Abstract:
Large scale production of cellulosic biofuel involves a spatially distributed system that stretches from the individual fields of biomass to transportation and logistics networks, to the biorefinery facilities where biomass is processed. Cellulosic biomass is an attractive source of renewable fuel because of the potentially higher greenhouse gas (GHG) mitigation potential as opposed to traditional sources of biofuel that compete with food production. However, the optimal fuel production technology, carbon capture and storage (CCS) options, and supply chain (SC) configuration all depend on the underlying spatial features of the system and contribute significantly to the GHG mitigation potential. Simultaneous optimization of the design and operation of the entire value chain, from field to product, can lead to both economic and environmental benefits (O’Neill et al. 2022) .
In this seminar, we present an analysis of the cost and GHG mitigation potential for a cellulosic biofuel supply chain, including CCS, using mixed-integer linear programming methods. Unlike previous studies which focus on a small region, or use coarse and spatially homogeneous data, we consider a high-resolution supply chain for a large 8-state region in the USA Midwest using realistic biomass data. We also consider technologically mature conversion routes (fermentation, gasification, and pyrolysis), and multiple CCS options for each route, to investigate which fuels and levels of CCS are preferred under different scenarios and how CCS incentives affect the SC design and operation under different fuel production levels (O’Neill et al., 2024).
Our analysis shows that the amount of biofuel produced, and the carbon sequestration credit contribute to substantial changes in the optimal SC configuration, biofuel conversion technology, and CCS technologies installed at the biorefinery. Furthermore, while significant GHG mitigation is possible, we find that current incentive structures, particularly in the USA (carbon sequestration credits) may neglect to incentivize the further mitigation that could be obtained from a carefully designed SC that considers the spatial characteristics of the system and considers all sources of emissions. We also show that a slightly more expensive SC can leverage spatial interactions between CCS, electricity prices, and biomass landscape design to achieve a disproportionate increase in GHG mitigation.
References
O’Neill, Eric G., Rafael A. Martinez-Feria, Bruno Basso, and Christos T. Maravelias. 2022. “Integrated Spatially Explicit Landscape and Cellulosic Biofuel Supply Chain Optimization Under Biomass Yield Uncertainty.” Computers & Chemical Engineering: 107724.
O’Neill EG, Geissler CH, Maravelias CT. Large Scale Spatially Explicit Analysis of Carbon Capture at Cellulosic Biorefineries. Nature Energy, 9, 828-838, 2024.
Bio:
Christos Maravelias is the Chair of the Department of Chemical and Biological Engineering, and the Anderson Family Professor in Energy and the Environment at Princeton University. His research interests lie in the general area of process and energy systems engineering and optimization. Specifically, he is studying production planning and scheduling, supply chain optimization, and energy systems synthesis and analysis with emphasis on renewable energy technologies. He has authored a research monograph on Chemical Production Scheduling and co-authored more than 200 journal articles.
Summary:
Focus: energy generation using renewable resources
Many options and choices:
Electrify vs liquid field
Biofuel pathway options
Solar power via electricity, liquid fuels, etc.
Optimization-based process synthesis
Traditional process synthesis
Hiearchical design
Reaction
Separation & recycle
Heat recovery network
Industrial utilities
Sequential decisions, optimized one at a time
Optimization-based design / network design
Solving constraint system that related different industrial components
Aiming to provide feedback to experimental chemists/process engineers to identify regions of design space that are most/least fruitful
Simulation
Input chemicals/economics
Model of chemistry
Design
->Output
Optimization: find design that optimizes the target “reward” function
Their approach: solve system of equations where design is embedded
BECCS: Bioenergy with Carbon Capture and Storage
Use biomass to generate energy / liquid fuels for energy storage, long distance energy transport
Emits CO2 in the process
Can capture at different points in the industrial process
Upto 75% can be captured with sufficient work to capture
Emissions due to transporting biomass to facility
Tradeoff
Larger facilities are more efficient at producing energy/fuels but transportation costs/emissions are higher
Smaller facilities have lower transport costs but are less efficient
Hybrid: intermediate facilities that densify feedstock
Can optimize system design around industrial constraints and revenues
Carbon credits due to capture
Ethanol price
Biomaterial sources (they focused on switchgrass grown on marginal lands but can expand to agricultural residue, forests, etc.)
Range of technological options
Gasification -> Electricity or H2
Fermentation
Pyrolysis
Capture in Flue, Biogas
Key to document the supply chain:
Emissions: harvesting, transportation, etc.
Capture: soils
Impacts of outputs:
Liquid fuels are burned: emissions
Electricity output can displace CO2 emitting power
Integrated Spatially Explicit Network model
Typically supply chain analyses focus on point solutions that ignore geography
Biomaterial availability varies both spatially and temporally
Different types of lands where different amounts of material can be grown
Biomaterial harvest varies over the year and yields vary across years
Optimizing facility design in a spatially-explicit way
A field-to-product analysis
Bioenergy lands
High quality land appropriate for food crops
Low quality lands best for grazing
Middle-quality lands good for bioenergy crops
ML and drone tech being used to identify marginal lands in the US
Intermittent and recently abandoned land
Formerly irrigated land
Estimate the productivity of various lands:
SALUS model: https://www.cibotechnologies.com/salus-model/
Yield maps for different crops
Distribution of yields for a given crop/land
10 weather/yield samples
Focus on switchgrass in US Midwest
Can analyse operating efficiency of biorefineries at any location, given material availability in any given location
Large-scale optimization of deployment of refineries across a large area
Stochastic Mixed-integer Program
Can do single-state analysis that places a biorefinery and densification sites
Investigation of Economic and Environmental Tradeoffs
Larger-scale analysis of 8 US Midwest states
Choosing from among a fixed set of sites for biorefineries, sequestration, depots
Constrained by geological structure, availability of rail transport
Accounting for price and emissions of electricity from regional grid
Need electricity for some types of sequestration (where heat biorefinery is not enough)
Goal is to compute optimal design, which informs decision makers about the regions of design space where more detailed investments should be explored
Optimal configuration depends on the types of emissions for which carbon credits are paid
If emissions in transport, harvest, electricity displacement are accounted, explicitly optimize location to be close to biomass, located in high emission grids