Economic Modeling of Woody Biomass
Utilization for Biofuels: A Case Study in
West Virginia
Jinzhuo Wu, Jingxin Wang, Joseph McNeel
West Virginia University
Division of Forestry and Natural Resources
Morgantown, WV 26506
Presented to 31st Annual Council on Forest
Engineering Meeting
June 22-25, 2008
Charleston, South Carolina
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Outline
„
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Introduction
Objective
Methodology
Results
Conclusion
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Introduction
ƒ U.S. Energy Overview and Biofuels from Woody Biomass
The interest of using woody biomass as feedstock for bioenergy
in the U.S. has been increasing.
20000
4
U.S. Crude Oil Field Production
U.S. Crude Oil and Petroleum Imports
U.S. Crude Oil and Petroleum Exports
U.S. Crude Oil and Petroleum Products Supplied
3.5
3
Quadrillion Btu
Thous a nd B a rre ls pe r D a y
25000
15000
10000
Biomass
Biofuels
Waste
Wood Derived Fuels
2.5
2
1.5
1
5000
0.5
0
1970
1975
1980
1985
1990
1995
2000
(Data Source: Energy Information Administration)
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2005
2010
0
2001
2002
2003
2004
2005
2006
2007
(Data Source: Energy Information Administration)
3
ƒ Factors Affecting Woody Biomass Utilization
Biomass sustainability
Efficient harvesting, extraction, and transportation
Investment cost for woody biomass based facility
Production cost
Social and environmental consideration
Policy
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ƒ Woody Biomass Sustainability in West Virginia
¾
History of Forest Lands Harvested in West Virginia
350,000
Acres of Harvested
300,000
250,000
200,000
150,000
100,000
50,000
0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
(Data Source: West Virginia Division of Forestry)
Of 12 million acres of forestland there were 230,000 acres
of forest lands harvested in West Virginia during 2007.
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¾
Annual Woody Biomass Potential in West Virginia
Million Dry tons Per Year
0
0.2
0.4
0.6
0.8
1
1.4
1.6
1.34
Logging Residue
0.942
Mill Residue
Urban Tree Residue
Pallet Residue
1.2
0.119
0.013
(Data Source: Wang et al. 2006)
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Annual woody biomass growth to removal ratio in West
Virginia
¾
500,000,000
Average net annual growth of sawtimber
Average of annual removals of sawtimber
450,000,000
400,000,000
Cubic feet
350,000,000
300,000,000
250,000,000
200,000,000
150,000,000
100,000,000
50,000,000
0
2000
2005
(Data source: Forest Inventory Analysis)
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ƒ Woody Biomass Utilization Process
Chipping at
landing
Transporting
Storage at
plant
Loading
Loose residue
Transporting
Processing at
plant
Forwarding
Loading
Transporting
Extraction
Logging residue
Bundling
Woody Biomass Supply Chain
(Source: Alakangas, VTT )
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Example Machines
(Sources: Rummer et al. 2004; Reis et al. 2007 )
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ƒ Summarized woody biomass conversion technologies
Woody Biomass
Thermo-chemical technologies
Gasification
* Steam
* Process heat
* Electricity
* Fuel gas
methane
Pyrolysis
* Charcoal
* Bio-oil
* Fuel gas
Combustion
* Steam
* Process heat
* Electricity
Biochemical technologies
Hydrolysis
fermentation
* Ethanol
Anaerobic
digestion
* Biogas
(methane, carbon
dioxide)
* Heat
* Steam
* Electricity
* Transportation
Fuels
* Chemicals
(Source: Caputo et al 2005, Wang et al 2006)
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Objective
Develop a mix integer programming (MIP) model to evaluate
woody biomass utilization for biofuels
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Methodology
Objective: Minimize the total annual delivered cost of woody
biomass from the supply locations to biorefinery locations.
I
H
I
J H
⎡ I H
Min z = ∑ ⎢ ∑∑ (α h + sc) xhihm + ∑∑ ϕ xpsihm + ∑∑∑ (csh + τ ijh + cph ) xtijhm
m =1 ⎣ i =1 h =1
i =1 h =1
i =1 j =1 h =1
M
⎤
+ ∑∑∑ (mcr + mtij ) xmijrm ⎥ + Lct
i =1 j =1 r =1
⎦
I
J
R
Cost components: Extraction cost, storage cost, chipping
cost, loading cost, transportation cost, and purchased cost.
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Methodology
Woody biomass handling systems considered:
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Cable skidder-loose material
Cable skidder-chip
Grapple skidder-loose material
Grapple skidder-chip
Forwarder-loose material
Forwarder-chip
Forwarder-bundle
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Methodology
Constraints
•
Logging residue availability constraints - slope constraint &
65% available;
•
Mill residue availability constraint - 90% available;
•
Number of woody biomass handling systems used – 1;
•
General storage system balance;
•
Storage constraints for slash bundling system;
•
Demand of woody biomass at the plant constraint - 1,000 dry
tons/day;
•
Number of plants requiring woody biomass – 1.
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Methodology
General transportation cost model
Tij =
2dij
fpg +
mpg
2dij
mph
( tp − ts ) + ⎛ (tp − ts)( N + 1) + ts ⎞ IITR
dwh +
N
⎜
⎝
2N
SMH ⋅ UT
⎟
⎠
2dij
mph
+
( tp − ts ) MR
2dij
N ⋅ SMH ⋅ UT mph
• Developed based on Jensen’s WTRANS program
• Fuel cost, driver wages, and overhead and maintenance costs
• A function of transportation distance
Within-county transportation cost model
⎛ 2.55xtlijh1 +5.35xtlijh2 +7.63xtlijh3 +10.42xtlijh4 +14.74xtlijh5 ⎞
Lct = ∑∑∑th ⎜
⎟⎟, wheredij ≤ rsi
⎜
xtl
xtl
xtl
xtl
+
19.08
+
22.60
+
25.63
+
30.17
i=1 j=1 h=1 ⎝
ijh6
ijh7
ijh8
ijh9
⎠
I
J
H
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Results
(Base case)
Woody Biomass Supply and Demand Locations
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Results
(Base case)
Delivered Cost of Woody
biomass by Handling
Systems (demand of
1,000 dry tons/day)
A v e r a ge de liv e re d c os t ($ /dr y ton)
Optimized Location for Woody Biomass-based
Facility (demand of 1,000 dry tons/day)
50
Extraction cost
Storage cost
Transport cost
Chipping cost
Purchased cost
40
30
20
10
0
Cable
Cable
Grapple
Grapple
skidder-loose skidder-chip skidder-loose skidder-chip
material
material
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Forwarderloose
material
Forwarderchip
Forwarderbundle
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Results
(Sensitivity analysis)
The delivered cost varied slightly
as logging residue availability
changed.
The delivered cost of woody
biomass was sensitive to mill
residue availability.
50
45
40
35
Cable skidder-loose material
Grapple skidder-loose material
Forw arder-loose material
Forw arder-bundle
30
Cable skidder-chip
Grapple skidder-chip
Forw arder-chip
25
20
25
30
35
40
45
50
55
60
65
70
75
80
Logging residue availability (%)
Delivered Cost vs. Logging Residue Availability
80
A verag e d elivered co st ($/d ry
to n )
Scenario 1: Woody Biomass
Availability
Average delivered cost ($/dry
ton)
55
Cable skidder-loose material
Grapple skidder-loose material
Forw arder-loose material
Forw arder-bundle
70
Cable skidder-chip
Grapple skidder-chip
Forw arder-chip
60
50
40
30
20
30
40
50
60
70
80
90
Mill residue availability (%)
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Delivered Cost vs. Mill Residue Availability
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Results
(Sensitivity analysis)
Scenario 2: Woody Biomass Demand at Plant
Due to defined biomass and
slope constraints for extraction,
the availability of woody
biomass can be up to 1,800 dry
tons/day.
70
A verag e d elivered co st ($/d ry
to n )
The delivered cost increased
dramatically as the demand at a
plant increased among woody
biomass handling systems.
60
50
Cable skidder-loose material
Cable skidder-chip
Grapple skidder-loose material
Grapple skidder-chip
Forw arder-loose material
Forw arder-chip
Forw arder-bundle
40
30
400
600
800
1000
1200
1400
1600
1800
2000
Woody biom ass dem and (dry tons/day)
Delivered Cost vs. Woody Biomass Demand
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Results
(Sensitivity analysis)
Scenario 3: Woody Biomass Inventory at Plant
ƒ The delivered cost
A verag e d elivered co st ($/d ry
ton )
55
50
45
40
35
Cable skidder-loose material
Grapple skidder-loose material
Forw arder-loose material
Forw arder-bundle
30
25
0
1
Cable skidder-chip
Grapple skidder-chip
Forw arder-chip
2
3
Inventory (w eeks)
increased as inventory
increased among different
systems.
ƒ The highest cost
occurred with cable
skidder handling systems
followed by grapple
skidder handling systems.
Delivered Cost vs. Woody Biomass Inventory
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Conclusions
•
West Virginia’s forests currently have a sustainable
growth/removal ratio.
•
Wood residue utilization will promote rural economic
development in WV.
•
The model developed can be applied to other areas of the
Appalachian region.
•
On-going research activities at WVU will lead to more efficient
utilization of woody biomass.
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Questions, Comments or Suggestions?
Thank you!
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