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- W4289209212 abstract "The aviation industry supports the world economy, contributing US$ 2.7 trillion to global the gross domestic product. However, aviation raises environmental concerns, where the industry has a 12% share of CO2 emissions within the transportation sector. Therefore, the International Civil Aviation Organization (ICAO) suggested the implementation of Carbon Offsetting Scheme for International Aviation (CORSIA) as a market-based measure to mitigate CO2. The CORSIA scheme may increase the operational costs by setting a carbon price on every extra tonne of CO2 beyond the baseline limits. In order to reduce operators' obligations, the integration of reduction measures such as Sustainable Aviation Fuels (SAF) may reduce the cost associated with offsetting requirements. As such, a multi-objective optimization model is presented in this study to identify optimal blending ratios of jet biofuels with conventional kerosene fuel for multiple aircrafts and destinations. The model considers the operators fuel cost, carbon price and renewable credit under CORSIA; aiming to minimize the total fuels’ associated costs. In addition, the model is implemented in a case study considering three fuel categories. The results indicate that Jatropha-based jet fuel, within the current tested data, is a preferable synthetic fuel to be blended with Jet-A at a maximum margin of 50%. Fuel prices highly influenced the results of the model. Whereas other factors including carbon prices, fuels’ lifecycle emissions, and supplied fuel quantity may directly or indirectly impact the process of incorporating SAF as an integrated mitigation tool under CORSIA." @default.
- W4289209212 created "2022-08-01" @default.
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- W4289209212 date "2022-01-01" @default.
- W4289209212 modified "2023-10-12" @default.
- W4289209212 title "Optimization of Biofuel and Kerosene Fuel Blends to Support Sustainable Aviation" @default.
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- W4289209212 doi "https://doi.org/10.1016/b978-0-323-95879-0.50015-1" @default.
- W4289209212 hasPublicationYear "2022" @default.
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