(303f) Large-Scale Spatially Explicit Landscape and Supply Chain Design for Biomass-Derived Sustainable Aviation Fuels (SAF)

The transition to sustainable biofuel production is a critical component in efforts to reduce greenhouse gas emissions and mitigate climate change. Lignocellulosic bioenergy crops have received much attention as biofuel feedstocks since they do not compete with food crops for land use. Due to the low density and geographically dispersed availability of these crops, along with the fact that these crops have yet to be planted in large quantities, integrated landscape and supply chain design can provide valuable insight into the design and operation of these biofuel networks. It can also be beneficial to study existing systems, such as the corn to ethanol network, to understand how existing networks might grow with increases in biofuel demand or how new feedstocks can affect the production and use of existing feedstocks. An integrated landscape and supply chain design approach for biofuels includes the design decisions at a field level for the production of biomass as well as the complex logistic networks that are needed to produce biofuels at a large scale. These networks include the transportation of biomass feedstock from fields to intermediate pre-processing stations, known as depots, and biorefineries. Along with the economic benefit that comes from the reduction in transportation costs when there is strategic placement of depots and biorefineries, there is an environmental benefit in choosing the right fields and agricultural practices, as biomass cultivation can sequester atmospheric CO₂ in the form of soil organic carbon, thereby reducing greenhouse gas emissions in the overall biofuel supply chain.

Previous work in the area of landscape and supply chain design for biofuel production often study the two problems separately. It is assumed that there is either a pre-existing biomass distribution network when determining the placement of biorefineries or that there are pre-existing refineries when determining landscape design decisions [1,2]. There has been recent work where these decisions have been studied together using realistic, detailed, large-scale data for switchgrass across the Midwest region of the United States [3,4]. We build upon these works and study the landscape and supply chain design for Sustainable Aviation Fuel (SAF). There is limited detailed analyses of this system and requires large amounts of realistic data and large models to understand how SAF supply chains can be developed in the United States. SAF is an alternative to conventional jet fuel that reduces lifecycle greenhouse gas emissions and is made from renewable resources, such as waste oils, agriculture waste, or non-food crops such as lignocellulosic biomass. Unlike traditional fossil-based jet fuel, SAF can lower carbon emissions when considering the entire production process, which is why it is essential to study the landscape and supply chain design of these new systems. SAF is a drop-in fuel, meaning it can be blended with conventional jet fuel and used in existing aircraft without modifications. This makes it an attractive solution for reducing the environmental impact of the aviation sector without requiring major infrastructure changes.

We developed two large-scale spatially explicit integrated landscape and supply chain design models for SAF based on field level biomass availability data across the United States. The first model is a static model which only considers the planting of fields and the installation of biorefineries at a single point in time. Static models are the most common landscape and supply chain design models for biomass systems. The solutions give a snapshot of what fields should be developed and what types of biorefineries should be built and their location, but the solutions do not account for how parameters change over time. The second model is a temporal model which includes the planting of fields and the installation of biorefineries as well as possible expansions to the fields or biorefineries based on changes in parameters over time, such as the demand of SAF or the yield of biomass. We developed the temporal model to provide insight into how the SAF landscape design and supply chain develop over time as this is less often studied in literature.

We consider two major SAF pathways that have received much research and advancement in the last few years: alcohol-to-jet (ATJ) and methanol-to-jet (MTJ). We consider fermentation to produce ethanol which is then upgraded to SAF for the ATJ pathway from corn or corn stover as feedstock. We also consider gasification to produce methanol which is upgraded to SAF for the MTJ pathway from poplar as feedstock. The data for the field level yield, sequestration potential, and the emissions due to land management and harvesting for each type of feedstock is based on simulations using large crop models with realistic inputs of the soil quality, weather, and land use history of fields across the United States [5]. In addition to the base technologies of fermentation and gasification, we include carbon capture and sequestration (CCS) at different points throughout the biorefineries. With the inclusion of these CCS technologies, there is potential to have a higher reduction in the greenhouse gas emissions of producing SAF. The carbon sequestration locations and prices vary across the United States, along with the electricity price and associated emissions for any grid power that a refinery may need to purchase. Potential refinery locations are based on the availability of biomass in a given region and the distance of the potential refinery to a road, railway, or pipeline. This means that a refinery must have enough biomass within a 400 km radius to process 2000 Mg of feedstock a day and must be less than a mile from any mode of transportation. With the collected and pre-processed data, we model the supply chain from harvesting biomass feedstock at the field level to the distribution of the final SAF product to airports in both the static and temporal models.

The solutions to our model help us analyze what areas in the United States have a high potential for producing the necessary biomass to meet a minimum SAF production target while sequestering the most CO₂ through soil organic carbon. Through sensitivity analyses, we understand how different incentives, such as increases in carbon capture credits or the social cost of carbon, can affect the design of the SAF supply chains. In particular, we are interested not only in which fields are chosen for cultivation, but also where biorefineries tend to be located, and how different transportation modes (trucks, rail, or pipelines) may affect biorefinery location decisions. The use of a large-scale spatially explicit integrated landscape and supply chain design model allows us to understand tradeoffs in complex systems, and is specifically helpful in the development of lignocellulosic biomass supply chains since these systems have not yet been established and understanding these tradeoffs can help these systems to be efficiently developed.

References

1. Ghaderi, H., Pishvaee, M.S., Moini, A. Biomass supply chain network design: an optimization-oriented review and analysis. Industrial Crops and Products 94, 972-1000 (2016).
2. Field, J.L., Evans, S.G., Marx, E. et al. High-resolution techno–ecological modelling of a bioenergy landscape to identify climate mitigation opportunities in cellulosic ethanol production. Nature Energy 3, 211–219 (2018).
3. O’Neill, E.G., Martinez-Feria, R.A., Basso, B., Maravelias, C.T. Integrated spatially explicit landscape and cellulosic biofuel supply chain optimization under biomass yield uncertainty. Computers and Chemical Engineering 160, 107724 (2022).
4. O’Neill, E.G., Geissler, C.H., Maravelias, C.T. Large-scale spatially explicit analysis of carbon capture at cellulosic biorefineries. Nature Energy 9, 828-838 (2024).
5. Maestrini, B., Basso, B. Subfield crop yields and temporal stability in thousands of US Midwest fields. Precision Agriculture 22, 1749-1767 (2021).