A comparison of methodologies for estimating delivered forest residue volume and cost to a wood-based biorefinery. (November 2017)

• Record Type: Journal Article
• Authors: Martinkus, Natalie; Latta, Greg; Morgan, Todd; Wolcott, Michael
• Is Part Of: Biomass and bioenergy. Volume 106(2017:Nov.)
• Page Start: 83
• Page End: 94
• Publication Date: 2017-11
• Subjects: Forest residue feedstock -- Biomass estimation -- Supply chain logistics -- Repurposed pulp mill -- Forest Inventory and Analysis -- Wood-based biorefinery; Biomass energy -- Periodicals; Biomass -- Periodicals; Energy-Generating Resources -- Periodicals; Bioénergie -- Périodiques; 333.9539
• DOI: 10.1016/j.biombioe.2017.08.023
• Languages: English
• ISSNs: 0961-9534

Abstract

Abstract: Plant location can be a major factor in the financial success of a company when feedstock transport costs are high, such as for wood-based biorefineries. Biorefineries sited near large amounts of forest residue can better mitigate against the risk of reduced feedstock availability due to exogenous market constraints. Two methodologies for estimating the volume and cost of delivered forest residues to a biorefinery are presented. Both methodologies are based on data provided by the U.S. Forest Service Forest Inventory and Analysis (FIA) program. The first methodology is past-predictive in that it uses individual state Timber Product Output (TPO) datasets, while the second methodology is future-predictive in that it uses a spatially explicit economic optimization model of the U.S. forestry sector coupled with stand data at FIA plot locations to project near- and medium-term residue volumes. A Total Delivered Feedstock Cost Model is used with both biomass estimation methods to enable comparison of facility supply curves. A case study assesses four pulp mills, considered as candidate repurposed biorefinery locations, for their ability to procure sufficient biomass under average- and low-yield scenarios utilizing both methods. The facility that procures sufficient feedstock to meet annual biorefinery demand at the least cost under both yield scenarios theoretically provides the least risk to investors in terms of insufficient feedstock availability. The past-predictive methodology was found to be best-suited for refining a list of candidate facilities for further analysis. The future-predictive methodology is best-suited for a robust analysis of facilities using multiple economic and policy scenarios. Highlights: Two biomass estimation methodologies are proposed and compared. Facility feedstock risk was assessed through average and low-yield biomass scenarios. The TPO model is best for refining a list of candidate facilities for further analysis. The LURA model is best for estimating future available biomass at a facility. Models compare best in regions with known forest residue recovery ratios.