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Cited by 12Forest Biomass Utilization and ManagementAgricultural and Food SciencesIntegrated Energy Systems Optimization

Modeling and Optimization of Woody Biomass Harvest and Logistics in the Northeastern United States

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Hartley, Damon S.·2019
DOI 10.33915/etd.5775
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Abstract

World energy consumption is at an all-time high and is projected to continue growing for the foreseeable future. Currently, much of the energy that is produced comes from non-renewable fossil energy sources, which includes the burden of increased greenhouse gas emissions and the fear of energy insecurity. Woody biomass is being considered as a material that can be utilized to reduce the burden caused by fossil energy. While the technical capability to convert woody biomass to energy has been known for a long period of time, the cost of the feedstock has been considered too costly to be implemented in a large commercial scale. Increasing the use of woody biomass as an energy source requires that the supply chains are setup in a way that minimizes cost, the locational factors that lead to development are understood, the facilities are located in the most favorable locations and local resource assessments can be made.;A mixed integer linear programming model to efficiently configure woody biomass supply chain configurations and optimize the harvest, extraction, transport, storage and preprocessing of the woody biomass resources to provide the lowest possible delivered price. The characteristics of woody biomass, such as spatial distribution and low bulk density, tend to make collection and transport difficult as compared to traditional energy sources. These factors, as well as others, have an adverse effect on the cost of the feedstock. The average delivered cost was found to be between {dollar}64.69-98.31 dry Mg for an annual demand of 180,000 dry Mg. The effect of resource availability and required demand was examined to determine the impact that each would have on the total cost.;The use of woody biomass for energy has been suggested as a way to improve rural economies through job creation, reduction of energy costs and regional development. This study examined existing wood using bio-energy facilities in the northeastern United States to define the drivers of establishment of bio-energy projects. Using a spatial econometric framework, a spatial autoregressive probit model was estimated based on the Bayesian methods to define the factors that impact the location of wood using bio-energy facilities in the United States. Through the analysis it was found that the energy policy of the state is the biggest driver of the choice of location for bioenergy facilities.;The choice of site is of great importance when trying to meet the goal of producing cost-effective biofuels, due to the spatial dispersion of

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  • The handling cost of biomass is usually higher than fossil fuels.

    “Although the utilization of biomass presents a relatively lower environmental burden, the handling cost of biomass is usually higher than fossil fuels 17-18 .”

    From Economic and life cycle assessments of biomass utilization for bioenergy products (2017) · cites this paper as [18]

    Section 2.4.2.2 discusses biomass harvesting, handling, and delivered costs, but does not compare them with fossil-fuel handling costs. Searches for that comparison found no passage in our copy establishing that biomass handling is usually more expensive. (LLM, conf=0.74)

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Research works printed but not linked: 16

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  16. *bio_fc.Cheat=100000 solve CBTL_Opt using mip minimizing Tot; $libinclude xldump hc.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx ChipHarv!A1 $libinclude xldump hr.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx ResHarv!A1 $libinclude xldump ctp.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx ChipTran!A1 $libinclude xldump rtp.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx ResToProc!A1 $libinclude xldump rts.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx ResToStor!A1 $libinclude xldump smp.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx StorMatProc!A1 $libinclude xldump mpf.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx MaterialProc!A1 $libinclude xldump msf.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx MaterialStored!A1 $libinclude xldump y.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx OpenFacility!A1 $libinclude xldump Harv.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx HarvestingCost!A1 $libinclude xldump TransC.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx TransCostChips!A1 $libinclude xldump TransR.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx TransCostRes!A1 $libinclude xldump Trans.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx TransCost!A1 $libinclude xldump Proc.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx ProcessCost!A1 $libinclude xldump Z.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx TotalCost(Period)!A1 $libinclude xldump Tot.l C:/Users/dhartle4/Documents/CBTL_RES.xlsx Total!A1

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Authors
Hartley, D. S.
Corresponding author
Hartley, Damon S.
ORCID
Hartley, Damon S.
Publisher
West Virginia University Libraries
Published
2014-01-01
DOI
10.33915/etd.5775
OpenAlex
W2273884493
Cited by
12
References
67
Citations by year
2022: 2 · 2020: 3 · 2018: 1 · 2017: 3 · 2016: 2 · 2015: 1
Type
dissertation
Language
en
Open access
Locations
The Research Repository @ WVU (West Virginia University) (submittedVersion)
Funders
West Virginia University
Topics
  • Physical Sciences › Engineering › Mechanics of Materials › Forest Biomass Utilization and Management
  • Life Sciences › Agricultural and Biological Sciences › Forestry › Agricultural and Food Sciences
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
Keywords
woody biomass, biomass supply chain, biomass harvesting, biomass logistics, feedstock cost, delivered cost, facility location, spatial econometrics, energy policy, biofuels, northeastern United States, mixed-integer linear programming

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