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(706e) Methods for the Design of Spatially Explicit Biofuel Supply Chains

Authors: 
Ng, R. T. L., University of Wisconsin-Madison
Maravelias, C. T., University of Wisconsin-Madison

Methods for the Design of Spatially Explicit Biofuel
Supply Chains

Rex T. L. Ng1,2 and Christos T. Maravelias1,2

1Department of Chemical and Biological Engineering and 2DOE
Great Lakes Bioenergy Research Center, University of Wisconsin-Madison,
Madison, WI

Cellulosic biomass is seen as one
of the promising sources for sustainable biofuels production. In recent years,
much effort has been made to design efficient biomass-to-biofuel supply chains
(SCs) in terms of economic, environmental and social aspects. In many studies,
biomass feedstock availability is estimated based on county-level data,
assuming a harvesting site is located at the centroid of a county [1,2],
or a county is divided into rectangular or square sub-areas, and a harvesting
site is located at the centroid of a sub-area [3,4].
Additionally, biomass is assumed to be uniformly distributed over the area. However,
unlike fossil fuels, biomass availability is highly distributed, so the
aforementioned assumptions often lead to biofuel SCs that are suboptimal or
even infeasible from an energy consumption point of view, that is, the primary
energy required to harvest and transport biomass is higher than the energy
content of the produced biofuel.

To overcome this, we develop a
framework that allows us to utilize high spatial resolution biomass data in
designing biomass-to-biofuel SCs. We first develop a tool based on the
Agricultural Model Intercomparison and Improvement Project (AgMIP) packages to
generate feedstock data using climate data from the user-specified general
circulation model and representative pathway [5].
This data is then combined with a historical cropland data layer (CDL) which is
provided by the U.S. Department of Agriculture [6],
to generate spatial data for an area based on a user-specified resolution (down
to 30 m × 30 m). Note that this generates a complex SC network with a very
large number of arcs, and therefore leads to a large-scale optimization
problem.

Accordingly, we then develop a
preprocessing algorithm to reduce the number of arcs. We first determine the
feedstock, harvesting and handling (FHH) cost up to the biorefinery gate
(includes transportation and depot installation and operation costs) for
different SC configurations (with or without depot and different transportation
modes). If depot is selected, we determine the maximum distance for collecting
distributed biomass to a potential depot given the depot capacity. Note that
the maximum distance greatly depends on biomass availability at every potential
depot location. Next, we compare the FHH costs for all configurations and
determine the threshold distances at which one configuration becomes the most
economical. For example, in a corn stover-to-bioethanol SC, depot should only
be installed if the distance between a harvesting site and the biorefinery is
more than 120 km. Similarly, rail transportation of densified corn stover is
cheaper than truck transportation when the distance between depot and
biorefinery is greater than 225 km. Based on these pre-calculated distances, we
remove arcs that will never be used in an optimal solution, and thus
dramatically reduce the number of defines variables.

Next, we develop a multi-period
mixed-integer linear programming (MILP) model that accounts for facility
location, technology selection, and capacity planning decisions in terms of
depots and biorefineries, as well as auxiliary decisions such as harvesting
site and biomass feedstock selection, biomass allocation, transportation mode,
and inventory planning decisions.

Finally, we illustrate the
applicability of the proposed model by considering a corn stover-to-bioethanol
SC in Wisconsin (420 km × 500 km). We consider a one-year horizon divided into
12 periods, and two transportation modes: truck and rail. The objective is to
minimize the total annual cost, which includes biomass acquisition, inventory,
operating, and annualized capital costs for depots and biorefineries.

References:

[1] F. You, B. Wang, Life Cycle
Optimization of Biomass-to-Liquid Supply Chains with Distributed–Centralized
Processing Networks, Ind. Eng. Chem. Res. 50 (2011) 10102–10127.

[2] W. Alex Marvin, L.D. Schmidt,
S. Benjaafar, D.G. Tiffany, P. Daoutidis, Economic Optimization of a
Lignocellulosic Biomass-to-Ethanol Supply Chain, Chem. Eng. Sci. 67 (2012)
68–79.

[3] R.T.L. Ng, C.T. Maravelias,
Design of Cellulosic Ethanol Supply Chains with Regional Depots, Ind. Eng.
Chem. Res. 55 (2016) 3420–3432.

[4] R.T.L. Ng, C.T. Maravelias,
Design of biofuel supply chains with variable regional depot and biorefinery
locations, Renew. Energy. 100 (2017) 90–102.

[5] N.B. Villoria, J. Elliott, C.
Müller, J. Shin, L. Zhao, C. Song, Rapid aggregation of global gridded crop
model outputs to facilitate cross-disciplinary analysis of climate change
impacts in agriculture, Environ. Model. Softw. 75 (2016) 193–201.

[6] National Agricultural
Statistics Service U.S. Department of Agriculture, CropScape - Cropland Data
Layer, (2016). https://nassgeodata.gmu.edu/CropScape/ (accessed April 5, 2017).