FBG-based sensors for oil and gas industry: assessment of heat transfer, structural health, liquid level, thermal conductivity and salinity

Detalhes bibliográficos
Ano de defesa: 2024
Autor(a) principal: Lazaro, Renan Costa
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal do Espírito Santo
BR
Doutorado em Engenharia Elétrica
Centro Tecnológico
UFES
Programa de Pós-Graduação em Engenharia Elétrica
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://repositorio.ufes.br/handle/10/17934
Resumo: This doctoral thesis focuses on the advancement of optical fiber-based sensors employing Fiber Bragg Gratings (FBG) for enhanced sensing in the oil and gas industry. The primary aim is to refine the evaluation of thermophysical parameters of fluids in classified and flammable environments. The research introduces FBG-based for tank structural health monitoring and parameters measurements in fluids, such as temperature, level, thermal conductivity and salinity. Experimental results demonstrate the efficacy of these sensors in challenging industrial conditions. The thermal experiments, utilizing an FBG-based temperature sensor, reveal insights into thermal power distribution in liquid processing systems. Specific heat and thermal conductivity of water are successfully estimated, demonstrating increased thermal stability with higher heat power. A method for measuring heat transfer rate in liquids is proposed, showing potential applications in industrial contexts. In the realm of structural health monitoring (SHM), the quasi-distributed FBG sensor, combined with supervised machine learning, exhibits high accuracy in monitoring stress and deformation in oil tank structures. The Random Forest algorithm enables precise liquid level estimation with minimal error, contributing to predictive maintenance strategies. The development of an all-optical hot-wire sensor showcases its precision in assessing thermal conductivity and salinity in various fluids. The sensor, integrating FBG with a hot-wire component, proves effective in discriminating substances with close thermal conductivity values. Future work aims at reducing measurement times and adapting the sensor for direct salinity measurement. Finally, worth highlighting the significant contributions of each sensor, emphasizing the practical applicability and promising results obtained in thermal analysis, structural health monitoring and all-optical sensing for the oil and gas industry. The research sets the stage for further exploration and refinement of these sensor technologies in complex industrial scenarios.
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spelling FBG-based sensors for oil and gas industry: assessment of heat transfer, structural health, liquid level, thermal conductivity and salinityTítulo alternativo do documento e/ou traduzido em outro idiomaSensores de fibra ópticaRedes de bragg em fibras (FBG)Monitoramento de integridade estrutural (SHM)Engenharia ElétricaThis doctoral thesis focuses on the advancement of optical fiber-based sensors employing Fiber Bragg Gratings (FBG) for enhanced sensing in the oil and gas industry. The primary aim is to refine the evaluation of thermophysical parameters of fluids in classified and flammable environments. The research introduces FBG-based for tank structural health monitoring and parameters measurements in fluids, such as temperature, level, thermal conductivity and salinity. Experimental results demonstrate the efficacy of these sensors in challenging industrial conditions. The thermal experiments, utilizing an FBG-based temperature sensor, reveal insights into thermal power distribution in liquid processing systems. Specific heat and thermal conductivity of water are successfully estimated, demonstrating increased thermal stability with higher heat power. A method for measuring heat transfer rate in liquids is proposed, showing potential applications in industrial contexts. In the realm of structural health monitoring (SHM), the quasi-distributed FBG sensor, combined with supervised machine learning, exhibits high accuracy in monitoring stress and deformation in oil tank structures. The Random Forest algorithm enables precise liquid level estimation with minimal error, contributing to predictive maintenance strategies. The development of an all-optical hot-wire sensor showcases its precision in assessing thermal conductivity and salinity in various fluids. The sensor, integrating FBG with a hot-wire component, proves effective in discriminating substances with close thermal conductivity values. Future work aims at reducing measurement times and adapting the sensor for direct salinity measurement. Finally, worth highlighting the significant contributions of each sensor, emphasizing the practical applicability and promising results obtained in thermal analysis, structural health monitoring and all-optical sensing for the oil and gas industry. The research sets the stage for further exploration and refinement of these sensor technologies in complex industrial scenarios.Esta tese de doutorado concentra-se no avanço de sensores baseados em fibras ópticas utilizando Redes de Bragg em Fibras (FBG) para aprimorar o sensoriamento na indústria de óleo e gás. O principal objetivo é aprimorar a medição de parâmetros termofísicos de fluidos em ambientes classificados e inflamáveis. A pesquisa apresenta uma abordagem baseada em FBG para monitoramento da saúde estrutural de tanques e medições de parâmetros em fluidos, como temperatura, nível, condutividade térmica e salinidade. Resultados experimentais demonstram a eficácia desses sensores em condições industriais desafiadoras. Os experimentos térmicos, utilizando um sensor de temperatura baseado em FBG, revelam insights sobre a distribuição de potência em sistemas de processamento de líquidos. Calor específico e condutividade térmica da água são estimados com sucesso, demonstrando maior estabilidade térmica com maior potência térmica. É proposto um método para medir a taxa de transferência de calor em líquidos, com potenciais aplicações industriais. No âmbito do monitoramento de integridade estrutural (SHM), o sensor FBG quase distribuído, combinado com aprendizado supervisionado, exibe alta precisão ao monitorar estresse e deformação em estruturas de tanques de óleo. O algoritmo Random Forest possibilita uma estimativa precisa do nível de líquido com erro mínimo, contribuindo para estratégias de manutenção preditiva. O desenvolvimento de um sensor óptico totalmente óptico destaca sua precisão na avaliação de condutividade térmica e salinidade em vários fluidos. O sensor, integrando FBG com um componente de fio quente, prova ser eficaz na discriminação de substâncias com valores de condutividade térmica próximos. Trabalhos futuros visam reduzir os tempos de medição e adaptar o sensor para medição direta de salinidade. Por fim, destaca-se as significativas contribuições de cada sensor, enfatizando a aplicabilidade prática e os resultados promissores obtidos na análise térmica, monitoramento de integridade estrutural do tanque e sensoriamento totalmente óptico para a indústria de petróleo e gás. A pesquisa estabelece bases para a exploração e aprimoramento contínuos dessas tecnologias de sensores em cenários industriais complexos.CAPESUniversidade Federal do Espírito SantoBRDoutorado em Engenharia ElétricaCentro TecnológicoUFESPrograma de Pós-Graduação em Engenharia ElétricaFrizera Neto, Anselmohttps://orcid.org/0000-0002-0687-3967http://lattes.cnpq.br/Leal Junior, Arnaldo Gomeshttps://orcid.org/0000-0002-9075-0619http://lattes.cnpq.br/Orientador2https://orcid.org/http://lattes.cnpq.br/https://orcid.org/http://lattes.cnpq.br/Pontes, Maria Joséhttps://orcid.org/0000-0002-9009-2425http://lattes.cnpq.br/Marques, Carlos Alberto Ferreirahttps://orcid.org/http://lattes.cnpq.br/Theodosiou, Antreashttps://orcid.org/http://lattes.cnpq.br/Lazaro, Renan Costa2024-10-11T20:40:56Z2024-10-11T20:40:56Z2024-06-03info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisTextapplication/pdfhttp://repositorio.ufes.br/handle/10/17934porptopen access, restricted access ou embargoed accessinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)instname:Universidade Federal do Espírito Santo (UFES)instacron:UFES2024-10-11T17:54:49Zoai:repositorio.ufes.br:10/17934Repositório InstitucionalPUBhttp://repositorio.ufes.br/oai/requestriufes@ufes.bropendoar:21082024-10-11T17:54:49Repositório Institucional da Universidade Federal do Espírito Santo (riUfes) - Universidade Federal do Espírito Santo (UFES)false
dc.title.none.fl_str_mv FBG-based sensors for oil and gas industry: assessment of heat transfer, structural health, liquid level, thermal conductivity and salinity
Título alternativo do documento e/ou traduzido em outro idioma
title FBG-based sensors for oil and gas industry: assessment of heat transfer, structural health, liquid level, thermal conductivity and salinity
spellingShingle FBG-based sensors for oil and gas industry: assessment of heat transfer, structural health, liquid level, thermal conductivity and salinity
Lazaro, Renan Costa
Sensores de fibra óptica
Redes de bragg em fibras (FBG)
Monitoramento de integridade estrutural (SHM)
Engenharia Elétrica
title_short FBG-based sensors for oil and gas industry: assessment of heat transfer, structural health, liquid level, thermal conductivity and salinity
title_full FBG-based sensors for oil and gas industry: assessment of heat transfer, structural health, liquid level, thermal conductivity and salinity
title_fullStr FBG-based sensors for oil and gas industry: assessment of heat transfer, structural health, liquid level, thermal conductivity and salinity
title_full_unstemmed FBG-based sensors for oil and gas industry: assessment of heat transfer, structural health, liquid level, thermal conductivity and salinity
title_sort FBG-based sensors for oil and gas industry: assessment of heat transfer, structural health, liquid level, thermal conductivity and salinity
author Lazaro, Renan Costa
author_facet Lazaro, Renan Costa
author_role author
dc.contributor.none.fl_str_mv Frizera Neto, Anselmo
https://orcid.org/0000-0002-0687-3967
http://lattes.cnpq.br/
Leal Junior, Arnaldo Gomes
https://orcid.org/0000-0002-9075-0619
http://lattes.cnpq.br/
Orientador2
https://orcid.org/
http://lattes.cnpq.br/
https://orcid.org/
http://lattes.cnpq.br/
Pontes, Maria José
https://orcid.org/0000-0002-9009-2425
http://lattes.cnpq.br/
Marques, Carlos Alberto Ferreira
https://orcid.org/
http://lattes.cnpq.br/
Theodosiou, Antreas
https://orcid.org/
http://lattes.cnpq.br/
dc.contributor.author.fl_str_mv Lazaro, Renan Costa
dc.subject.por.fl_str_mv Sensores de fibra óptica
Redes de bragg em fibras (FBG)
Monitoramento de integridade estrutural (SHM)
Engenharia Elétrica
topic Sensores de fibra óptica
Redes de bragg em fibras (FBG)
Monitoramento de integridade estrutural (SHM)
Engenharia Elétrica
description This doctoral thesis focuses on the advancement of optical fiber-based sensors employing Fiber Bragg Gratings (FBG) for enhanced sensing in the oil and gas industry. The primary aim is to refine the evaluation of thermophysical parameters of fluids in classified and flammable environments. The research introduces FBG-based for tank structural health monitoring and parameters measurements in fluids, such as temperature, level, thermal conductivity and salinity. Experimental results demonstrate the efficacy of these sensors in challenging industrial conditions. The thermal experiments, utilizing an FBG-based temperature sensor, reveal insights into thermal power distribution in liquid processing systems. Specific heat and thermal conductivity of water are successfully estimated, demonstrating increased thermal stability with higher heat power. A method for measuring heat transfer rate in liquids is proposed, showing potential applications in industrial contexts. In the realm of structural health monitoring (SHM), the quasi-distributed FBG sensor, combined with supervised machine learning, exhibits high accuracy in monitoring stress and deformation in oil tank structures. The Random Forest algorithm enables precise liquid level estimation with minimal error, contributing to predictive maintenance strategies. The development of an all-optical hot-wire sensor showcases its precision in assessing thermal conductivity and salinity in various fluids. The sensor, integrating FBG with a hot-wire component, proves effective in discriminating substances with close thermal conductivity values. Future work aims at reducing measurement times and adapting the sensor for direct salinity measurement. Finally, worth highlighting the significant contributions of each sensor, emphasizing the practical applicability and promising results obtained in thermal analysis, structural health monitoring and all-optical sensing for the oil and gas industry. The research sets the stage for further exploration and refinement of these sensor technologies in complex industrial scenarios.
publishDate 2024
dc.date.none.fl_str_mv 2024-10-11T20:40:56Z
2024-10-11T20:40:56Z
2024-06-03
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.ufes.br/handle/10/17934
url http://repositorio.ufes.br/handle/10/17934
dc.language.iso.fl_str_mv por
pt
language por
language_invalid_str_mv pt
dc.rights.driver.fl_str_mv open access, restricted access ou embargoed access
info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access, restricted access ou embargoed access
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv Text
application/pdf
dc.publisher.none.fl_str_mv Universidade Federal do Espírito Santo
BR
Doutorado em Engenharia Elétrica
Centro Tecnológico
UFES
Programa de Pós-Graduação em Engenharia Elétrica
publisher.none.fl_str_mv Universidade Federal do Espírito Santo
BR
Doutorado em Engenharia Elétrica
Centro Tecnológico
UFES
Programa de Pós-Graduação em Engenharia Elétrica
dc.source.none.fl_str_mv reponame:Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
instname:Universidade Federal do Espírito Santo (UFES)
instacron:UFES
instname_str Universidade Federal do Espírito Santo (UFES)
instacron_str UFES
institution UFES
reponame_str Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
collection Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
repository.name.fl_str_mv Repositório Institucional da Universidade Federal do Espírito Santo (riUfes) - Universidade Federal do Espírito Santo (UFES)
repository.mail.fl_str_mv riufes@ufes.br
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