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.
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.
New and improved ways are required for increasing the operational efficiency of
agricultural operations especially in complicated production systems
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.
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 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.
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.
Productivity (min/ha)
Capacity
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 of the truck is created when the pick up machine collects bales from the field and the truck waits for being loaded.
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.
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.