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Integrated Biomass Supply Analysis & Logistics IBSAL. Shahab Sokhansanj and Anthony Turhollow Oak Ridge National Laboratory. Organization. Overview Example Elements of the model IBSAL demonstration Conclusions. Integrated Biomass Supply Analysis and Logistics (IBSAL) Model.
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Integrated Biomass Supply Analysis & Logistics IBSAL Shahab Sokhansanj and Anthony Turhollow Oak Ridge National Laboratory
Organization • Overview • Example • Elements of the model • IBSAL demonstration • Conclusions
Integrated Biomass Supply Analysis and Logistics (IBSAL) Model
Feedstock Cost Reduction Target 2000 2005 2010 2015 2020 2025
At the present, average delivered cost of agricultural residues is about $52 per dry ton.The target is to reduce the delivered costs to $30 per dry ton.
Baseline costs based on spreadsheet analysis $60 Other costs $50 Processing $40 Haul to plant $/dry ton $30 Storage $20 Move to storage $10 Collection $0 Base case - Chopper Chopper Farmer - Large head w/ low head w/o payments round bales cost storage storage
IBSAL Model - Description • The Integrated Biomass Supply Analysis and Logistics (IBSAL) model has been developed to simulate physical flow of biomass from collection to biorefinery. • IBSAL integrates technology and management information system for collecting, storing, processing, and transporting biomass. • IBSAL model functions similar to ASPEN model used for analysis of process engineering in chemical industry. • IBSAL model is made up of modules each representing a biomass handling process.
Elements of the model • Mathematical model of field and off-the-field operations. • Data on biomass physical characteristics - yield, physical and chemical changes in dry matter, moisture relations, density. • Climate variables (temperature, precipitation) • Quantities and geographical distribution of biomass material. • Cost data and costing method. • Emissions and soil compaction data (not yet implemented). • Biomass management information system.
Three scenarios for collection and supply of stover and straw to biorefinery
Dry ton, yield, acreage, and number of farms for a 1500 dry ton per day biorefining operation
Input data BIOMASS AVAILABILITY vs. time • The National Agricultural Statistics Service of the U.S. Department of Agriculture publishes the progress of a number of major crops during a growing season. • The data is available at http://www.nass.usda.gov:81/ipedb.
Location model Three intermediate stores: S1, S2, S3 One conversion plant: P 1/n1=1 A1, f1 1/n2=8/9 A2, f2 ds2 S1 (xs2,ys2) S2 ds1 P R2 1/n3=1 A3, f3 ds3 S3 Af,i Y2,i (xi,yi)
Scenario 1 - baling 55 shredders $4.70/dt 40 balers $9.30 /dt 18 sp bale movers $18.46/dt 10 tarping crew $2.88/dt 12 bale loaders & 8 bale trucks $9.46/dt 3 grinders $6.99/dt Overall cost $50.85/dt Operating cost $21,068,482 Dry ton in 516,232 Dry ton out 509,931 Energy in/Energy out 0.12 Collection & supply of stover • Scenario 2 - loafing • 55 shredders $4.84/dt • 40 loafers (stackhand) $6.68 /dt • 3 grinder and 8 loaders $7.79/dt • 10 trucks $11.90/dt • Overall cost $34.89/dt • Operating cost $16,592,453 • Dry ton in 554,094 • Dry ton out 473,341 • Energy in/Energy out 0.12
Scenario 3 – Wet storage (ensiling) 50 forage sp harvesters & 80 forage wagons $22.75/dt 1 silage pit/100 ha & 8 Compactors (85 hp tractor) $13.09/dt 18 wheel loaders & 19 trucks $16.22/dt Overall cost $55.13/dt Operating cost $21,003,064 Dry ton in 565,059 Dry ton out 531,155 Collection & supply of stover
Progress of biomass availability, collection, and transport operations – cool and wet climate Available Collected Collection Completion 224 days Stored Delivered
Conclusions • EXTENDTM provides a convenient object oriented language to implement the dynamic simulation of biomass supply. • IBSAL considers time-dependent availability of biomass and the effects weather conditions have on the progress of harvest. • The program predicts the number and size of equipment and storage spaces to meet the rate of harvest and biorefinery demand schedule for feedstock. • The delivered cost of biomass is calculated based on the utilization rate of the machines and storage spaces. • The energy efficiency of the system is calculated