Seleção do modo de transporte de hidrogênio verde no Brasil: uma aplicação do método PROMETHEE II fuzzy esférico

• Ano de defesa: 2024
• Autor(a) principal: Pinheiro, Gabriel Henrique Mesquita
• Orientador(a): Prata, Bruno de Athayde
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA MECANICA
• Link de acesso: http://repositorio.ufc.br/handle/riufc/79572

Brazil’s participation in the Paris Agreement drives the energy transition of its industry. In this context, the appropriate use of Green Hydrogen (GH2) allows the replacement of polluting fuels and its integration into production chains, reducing the carbon footprint in the process. Planning its distribution logistics is thus an essential step to ensure safe transportation to potential consumers. In green logistics, multi-criteria methods combined with fuzzy approaches are commonly applied, enabling the analysis of multiple, sometimes conflicting, objectives and subjective or uncertain opinions. Therefore, this study aims to rank the transportation modes for GH2 to select the best-performing option for supplying the Brazilian domestic market, applying the spherical fuzzy PROMETHEE II method. To this end, we gathered the opinions of six experts in hydrogen on three transportation alternatives and six evaluation criteria, across five national distribution scenarios. The alternatives include: adapted natural gas pipeline, truck transportation with compressed GH2, or truck transportation with liquefied GH2; the criteria are: transportation cost, volume of transported GH2, risk of transportation accidents, operational risk throughout the logistics process, carbon footprint, and distance to the consumer. The results show consistency across the five cases, with the pipeline emerging as the top-performing alternative, followed by liquefied GH2, and compressed GH2 transport ranking last. Additionally, we highlight the dynamic nature of the decision-making process, which strongly depends on decision-makers’ perceptions of cost, risks, and distance. Finally, we calculate the model’s robustness to be 87% through a sensitivity analysis.