The Fright/Fuel Transportation Optimization Tool: A model to explore future canola utilization

The Freight/Fuel Transportation Optimization Tool:

A model to explore future canola utilization scenarios

The National Transportation Systems Center Advancing transportation innovation for the public good

U.S. Department of Transportation Office of the Secretary of Transportation John A. Volpe National Transportation Systems Center

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Future Scenarios

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What is the Freight / Fuel Transportation Optimization Tool? 

Flexible scenario-testing tool designed to analyze future fuel scenarios for various commodities, datasets, and assumptions

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Optimizes routing and flows at scenario level using a Geographic Information System (GIS) module and an optimization module

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Multimodal network: road, rail, waterway, pipeline, intermodal facilities

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Outputs of optimized scenarios:      

material/commodity flows costs CO2 emissions fuel burn number of vehicle trips distance, vehicle miles traveled

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Analysis Steps

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Set Origins and Ultimate Destinations

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Example: waste feedstocks across U.S. Example origins located at county population centroid (can also be points or gridded data) Raw material travel over network can be limited or not Tool can track multiple products (e.g., diesel and jet fuel, etc.) and processing types (e.g., crushers, biorefineries, etc.)

Example Results – Do Not Distribute or Cite

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Locate Potential Processing Points

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Can incorporate existing facilities Tool can generate potential candidate processing points based on crop availability and minimum aggregation threshold along the transportation network “en route” to destinations

Example Results – Do Not Distribute or Cite

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Identify Best Routes

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Based on: • Transportation costs by mode • Transloading costs • Weightings and penalties (e.g., prefer interstates over smaller roads) • Capex if want to “build” new processing locations.

Example Results – Do Not Distribute or Cite

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Optimize Movements: Feedstock-to-Processor Routes

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Initial routes from raw material origin(as if it were all converted to fuel) to destinations are shown.

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Minimizes overall scenario “cost” Selects among origins, processing points, destinations Provides optimal flows by mode in the final optimal solution Note that some modal movements may be obscured by others (e.g., rail routes may parallel and cover roadway routes)

Example Results – Do Not Distribute or Cite

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Optimize Movements: Processors to Destinations Destination Examples

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% Demand

Lubbock Preston Smith International Airport

30.0

Midland International Airport

30.0

Salt Lake City International Airport

6.5

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All Destinations (28 airports total)

5.7

Shows delivery to individual destinations for each product Provides screening level data for estimating transportation costs, emissions, VMT, and fuel burn associated with future scenarios

Example Results – Do Not Distribute or Cite

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Key metrics by commodity and mode

Example Results – Do Not Distribute or Cite

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Performance metrics  Transportation

CO2 emissions

 Fuel

burn  Scenario transportation cost  Transportation cost per unit of delivered fuel  Material/commodity flows  Mileage (total, by mode, etc.) / VMT  Number of vehicle trips  Strategic network mileage  Geospatial data  Commodity flow maps  Scenario comparisons  Aggregation

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options

Commodity/commodity type By mode Total network results Regional information Flow comparison

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Publications / Reports 

Shi, Rui, Suchada Ukaew, David W. Archer, Joon Hee Lee, Matthew Pearlson, Kristin C. Lewis, David R. Shonnard. 2017. Life cycle water footprint analysis for rapeseed derived jet fuel in North Dakota. Sustainable Chemistry and Engineering: submitted.

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Ukaew, Suchada, Rui Shi, Joon Hee Lee, David W. Archer, Matthew Pearlson, Kristin C. Lewis, Leanne Bregni, and David R. Shonnard, 2016. Full chain life cycle assessment of greenhouse gases and energy demand for canola-derived jet fuel in North Dakota, United States. ACS Sustainable Chemistry and Engineering. DOI: 10.1021/acssuschemeng.6b00276.

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Lewis, Kristin C., Gary M. Baker, Matthew N. Pearlson, Olivia Gillham, Scott Smith, Stephen Costa, Peter Herzig, 2015. Alternative Fuel Transportation Optimization Tool: Description, Methodology and Demonstration Scenarios. DOT/FAA/AEE/2015-12.

Available from the National Transportation Library at: http://ntl.bts.gov/lib/56000/56200/56200/Alt_Fuel_Trn_Optimization_Tool.pdf .

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Future Scenarios

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Thank you!

Kristin Lewis, Ph.D. Environmental Biologist Energy Analysis and Sustainability Division Volpe National Transportation Systems Center Kristin.lewis@dot.gov 617-494-2130 Sponsors: Federal Aviation Administration - Office of Environment and Energy Department of Energy – Energy Policy and Systems Analysis U.S. Navy - Office of Naval Research

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