Towards Net-negative Bioenergy: Investigation of Economic and Environmental Tradeoffs Using Large-scale Spatially Explicit Models

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.

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.

Speaker 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. Specifically, his group develops optimization methods for (1) production and supply chain planning, and (2) process and energy systems synthesis and analysis, with emphasis on renewable energy technologies. He has authored a research monograph on Chemical Production Scheduling. Princeton Group website available here