(680d) Carbon and Energy Implications of Fast Pyrolysis Biorefineries with Blended Feedstocks and Decentralized Supply Chain Design in the Southeastern United States

Variations in feedstock cost, quality, and availability are common risks for traditional biomass supply chains that rely on centralized design and a single type of feedstocks.¹ Two risk mitigation strategies have been suggested by the literature: using blended feedstocks² and decentralized preprocessing facilities.³ Many Life Cycle Assessment (LCA) have been developed to either investigate the benefits of producing biofuels from varied feedstocks or decentralized biomass supply chains.^4,5 However, few of previous studies have investigated the environmental implications of using both blended biomass and decentralized preprocessing sites, nor did they quantified the impacts of variations of biomass quality, blending ratios, and preprocessing technologies.

This study addressed this knowledge gap by developing a cradle-to-gate LCA model for fast pyrolysis biorefineries using blended feedstocks with decentralized preprocessing depots.⁶ Pine residues and switchgrass are two types of biomass included in this study. The primary tasks for depot preprocessing include size reduction, moisture reduction, and feedstock blending and densification. This study explored two different preprocessing technologies, conventional pelleting processing (CPP) and high moisture pelleting process (HMPP).⁴ The life-cycle inventory (LCI) data of depot preprocessing and biorefining were generated by process simulations. A scenario analysis was conducted to evaluate the impacts of depot sizes (i.e., 250, 500 oven dry tonne (ODT)/day), biorefinery capacities (i.e., 1,000, 1,500, 2,000 ODT/day), blending ratios (i.e., pine residue/switchgrass ratio: 100%/0%, 75%/25%, 50%/50%, 25%/75%), and allocation methods for surplus electricity (energy allocation and system expansion). This study mainly focused on Global Warming Potential (GWP) and primary energy consumption.

The results showed that the life-cycle energy consumption and GWP of decentralized systems with depots are 0.7–1.1 MJ/MJ biofuel produced and 43.2–76.6 g CO₂ eq./MJ, respectively. The largest contributors to those results are biorefinery processing and depot preprocessing. Compared to the traditional centralized system without depots, the decentralized system reduces the energy consumption in biorefinery but increases the overall life-cycle environmental impacts due to the drying stage in depot preprocessing. However, by blending more switchgrass in the feedstock, both energy and GWP of depot preprocessing can be largely reduced. For example, increasing switchgrass blend to 75% would lower the energy and GWP of the decentralized system to a level similar to the centralized system using 100% pine residues. For the two preprocessing technologies, HMPP has lower energy consumption and GWP. In addition, allocation methods for the surplus electricity have large impacts on the results but do not change the comparative conclusions.

References

(1) Festel, G.; Würmseher, M.; Rammer, C. Scaling and Learning Effects of Biofuels Conversion Technologies. Energy Technology 2014, 2 (7), 612–617.

(2) Edmunds, C. W.; Reyes Molina, E.; André, N.; Hamilton, C.; Park, S.; Fasina, O.; Adhikari, S.; Kelley, S. S.; Tumuluru, J. S.; Rials, T. G.; et al. Blended Feedstocks for Thermochemical Conversion : Biomass Characterization and Bio-Oil Production From Switchgrass-Pine Residues Blends. Frontiers in Energy Research 2018, 6 (August), 1–16.

(3) Lamers, P.; Roni, M. S.; Tumuluru, J. S.; Jacobson, J. J.; Cafferty, K. G.; Hansen, J. K.; Kenney, K.; Teymouri, F.; Bals, B. Techno-Economic Analysis of Decentralized Biomass Processing Depots. Bioresource Technology 2015, 194, 205–213.

(4) Iglesias, L.; Laca, A.; Herrero, M.; Díaz, M. A Life Cycle Assessment Comparison between Centralized and Decentralized Biodiesel Production from Raw Sunflower Oil and Waste Cooking Oils. Journal of Cleaner Production 2012, 37, 162–171.

(5) Hiloidhari, M.; Baruah, D. C.; Singh, A.; Kataki, S.; Medhi, K.; Kumari, S.; Ramachandra, T. V.; Jenkins, B. M.; Thakur, I. S. Emerging Role of Geographical Information System (GIS), Life Cycle Assessment (LCA) and Spatial LCA (GIS-LCA) in Sustainable Bioenergy Planning. Bioresource Technology 2017, 242, 218–226.

(6) Lan, K.; Ou, L.; Park, S.; Kelley, S. S.; Yao, Y. Life Cycle Analysis of Decentralized Preprocessing Systems for Fast Pyrolysis Biorefineries with Blended Feedstocks in the Southeastern United States. Energy Technology 2019, 1–13.