze based on deep chaotic Levy gazelle optimization

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A green hydrogen production model from solar powered water electrolyze based on deep chaotic Levy gazelle optimization

Popis výsledku v původním jazyce

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&lt;acute accent&gt;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&apos;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.

Název v anglickém jazyce

A green hydrogen production model from solar powered water electrolyze based on deep chaotic Levy gazelle optimization

Popis výsledku anglicky

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&lt;acute accent&gt;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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Engineering Science and Technology, an International Journal

ISSN

2215-0986

e-ISSN

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Svazek periodika

60

Číslo periodika v rámci svazku

Dec

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

28

Strana od-do

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Kód UT WoS článku

001356121800001

EID výsledku v databázi Scopus

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