# (527h) A Composite-Curve-Based Biomass Procurement Planning Approach

- Conference: AIChE Annual Meeting
- Year: 2016
- Proceeding: 2016 AIChE Annual Meeting
- Group: Computing and Systems Technology Division
- Session:
- Time: Wednesday, November 16, 2016 - 2:43pm-3:02pm

**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 concerns^{1}. 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

methods^{2}. In many studies, the farms are treated as points without

shape or area^{3}. 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 networks^{4} 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.

**Fig****ure 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.

**References**

[1] DOE

Bioenergy Technologies Office, 2014. Multi-year program plan, Washington DC,

USA: DOE.

[2] DOE/EERE, 2013c. Feedstock supply and

logistics: biomass as a commodity, US: Department of Energy, Office of Energy

Efficiency & Renewable Energy.

[3] 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.

[4] Linnhoff,

B. & Hindmarsh, E., 1983. The pinch design method for heat exchanger

networks. Chemical Engineering Science, 38(5), pp. 745-763.