Use este identificador para citar ou linkar para este item: https://repositorio.ufba.br/handle/ri/37029
Registro completo de metadados
Campo DCValorIdioma
dc.creatorAlves Junior, Ozaná Rocha-
dc.date.accessioned2023-05-22T15:11:21Z-
dc.date.available2023-05-22T15:11:21Z-
dc.date.issued2023-05-05-
dc.identifier.urihttps://repositorio.ufba.br/handle/ri/37029-
dc.description.abstractThe opening of the market and the possibility of the participation of several suppliers in Natural Gas (NG) distribution networks has raised the level of complexity associated with the quality control of the final stream to be made available to the consumer market. There are suppliers that offer lower prices but supply a lower quality of fuel which can contribute to an out-of-spec final stream (after mixing the different suppliers). On the other hand, it is not always economically viable to build Processing Units and acquire analytical equipment for gas quality control. In general, simulations in natural gas distribution networks focus on the evaluation of physical criteria and energy balance. This work presents a dynamic optimization model, validated by real case studies, for the monitoring and quality control of a natural gas stream mixing process that involves several suppliers providing varying quality levels (including out of specification streams) and prices. The results show the feasibility of executing projects for new suppliers, as well as supplying natural gas to customers who use it as a raw material and who establish more restrictive specification limits than those established by the regulatory agency. Comparing with the conventional operating strategy, the results obtained by the proposed optimization model show a reduction in the cost of distribution equal to 13.5% and 22.6% in the two case studies analyzed, respectively.pt_BR
dc.description.sponsorshipCNPqpt_BR
dc.languageporpt_BR
dc.publisherUniversidade Federal da Bahiapt_BR
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectGás naturalpt_BR
dc.subjectRedes de distribuiçãopt_BR
dc.subjectProcessos de misturapt_BR
dc.subjectOtimização dinâmicapt_BR
dc.subjectControle de qualidadept_BR
dc.subject.otherNatural gaspt_BR
dc.subject.otherDistribution networkspt_BR
dc.subject.otherMixing processespt_BR
dc.subject.otherDynamic optimizationpt_BR
dc.subject.otherQuality controlpt_BR
dc.titleModelagem e otimização de redes de distribuição de gás natural para projetos de novos supridorespt_BR
dc.title.alternativeModeling and optimization of natural gas distribution networks for new supplier projectspt_BR
dc.typeDissertaçãopt_BR
dc.publisher.programPrograma de Pós-Graduação em Engenharia Industrial (PEI) pt_BR
dc.publisher.initialsUFBApt_BR
dc.publisher.countryBrasilpt_BR
dc.subject.cnpqCNPQ::ENGENHARIAS::ENGENHARIA QUIMICA::OPERACOES INDUSTRIAIS E EQUIPAMENTOS PARA ENGENHARIA QUIMICA::OPERACOES DE SEPARACAO E MISTURApt_BR
dc.contributor.advisor1Fontes, Cristiano-
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/8533422209857268pt_BR
dc.contributor.referee1Fontes, Cristiano Hora-
dc.contributor.referee2Chavez Ruiz, Juan Alberto-
dc.contributor.referee3Pacheco Filho, José Geraldo A.-
dc.creator.ID0000-0003-0116-6099pt_BR
dc.creator.Latteshttp://lattes.cnpq.br/8192996503579746pt_BR
dc.description.resumoA abertura do mercado e a possibilidade da participação de diversos supridores em redes de distribuição de Gás Natural (GN) elevaram o nível de complexidade associado ao controle de qualidade do produto final a ser disponibilizado ao mercado consumidor. Existem fornecedores que oferecem preços mais baixos, porém com um nível de qualidade inferior do combustível, o que pode contribuir para uma corrente final fora da especificação (após a mistura com outros fornecedores). Por outro lado, nem sempre é economicamente viável construir Unidades de Processamento e adquirir equipamentos analíticos para controle de qualidade do gás. Em geral, as simulações nas redes de distribuição de gás natural focam na avaliação de critérios físicos e nos aspectos associados ao equilíbrio energético. Este trabalho apresenta um modelo dinâmico de otimização, validado por estudos de caso real, para o monitoramento e controle de qualidade de um processo de mistura de gás natural que envolve diversos supridores com diferentes custos de fornecimento e diferentes níveis de qualidade (inclusive fora de especificação). Os resultados mostram a viabilidade da execução de projetos para novos supridores, bem como o fornecimento de gás natural aos clientes que o utilizam como matéria-prima e que estabelecem limites de especificação mais restritivos do que os estabelecidos pela agência reguladora. Comparando com a estratégia operacional convencional, os resultados obtidos pelo modelo de otimização proposto mostram uma redução no custo de distribuição de até 13,5% e 22,6% nos dois estudos de caso analisados, respectivamente, a saber: i) uma mistura com apenas dois fornecedores e um deles com fluxo de gás não especificado e ii) vários fornecedores, alguns fluxos de gás não especificados, misturas intermediárias ao longo da rede e uma mistura final no header pipe.pt_BR
dc.publisher.departmentEscola Politécnicapt_BR
dc.relation.referencesAkatsu, S., Tomita, S., Mori, Y. H., & Ohmura, R. (2013). Thermodynamic simulations of hydrate-based removal of carbon dioxide and hydrogen sulfide from low-quality natural gas. Industrial and Engineering Chemistry Research, 52(43), 15165–15176. https://doi.org/10.1021/ie402010p Alfaki, M., & Haugland, D. (2013). Strong formulations for the pooling problem. Journal of Global Optimization, 56(3), 897–916. https://doi.org/10.1007/s10898-012-9875-6 Audet, C., Brimberg, J., Hansen, P., Le Digabel, S., & Mladenović, N. (2004). Pooling problem: Alternate formulations and solution methods. Management Science, 50(6), 761–776. https://doi.org/10.1287/mnsc.1030.0207 Chaczykowski, M., & Zarodkiewicz, P. (2017). Simulation of natural gas quality distribution for pipeline systems. Energy, 134, 681–698. https://doi.org/10.1016/j.energy.2017.06.020 Cheli, L., Guzzo, G., Adolfo, D., & Carcasci, C. (2021). Steady-state analysis of a natural gas distribution network with hydrogen injection to absorb excess renewable electricity. International Journal of Hydrogen Energy, 46(50), 25562–25577. https://doi.org/10.1016/j.ijhydene.2021.05.100 Dell’Isola, M., Ficco, G., Lavalle, L., Moretti, L., Tofani, A., & Zuena, F. (2020). A resilience assessment simulation tool for distribution gas networks. Journal of Natural Gas Science and Engineering, 84(June), 103680. https://doi.org/10.1016/j.jngse.2020.103680 Dyachenko, S. A., Zlotnik, A., Korotkevich, A. O., & Chertkov, M. (2017). Operator splitting method for simulation of dynamic flows in natural gas pipeline networks. Physica D: Nonlinear Phenomena, 361, 1–11. https://doi.org/10.1016/j.physd.2017.09.002 Elaoud, S., Hafsi, Z., & Hadj-taieb, L. (2017). Journal of Petroleum Science and Engineering Numerical modelling of hydrogen-natural gas mixtures flows in looped networks. Journal of Petroleum Science and Engineering, 159(September), 532–541. https://doi.org/10.1016/j.petrol.2017.09.063 Esfandiari, K., Banihashemi, M., Mokhtari, A., & Soleimani, P. (2021). Experimental investigation of influencing parameters on natural gas odor fading in gas distribution networks. Journal of Natural Gas Science and Engineering, 95(March), 104191.https://doi.org/10.1016/j.jngse.2021.104191 Ficco, G., Frattolillo, A., Zuena, F., & Dell’Isola, M. (2022). Analysis of Delta In-Out of natural gas distribution networks. Flow Measurement and Instrumentation, 84(December 2021), 102139. https://doi.org/10.1016/j.flowmeasinst.2022.102139 Gaykema, E. W., Skryabin, I., Prest, J., & Hansen, B. (2021). Assessing the viability of the ACT natural gas distribution network for reuse as a hydrogen distribution network. International Journal of Hydrogen Energy, 46(23), 12280–12289. https://doi.org/10.1016/j.ijhydene.2020.11.051 Guandalini, G., Colbertaldo, P., & Campanari, S. (2017). Dynamic modeling of natural gas quality within transport pipelines in presence of hydrogen injections. Applied Energy, 185, 1712–1723. https://doi.org/10.1016/j.apenergy.2016.03.006 Herrán-González, A., De La Cruz, J. M., De Andrés-Toro, B., & Risco-Martín, J. L. (2009). Modeling and simulation of a gas distribution pipeline network. Applied Mathematical Modelling, 33(3), 1584–1600. https://doi.org/10.1016/j.apm.2008.02.012 Jianwen, Z., Da, L., & Wenxing, F. (2014). An approach for estimating toxic releases of H2Scontaining natural gas. Journal of Hazardous Materials, 264(March), 350–362. https://doi.org/10.1016/j.jhazmat.2013.09.070 Karpash, O., Darvay, I., & Karpash, M. (2010). New approach to natural gas quality determination. Journal of Petroleum Science and Engineering, 71(3–4), 133–137. https://doi.org/10.1016/j.petrol.2009.12.012 Nourian, R., & Mousavi, S. M. (2019). Design and implementation of an expert system for periodic and emergency control under uncertainty: A case study of city gate stations. Journal of Natural Gas Science and Engineering, 66(September 2018), 306–315. https://doi.org/10.1016/j.jngse.2019.04.007 Ma, T., Wu, J., & Hao, L. (2017). Energy flow modeling and optimal operation analysis of the micro energy grid based on energy hub. Energy Conversion and Management, 133, 292– 306. https://doi.org/10.1016/j.enconman.2016.12.011 Malode, S. J., Prabhu, K. K., Mascarenhas, R. J., Shetti, N. P., & Aminabhavi, T. M. (2021). Recent advances and viability in biofuel production. Energy Conversion and Management: X, 10(December 2020), 100070. https://doi.org/10.1016/j.ecmx.2020.100070 Osiadacz A. J. and Chaczykowski M. Comparison of isothermal and non-isothermal pipelinegas flow models. Chemical Engineering Journal, 81(1):41–51, 2001. https://doi.org/10.1016/S1385-8947(00)00194-7 Poling, B, E,, & Prausnitz, J, M, (2011), Sciencedirect_articles_08Jan2016_02-19-13,101, In Solutions (Issue C), Ríos-Mercado, R. Z., & Borraz-Sánchez, C. (2015). Optimization problems in natural gas transportation systems: A state-of-the-art review. Applied Energy, 147, 536–555. https://doi.org/10.1016/j.apenergy.2015.03.017 Sabo, K., Scitovski, R., Vazler, I., & Zekić-Sušac, M. (2011). Mathematical models of natural gas consumption. Energy Conversion and Management, 52(3), 1721–1727. https://doi.org/10.1016/j.enconman.2010.10.037 Skorek-Osikowska, A., Martín-Gamboa, M., & Dufour, J. (2020). Thermodynamic, economic and environmental assessment of renewable natural gas production systems. Energy Conversion and Management: X, 7, 100046. https://doi.org/10.1016/j.ecmx.2020.100046 Viana, F. F. C. L., Alencar, M. H., Ferreira, R. J. P., & De Almeida, A. T. (2021). Multidimensional risk classification with global sensitivity analysis to support planning operations in a transportation network of natural gas pipelines. Journal of Natural Gas Science and Engineering, 96(October). https://doi.org/10.1016/j.jngse.2021.104318 Woldeyohannes, A. D., & Majid, M. A. A. (2011). Simulation model for natural gas transmission pipeline network system. Simulation Modelling Practice and Theory, 19(1), 196–212. https://doi.org/10.1016/j.simpat.2010.06.006 Zhang, B., Hu, W., Li, J., Cao, D., Huang, R., Huang, Q., Chen, Z., & Blaabjerg, F. (2020). Dynamic energy conversion and management strategy for an integrated electricity and natural gas system with renewable energy: Deep reinforcement learning approach. Energy Conversion and Management, 220(February), 113063. https://doi.org/10.1016/j.enconman.2020.113063 Zhang, X., Lei Da., & Feng Wenxing. (2014). An approach for estimating toxic releases of H2S-containing natural gas. Journal of Hazardous Materials, 264 (July 2019), 116268. https://doi.org/10.1016/j.fuel.2019.116268pt_BR
dc.type.degreeMestrado Profissionalpt_BR
Aparece nas coleções:Dissertação (PEI)

Arquivos associados a este item:
Arquivo Descrição TamanhoFormato 
Alves_Ozaná_Dissertação_Modelagem e Otimização de RDGN.pdfAlves_Ozaná_Dissertação_Modelagem e Otimização de Redes de Distribuição de Gás Natural11,34 MBAdobe PDFVisualizar/Abrir


Este item está licenciada sob uma Licença Creative Commons Creative Commons