=Paper=
{{Paper
|id=Vol-1498/HAICTA_2015_paper88
|storemode=property
|title=Efficiency Assessments for a Biomass Harvesting and Handling System
|pdfUrl=https://ceur-ws.org/Vol-1498/HAICTA_2015_paper88.pdf
|volume=Vol-1498
|dblpUrl=https://dblp.org/rec/conf/haicta/OrfanouPB15
}}
==Efficiency Assessments for a Biomass Harvesting and Handling System==
https://ceur-ws.org/Vol-1498/HAICTA_2015_paper88.pdf
Efficiency Assessments for a Biomass Harvesting and
Handling System
Anna Orfanou1, Dimitrios Pavlou2, Dionysis Bochtis3
1
Department of Engineering, Aarhus University, Denmark, e-mail: annaorf@yahoo.gr
2
Department of Engineering, Aarhus University, Denmark, e-mail: dmpavlou@gmail.com
3
Department of Engineering, Aarhus University, Denmark, e-mail: dionysis.bochtis@eng.au.dk
Abstract. A simulation model, which depicts the harvesting operation of
biomass supply chain, is presented in this paper. ExtendSim8 simulation
software was used for the development of the model. There are a number of
sequential operations, i.e. mowing, drying, baling, picking-up, loading, and
transporting, for harvesting biomass until the final product arrives at bio-energy
generation plant. Different scenarios, in terms of the operational system
configuration, are analyzed in order to show how the operational time and cost
are affected.
Keywords: Biomass supply chain, harvesting operations, simulation model,
optimisation.
1 Introduction
The interest in new and renewable energy has been increased over the years because
of the limited fossil fuel resources and the related caused environmental problems,
such as atmospheric pollution. (Goldenberg, 2000; Richardson and Verwijst, 2007)
Biomass utilization is important for energy production (McKendry, 2001; Veringa,
2006), such as electricity, heat and biofuels. The use of biomass is expected to be
significantly increased in the future (Berndes et al., 2003; Yamamoto, 2001; Jager-
Waldau and Ossenbrink, 2004), which is a great opportunity for agriculture, although
there should be efficient ways for retrieving it from the field in order to maintain the
operational cost at reasonable level (Sambra et al., 2008). Improvements in biomass
supply chain should be done for minimizing not only the cost but also the time
consumption. The demand and the use of biomass can be increased by several ways,
such as new conversion technologies, better planning and handling systems etc.
(Sambra et al., 2009).
New and improved ways are required for increasing the operational efficiency of
agricultural operations especially in complicated production systems (Sørensen and
Bochtis, 2010). Advanced management models, such as fleet management tools for
operations of multiple machines in multiple fields are required in order to analyse
these processes (Sørensen and Bochtis, 2010; Orfanou et al., 2011). Simulation
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models of a biomass supply chain are important for making the process more efficient
by examine different parameters that can affect the process.
This paper refers to a simulation model of biomass supply chain, which consists of
the operations of mowing, drying, balling, picking up, loading, traveling and
unloading as it is shown in Fig. 1. The purpose of building the simulation model was
for demonstrating the process of biomass supply chain and showing how different
parameters can affect the whole process in terms of time and cost.
Fig. 1. Graphical representation of the biomass supply chain
2 Materials and Methods
A simulation model was created by using ExtendSim8 simulation software. A
number of blocks were utilized for representing biomass supply chain. The activities
(i.e. mowing, baling, loading, unloading, transportation) and resources (i.e. machines,
labor) are represented by the blocks of Item library. The blocks that belong to Value
library were used for importing data (inputs), making equations and taking decisions
(e.g. to start an operation when the previous one is terminated). Furthermore, the
blocks from Plotter library were used for the graphical representation of the results.
The inputs are separated into field data (e.g. field area, yield, etc.), machinery data
(e.g. number and capacity of the machines in each task, etc.), and cost data (labor, fuel
cost, etc.). The output of the simulation process provides the total time and the
variable cost of the harvesting process according to different operational scenarios and
a range of travel distances between the field and the bio-energy generation plant. It
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shows also the identification of different bottlenecks for pick-up machine and truck in
each scenario.
The architecture of the model is presented in Fig. 2. Every box in the diagram
represents an activity and the constrain parameters of it. Inputs and outputs are
presented by arrows on the left and on the right of each box respectively. The physical
aspects of each activity are shown by the arrows at the bottom of each box.
Fig. 2. Architecture of the simulation model
3 Implementation
In the presented case study, it is shown a harvesting process of crops for bio-energy
production purposes. Mowing, baling, picking up, loading truck, transporting, and
unloading truck, are the sequential operations of the system. Table 1 shows the
parameters of the selected machines. In a field of 5 ha, different parameters of
distance (5 km, 15 km and 25 km), number of trucks (1 and 2 trucks), and capacity of
each truck (34 bales, 48 bales, 62 bales) were examined.
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Table 1. Machinery Parameters
Machines Repair List Fuel Cost Accum Productivity Capacity Travel
factorsa Priceb (€/h) .Use (min/ha) speed
RF1 RF2 (€) (h/y) (km/h)
Tractor 0.003 2.0 60,000 - 1,000 - - -
(150 hp)
Mower 0.44 2.0 15,000 11.89 400 42.00 - -
Round 0.43 1.8 32,000 14.18 400 65.00 - -
Baler
Pick-Up 0.16 1.6 34,000 13.03 400 62.00 18 15.0
Forklift 0.40 1.7 9,000 8.46 400 17.86 2
Truck 0.003 2.0 110,000 Full: 17.92 1,750 - 48 51.5
Empty:
12,46
a: ASAE D497.5 (2006), b: DAAS (2011)
Table 2. Tested Scenarios for biomass supply chain
Number Capacity of each Travel Distance
of Trucks Truck (bales) (km)
5
34 15
25
5
1 48 15
25
5
62 15
25
5
34 15
25
5
2 48 15
25
5
62 15
25
4 Results
Fig. 3 shows (a) the total operational time per ha and (b) the total cost per ha for the
selected scenarios presented on Table 2. At the x axis (Combinations), the first row
refers to the travel distances between field and bio-energy generation plant, while the
second row shows the capacity of each truck.
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(a)
(b)
Fig. 3. (a) Total operational time and (b) total cost of biomass supply chain
Table 3 lists the results of total dead time (bottleneck) of pick up machine and
truck, total operating time and cost of the biomass supply chain, regarding specific
scenarios (1-34, 1-48, 1-62, etc) in a field of 5 ha and travel distance between field
and bio-energy plant of 15 km. The first number of the combination (1, 2) represents
the number of trucks used in the process. The second number (34, 48, or 62) refers to
the capacity (bales) of the truck. Fig. 4 shows graphically the total dead time of pick
up machine and truck during the process for each combination.
Dead time is a stage in a process that causes a part of the process or the whole
process to slow down or stop. The dead time of pick up machine is created when there
is no available truck and the pick up machine waits for being unloaded. The dead time
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of the truck is created when the pick up machine collects bales from the field and the
truck waits for being loaded.
Table 3. Dead Time, Total Operational Time and Total Cost
No Trucks- Dead Time Dead Time Total Time Total Cost
Capacity PU (min) Truck (min) (min) (€)
1-34 153 139 964 1085
1-48 116 139 930 1060
1-62 130 139 930 1063
2-34 0 169 811 1074
2-48 0 173 814 1066
2-62 0 180 817 1067
Fig. 4. Total dead time of Pick Up Machine and Truck by changing the number and/ or the
capacity of the truck
5 Discussion
Fig. 3(a) shows that more time is consumed for biomass supply chain when one
truck is used instead of two. This difference is greater in long distances (25 km) than
in short distances (5 km). For stable travel distances, the increase of capacity after a
certain point does not reduce the total operating time because even if the number of
transportations is less, the loading and unloading time is increased. A solution could
be more forklifts in use in both locations (field and bio-energy plant). Also, the
capacity and/ or the number of pick up machines could affect the total operating time.
It should be noticed that when two trucks of low capacity (34 bales) are used, less
time is needed than in the case of one truck of high capacity (62 bales) for same travel
distances. The period of absence of a low capacity truck is less than a high capacity
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truck, while at the same period the second truck continues the operation making the
entire process faster.
As it is shown in Fig. 3(b), the total cost is higher in long distances (25 km) than
short distances (5 km) for both cases. The biomass supply chain costs less in short
distances when one truck is used and in long distances when two trucks are used. The
cost is reduced when the capacity is increased, but when the capacity overcomes the
optimal, then the cost is not reduced anymore (e.g. 62 bales capacity).
As it is presented in Table 3 and Fig. 4, in the case of one truck in use, the dead
time of pick up machine is reduced as the capacity of the truck is increased from 34
bales to 48 bales. Although, when the capacity is increased to 64 bales the dead time is
greater than the case of 48 bales capacity, because the truck of 64 bales capacity needs
more time to be unloaded when it arrives to bio-energy generation plant delaying the
activity of pick up machine. This implies that truck with high capacity does not
always minimise the bottlenecks of an activity due to the interaction that capacity has
to other parameters, such as unloading time.
By using two trucks in the process, the dead time of pick up machine reaches zero
because there is always an available truck. However, the dead time of the truck is
getting higher in comparison with the case of one truck because the second truck is
always waiting for the first to be loaded and leave. As the capacity of the trucks
becomes higher, the dead time of the trucks is increased because the second truck
waits longer. For minimizing the dead time of a truck, increased number and/ or
capacity of pick up machines should be used. Also, the total number of forklifts in use
should be considered in both locations (field and bio-energy plant) in order the
bottlenecks of the truck to be reduced.
By analysing Table 3, it occurs that higher dead time of a machine does not
necessarily mean higher total operating time or cost. For long travel distances between
the field and the bio-energy plant, when low capacity trucks are used, the process is
more expensive because of the increased number of transportations.
6 Conclusion
A simulation model for biomass supply chain including the operations of mowing,
baling, picking up, loading, transporting and unloading was created. Different
scenarios concerning how the number and/or the capacity of the truck(s) can affect the
process in terms of time, cost and bottlenecks were examined. The increased number
and/or capacity of trucks make the process less time consuming but not always less
cost consuming. Factors like the area of the field and travel distance should be
considered for the best choice of number and capacity of the truck. However, the
biomass supply chain can be more optimized in terms of time, cost and bottlenecks, if
the number and capacity of pick up machine as well as the number of forklifts are
taken into consideration.
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References
1. Berndes, G., Hoogwijk, M. and Van Den Broek, R. (2003) The contribution of
biomass in the future global energy supply: A review of 17 studies. Biomass and
Bioenergy, 25, p.1-28.
2. Goldemberg, J. (2000) World Energy Assessment. United Nations Development
Programme, New York, USA. P.508.
3. Jager-Waldau, A. and Ossenbrink, H. (2004) Progress of electricity from biomass,
wind and photovoltaics in the European Union. Renewable and Sustainable Energy
Reviews, 8, p.157- 82.
4. McKendry, P. (2001) Energy production from biomass (part 2): conversion
technologies. Bioresource Technology, 83, p.47-54.
5. Orfanou, A., Busato, P., Bochtis, D.D., Edwards G., Pavlou, D., Sørensen, C.G.
and Berruto, R. (2011) Scheduling for machinery fleets in biomass multiple-field
operations. Computers and Electronics in Agriculture, 94, p.12-19.
6. Richardson, J., and Verwijst, T. (2007) Multiple benefits from sustainable
bioenergy systems. Biomass and Bioenergy, 31, p.599-600.
7. Sambra, A., Sørensen, C.G. and Kristensen, E.F. (2009) Supply chain optimization
of rapeseed as biomass applied on the Danish conditions. Joint International
Agricultural Conference 2009 (jiac2009), 05 – 07 July 2009, Wageningen,
Netherlands.
8. Sambra, A., Sørensen, C.G. and Kristensen, E.F. (2008) Optimized harvest and
logistics for biomass supply chain. Proceedings of European Biomass Conference
and Exhibition, Valencia, Spain.
9. Sørensen, C.G. and Bochtis, D.D. (2010) Conceptual model of fleet management
in agriculture. Biosystems Engineering, 105(1), p.41-50.
10. Veringa, H. J. (2006) Advanced techniques for generation of energy from biomass
and waste, Website ECN.
11. Yamamoto, H., Fujino, J. and Yamaji K. (2001) Evaluation of bioenergy potential
with a multi-regional global-land-use-and-energy model. Biomass and
Bioenergy,21, p.185-203.
797