(364ac) Sustainable Aviation Fuels (SAF) from Ethanol: An Integrated Systems Modeling Approach

Process modeling and simulation, optimization under uncertainty, supply chain design, and sustainable energy.

Bio:

Madelynn Watson is a dedicated PhD candidate in chemical engineering at the University of Notre Dame. Advised by Prof. Alexander Dowling, her research is part of an international interdisciplinary collaboration with colleagues at the University of Sao Paulo in Brazil. Her research centers on leveraging optimization and analysis tools to enable economic and environmentally friendly bio-jet fuel production in Brazil. She enjoys optimization and simulation-based research and will be seeking full-time employment starting in 2026 summer.

Abstract:

Brazil is a global leader in renewable energy and sustainability efforts and was one of the first countries to use biofuels for transportation. They are currently the second-largest producer of the world’s bioethanol and the first-largest producer of bioethanol from sugarcane. Biofuels, especially those produced at large scales, have gained increasing interest, specifically from the aviation industry that cannot rely on standard decarbonization solutions (e.g., electrification or hydrogen) in the short- to medium-term (2030-2050) as they require two or three decades of development and capital-intensive investments.

Brazil’s experience in renewable energy development, historical government support of biofuels, and existing biomass infrastructure create a unique opportunity for bio-jet fuel capacity development from the sugarcane industry’s bioethanol. However, several challenges exist for commercial implementation. First, technologies to produce bio-jet fuels cost significantly more than conventional fossil-based jet fuel. Furthermore, underdeveloped biomass-to-bioenergy supply chains lead to high biomass and bio-jet fuel costs. Additionally, there is considerable uncertainty in sugar, ethanol, and electricity prices, hindering risk-conscious mill owners from investing in new technologies.

In previous work, we developed a new optimization model to inform risk-conscious investment decisions on bio-jet fuel production capacity in sugarcane mills using historical price data to de-risk decisions. In this work, we strategically design a supply chain model to optimize bio-jet fuel capacity distribution in Brazil to meet 10% of the country’s jet fuel demand with bio-jet fuel from sugarcane. Specifically, we proposed a mixed-integer linear programming (MILP) superstructure model to optimize the location of sugarcane mills that invest in bio-jet fuel capacity, assignment of airports to integrated biorefineries, and transportation links that connect sites across the supply chain network. The optimal supply chain network minimizes the cost of bio-jet fuel from sugarcane based on existing biomass infrastructure (sugarcane mill and airport locations), bio-jet fuel policy incentives, and market prices of sugar, ethanol, and jet fuel. Finally, we identify opportunities to quantify multi-objective trade-offs of costs and environmental impact with sensitivity studies to guide biofuel infrastructure and policy development.