2nd INTERNATIONAL CONFERENCE ON SUPPLY CHAINS
A GIS-based Methodology for defining Biomass
Potential and Facilities in Greece with the optimization
of transportation cost
Paliogiannis Grigorios1, Chondrocoukis Gregory2
1MSc
in Logistics, University of Piraeus, Greece
University of Piraeus, Greece
1paliogiannis.grigoris@gmail.com
2Professor,
Abstract
The aim of this study is the identification of the locations where biomass facilities can
be established based on least logistics cost. For this purpose, a database which
contains quantitative and spatial data about the agricultural residues of specific
plants have been developed, in order to support the selection of the appropriate
region that can facilitate biomass industries. This database is linked with a map that
provides geographic information. The geographic information system (GIS) is used
as a tool that contributes to the selection of the best location where biomass facilities
can be established. The decision is made with the aid of CORINE LAND USE map
which also identifies the topological, geological, etc features of each selected region
that can facilitate the biomass plant. An algorithm calculates the transportation cost
for each region of Greece. The cases of the location of a bio-refinery and a biomass
power plant are examined and indicate the supply chain of biomass resources
considering various feedstock types. The region that is selected in each scenario
depends on the transportation cost of agricultural residues, which includes the cost of
loading and unloading biomass and variable transportation costs.
Keywords: Geographic information System (GIS), biomass logistics, agricultural
residues, transportation cost, green logistics.
1. Introduction
In Greece the total agricultural land reaches almost 4 million ha, of which 60%
is arable land, 25% is cultivated land with trees and vines, 3% is garden area
and 12% is fallow land (EUBIONET, 2003). Agricultural residues and
specifically annually crop residues that remain in the field after the harvesting
process can be consider as biomass resource. (CENTER FOR RENEABLE
ENERGIES AND SAVING,2009). All these materials have a great potential to
provide renewable energy and added value products, such as cosmetic
ingredients, green surfactants, succinic acid, ethanol, fiber plants, etc. ( US
Department of Energy, US Department of Agriculture, 2005, Biocore Summer
School proceedings, 2011).
Biomass to energy and biomass to products facilities located in a
specific area are highly dependent on the spatial variation of the available
biomass of the current prefecture as well as from the surrounding regions and
the energy content and type of the harvested crops. A significant point is to
obtain sufficient biomass quantities to meet the respective demand of the
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specific capacity of the biomass plant at a minimum cost (Gnansounou,
2007).
Geographic Information Systems (GIS) can be used as appropriate tools
in order to create databases of the available biomass quantities, energy
contents for each selected crop and total potential energy produced from
biomass per prefecture. Moreover, they aid in decision-making processes
about the sites of the facilities and the presentation of the best-fit region on
map, (Perpina C et al, 2008).
In this study, logistics cost is estimated as constitutes a key component
during the decision making process in the cases of the establishment of
biomass facilities. Logistics cost represents the cost of moving the feedstock
or raw materials from the location where they are produced to the location
where biomass plant facility is established and this cost is a factor that affects
primarily the location of biomass plants (Flynn, 2007).
The transportation costs of biomass for each Greek prefecture that can
be considered both as a supply and a demand location/point of biomass is
estimated and also this study makes clear that the selection of the appropriate
location is highly affected by the regional and the surrounding areas biomass
availability and the corresponding transportation cost. Also, GIS based
applications enrich the decision factors, adding potential constraints such as
wetlands, lakes, protected areas, road ways and residential areas.
2. Methodology
In this study a series of steps that constitute the methodology are applied in
order to estimate the prefecture with the minimum transportation cost where
biomass facilities can be established. For this purpose, the transportation cost
of the feedstock is calculated in order to meet the demand of a biomass
facility with a particular capacity. This procedure is repeated for all the
candidate prefectures in Greece and finally the region with the minimum
transportation cost is selected as the appropriate location.
The two main scenarios of the establishment of biomass facilities are
presented:
 Best Location according to transportation costs for a Biorefinery, which
has a 750 thousand tones annual capacity of biomass as feedstock.
 Best Location according to transportation costs for a Biomass Energy
Plant with nominal power capacity of 40MW.
In the first steps, data for the quantities of specific agricultural residues
(biomass) for each region of Greece (the selected agricultural residues are
wheat, barley, oats, maize, rice, tobacco, cotton, sunflower and sugar beets)
are collected. A GIS tool is used for the creation of relational databases about
the available biomass and the potential energy content of each prefecture of
Greece and to generate thematic maps, making clear the regions with the
greatest biomass potential.
Then an equation of the total transportation cost, that consists of two
parts, the fixed and variable cost, is used in order to assist the decision of the
location of the plants.
2nd INTERNATIONAL CONFERENCE ON SUPPLY CHAINS
Finally, the supply network of biomass quantities (transported to the
selected prefecture/location from the surrounding prefectures) that are used
as feedstocks in biomass facilities is illustrated in the map of Greece for each
scenario.
More specifically, according to the methodological approach presented
below (Figure 1), the selection of the location with the minimum transportation
cost consists of several steps that include data acquisition for biomass
quantities and crop characteristics (e.g. availability for energy production,
heating value, product-to-residue ratio) and the estimation of the potential
energy content biomass per prefecture. In the next steps, the transportation
cost of the feedstock is calculated in order to meet the demand of a biomass
facility with a particular capacity. This calculation procedure is repeated for all
the candidate prefectures and finally the region with the minimum
transportation cost is selected as the appropriate location.
Figure 1. Methodology diagram of the study
Agriculture Statistics
of Greece
Production of specific Agricultural
Plants
Product to Residue
Ratios per Plant
Theoretical Biomass Potential
Availability percentages
for each residue
Available Biomass
-Heating Value (MJ/tn)
-Moisture Content
-Distances (km)
-Transportation Cost Equation
-Mainland Storage Locations
-Mass or Energy Demand
GIS Databases
and
Thematic Maps
Potential Energy Content
Calculation of Transportation Costs
of residues
Region with minimum transporting
cost
Corine Land Use
Identification of best location
As presented above (Figure 1) data are collected first including the
tonnage of a group of selected crops (wheat, barley, oats, maize, rice,
tobacco, cotton, sunflower and sugar beets) (Agriculture Statistics of Greece,
2010). Then using the Product to Residue Ratio coefficients per plant,
available in literature for Greek crops and the availability coefficients of the
residues (Apostolakis, 1987), it is possible to calculate the available biomass
from the agricultural residues as follows:
𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 (𝑡𝑛)
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝐵𝑖𝑜𝑚𝑎𝑠𝑠(𝑡𝑛) = 𝑃𝑟𝑜𝑑𝑢𝑐𝑡 𝑡𝑜 𝑅𝑒𝑠𝑖𝑑𝑢𝑒 𝑅𝑎𝑡𝑖𝑜 ∙ 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦(%) (Equation 1)
After these steps, MapInfo databases and thematic maps can be created
to assist a preliminary stage of decision making (figure 2). It is also possible to
calculate and present in MapInfo the potential energy produced from these
2nd INTERNATIONAL CONFERENCE ON SUPPLY CHAINS
crop residues using the following table of Lower Heating Values (LHV - MJ/kg)
as presented in Table 1 .
Table 1.Lower Heating Values (LHV - MJ/kg) and Product to Residue Ratios (PRR) of plants.
Types of Residues
LHV (MJ/kg) PRR
wheat straw
15,0
1,0
barley
11,4
1,2
oats
14,8
1,3
maize cobs
12,1
1,4
rice
14,1
1,0
Tobacco
13,5
0,9
Cotton
15,6
0,2
Sunflower
22,4
0,5
Sugarbeets
12,0
2,5
Figure 2. Thematic Map illustrating the total potential of the agricultural residues that
have been selected (wheat, barley, oats, maize, rice, tobacco, cotton, sunflower and
sugar beets).
From a logistics point of view, only 39 regions of the of the mainland of
Greece out of 54 were used in this study because the other represent urban
regions with commercial and industrial facilities in their great percentage.
The cost of transportation TCi from each region j to each candidate
destination i point is the sum of the Total Fixed Cost and the Total Variable
Cost respectively as shown in the following equation:
N
TC i (Euro)  Cost Fixed  Number of Trips i, j  Cost Variable   d i, j (Equation 2)
j1
where, the Total Fixed Cost is a function of the Number of Trips, which is
calculated by the available biomass quantity of each region j that is
transported to the candidate region i, divided by the capacity of a truck (it is
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assumed only one mode of transport, trucks, with capacity equal to 15 tones)
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒𝐵𝑖𝑜𝑚𝑎𝑠𝑠(𝑡𝑛)
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑇𝑟𝑖𝑝𝑠 (𝑎𝑛𝑛𝑢𝑎𝑙𝑙𝑦) =
(Equation 3).
15 𝑡𝑛/𝑡𝑟𝑢𝑐𝑘
This variable is multiplied with the factor CostFixed that represents the
cost of compacting, loading and unloading of biomass and its value is
assumed equal to 154 €/trip based on literature data.
The Total Variable Cost depends on the distance travelled to haul
biomass from the regions j to the candidate region i, thus distance is
multiplied with the driver’s and fuel’s cost CostFixed , and the value of this
parameter was set equal to 2.05 (€/km) (literature data).
The distances di,j between each destination point and the other regions,
were obtained using the digital map CORINE for land use. In order to
calculate the distances among the regions, the centroids of the total
agricultural area of each prefecture was indentified. Centroids were assumed
to represent a potential place for the storage of residues and finally a biomass
processing facility.





In each scenario the data used are the following:
A 39x39 table of distances between the regions of mainland
A 1x9 table of lower heating values of each type of residue
A 39x9 table of available biomass
A 39x9 table of available energy
Α 39x1 table of the names of counties and their state ID (accordingly to
the Hellenic Statistical Authority)
3. Analysis of Scenarios
The algorithms that calculate the total transportation cost of each region was
developed in MATLAB
The solution of the algorithm for the selection of the ideal location with
the minimum transportation cost varies according to: the kind of the biomass
facility (biorefinery/power plant), the demand and the supply of biomass
quantities for each facility and the set of assumptions related to the type of the
feedstock supplied in each case.
During the calculation procedure the algorithm sorts the matrix of
distances so that for each candidate region the selected types of biomass
residues are transported first by the nearest regions.
3.1 Scenario 1. The Biorefinery Case Study
3.1.1 Description
This scenario aims at the identification of the best logistically location for the
construction of a biorefinery facility. Biorefineries convert biomass into added
value products and the provided feedstock to a biorefinery is assumed to be in
this study almost 2,000 tones daily or equally 750,000 tons annually. In this
case an assumption is set to the types of residues that are going to be hauled.
The feedstock of the biorefinery can be only sugar beet leaves and cotton
stalks.
2nd INTERNATIONAL CONFERENCE ON SUPPLY CHAINS
3.1.2 Algorithm analysis
In the “biorefinery case” priority is given to sugar beets and then to cotton
residues. The transported amount is set 750,000 tons. The bubbleSort
function is used in order to sort the distances of the 39 candidate regions of
Greek mainland. Then the transportation cost is calculated for each candidate
region (fixed cost) and the regions which provide it with biomass (fixed and
variable costs) are selected. For every candidate region the algorithm is valid
only when there sugar beets or cotton residues are existing or till the sum of
the quantity in the region and those that send biomass has reached the goal
of transporting amount. The algorithm is presented on figure 3.
Figure 3. The Biorefinery Case algorithm
For i = 1,39
Transferred
Biomass
≤
750,000 tn
For Candidate region i
Sugar beet and cotton
residues trips
Fixed
Transportation
Cost calculation
For sending region j=2,39
For minimum distance
Sugar beet and cotton
residues trips
Total
Transportation
Cost Calculation
Total
Biomass
≤
NO
750,000 tn
Selection of the region
with the minimum TC
3.2 Scenario 2. Biomass Power Plant
3.2.1 Description
The aim of the second scenario is to identify the best location for a biomass
power plant with nominal installed power 40MW annually. The power plant will
have as input residues of wheat, oats, barley, maize, rice, sunflower and
tobacco.
The algorithm has many similarities as the previous one. A data file
including all the combination of distances, the quantities of biomass and the
names of the regions is loaded. Priority now is given to the types of residues
with high LHV (MJ/kg); for that reason bubbleSort function is used to sort the
species of biomass according to their energy content.
Each evaluated region along with the regions that provide biomass to the
power plant must collect the energy that applies to a 40MW installation. The
2nd INTERNATIONAL CONFERENCE ON SUPPLY CHAINS
suitable location achieves the energy target at the minimum transportation
cost.
3.3 Results
3.3.1 The bio-refinery Case
The solution indicates the best location of a bio-refinery which converts only
sugar beets and cotton residues, is the region of Larissa (P1) (Figure 4.1).
The black lines represent the connection of the plant with 5 other regions (The
prefectures of Larisa, Karditsa, Magnesia, Trikala, Pieria and Fthiotis will
provide the biorefinery with sugar beets and cotton residues) of Greece which
also will provide the plant with feedstock at a cost of 10.2 €/tn. The red stars
represent the centroids of the agricultural areas studied and we assume that
they can also be locations where biomass residues (irrespective of their type)
can be in periods collected and stored. This solution could be acceptable not
only for the minimum logistics cost, but also for the agriculture development
that holds for years; for that reason it would be an acceptable solution.
Figure 4.
1.
The solution for the ideal location of a biorefinery
which converts only sugar beets and cotton
residues, in Larissa
2.
The region of Pieria is the best fit location, where a
biomass power plant (40MW) can be constructed.
2nd INTERNATIONAL CONFERENCE ON SUPPLY CHAINS
3.3.2 The power plant case
The region of Pieria (P2) is proposed as the best location according to the
results of the algorithm, where a biomass power plant (40MW) can be
constructed (Figure 4.2). The supply chain of the biomass power plant
includes 10 regions (Pieria, Thessaloniki, Pella, Imathia, Larisa, Kilkis, Kozani,
Chalcidice, Magnesia, Serres and Karditsa) and the transportation cost is
calculated equal to 7.99 €/tn. The power plant have as input residues wheat,
oats, barley, maize, rice, sunflower and tobacco. The topological and
geographical characteristics of this region (there is a river and water can be
withdrawn for cooling processes) may facilitate the construction of a biomass
power plant.
4. Conclusions
This study presents some proposals about the construction of biomass
facilities based on the least transportation cost of crop residues following a
logistics approach. The construction of a bio-refinery and the construction of
biomass power plant of 40 MW installed power were examined.
The appropriate location and the supply chain of the agricultural residues
were defined for each kind of facility, according to the transportation cost,
taking into account the biomass production of each prefecture, the distances
of each candidate location from the various storage locations and the energy
content of each type of residue.
The Geographic Information Systems provide a significant help in
decision making about the establishment of biomass-processing facilities. The
data about agricultural residues can be collected in databases which are
related to maps providing geographical information (longitude and latitude).
Also, maps such as CORINE can be edited with queries in order to conclude
whether a suggested by the algorithm location has morphological, geological
and other features that can support the construction of a biomass facility.
The region of Larisa seems to be the location for the establishment of a
biorefinery. This place has the minimum cost of feedstock transportation from
the other 5 regions. On the other hand, when considering the construction of a
biomass power plant (40MW), the region of Pieria is the ideal location
combining both the minimum transportation cost.
The decision-making process of the establishment of biomass facilities
should take into account many different and multiple parameters. Except for
the transportation cost of raw materials, some other parameters should be
taken into account like production costs, investment cost and the
topological/geographical features and constraints of the candidate location.
References
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US Department of Energy, US Department of Agriculture (2005), “Biomass as
feedstock for a Bioenergy and Bioproducts Industry” pp.18-22
2nd INTERNATIONAL CONFERENCE ON SUPPLY CHAINS
BIOCORE Summer School Proceedings, 2011
CENTER FOR RENEABLE ENERGIES AND SAVING (2009), Biomass Guide, pp. 35.
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