Biomass Transportation Model and Optimum Plant Size for the Production of Ethanol

Introduction

            The increasing costs of fossil fuels and environmental concerns linked with greenhouse gas emissions have sparked great interest in cellulosic-based second generation biofuels. Despite considerable efforts over the last five years to develop economically feasible process, some challenges still remain to commercialize cellulosic-based biofuels. In the Midwest, corn stover is an abundant agricultural residue and thus a potential feedstock for biofuels. To be used in biofuel production, agricultural residues must be collected from many farms spread over a large area and transported to a single production facility giving rise to challenges associated with the logistics of harvesting, collection, storage, and transportation. The economic success of biofuels from agricultural residues and the reduction of greenhouse gas emissions during their life-cycle depend on resolving the logistic complexity associated with biomass collection (Overend, 1982).

            The low energy density of agricultural residues along with the logistic hurdles limit the scale of bio-refineries; this issue does not affect petroleum refineries. As the plant size increases, the reduction of depreciation (from capital investment) due to economies of scale leads to a reduction in the production cost; on the other hand, the increase in biomass needed to meet plant demand leads to an increase in the transportation distances (due to a larger collection area), thus increasing the transportation cost. As a result of these competing factors, bio-refineries present an optimum plant size that minimizes the total production cost. Historical data from the chemical industry indicates that the scaling exponent is around 0.6, commonly known as the six-tenth rule of thumb (Peters and Timmerhaus, 1991); the rule has great influence from petroleum refineries and petrochemicals plants. Some ambiguity exists regarding if the rule applies when scaling capital investment for bio-refineries.

Model Description

            A detailed model is developed to quantify the collection of agricultural residues and transportation costs as well as to assess the optimum plant size for biorefineries. The model has two main elements; specifically, the production and transportation cost components. A simplified case study for an ethanol plant, using a dilute acid hydrolysis process, located in Macon county in central Illinois is used for the production model. The economic analysis of the production process is based on work performed at National Renewable Energy Laboratory (NREL) and reported by Aden et al. (2002); an Aspen Plus model is used to assess the cost of the production process. A two-step approach is used to develop the transportation component. First, a detailed model referred to as the "Farm Model", in which individual farms are modeled, is used to obtain a non-dimensional transportation factor for a base case plant size. In a second step, the dimensionless factor in a simplified transportation model that scales the transportation cost with plant capacity. The simplified transportation model coupled with the production cost model are used to perform the optimization analysis; this model is referred to as the "Optimization Model".

            The model accounts for collection costs, transportation, storage of biomass, and cost associated with the conversion of corn stover to ethanol. The model assumes the farmland is uniformly distributed around the processing facility and that there are no local variations in the characteristics of the crop (i.e., the yield and the other factors do not vary within the collection area). The harvesting cost is based on the collection of round bales on a second pass. The bailing cost includes collecting, shredding, raking, and staging of bales at the edge of field. Storage of the corn stover is considered in the model; corn stover is only harvested a few months per year, leading to the need for storing the feedstock to operate the bio-refinery year-round. Fixed cost and variable costs are accounted for in the production cost model. The former includes capital depreciation, maintenance, insurance and taxes, and labor. The later includes raw materials, utilities, waste handling, and consumables.

Results

            The Farm Model is used to assess three transportation schemes. Scheme I, assumes the corn stover is transported from the farm to the processing facility in a straight line. Scheme II, assumes the corn stover is transported along the catheti (i.e., sides) of the right triangle formed by the radius connecting the farm to the plant and the axes of the Cartesian coordinate system. Finally, in Scheme III, the road distance is calculated from the straight-line distance (from Scheme I) by means of a winding factor. An average winding factor was calculated from the average transportation distance from Scheme II (the average transportation distance from Scheme I is also required) and used for Scheme III. A shift of the distribution towards lower transportation distances is observed when using the winding factor to calculate road distances compared to actual road distances even though the average transportation distance is equal. In the transportation industry, the hauling cost per distance decreases with increasing transportation distance; higher unit cost per distance are charged for shorter hauls (Glassner et al., 1998). The difference in the distributions of transportation distances between actual road distances (Scheme II) and the ones obtained through the winding factor (Scheme III) would lead to different total transportation costs (i.e., for all the biomass transported to a given plant). The transportation cost estimated from the use of the winding factor would be greater than that paid from actual road distances due to the higher number of shorter hauls.

            The optimization problem requires that the transportation and production components scale with capacity. One of the main components of the production cost is the capital depreciation which is calculated from the capital investment. As commonly done, the capital cost is expressed with an exponential function to capture the effect of economy of scale. The investment for 2000 tonne/d and 907 tonne/d plants were obtained from the detailed model developed at the NREL (Aden et al., 2002). The capital investment scaling exponent was calculated. The impact of plant size on the scaling exponent is also investigated.

            The optimum capacity and minimum total production is greatly impacted by the farmer participation. The rate of change of production cost (i.e., slope of the curve) decreases as farmer participation increases; the rate of change decreases significantly (i.e., the slope of the curve flatness) for farmer participations greater than 0.4. The observed behavior is a result of the relationship between the cost of delivered corn stover and farmer participation. The cost of delivered corn stover is proportional to the square root of the inverse of the farmer participation. As a consequence of the proportionality, the reduction in cost for increasing farmer participation from 0.2 to 0.5 is greater than that of increasing it from 0.5 to 0.8. Biofuel producers should target at least a farmers participation of 0.5; this point is in the zone where the minimum production cost has a reduced sensitivity to the farmer participation (greater than 0.4). An increase of farmer participation to 1.0 may require significantly more resources and the economic benefit, reduction of the production cost, might not justify it. The optimum plant size increases with increasing farmer participation.

Conclusion

            A detailed model that includes cost of collection, transportation, storage, and chemical transformation was developed to evaluate the optimum plant size of bio-refineries. A simplified bio-ethanol refinery via dilute acid hydrolysis from corn stover is presented as a case study. The conversion of straight-line, farm-to-plant distances to road distance via a winding factor shifts the distribution of transportation distances towards shorter hauls. The capital investment scaling exponent was calculated. The cost of the delivered corn stover is proportional to the square root of the inverse of the farmer participation; as a consequence, biofuel producers using agricultural residues as feedstock should target a farmer participation of fifty percent.

References

Aden, A., Ruth, M., Ibsen, K., Jechura, J., Neeves, K., Sheehan, J., Wallace, B., Montague, L., Slayton, A., Lukas, J., 2002. Lignocellulosic biomass to ethanol process design and economics utilizing co-current dilute acid prehydrolysis and enzymatic hydrolysis for corn stover. U.S. Department of Energy Laboratory, National Reweable Energy Laboratory, Golden, CO.

Glassner, D., Hettenhaus, J., Schechinger, T., 1998. Corn stover collection project. Procedings of BioEnergy'98—Expanding Bioenergy Partnerships, Madison, WI, pp. 1100-1110.

Overend, R.P., 1982. The average haul distance and transportation work factors for biomass delivered to a central plant. Biomass 2, 75-79.

Peters, M.S., Timmerhaus, K.D., 1991. Plant design and economics for chemical engineers,  fourth ed. McGraw-Hill, New York.

 

nomics for chemical engineers,  fourth ed. McGraw-Hill, New York.