TY - JOUR
T1 - Multi-objective optimization of sustainable aviation fuel production pathways in the U.S. Corn Belt
AU - Ari Akdemir, Ece
AU - Kern, Jordan
AU - Smith, Jack P.
AU - Limb, Braden J.
AU - Quinn, Jason C.
AU - Field, John L.
AU - Pack, Taylor
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/2
Y1 - 2025/2
N2 - As a potential source of low-carbon transportation energy, biofuels offer certain advantages over vehicle electrification (e.g., lower societal vulnerability to grid failures, and improved range of sustainable aviation), but also several challenges, including cost, carbon intensity, and land usage. There are also well-founded concerns that biofuel supply chains could be disrupted if extreme weather events impact feedstock yields. In this paper, we explore the use of multi-objective optimization to identify biofuel production pathways that balance cost, greenhouse gas emissions, and supply vulnerability to extreme weather. We compare the use of three different many-objective evolutionary algorithms and linear programming in optimizing biomass cultivation decisions in the U.S. Corn Belt under weather uncertainty using historical, modeled, and synthetic yield data. We consider four feedstock choices (corn, soy, switchgrass, and algae) with two land types (agricultural and marginal lands) and evaluate decisions using three alternative spatial resolutions (ranging from the USDA agricultural district level to the state level). Results show that feedstock choice is the primary driver of objective performance (i.e., the position and shape of 3D, approximate Pareto frontiers). Spatial diversification is a less effective tool in reducing exposure to weather-caused drops in crop yield.
AB - As a potential source of low-carbon transportation energy, biofuels offer certain advantages over vehicle electrification (e.g., lower societal vulnerability to grid failures, and improved range of sustainable aviation), but also several challenges, including cost, carbon intensity, and land usage. There are also well-founded concerns that biofuel supply chains could be disrupted if extreme weather events impact feedstock yields. In this paper, we explore the use of multi-objective optimization to identify biofuel production pathways that balance cost, greenhouse gas emissions, and supply vulnerability to extreme weather. We compare the use of three different many-objective evolutionary algorithms and linear programming in optimizing biomass cultivation decisions in the U.S. Corn Belt under weather uncertainty using historical, modeled, and synthetic yield data. We consider four feedstock choices (corn, soy, switchgrass, and algae) with two land types (agricultural and marginal lands) and evaluate decisions using three alternative spatial resolutions (ranging from the USDA agricultural district level to the state level). Results show that feedstock choice is the primary driver of objective performance (i.e., the position and shape of 3D, approximate Pareto frontiers). Spatial diversification is a less effective tool in reducing exposure to weather-caused drops in crop yield.
KW - Biofuel supply chains
KW - Linear programming
KW - Multi-objective optimization
KW - Weather risk
UR - http://www.scopus.com/inward/record.url?scp=85214343913&partnerID=8YFLogxK
U2 - 10.1016/j.biombioe.2025.107590
DO - 10.1016/j.biombioe.2025.107590
M3 - Article
AN - SCOPUS:85214343913
SN - 0961-9534
VL - 193
JO - Biomass and Bioenergy
JF - Biomass and Bioenergy
M1 - 107590
ER -