From biomass to jet fuel: A systematic review of modeling and simulation approaches for SAF production

The adoption of Sustainable Aviation Fuels (SAF) is essential to mitigate the aviation sector's carbon footprint, yet large-scale production faces significant technological and economic challenges. This systematic literature review, conducted under PRISMA 2020 guidelines and supported by AI-assisted screening via the Elicit tool, addresses a specific gap in existing SAF reviews: the systematic analysis of computational modeling and process simulation approaches. Distinct from previous reviews focusing on technology comparison or economic feasibility in isolation, this review targets the specific role and effectiveness of simulation tools (Aspen Plus, MATLAB, Python) in optimizing SAF production pathways. A total of 26 peer-reviewed articles (2018–2025) were selected from Scopus, IEEE Xplore, ScienceDirect, and Web of Science using PRISMA-aligned selection criteria and quantitative scoring via StArt software. Results show Aspen Plus as the dominant tool (96% adoption) for process simulation across thermochemical and biochemical pathways, while MATLAB and Python are increasingly employed for optimization and machine learning. Computational modeling demonstrates essential value in enhancing efficiency, reducing energy losses, and optimizing yields; however, critical methodological gaps persist, including limited pilot-scale validation, insufficient uncertainty quantification, inadequate integration of environmental and economic assessments, and scarce region-specific feedstock modeling. Hybrid approaches that combine process simulators with data-driven tools show promise for improving predictive accuracy and scalability. This review identifies concrete opportunities to advance SAF modeling through robust validation protocols, improved uncertainty quantification frameworks, and broader integration of life-cycle and techno-economic analyses. Recommendations include expanded adoption of artificial intelligence for adaptive process control, scaling pilot-scale demonstrations with computational support, and implementing effective policies aligned with modeling predictions. Overall, computational modeling represents a strategic and validated pathway to strengthen reliability and predictability in SAF production, guiding research and industrial innovation toward economically and environmentally sustainable aviation. • PRISMA-guided review of 26 studies (2018–2025) on SAF modeling/simulation • Aspen Plus dominates process simulation; MATLAB/Python used for optimization. • Modeling improves efficiency and product yield while reducing energy losses. • Gaps: pilot validation, weak assessments, scarce feedstocks • Hybrid simulator-AI models boost accuracy and scalability.