Potential for Sustainable Deployment of Biofuels Under EISA - - PowerPoint PPT Presentation

Potential for Sustainable Deployment of Biofuels Under EISA

American Chemical Society Science & the Congress Briefing on Cellulosic Biofuels Virginia H. Dale Oak Ridge National Laboratory

Washington, D.C. January 30, 2012

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Feedstock type Land conditions Management Processing Harvesting and collection Storage Transport Fuel type Conversion process Co-products Storage Transport Blend conditions Engine type and efficiency

Based on McBride et al. (2011) and Dale et al. (in review)

Looking at the biofuel supply chain in terms of sustainability indicators

Feedstock production Feedstock logistics Conversion to biofuel Biofuel logistics Biofuel End uses Environmental Categories without major effects Profitability Social well being External trade Energy security Resource conservation Social acceptability Socioeconomic Soil quality Water Greenhouse gases Biodiversity Air quality Productivity

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Approach

• Develop modified or natural biomass

that is designed to release sugars more easily

• Engineer microbes to consolidate multiple

costly processes into a single step

Outcomes to date

• Modified

biomass: Switchgrass

– Yields 30% more biofuel – Requires less commercial enzyme – Now in commercial field trials

• Improved yeast:

– Combines ability to digest cellulose and ferment – Now the basis of a commercial plant

Ready access to biomass sugars can reduce cost of processing cellulosic biomass

Field trials of modified switchgrass at Ceres facility Source: U.S. Department of Energy BioEnergy Science Center (http://bioenergycenter.org) Sugars Cellulosic biomass Fuel(s) Recalcitrance: Resistance to breakdown into sugars

Prognosis: Needed technology improvements will impact industry within the next 5 years

Subsequent change drivers

Land use is dynamic and bioenergy contributes to ongoing land-use change No empirical evidence for land-use changes due to bioenergy

120 130 140 150 160 170 Cultivated crop land Range land Forest land

Area (Mha)

1982 1987 1992 1997 2002 2007 Initial change drivers (cultural, technical, biophysical, political, economic, demographic) Initial land-use change Land cover (typically measured by remote sensing methods at one place and time) Global economic models Prices, quantities, and distribution of goods Carbon stocks Ongoing land-use changes Demand Source: CBES 2010 (http://www.ornl.gov/sci/besd/cbes/) Based on data from USDA 2009-NRI (Dale et

• al. 2011), supported by recent USDA reports

Evaluate land-use effects based on empirical evidence

20 40 60 Non cultivated crop land CRP land Pasture land Developed land

Area (Mha)

Filters: Land-cover data, scale, sources Filters: Private land, rents Filters: Land-cover, carbon change data

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Stommel diagrams show spatial extent and duration of effects (Parish et al., in prep)

Compare bioenergy to other energy alternatives

Environmental effects associated with gasoline production Projected environmental effects

• f ethanol production

Spatial scale (km2) 0.01 100 1,000,000 Field Region

• 1. Establish biomass

feedstock

• 3. Distribute

biomass

• 4. Produce

biofuel

• 2. Harvest

and collect biomass 100,000 1,000 10 0.1 0.01 100 1,000,000 Field Region Globe Temporal scale (days)

• 4. Produce

gasoline

• 2. Extract oil
• 1. Explore for oil
• 3. Distribute

crude oil

• 1. Establish fuel sources
• 2. Obtain raw material
• 3. Distribute raw material
• 4. Produce fuel

Spatial scale (km2)

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Adopt landscape perspective

Consider fuel production within entire system (interactions and feedbacks) as an opportunity to design landscapes that add value

Dale et al. (2010)

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Parish et al., Biofuels, Bioprod. Bioref. 6,58–72 (2012)

An optimization model can identify “ideal” sustainability conditions

Spatial optimization model

• Identifies where to locate plantings
• f bioenergy crops given feedstock

needs for Vonore refinery

• Considering

– Farm profit – Water quality constraints

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Objective: Ag land converted 58% 51% 44% 27% 60% 25%

20 40 60 80 Max N reduction Max P reduction Max sed reduction Max profit Balanced <25% ag conversion Percent achieved Total Profit Reduction in N Reduction in P Reduction in Sediment

Land area recommended for switchgrass in this watershed: 1.3% of the total area (3,546 ha of 272,750 ha)

Balancing objectives:

A landscape design of cellulosic bioenergy crop plantings may simultaneously improve water quality and increase profits for farmer-producers while achieving a feedstock-production goal

Recognize place-based options

Collectable stover if tillage practices change: 111M tons/year Collectable stover: 64M tons/year Wilhelm et al. (2007) Dry tons <1,000 1,000–50,000 50,000–100,000 100,000–200,000 200,000–400,000 >400,000

• Hybrid poplars
• Switchgrass
• Willows
• Hybrid poplars
• Miscanthus
• Pine
• Sorghum
• Sweetgum
• Switchgrass
• Energy cane
• Ecalyptus
• Pine
• Hybrid poplars
• Miscanthus
• Sorghum
• Switchgrass
• Sorghum
• Switchgrass
• Hybrid poplars
• Switchgrass

Different crops grow better in specific places Residue availability is specific to each place and management Dale et al. (2011)

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http://www.ornl.gov/sci/ees/cbes/

Thank you!