(637d) Techno-Economic Analysis and Life Cycle Assessment of Decentralized Preprocessing System for Fast Pyrolysis Biorefineries with Blended Feedstocks in the Southeastern USA | AIChE

(637d) Techno-Economic Analysis and Life Cycle Assessment of Decentralized Preprocessing System for Fast Pyrolysis Biorefineries with Blended Feedstocks in the Southeastern USA


Lan, K. - Presenter, North Carolina State University
Ou, L., Uchicago Argonne, LLC
Park, S., North Carolina State University
Kelley, S. S., North Carolina State University
English, B. C., University of Tennessee
Yao, Y., Yale University
Biofuel production has a rapid growth in the past decades. Since 2002, the bioenergy portion contributed by biofuel has reached over five times in the United States and bioenergy was over 5% of the total U.S. energy consumption in 2013.1 Rapid expansion of biofuel production results in the challenges in the traditional biomass supply chains that have large impacts on the economic effectiveness and production robustness of biorefineries. Traditional biorefinery systems with a single type of feedstock and centralized design may be vulnerable to variations in feedstock cost, quality, and availability. One possible solution is to introduce blended feedstocks and decentralized preprocessing facilities. Many Life Cycle Analysis (LCA) and Techno-Economic Analysis (TEA) have been developed to either investigate the benefits of decentralized biofuel supply chains2 or blended feedstocks3. However, few of them have studied the potential economic and environmental trade-offs of using blended biomass with decentralized preprocessing sites, nor did they quantify the impacts of biomass quality and regional availability, and selection of preprocessing technologies on the overall life-cycle impacts.

This study addressed this knowledge gap by developing TEA and LCA models for biorefineries treating blended feedstocks (i.e., pine residues and switchgrass) with fast pyrolysis. “Depot”, the decentralized preprocessing site, was introduced to preprocess biomass for size reduction, mixing, drying, and pelletization. Two different preprocessing technologies were explored: conventional pelleting processing (CPP) and high moisture pelleting process (HMPP).4 Different scenarios were developed to investigate the impacts of blending ratio of pine and switchgrass, biorefinery and depot capacities, the number of depots, and preprocessing technologies. A Geospatial Information System (GIS) coupled with real-world geographic and biomass data was used to identify the locations of depots and biorefinery along with transportation distances. The TEA model calculated the Minimum Fuel Selling Price (MFSP) based on feedstock costs, the inputs from GIS, and the mass and energy balance in biorefinery from Aspen Plus process simulations. Those data were also used in combinations with the Life Cycle Inventory (LCI) data collected from literature to estimate the cradle-to-gate environmental footprints if different scenarios. The results of TEA and LCA were compared to further understand the trade-offs.

Preliminary results showed that MFSP highly depended on the biorefinery capacity, the blending ratio of feedstocks, and the process pathways of the depot. For biorefinery with a capacity from 1,000 to 2,500 oven dry ton (ODT)/day, MFSP decreased as the capacity increased. The impacts of blending ratio on MFSP were different for biorefineries with and without depots. For biorefinery without depot at 1,000 ODT/day, using a higher ratio of pine residues was more economically beneficial. As the capacity expands, the advantage of using pine residues gradually vanished. It was found that using more switchgrass at 2,000 and 2,500 ODT/day biorefineries resulted in lower MFSP. For biorefineries with depot, MFSP was lower for blending feedstocks than single feedstock type (pine residues). Additionally, introducing the decentralized depots showed the potential to lower the MFSP for biorefinery larger than 2,500 ODT/day. The results also showed that the HMPP preprocessing pathway had lower costs than CPP pathway.

Preliminary LCA results showed that the depot capacities or biorefinery capacities had relatively minor impacts on the life cycle primary energy consumption and Greenhouse Gas (GHG) emissions. Biorefineries, with or without depots, accounted for the major portion of primary energy consumptions. By using real biomass availability data, and investigating the impacts of variabilities in biomass components, preprocessing pathways, depot capacities, and biorefinery capacities, this study provides useful information for a variety of stakeholders for decision making related to investment and design of low-cost and large-scale biorefineries.


  1. US EIA. Biofuels production drives growth in overall biomass energy use over past decade https://www.eia.gov/todayinenergy/detail.php?id=15451. Accessed March 15, 2019.
  2. Pérez, A. T. E., Camargo, M., Rincón, P. C. N., & Marchant, M. A. Key challenges and requirements for sustainable and industrialized biorefinery supply chain design and management: a bibliographic analysis. Renewable and Sustainable Energy Reviews. 2017, 69, 350-359.
  3. Akgul, O., Shah, N., & Papageorgiou, L. G. An optimisation framework for a hybrid first/second generation bioethanol supply chain. Computers & Chemical Engineering.2012, 42, 101-114.
  4. Lamers, P., Roni, M. S., Tumuluru, J. S., Jacobson, J. J., Cafferty, K. G., Hansen, J. K., Kenney, K., Teymouri, F. and Bals, B. Techno-economic analysis of decentralized biomass processing depots. Bioresource technology. 2015, 194, 205-213.