(695e) Understanding the Uncertainties in Environmental Life Cycle Energy and Carbon Analysis for Biofuel from Forest Residue in the United States

Authors: 
Lan, K., North Carolina State University
Ou, L., Argonne National Laboratory
Kelley, S. S., North Carolina State University
Park, S., North Carolina State University
Kwon, H., Argonne National Laboratory
Cai, H., Argonne National Laboratory
Wang, M., Argonne National Laboratory
Yao, Y., North Carolina State University
Converting renewable biomass to biofuel has great potential to reduce the environmental impacts of the fuel sector and enhance energy security. Among different types of biomass, forest residues, a type of woody biomass, are promising in supporting large-scale applications. In the United States, a significant amount of forest residues are generated in thinning, logging, and wood product manufacturing. According to the “Billion-Ton” report by the U.S. Department of Energy,1 there are potentially 30-108 million dry metric tons of forest residues per year in the United States. Forest residues are currently underutilized as they are either left on site for decay or burned for energy recovery. To support the development and implementation of biofuel production from forest residue, it is critical to provide decision makers with quantitative information regarding potential environmental benefits and possible tradeoffs. Many studies used Life Cycle Analysis (LCA) to investigate the environmental footprints of converting forest residues to biofuels.2,3 But most of them are static studies without fully considering the impacts of variations and uncertainties in both the biomass production in the forest and biomass conversion in biorefineries. These variations may have significant impacts on modeling choices and LCA results. For example, tree growth rates, management intensities, and end-of-life of residues (e.g., left for decay, burning, or sent for biofuel) may affect the biogenic carbon accounting and the development of counterfactual scenarios. Hence, a better understanding of these variabilities and their corresponding impacts on environmental footprints is critical.

In this work, a cradle-to-gate life-cycle energy and carbon analysis was developed for producing biofuels from pine residues through fast pyrolysis in the Southern U.S.. The system included three life-cycle stages: biomass production, transportation, and biorefinery. The Life Cycle Inventory data were collected either from simulation models (e.g., LobDSS4 for pine growth, Aspen Plus for biorefining) or from literature. The uncertain parameters that might have large impacts on the results were identified through literature review and sensitivity analysis. In biomass production stage, most of those parameters identified are related to pine production or forest management methods, such as thinning schedule, rotation length, or forest operations. In biomass conversion stage, uncertain parameters are mostly related to feedstock quality (i.e. carbon content, ash content, and moisture content) that has large impacts on the mass and energy balance. Quantitative information of those parameters was collected from literature and statistically analyzed to generate the probability density functions that were used as inputs to the Monte Carlo Simulation (MCS). MCS is a widely accepted tool that helps understand the effects of uncertainties or variations and has been largely used in LCA studies.5 The results were used to quantify the impacts of variations on life cycle primary energy consumptions and Greenhouse Gas (GHG) emissions. Different counterfactual scenarios were developed to understand the tradeoffs and potential environmental benefits of turning forest residue into biofuels. Preliminary results showed that the parameters that were related to the residue production such as planting strategies, rotation length, and thinning schedule were critical to the results of life-cycle GHG emissions. For the life cycle primary energy consumption, parameters related to biorefinery were the main contributors to the variations of the results.

Reference

  1. Perlack, R. D., Eaton, L. M., Turhollow Jr, A. F., Langholtz, M. H., Brandt, C. C., Downing, M. E., Graham, R. L., Wright, L. L., Kavkewitz, J. M., Shamey, A. M., & Nelson, R. G. US billion-ton update: biomass supply for a bioenergy and bioproducts industry. 2011.
  2. Hsu, D. D. Life cycle assessment of gasoline and diesel produced via fast pyrolysis and hydroprocessing. Biomass and bioenergy. 2012, 45, 41-47.
  3. Fan, J., Kalnes, T. N., Alward, M., Klinger, J., Sadehvandi, A., & Shonnard, D. R. Life cycle assessment of electricity generation using fast pyrolysis bio-oil. Renewable Energy. 2011, 36(2), 632-641.
  4. Amateis, R. L., Allen, H. L.; Montes, C. R.; Fox, T. R. Overview of the Forest Nutrition Cooperative’s Silvicultural Decision Support System. 2005. North Carolina State University Raleigh, NC; Virginia Polytechnic Institute and State University, Blacksburg, VA; Forest Nutrition Cooperative.
  5. Sonnemann, G. W., Schuhmacher, M., & Castells, F. Uncertainty assessment by a Monte Carlo simulation in a life cycle inventory of electricity produced by a waste incinerator. Journal of Cleaner Production. 2003, 11(3), 279-292.
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