A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms

This article presents a multivariable optimization of the energy and exergetic performance of a power generation system, which is integrated by a supercritical Brayton Cycle using carbon dioxide, and a Simple Organic Rankine Cycle (SORC) using toluene, with reheater (S CORH 2 SORC), and without rehe...

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Autores:
Valencia Ochoa, Guillermo
Tipo de recurso:
Fecha de publicación:
2020
Institución:
Universidad del Atlántico
Repositorio:
Repositorio Uniatlantico
Idioma:
eng
OAI Identifier:
oai:repositorio.uniatlantico.edu.co:20.500.12834/991
Acceso en línea:
https://hdl.handle.net/20.500.12834/991
Palabra clave:
Energy Mechanical engineering Thermodynamics Mathematical optimization Energy conservation Swarm intelligence algorithms Brayton supercritical CO2 cycle Organic Rankine cycle Exergetic analysis Energy analysis
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openAccess
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http://creativecommons.org/licenses/by-nc/4.0/
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network_acronym_str UNIATLANT2
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dc.title.spa.fl_str_mv A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms
title A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms
spellingShingle A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms
Energy Mechanical engineering Thermodynamics Mathematical optimization Energy conservation Swarm intelligence algorithms Brayton supercritical CO2 cycle Organic Rankine cycle Exergetic analysis Energy analysis
title_short A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms
title_full A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms
title_fullStr A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms
title_full_unstemmed A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms
title_sort A comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithms
dc.creator.fl_str_mv Valencia Ochoa, Guillermo
dc.contributor.author.none.fl_str_mv Valencia Ochoa, Guillermo
dc.contributor.other.none.fl_str_mv Duarte Forero, Jorge
Rojas, Jhan Piero
dc.subject.keywords.spa.fl_str_mv Energy Mechanical engineering Thermodynamics Mathematical optimization Energy conservation Swarm intelligence algorithms Brayton supercritical CO2 cycle Organic Rankine cycle Exergetic analysis Energy analysis
topic Energy Mechanical engineering Thermodynamics Mathematical optimization Energy conservation Swarm intelligence algorithms Brayton supercritical CO2 cycle Organic Rankine cycle Exergetic analysis Energy analysis
description This article presents a multivariable optimization of the energy and exergetic performance of a power generation system, which is integrated by a supercritical Brayton Cycle using carbon dioxide, and a Simple Organic Rankine Cycle (SORC) using toluene, with reheater (S CORH 2 SORC), and without reheater (S CONRH 2 SORC) using the PSO algorithm. A thermodynamic model of the integrated system was developed from the application of mass, energy and exergy balances to each component, which allowed the calculation of the exergy destroyed a fraction of each equipment, the power generated, the thermal and exergetic efficiency of the system. In addition, through a sensitivity analysis, the effect of the main operational and design variables on thermal efficiency and total exergy destroyed was studied, which were the objective functions selected in the proposed optimization. The results show that the greatest exergy destruction occurs at the thermal source, with a value of 97 kW for the system without Reheater (NRH), but this is reduced by 92.28% for the system with Reheater (RH). In addition, by optimizing the integrated cycle for a particle number of 25, the maximum thermal efficiency of 55.53% (NRH) was achieved, and 56.95% in the RH system. Likewise, for a particle number of 15 and 20 in the PSO algorithm, exergy destruction was minimized to 60.72 kW (NRH) and 112.06 kW (RH), respectively. Comparative analyses of some swarm intelligence optimization algorithms were conducted for the integrated S-CO2-SORC system, evaluating performance indicators, where the PSO optimization algorithm was favorable in the analyses, guaranteeing that it is the ideal algorithm to solve this case study.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020-06-01
dc.date.submitted.none.fl_str_mv 2020-03-18
dc.date.accessioned.none.fl_str_mv 2022-11-15T21:23:47Z
dc.date.available.none.fl_str_mv 2022-11-15T21:23:47Z
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
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dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.hasVersion.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.spa.spa.fl_str_mv Artículo
status_str publishedVersion
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12834/991
dc.identifier.doi.none.fl_str_mv 10.1016/j.heliyon.2020.e04136
dc.identifier.instname.spa.fl_str_mv Universidad del Atlántico
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad del Atlántico
url https://hdl.handle.net/20.500.12834/991
identifier_str_mv 10.1016/j.heliyon.2020.e04136
Universidad del Atlántico
Repositorio Universidad del Atlántico
dc.language.iso.spa.fl_str_mv eng
language eng
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dc.rights.cc.*.fl_str_mv Attribution-NonCommercial 4.0 International
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rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0/
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eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.place.spa.fl_str_mv Barranquilla
dc.publisher.discipline.spa.fl_str_mv Ingeniería Mecánica
dc.publisher.sede.spa.fl_str_mv Sede Norte
dc.source.spa.fl_str_mv Elsevier Ltd
institution Universidad del Atlántico
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spelling Valencia Ochoa, Guillermo1601011b-0fa9-473b-b829-dad629428f37Duarte Forero, JorgeRojas, Jhan Piero2022-11-15T21:23:47Z2022-11-15T21:23:47Z2020-06-012020-03-18https://hdl.handle.net/20.500.12834/99110.1016/j.heliyon.2020.e04136Universidad del AtlánticoRepositorio Universidad del AtlánticoThis article presents a multivariable optimization of the energy and exergetic performance of a power generation system, which is integrated by a supercritical Brayton Cycle using carbon dioxide, and a Simple Organic Rankine Cycle (SORC) using toluene, with reheater (S CORH 2 SORC), and without reheater (S CONRH 2 SORC) using the PSO algorithm. A thermodynamic model of the integrated system was developed from the application of mass, energy and exergy balances to each component, which allowed the calculation of the exergy destroyed a fraction of each equipment, the power generated, the thermal and exergetic efficiency of the system. In addition, through a sensitivity analysis, the effect of the main operational and design variables on thermal efficiency and total exergy destroyed was studied, which were the objective functions selected in the proposed optimization. The results show that the greatest exergy destruction occurs at the thermal source, with a value of 97 kW for the system without Reheater (NRH), but this is reduced by 92.28% for the system with Reheater (RH). In addition, by optimizing the integrated cycle for a particle number of 25, the maximum thermal efficiency of 55.53% (NRH) was achieved, and 56.95% in the RH system. Likewise, for a particle number of 15 and 20 in the PSO algorithm, exergy destruction was minimized to 60.72 kW (NRH) and 112.06 kW (RH), respectively. Comparative analyses of some swarm intelligence optimization algorithms were conducted for the integrated S-CO2-SORC system, evaluating performance indicators, where the PSO optimization algorithm was favorable in the analyses, guaranteeing that it is the ideal algorithm to solve this case study.application/pdfenghttp://creativecommons.org/licenses/by-nc/4.0/Attribution-NonCommercial 4.0 Internationalinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Elsevier LtdA comparative energy and exergy optimization of a supercritical-CO2 Brayton cycle and Organic Rankine Cycle combined system using swarm intelligence algorithmsPúblico generalEnergy Mechanical engineering Thermodynamics Mathematical optimization Energy conservation Swarm intelligence algorithms Brayton supercritical CO2 cycle Organic Rankine cycle Exergetic analysis Energy analysisinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1BarranquillaIngeniería MecánicaSede Norte[1] G. 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(2010) 815–819.http://purl.org/coar/resource_type/c_6501ORIGINAL1-s2.0-S2405844020309804-main.pdf1-s2.0-S2405844020309804-main.pdfapplication/pdf2761519https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/991/1/1-s2.0-S2405844020309804-main.pdf0e1e617daba9643300cd05a872bb66faMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/991/2/license_rdf24013099e9e6abb1575dc6ce0855efd5MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81306https://repositorio.uniatlantico.edu.co/bitstream/20.500.12834/991/3/license.txt67e239713705720ef0b79c50b2ececcaMD5320.500.12834/991oai:repositorio.uniatlantico.edu.co:20.500.12834/9912022-11-15 16:23:48.183DSpace de la Universidad de Atlánticosysadmin@mail.uniatlantico.edu.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