A Genetic Algorithm Applied to a PWR Turbine Extraction Optimization to Increase Cycle Efficiency
Autor(a) principal: | |
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Data de Publicação: | 2002 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional do IEN |
Texto Completo: | http://carpedien.ien.gov.br:8080/handle/ien/1707 |
Resumo: | In nuclear power plants feedwater heaters are used to heat feedwater from its temperature leaving the condenser to final feedwater temperature using steam extracted from various stages of the turbines. The purpose of this process is to increase cycle efficiency. The determination of the optimal fraction of mass flow rate to be extracted from each stage of the turbines is a complex optimization problem. This kind of problem has been efficiently solved by means of evolutionary computation techniques, such as Genetic Algorithms (GAs). GAs, which are systems based upon principles from biological genetics, have been successfully applied to several combinatorial optimization problems in nuclear engineering, as the nuclear fuel reload optimization problem. We introduce the use of GAs in cycle efficiency optimization by finding an optimal combination of turbine extractions. In order to demonstrate the effectiveness of our approach, we have chosen a typical PWR as case study. The secondary side of the PWR was simulated using PEPSE, which is a modeling tool used to perform integrated heat balances for power plants. The results indicate that the GA is a quite promising tool for cycle efficiency optimization. |
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SACCO, Wagner FigueiredoPEREIRA, Cláudio Márcio do Nascimento AbreuSCHIRRU, Robertohttp://lattes.cnpq.br/7328915770509368http://lattes.cnpq.br/2341184189645578http://lattes.cnpq.br/57665923154489112016-05-03T17:44:24Z2016-05-03T17:44:24Z2002http://carpedien.ien.gov.br:8080/handle/ien/1707Submitted by Sherillyn Lopes (sherillynmartins@yahoo.com.br) on 2016-05-03T17:44:24Z No. of bitstreams: 1 A genetic algorithm applied to a PWR turbine.PDF: 71691 bytes, checksum: be2d77704a4e0e57a0eead144fcbdb68 (MD5)Made available in DSpace on 2016-05-03T17:44:24Z (GMT). No. of bitstreams: 1 A genetic algorithm applied to a PWR turbine.PDF: 71691 bytes, checksum: be2d77704a4e0e57a0eead144fcbdb68 (MD5) Previous issue date: 2002In nuclear power plants feedwater heaters are used to heat feedwater from its temperature leaving the condenser to final feedwater temperature using steam extracted from various stages of the turbines. The purpose of this process is to increase cycle efficiency. The determination of the optimal fraction of mass flow rate to be extracted from each stage of the turbines is a complex optimization problem. This kind of problem has been efficiently solved by means of evolutionary computation techniques, such as Genetic Algorithms (GAs). GAs, which are systems based upon principles from biological genetics, have been successfully applied to several combinatorial optimization problems in nuclear engineering, as the nuclear fuel reload optimization problem. We introduce the use of GAs in cycle efficiency optimization by finding an optimal combination of turbine extractions. In order to demonstrate the effectiveness of our approach, we have chosen a typical PWR as case study. The secondary side of the PWR was simulated using PEPSE, which is a modeling tool used to perform integrated heat balances for power plants. The results indicate that the GA is a quite promising tool for cycle efficiency optimization.engInstituto de Engenharia NuclearIENBrasilCycle Efficiency OptimizationRankine CycleGenetic AlgorithmsA Genetic Algorithm Applied to a PWR Turbine Extraction Optimization to Increase Cycle Efficiencyinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject2002info:eu-repo/semantics/openAccessreponame:Repositório Institucional do IENinstname:Instituto de Engenharia Nuclearinstacron:IENLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://carpedien.ien.gov.br:8080/xmlui/bitstream/ien/1707/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52ORIGINALA genetic algorithm applied to a PWR turbine.PDFA genetic algorithm applied to a PWR turbine.PDFapplication/pdf71691http://carpedien.ien.gov.br:8080/xmlui/bitstream/ien/1707/1/A+genetic+algorithm+applied+to+a+PWR+turbine.PDFbe2d77704a4e0e57a0eead144fcbdb68MD51ien/1707oai:carpedien.ien.gov.br:ien/17072016-05-03 14:44:24.419Dspace IENlsales@ien.gov.brTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo= |
dc.title.pt_BR.fl_str_mv |
A Genetic Algorithm Applied to a PWR Turbine Extraction Optimization to Increase Cycle Efficiency |
title |
A Genetic Algorithm Applied to a PWR Turbine Extraction Optimization to Increase Cycle Efficiency |
spellingShingle |
A Genetic Algorithm Applied to a PWR Turbine Extraction Optimization to Increase Cycle Efficiency SACCO, Wagner Figueiredo Cycle Efficiency Optimization Rankine Cycle Genetic Algorithms |
title_short |
A Genetic Algorithm Applied to a PWR Turbine Extraction Optimization to Increase Cycle Efficiency |
title_full |
A Genetic Algorithm Applied to a PWR Turbine Extraction Optimization to Increase Cycle Efficiency |
title_fullStr |
A Genetic Algorithm Applied to a PWR Turbine Extraction Optimization to Increase Cycle Efficiency |
title_full_unstemmed |
A Genetic Algorithm Applied to a PWR Turbine Extraction Optimization to Increase Cycle Efficiency |
title_sort |
A Genetic Algorithm Applied to a PWR Turbine Extraction Optimization to Increase Cycle Efficiency |
author |
SACCO, Wagner Figueiredo |
author_facet |
SACCO, Wagner Figueiredo PEREIRA, Cláudio Márcio do Nascimento Abreu SCHIRRU, Roberto http://lattes.cnpq.br/7328915770509368 http://lattes.cnpq.br/2341184189645578 http://lattes.cnpq.br/5766592315448911 |
author_role |
author |
author2 |
PEREIRA, Cláudio Márcio do Nascimento Abreu SCHIRRU, Roberto http://lattes.cnpq.br/7328915770509368 http://lattes.cnpq.br/2341184189645578 http://lattes.cnpq.br/5766592315448911 |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
SACCO, Wagner Figueiredo PEREIRA, Cláudio Márcio do Nascimento Abreu SCHIRRU, Roberto http://lattes.cnpq.br/7328915770509368 http://lattes.cnpq.br/2341184189645578 http://lattes.cnpq.br/5766592315448911 |
dc.subject.por.fl_str_mv |
Cycle Efficiency Optimization Rankine Cycle Genetic Algorithms |
topic |
Cycle Efficiency Optimization Rankine Cycle Genetic Algorithms |
dc.description.abstract.por.fl_txt_mv |
In nuclear power plants feedwater heaters are used to heat feedwater from its temperature leaving the condenser to final feedwater temperature using steam extracted from various stages of the turbines. The purpose of this process is to increase cycle efficiency. The determination of the optimal fraction of mass flow rate to be extracted from each stage of the turbines is a complex optimization problem. This kind of problem has been efficiently solved by means of evolutionary computation techniques, such as Genetic Algorithms (GAs). GAs, which are systems based upon principles from biological genetics, have been successfully applied to several combinatorial optimization problems in nuclear engineering, as the nuclear fuel reload optimization problem. We introduce the use of GAs in cycle efficiency optimization by finding an optimal combination of turbine extractions. In order to demonstrate the effectiveness of our approach, we have chosen a typical PWR as case study. The secondary side of the PWR was simulated using PEPSE, which is a modeling tool used to perform integrated heat balances for power plants. The results indicate that the GA is a quite promising tool for cycle efficiency optimization. |
description |
In nuclear power plants feedwater heaters are used to heat feedwater from its temperature leaving the condenser to final feedwater temperature using steam extracted from various stages of the turbines. The purpose of this process is to increase cycle efficiency. The determination of the optimal fraction of mass flow rate to be extracted from each stage of the turbines is a complex optimization problem. This kind of problem has been efficiently solved by means of evolutionary computation techniques, such as Genetic Algorithms (GAs). GAs, which are systems based upon principles from biological genetics, have been successfully applied to several combinatorial optimization problems in nuclear engineering, as the nuclear fuel reload optimization problem. We introduce the use of GAs in cycle efficiency optimization by finding an optimal combination of turbine extractions. In order to demonstrate the effectiveness of our approach, we have chosen a typical PWR as case study. The secondary side of the PWR was simulated using PEPSE, which is a modeling tool used to perform integrated heat balances for power plants. The results indicate that the GA is a quite promising tool for cycle efficiency optimization. |
publishDate |
2002 |
dc.date.issued.fl_str_mv |
2002 |
dc.date.accessioned.fl_str_mv |
2016-05-03T17:44:24Z |
dc.date.available.fl_str_mv |
2016-05-03T17:44:24Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
status_str |
publishedVersion |
format |
conferenceObject |
dc.identifier.uri.fl_str_mv |
http://carpedien.ien.gov.br:8080/handle/ien/1707 |
url |
http://carpedien.ien.gov.br:8080/handle/ien/1707 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Instituto de Engenharia Nuclear |
dc.publisher.initials.fl_str_mv |
IEN |
dc.publisher.country.fl_str_mv |
Brasil |
publisher.none.fl_str_mv |
Instituto de Engenharia Nuclear |
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Instituto de Engenharia Nuclear |
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IEN |
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IEN |
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