460724 A Composite-Curve-Based Biomass Procurement Planning Approach
A Composite-Curve-Based Biomass Procurement Planning Approach
Wenzhao Wu, Daniel Kurniawan, WenBo Zhu, Christos T. Maravelias*
Dept. of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706
The production of fuels and chemicals from biomass has received considerable attention recently due to environmental concerns1. Since the production of biofuels involves relatively expensive feedstock and energy-intensive biomass transportation, any biomass-to-fuels strategy should include an efficient, both in terms of cost and environmental impact, biomass (feedstock) supply chain. Unlike fossil fuels, biomass, as a low-energy density resource, is sparsely distributed. The efficient biomass transportation thus requires biomass procurement planning methods2. In many studies, the farms are treated as points without shape or area3. This is a reasonable assumption when the transportation distance is so large that the shape and size of the farms can be neglected. In this case, the transportation problem is modeled as a point-to-point (farm-to-refinery) problem. However, the shape and size of the farms cannot be neglected when the refinery is close to the farm, which means that the size of the farm is not significantly smaller than the transportation distances, which in turn means that the error in approximating the real transportation distance with the distance between the center of the farm and the bio-refinery can be quite large. In this case, transportation should be treated as a region-to-point problem. To this end, we discuss a novel approach to biomass procurement planning on a region-to-point basis.
In terms of transportation, we propose a region-to-point modeling approach based on mathematical integration (in a polar coordinate system) over the sourcing region that has unique characteristics such as shape, location, and productivity. The transportation cost is correlated with the amount of biomass (mass) procured from each farm. The final result is a function C = f (M), where M is the mass, and C is the transportation cost. In other words, the proposed methods generate a function that returns the total cost of procuring M mass, which is graphically represented by a procurement curve. Both algebraic and numerical solution methods are discussed and demonstrated with examples.
In terms of system-level procurement planning, we develop a composite-curve-based approach that incorporates the regional transportation modeling method, and aims at identifying the biomass procurement plan that minimizes the total procurement cost (including biomass purchasing, harvesting and transportation). The specific steps for the generation of the composite curve using the individual procurement curves, as well as insights into the procurement planning problem are discussed. An analogy to the cold/hot composite curves in the pinch design method for heat exchanger networks4 is also presented. A case study involving 12 farms (which are approximated as polytopes) and one refinery is presented (see Figure 1A). The corresponding composite curve is shown in Figure 2, and the final procurement strategy is graphically represented in Figure 1B.
Figure 1. (A) Map of farms (the polytopes) surrounding a bio-refinery (the black dot at the origin) for the case study; (B) procurement strategy (represented by the green dashed areas) for a 11800 T/year demand. The farm numbers are labeled accordingly.
Figure 2. Individual procurement curves and the composite curve for the case study. The composite curve is marked thick. The demand of 11800 T/year and the corresponding per-mass supply cost () on the y-axis are marked with dashed lines. The farm numbers are labeled accordingly.
The proposed composite-curve-based method allows us to more accurately calculate transportation distance, and thus transportation costs and GHG emissions due to transportation. It also provides some key insights into the design of biofuel supply chains. In addition, the methods proposed in this work are integrated with mathematical programming to address complicated problems involving, for example, multiple feedstocks, multiple refineries, and multiple periods. We can solve such problems by either generating multiple composite curves, or directly incorporating the C = f (M) function for each farm into a general supply chain optimization model.
 DOE Bioenergy Technologies Office, 2014. Multi-year program plan, Washington DC, USA: DOE.
 DOE/EERE, 2013c. Feedstock supply and logistics: biomass as a commodity, US: Department of Energy, Office of Energy Efficiency & Renewable Energy.
 You, F., Tao, L., Graziano, D. & Snyder, S. W., 2012. Optimal Design of Sustainable Cellulosic Biofuel Supply Chains: Multiobjective Optimization Coupled with Life Cycle Assessment and Input-Output Analysis. AIChE Journal, Volume 58, pp. 1157-1180.
 Linnhoff, B. & Hindmarsh, E., 1983. The pinch design method for heat exchanger networks. Chemical Engineering Science, 38(5), pp. 745-763.