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dc.contributor.authorHajialigol, Nen_US
dc.contributor.authorFattahi, Aen_US
dc.contributor.authorKarimi, Nen_US
dc.contributor.authorJamali, Men_US
dc.contributor.authorKeighobadi, Sen_US
dc.date.accessioned2024-01-22T08:52:07Z
dc.date.issued2024-03-01en_US
dc.identifier.issn0360-5442en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/94106
dc.description.abstractThis paper investigates different configurations of organic Rankine flash cycles combined with a Brayton cycle by performing thermodynamic, exergy, and exergoeconomic analyses. The thermal energy of the cycle is produced through burning gaseous methane generated via gasification of biomass. A systematic analysis of these configurations is conducted to enhance the exergy efficiency of the cycles. Additionally, the reutilization of the thermal energy that would otherwise be wasted in the Brayton cycle contributes to a notable enhancement in the overall thermal efficiency of the combined cycle. A range of working fluids, namely m-Xylene, o-Xylene, p-Xylene, toluene, and ethylbenzene are analyzed for the organic Rankine cycle. Predictions using an artificial neural network (radial base function) are also carried out. The results indicate that the p-Xylene increases exergy efficiency more than other working fluids. Further, the improved organic Rankine cycle mitigates exergy destruction by 10 %. Although applying double flash evaporators improves the exergy efficiency by 3 %, it increases the unit cost of power generated by more than 10 %. The application of a data-driven model to predict various configurations of combined organic Rankin cycle with a Brayton cycle fed by biomass has rarely been investigated.en_US
dc.relation.ispartofEnergyen_US
dc.rights© 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleHybridized power-hydrogen generation using various configurations of Brayton-organic flash Rankine cycles fed by a sustainable fuel: Exergy and exergoeconomic analyses with ANN predictionen_US
dc.typeArticle
dc.identifier.doi10.1016/j.energy.2023.130166en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume290en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US


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