Holistic Optimization of Aviation Sustainability: From SAF Feedstocks to a Decision Support System for Flight Routes

The aviation industry is shifting towards Sustainable Aviation Fuel (SAF), yet gaps remain in understanding how different SAF blends impact aircraft selection, engine efficiency, and route optimization. This research integrates fuel science, thermodynamic modeling, machine learning, and techno-economic analysis to develop an optimization framework linking SAF formulation, aircraft performance, and flight operations for large-scale adoption. Addressing the lack of comprehensive studies on SAF’s influence on aircraft selection and flight planning, this study systematically evaluates Fischer-Tropsch (FT), Alcohol-to-Jet (ATJ), and Hydroprocessed Esters and Fatty Acids (HEFA) pathways, optimizing hybrid blending strategies for improved fuel economy, extended flight range, and reduced lifecycle emissions. The economic feasibility of SAF remains a key bottleneck, with uncertainties in production costs and blending strategies limiting large-scale adoption. By integrating techno-economic evaluation with AI-driven stochastic optimization, this research develops predictive tools for airlines, aiding in real-time decision-making concerning SAF pricing, carbon offset mechanisms, and flight demand fluctuations. Specific objectives include identifying optimal feedstocks, developing hybrid SAF blends, evaluating SAF performance in aviation systems, optimizing flight routes using AI, and creating decision-support tools for airlines and policymakers.

Hamad Bin Khalifa University (HBKU)