Uncertainties in Life Cycle Greenhouse Gas ...

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Train. REFINERY. OPEARTION. Fuel Conversion. Corn Stover. Corn Stover. Densified Corn Stover ... (St. Louis Southwestern,. Missouri Pacific and Atchison,. Topeka & Santa Fe Railway and. Chicago, Rock Island and Pacific. Railroad.).
Uncertainties in Life Cycle Greenhouse Gas Emissions from Advanced Biomass Feedstock Logistics Supply Chains Long Nguyen1, Kara G. Cafferty2, Erin M. Searcy2 and Sabrina Spatari1 1Department of Civil, Architectural, and Environmental Engineering, Drexel University 2Department of Biofuels and Renewable Energy Technologies, Idaho National Laboratory Introduction

Life Cycle Inventory Model System Boundary of Corn Stover to Ethanol Supply and Logistics System

 Commodity systems have been proposed and designed to deliver quality-controlled biomass feedstocks at preprocessing “depots”.

Energy

Bailing

Energy

Combine Harvest/CH

 Preprocessing depots densify and stabilize the biomass prior to long-distance transport and delivery to centralized biorefineries.  To access available biomass resources from areas with varying yields  To improve the feedstock logistics of lignocellulosic biofuels  The logistics of biomass commodity supply chains could introduce spatially variable environmental impacts

RAW MATERIALS FROM FIELDS

FEEDSTOCK PRODUCTION Production/Harvest/ Corn feedstock Collection/Short-Term Storage

Corn Stover

Unloading Bales

TRANSPORT FROM FIELD Lorry 28t

Corn Stover

Energy

Energy

PREPROCESSING DEPOT Preconversion/Formulation/ Stabilization/Densification

Grinder Infeed System

Densified Corn Stover

TRANSPORT FROM DEPOT Train

Densified Corn Stover

Bio-Ethanol Energy Energy

Self Propelled Stacker

Loading Bales

Horizontal Grinder

Operations at fields Operations at depots Transportation

Objective  Evaluate the spatial variability of life cycle environmental impacts owing to characteristics along the biomass feedstock supply chain.  The incur variability as a result of the quantity of biomass harvested, collected, stored, moved, and preprocessed prior to long-distance transport to a centralized biorefinery.

REFINERY OPEARTION Fuel Conversion

Figure 3. Framework of lignocellulosic supply chain.

Energy

Twin Bar Rack with 180 HP

Metering Bin

Energy

Dust Collection

Energy Inputs

ETHANOL TRANSPORT, DISTRIBUTION AND BLENDING

Bio-Ethanol

Material flows VEHICLE OPERATION

Scenario Analysis

Sensitivity Analysis

 Our focus is on identifying processes within the feedstock logistics sequence that introduce the most significant uncertainty in life cycle impact assessment (LCIA).

Life Cycle Assessment Framework  Goal and Scope:  Compare two scenarios of supply chain  Corn stover-to-ethanol  1 MJ of ethanol

Figure 6. Sensitive Analysis on GHG emissions for Scenario 1.

 Life Cycle Inventory  Energy inputs from unit processes and transportation.  Related greenhouse gas emissions

Figure 7. Sensitive Analysis on GHG emissions for Scenario 2.

Monte Carlos Simulation and Results Figure 4. Scenario 1 depot configuration based on equal spatial siting between depots and infrastructure availability

 Life Cycle Impact Assessment

Figure 1. LCA Framework

 Evaluate the impacts of releases on the environment (100-year global warming potential (GWP 100)).

Figure 8. Stochastic gate-to-gate logistics GHG emissions over the 90% confidence interval of Scenario 1.

Biorefinery Location  Reno County:  Proximity to five major highways (K14, K17, U50, K61 and K96)  Proximity to four major railroads (St. Louis Southwestern, Missouri Pacific and Atchison, Topeka & Santa Fe Railway and Chicago, Rock Island and Pacific Railroad.) Figure 2. Highway (red) and rail systems (black) of Reno County

Figure 9. Stochastic gate-to-gate logistics GHG emissions over the 90% confidence interval of Scenario 2.

Figure 5. Scenario 2 depot configuration based on biomass density and infrastructure availability

Conclusions  The preprocessing depots should be located near major railroads  Transport distance and the volume of transported commodity are significant  The uncertainty in GHG emissions declines with increasing number of preprocessing depots.

Figure 10. Scenario 1 can possibly produce higher GHG emissions than the conventional case (39.7 MJ of ethanol)

Acknowledgements This project was supported by Agriculture and Food Research Initiative Competitive Grant No. 2012-68005-19703 from the USDA National Institute of Food and Agriculture. Long Nguyen was supported by the Freshman Design fellowship program at Drexel.