The inbound logistics of delivering biomass from the field to a biorefinery has been investigated by the autors (Mukunda et al. 2006; Mukunda 2007). A model named, Biomass Feedstock Logistic Simulator (BmFLS) using discrete event simulation was developed on EXTEND™ . The model consisted of four blocks that simulated the following: (1) feed­stock generation from the field at harvest; (2) feedstock storage and loading at the field; (3) transportation to the biorefinery; and (4) inbound logistics operations at the biorefinery.

The analysis discussed here would only pertain to the feedstock inbound logistics. In developing a scenario for the analysis, an existing 102 MMGY corn ethanol fuel plant located in Indiana was assumed to be supplied corn stover feedstock. The inventory to be maintained was for 10 days of production. A conversion rate of 72gallons/dry ton of corn stover was used and 900 lbs of 8’x4′ x 3′ was assumed at the bale weight delivered to the plant from seven different farms that were categorized based on their sizes. These farms ranged from 10 to 80 miles of supply radius to the biorefinery and corn acreage data were taken from the National Agricultural Statistics Service (NASS) to compute the feedstocks collected from these radii mileage. Figure 7.10 (Mukunda 2007) shows the percentage of feedstock at various distances from the plant. It is important to observe that feedstock will be delivered from farms located at a range of mileage. This needs to be factored in when analyzing the travel times and arrival distances of trucks to the plant.

One of the first goals to tackle in analyzing the inbound logistics is to determine the fre­quency of feedstock delivery needed for maintaining the inventory level at the biorefinery. Feedstock delivery will have some restrictions based on the working hours of the plant deliv­ery schedule, the number of trucks that can be processed through, and the plant sampling, and unloading stations. The simulation tracked inventory at the plant and truck queuing inside the plant (Mukunda 2007) and the following statistics were collected: average feedstock inventory, total trip time, total service time, waiting time and utilization of the weighing, sampling, and unloading stations.

As mentioned before, the number of delivery trucks needs to match the sampling and unloading station capacity at the plant for trucks to be serviced within the limit of service hours (8-16 hours/day). Station utilization (sampling, weighing, and unloading) must be optimized to reduce redundancy. The number of stations needed is dictated by the numbers of inbound truck deliveries to the biorefinery and there is a trade-off between the number of trucks and unloading station capacity (Figure 7.11; Mukunda 2007) . The total trip time, service time, and waiting time all depends on the size of the biorefinery. On average, all these times increase with increasing plant sizes as shown by the analyses of plants capacities from 40 to 200 (Figure 7.11; Mukunda 2007). Reducing these times will reduce the cost of feedstock delivered.

As mentioned previously, the trucking cost is by far the most expensive of all the logistics components. Analysis conducted for 100MMGY showed that the trucking cost to the plant over 10 years can be 30 to 40 times more than the handling cost at the plant (Mukunda 2007). This means more emphasis should be placed in optimizing transportation tonnage in order to have a real impact on the logistics of handling bulky plant biomass feedstocks.