CHEMICAL ENGINEERING TRANSACTIONS
VOL. 81, 2020
A publication of
The Italian Association
of Chemical Engineering
Online at www.cetjournal.it
Guest Editors: Petar S. Varbanov, Qiuwang Wang, Min Zeng, Panos Seferlis, Ting Ma, Jiří J. Klemeš
Copyright © 2020, AIDIC Servizi S.r.l.
ISBN 978-88-95608-79-2; ISSN 2283-9216
Supply Chain Design of Poultry Waste Valorization through
Pyrolysis: Economic and Spatial Analysis for New York State
Yanqiu Tao*, Fengqi You
Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, 14853, USA
yt554@cornell.edu
As an organic waste with high nutrient contents, the conventional landfill or incineration of poultry waste has led
to growing concerns. This study applies spatial analysis to two scenarios of poultry waste valorization supply
chain design in New York State (NYS). Ultimately, the optimal distribution of slow and fast pyrolysis biorefineries
within NYS based on the operational and economic parameters, locations of major poultry farms, crude
refineries and corn croplands is discerned and mapped. Additionally, the biochar transportation costs are
distributed in the form of heat maps. The outcome indicates that building a single centralized biorefinery in NYS
for both slow pyrolysis and fast pyrolysis is more economically feasible than building multiple smaller
biorefineries for ten slow pyrolysis and eight fast pyrolysis distributed biorefineries in NYS.
1. Introduction
The explosive growth in global human population over the past few decades has urged the drastic increase in
food supply (Kaza et al., 2018), and subsequently led to the waste flooding (Demirbas et al., 2011). Poultry litter,
as one of the most common organic waste, if disposed by conventional disposal method such as land
application, has brought various concerns about air pollution, nutrient loss (Osorio et al., 2017), water
contamination and health risks (Seidavi et al., 2019). More sustainable alternatives are being considered for
treating organic waste such as poultry litter (Kantarli et al., 2016), owing to their potentials to sequestrate
biogenic carbon (Isemin et al., 2019), retain nutrients and recover energy through products such as biochar,
bio-oil and pyrolysis gas (Skaggs et al., 2018). Specifically, slow and fast pyrolysis have been shown to be
environmentally beneficial as a result of waste-to-energy conversion (Ning et al., 2019), as well as nutrient
recycling and carbon sequestration through biochar (Nicoletti et al., 2019). In the United States, 50 Mt/y of
poultry litter is produced, with most of it being either land applied or landfilled (Bolan et al., 2010). Some states
have identified thermochemical technologies to treat a part of their wastes and they are actively investigating
the large-scale application of these technologies (EIA, 2019). New York State (NYS) is a prime example with a
number of policies being deliberated upon currently to tackle organic wastes and produce sustainable energy
(EIA, 2019). Since the spatial data for the large poultry farms and the crop fields is also available for NYS, it
provides a great opportunity to carry out a spatial analysis for the region to investigate the feasibility of
implementing thermochemical technologies, along with the determination of optimal plant capacities (Garcia et
al., 2017). Most techno-economic studies assessing thermochemical technologies are found to choose a
predetermined downstream processing option for each technology (Yue et al., 2014), without investigating the
impacts for other downstream processing options or locations (Satrio et al., 2010). In this study, by utilizing
results and parameter values from a previous study, a spatial analysis to investigate how pyrolysis technologies
would perform if implemented at different scales for NYS is conducted. Novel contributions of this study is a
detailed spatial analysis scenario studies presenting the optimal plant locations and capacities for both pyrolysis
technologies in NYS.
2. Material and methods
To illustrate the economics of both centralized and distributed design of the pyrolysis biorefineries, a scenario-
based study for concentrated animal feeding operations (CAFOs) in NYS is presented. The following sections
DOI: 10.3303/CET2081187
Paper Received: 25/03/2020; Revised: 24/06/2020; Accepted: 24/06/2020
Please cite this article as: Tao Y., You F., 2020, Supply Chain Design of Poultry Waste Valorization through Pyrolysis: Economic and Spatial
Analysis for New York State, Chemical Engineering Transactions, 81, 1117-1122 DOI:10.3303/CET2081187
1117
provide details about the technical and economic parameters and constraints used in this study, as well as the
selection of different scenarios for NYS. The data are either available in the form of a county-level distribution
or based on the CAFOs for poultry litter in NYS. The latter is selected for this study as the fourteen CAFOs are
found to produce approximately 175 kt of poultry litter per year, which accounts for roughly 63 % of the total
production in the state. Additionally, they are found to be hotspots in terms of poultry litter density distribution,
providing ideal locations to build a plant, as against the county centers which would not always have the highest
densities owing to much smaller, distributed farms.
2.1 Parameters and constraints
The poultry CAFO data including name, geographic information and annual capacities are collected from the
NYS Organic Resource Locator, and presented in Figure 1 (RIT, 2019). It is assumed that the pyrolysis
biorefineries can only be built on the CAFOs themselves to minimize transportation of the poultry litter as
explained in an earlier section. Additionally, all the poultry litter feedstock generated by the 14 poultry CAFOs is
utilized. The minimum capacity of a slow pyrolysis biorefinery (Zhao et al., 2020) and a fast pyrolysis biorefinery
(Zhang et al., 2014) is assumed to be 4.38 and 8.76 kt/y, which corresponds to an input feed rate of 0.5 t/h and
1 t/h (Wright et al., 2010). Any biorefinery smaller than these minimum capacities for this study would prove to
be economically infeasible. Along similar lines, the minimum capacity of a CHP unit in a slow pyrolysis
biorefinery and a fast pyrolysis biorefinery is assumed as 4.60 and 3.68 kt/y (Zhao et al., 2014). These
correspond to production of 0.5 MW and 0.25 MW of electricity in that order (Zhu et al., 2019). Based on these
constraints, it is found that all fast pyrolysis biorefineries in our system can process the pyrolysis gas through
CHP, to generate heat to offset part of the O&M cost, and to produce electricity to generate additional revenue.
The bio-oil can either be upgraded on-site or sent to an existing crude refinery and the biochar is to be applied
on corn cropland.
Figure 1: Map containing distribution, names, and annual poultry litter generation amounts (t/y) for the CAFOs
in NYS, as well as existing crude refineries near the State.
Annual net revenue is calculated based on the difference between the sum of bio-oil income, biochar income,
electricity income and carbon tax income and the sum of capital cost, O&M cost, poultry litter transportation cost
and bio-oil transportation cost along with a fixed biochar price of $100 / t and without consideration of the biochar
transportation cost (You et al., 2012). All the parameters needed for the annual net revenue calculations are
assigned values based on the techno-economic analysis from a previous study (Bora et al. 2020). It is worth
mentioning that due to the difficulty in estimating the transportation cost for biochar, it is excluded from the
consideration of the biorefinery location and the annual net revenue is calculated with the premise of trading
biochar without transportation. Biochar breakeven price is subsequently calculated to illustrate the biochar
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application throughout the NYS. It is worth mentioning that when annual net revenue turns out to be positive, a
negative biochar breakeven price is obtained, and vice versa. Capital cost, O&M cost, biochar income, bio-oil
income, electricity income and carbon tax income are found to be dependent on the capacity and technology
choices for the biorefinery, while the poultry litter, bio-oil and biochar transportation costs are related closely
with the location of the biorefineries and corn croplands. To determine the optimal locations of the biorefineries
among the 14 CAFOs, the pairwise route distances between each crude refinery and CAFO, from one CAFO to
another, and between corn cropland pixels and each CAFO are computed and integrated in the calculation of
the breakeven price for biochar.
2.2 Choice of scenarios and cases for the spatial analysis
In this case study, two scenarios for both slow and fast pyrolysis technologies are chosen (Table 1). Scenario 1
involves building only one pyrolysis biorefinery for the entire NYS. According to the CAFO data, the overall
estimated poultry manure generated through NYS’s CAFOs is 175.3 kt/y, and that is chosen as the capacity for
the pyrolysis biorefinery in Scenario 1. (RIT, 2019) This scenario takes advantage of the economy of scale that
helps to reduce the unit production costs, but the transportation costs of both feedstock and products is
expectedly higher. Scenario 2 is designed to minimize the transportation of poultry litter. Based on the previously
defined cutoffs (minimum capacity) for building a pyrolysis biorefinery, the scales of some CAFOs are found to
be too small to build a biorefinery, and it is still necessary to transport poultry litter from those CAFOs to their
nearest biorefineries. Consequently, there are ten biorefineries for Scenario 2 of slow pyrolysis but only eight
biorefineries for fast pyrolysis due to the different cutoffs (minimum capacity) for building slow and fast pyrolysis
plants. The location of biorefineries in Scenarios 1 and 2 will be revealed and analyzed in Results and discussion
section.
Table 1: Description of scenarios for spatial analysis in terms of number of plants, technology and capacity.
Scenario name No. of plants Technology Capacity (kt/y)
SP Scenario 1 1 Slow
pyrolysis
175.3
SP Scenario 2 10 49.5, 39.4, 26.4, 17.3, 9.9, 8.3, 7.6, 7.1, 5.4, 4.6
FP Scenario 1 1 Fast
pyrolysis
175.3
FP Scenario 2 8 49.5, 39.4, 26.4, 17.3, 13.2, 9.97, 9.9, 9.7
3. Results and discussion
The spatial analysis results are presented based on the two different scenarios that are considered to treat
NYS’s poultry litter through either slow or fast pyrolysis. The location of poultry CAFOs is depicted using purple
dots with their size proportional to the farm’s capacity. A star is used to identify the optimal pyrolysis biorefinery
built for each scenario, and its size represents the original capacity of the chosen CAFO. The capacity of the
biorefinery is portrayed through the orange lines. In Figure 2, the arrows represent the transportation of poultry
litter from CAFOs to the pyrolysis biorefineries. Similarly, the location and capacity of crude refineries is shown
in the figures using a black rhombus with its size proportional to capacity. The arrows represent the
transportation of bio-oil from pyrolysis plants to the crude refineries. Transportation volumes are also labeled
besides the arrow or pointed out with lines.
3.1 Scenario 1: Building a single pyrolysis biorefinery in NYS
The heat maps of biochar transportation cost across NYS and the optimal supply chain design for SP Scenario
1 and FP Scenario 1 are illustrated in Figure 2a and 2b (where SP stands for slow pyrolysis and FP stands for
fast pyrolysis). It can be observed that the CAFO named Wayne County Eggs (Figure 1) is chosen to be the
location of the pyrolysis biorefinery for both SP Scenario 1 and FP Scenario 1. The large capacity (175.3 kt/y)
helps satisfy the constraints associated with building the biorefineries and their downstream processing facilities,
and both scenarios choose to process pyrolysis gas with a CHP unit. The FP Scenario 1 also chooses to
upgrade the bio-oil instead of transporting and selling it to existing crude refineries. Under the given choice of
technology and biorefinery capacity, the biorefinery location for FP Scenario 1 is only determined by the poultry
litter transportation, while the biorefinery location for SP Scenario 1 is a result of the combined effect of poultry
litter transportation and bio-oil transportation.
Transportation cost is based on the interaction between transportation distances and loads, so a biorefinery is
more likely to be built on a large CAFO with low total turnover of transportation (calculated through the
multiplication of load and distance), in order to avoid as much poultry litter transportation as possible. The 39.4
kt/yr capacity of Wayne County Eggs is the second highest among all the CAFOs and is not far from the 48.2
kt/y capacity of Giroux’s Poultry Farm (ranked first). While Giroux’s Poultry Farm is comparatively far from 12 of
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the CAFOs and the two crude refineries, Wayne County Eggs is closer to most CAFOs, among which there are
two CAFOs - Kreher's Farm Fresh Eggs and Whitesville Farms, whose capacities are the third and fourth
highest. Consequently, the summation of poultry litter capacity of the three CAFOs, namely Wayne County
Eggs, Kreher's Farm Fresh Eggs and Whitesville Farms, accounts for 47 % of all poultry litter feedstock from
the 14 CAFOs. Since the remaining CAFOs do not have capacities comparable to the four largest ones, the fast
pyrolysis biorefinery is most likely to be built at Wayne County Eggs.
While considering bio-oil transportation for slow pyrolysis, a biorefinery located towards the lower half of NYS
would be preferable, such as at Whitesville Farms and Ace Farms (Figure 1). However, the amount of bio-oil
produced from slow pyrolysis (33 kt/y) is much less than the amount of poultry litter to be transported (127 to
175 kt/y), and the bio-oil transportation distance for each CAFO is not significantly higher than the average
poultry litter transportation distance. The bio-oil transportation did not prove to be an influential factor on the
choice of biorefinery location in SP Scenario 1. As a consequence, Wayne County Eggs is chosen to be the
optimal location to build the pyrolysis biorefinery for both scenarios and the bio-oil from slow pyrolysis is
transported to both crude refineries, since the closest crude refinery is not able to accommodate all the produced
bio-oil based on the cutoff for the maximum bio-oil permissible in a conventional crude refinery.
For both scenarios, the radial color pattern of the heat map arising from the biorefinery represents the biochar
transportation cost varying from lower to higher values (Figure 2). The biochar breakeven price is found to vary
from $59 / t to $96 / t for slow pyrolysis while it changes from -$128 / t to -$91 / t for fast pyrolysis (summarized
in Table 2). The differences can be explained through the breakdown analysis of the economics (Figure S6 in
the Supporting Information). All the costs and sources of revenue are considered on an annual basis, and the
revenue generated through bio-oil, biochar, carbon tax and electricity account for 31.6 %, 47.0 %, 20.6 % and
0.8 % of the total income for the SP Scenario 1. In contrast, the FP scenario 1 has the bio-oil, biochar, carbon
tax and electricity account for 76.7 %, 17.3 %, 5.7 % and 0.3 % of the total income. Due to higher biochar
production in slow pyrolysis, its biochar and carbon tax revenues are found to be $6.98 M and $4.06 M more
than that of fast pyrolysis. On the other hand, since the diesel ($3.26/gallon) and gasoline prices ($2.79/gallon)
are much higher than the bio-oil price ($45/barrel), the upgraded fast-pyrolysis bio-oil is able to generate $30.9
M more than the slow pyrolysis bio-oil.
In terms of fixed and variable costs, the capital cost, O&M cost, poultry litter transportation cost and bio-oil
transportation cost account for 16.6 %, 59.8 %, 18.0 % and 5.6 % of the total costs in SP Scenario 1. For FP
Scenario 1, the capital cost, O&M cost and poultry litter transportation cost account for 18.2 %, 72.4 % and 9.4
% of the total costs with the poultry litter transportation cost having the same values for both scenarios. As a
result of the additional bio-oil upgrading equipment required for fast pyrolysis, FP Scenario 1 has $16.85 M
higher annual O&M costs and $3.91 M higher annualized capital costs compared to SP Scenario 1. The resultant
net revenues (derived from the earlier techno-economic analysis) are $13.42 M and $13.60 M for SP Scenario
1 and FP Scenario 1.
Figure 2: Transportation cost of biochar and illustration of biorefinery location, technology selection, capacity,
transportation of feedstocks and products in Scenario 1 for a. slow pyrolysis and b. fast pyrolysis.
3.2 Scenario 2: Building multiple pyrolysis biorefineries for NYS
The heat maps of biochar transportation cost distributed across NYS, and the optimal supply chain design for
SP and FP Scenario 2 are illustrated in Figures 3a and 3b. Under this scenario, we aim to build biorefineries on
all CAFOs. However, some CAFOs are too small to construct a pyrolysis biorefinery on, and poultry litter from
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these CAFOs is transported to the nearest biorefinery and further processed. Ten CAFOs (namely Kreher’s
Farm Fresh Eggs, Whitesville Farms, Wayne County Eggs, Hudson Egg Farms, Smith Quality Eggs, Harold
Brey & Sons, Bella Poultry, Ace Farm, Tomas Poultry Farm of Schuylevil and Giroux’s Poultry Farm) are chosen
as slow pyrolysis biorefineries with annual capacities of 26.4, 17.3, 39.4, 5.4, 4.6, 9.9, 7.6, 7.1, 8.3 and 49.5
kt/y. Among these, only Smith Quality Eggs processes its pyrolysis gas with a combustor due to the minimum
capacity limitation for CHP units, while the other biorefineries could utilize CHP units. Eight CAFOs are chosen
as fast pyrolysis biorefineries, namely, Kreher's Farm Fresh Eggs, Whitesville Farms, Wayne County Eggs,
Hudson Egg Farms, Harold Brey & Sons, Bella Poultry, Tomas Poultry Farm of Schuylevil and Giroux’s Poultry
Farm with annual capacities of 26.4, 17.3, 39.4, 9.97, 9.9, 13.2, 9.7 and 49.5 kt/y. Among these, all biorefineries
process their pyrolysis gas with a CHP unit. Three biorefineries, namely Hudson Egg Farms, Harold Brey &
Sons and Tomas Poultry Farm of Schuylevil, transport and sell their bio-oil to crude refineries while the others
satisfy the capacity constraint to upgrade bio-oil onsite.
In terms of bio-oil transportation, all slow pyrolysis biorefineries have to transport and sell bio-oil to their nearest
crude refineries. Some biorefineries have geographic advantages while the others do not. For fast pyrolysis,
only biorefineries which do not have sufficient capacity for the upgrading facilitites to be feasible would have to
transport and sell bio-oil to their nearest crude refineries. As mentioned previously in Scenario 1, the radial color
pattern of the heat map arising from all biorefineries represents biochar transportation cost varying gradually
from low to high value. As shown in Table 2, the biochar breakeven price varies from $76 / t to $91 / t for slow
pyrolysis, while it ranges from $74 / t to $93 / t for fast pyrolysis. Notably, the variance of biochar breakeven
price becomes less compared to that under Scenarios 1, suggesting that the biochar transportation cost does
not vary much for each pixel of corn cropland across NYS. This benefits from the sparse distribution of
biorefineries and greatly reduces the minimum transportation distance between corn cropland and biorefineries.
However, the distributed design of biorefineries leads to economic infeasibility here. Total annual biochar
income, carbon tax income and O&M costs are the same for all scenarios of slow pyrolysis or fast pyrolysis.
Under SP Scenario 2, annual electricity income is $7,620 less than that in SP Scenario 1 as a result of pyrolysis
gas combustion on Smith Quality Eggs. Under FP Scenario 2, annual electricity income is the same as that in
FP Scenario 1. Bio-oil income does not change for SP Scenario 2, compared to SP Scenario 1. Fast pyrolysis,
in contrast, earns 8.5 % or $3.27 M less revenue through bio-oil than that earned in FP Scenario 1, suggesting
that the distributed design of biorefineries is economically infeasible when CAFOs with small capacities exist.
Table 2: Range of biochar breakeven price for Scenarios 1 and 2 of slow and fast pyrolysis across NYS
Scenario name No. of plants Technology Range of biochar breakeven price ($ / t)
SP Scenario 1 1 Slow
pyrolysis
59 - 96
SP Scenario 2 10 76 - 91
FP Scenario 1 1 Fast
pyrolysis
-128 - -91
FP Scenario 2 8 74 - 93
Total annualized capital costs for SP and FP Scenario 2 are much higher compared to Scenario 1. To be
specific, the total annualized capital cost in Scenario 2 is 131.4 % and 99.7 % higher than that in Scenario 1 for
slow and fast pyrolysis. In contrast, the poultry litter transportation cost in Scenario 1 is 95 and 28 times higher
than that in Scenario 2 for slow and fast pyrolysis. Bio-oil transportation costs in Scenario 2 are slightly higher
than that of Scenario 1. The annual net revenue for Scenario 2, which is calculated with a fixed biochar price of
$300 / t and without biochar transportation cost, is found to be $12.48 M and $15.65 M less than those in
Scenario 1 for slow and fast pyrolysis. The reduction in biochar transportation cost is found to offset part of the
reduction in the net revenue, bringing the range of biochar breakeven price for slow pyrolysis lower and
comparable to that in SP Scenario 1. However, for fast pyrolysis, the reduction in net revenue is too much to be
offset significantly. Particularly, only two out of eight fast pyrolysis biorefineries show slightly positive annual net
revenues, while the others all possess negative net revenues. The biochar breakeven price is brought up
significantly from previous negative values to the range of $74 / t to $93 / t.
4. Conclusions
The poultry litter supply chain in NYS illustrates the variability in transportation of feedstock and products
associated with real practice. The centralized treatment of poultry litter is found to outperform the distributed
system for fast pyrolysis in NYS by a large margin, revealing the advantage that large-scale facilities possess.
For slow pyrolysis on the other hand, the differences in the biochar breakeven price between the two systems
are much smaller, portraying that either of the two could be suitable for NYS based on policy and market
demand. Fast pyrolysis is found to outperform slow pyrolysis under both centralized and distributed supply chain
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design, emphasizing the tremendous economic value that bio-oil currently possesses. The proposed model
provides a basis for decisions regarding the choice of technologies in the future, as a particular pathway would
be more suitable in some cases as compared to the others depending on scale, feedstock, operating conditions,
products desired, finances, and geospatial distribution of the entities involved.
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