Bioresource Technology Reports 7 (2019) 100210

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Bioresource Technology Reports

journal homepage: www.journals.elsevier.com/bioresource-technology-reports

Comparative energy and techno-economic analyses of two different T

configurations for hydrothermal carbonization of yard waste
Maryam Akbari, Adetoyese Olajire Oyedun, Amit Kumar
⁎

Department of Mechanical Engineering, 10-263 Donadeo Innovation Centre for Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada

ARTICLE INFO ABSTRACT

Keywords: Hydrothermal carbonization (HTC) includes conversion of wet biomass to hydrochar, a coal-like product,
Bio-coal eliminating the need for biomass pre-drying. There has been limited focus on the techno-economic assessment of
Hydrothermal carbonization the HTC process in the literature. In this study, techno-economic models were developed to assess the economics
Techno-economic model of bio-coal production from yard waste for two different HTC plant configurations. In configuration A, heat is
Production cost
recovered by steam using several flash separators while in configuration B special heat exchangers are used.
Net energy ratio
Scale factor
Process models were developed for the two configurations and then further used to estimate bio-coal production
costs. The bio-coal cost and the net energy ratio (NER, or ratio of energy output to the energy input in the
system) indicate that configuration A is preferable in terms of energy (NER of 5.2 versus 1.4 for configuration B)
but less desired economically because it costs 3.3 $/GJ more than configuration B.

1. Introduction Thus coal, the most polluting feedstock for electricity generation,
needs to be replaced by cleaner feedstocks (Government of Alberta,
In 2013, about 41% of world electricity production was from coal 2016a; Mody et al., 2014). Bio-coal, which is produced through the
(International Energy Agency, 2018) and the coal share in electricity artificial coalification of biomass feedstock (Wang et al., 2014), is re-
production was around 31% in 2017 in the US (Energy Information ceiving increased attention, particularly as a substitute for coal because
Administration, 2018). The use of coal contributes greatly to climate of their similar characteristics (Agar and Wihersaari, 2012). The coal-
change and global warming, the greatest global threats of this century like properties of bio-coal eliminate the need for any additional infra-
(Howard, 2017). There are many opponents to the global use of coal. structural changes in coal-fired power plants, which offers opportu-
Putting a stop to coal use would reduce global carbon dioxide emissions nities for both co-firing with and complete replacement of coal (Agar
by 44%, and professionals believe coal phase-out is the only way to and Wihersaari, 2012).
mitigate climate change (Howard, 2017). Thus 20 countries, including Co-firing bio-coal with coal in power plants has several advantages,
Canada, have joined a global alliance to phase out coal by 2030 (Doyle, including extending power plant lifetime and reducing GHG emissions
2017). (Wang et al., 2014). Unlike bio-coal, co-firing raw biomass with coal
Alberta is one of the three largest electricity producers in Canada, has drawbacks such as high transportation cost due to low energy
and about 47% of its electricity is from the firing of coal (National density (Rautiainen et al., 2012), low heating value, high moisture
Energy Board, 2018). Alberta is responsible for the majority of coal- content (Mody et al., 2014), and a lower co-firing rate (lower biomass
related pollution in Canada, more than all other provinces' coal-related to coal ratio) (Agar and Wihersaari, 2012). Raw biomass storage is
emissions combined (Government of Alberta, 2016a). Under federal another issue because of its hydrophilic properties (Mody et al., 2014)
regulations such as the Climate Leadership Plan (Government of and mass loss caused by fungal decay and microbial activity
Alberta, 2016a), Alberta's pollution level must decrease so that Canada (Rautiainen et al., 2012). Grinding raw biomass to sizes suitable for co-
can meet its carbon pollution reduction standards (83% of its 2005 firing in pulverized coal power plants is difficult, and firing raw bio-
levels by 2020). Hence 12 of Alberta's 18 coal power plants will be mass does not provide a stable thermal energy and may produce un-
phased out by 2030 (Government of Alberta, 2016a). However, phasing desirable tars (Mody et al., 2014). During the coalification processes,
out coal has consequences. The world's strong reliance on coal may give volatiles are released and fixed carbon increases, and so the ratio of
rise to higher electricity prices (Simes, 2016). oxygen to carbon (O/C) decreases. Hence bio-coal is safer than raw

⁎
Corresponding author.
E-mail address: Amit.Kumar@ualberta.ca (A. Kumar).

https://doi.org/10.1016/j.biteb.2019.100210
Received 7 March 2019; Received in revised form 26 April 2019; Accepted 26 April 2019
Available online 29 April 2019
2589-014X/ © 2019 Elsevier Ltd. All rights reserved.
M. Akbari, et al. Bioresource Technology Reports 7 (2019) 100210

2012). Unlike pyrolysis, in torrefaction, the goal is to maximize the

Nomenclature
solid yield (Nhuchhen et al., 2014). When torrefaction is done in a dry
environment, it is called dry torrefaction, and if water is present, it is
COP cost of production
called wet torrefaction or hydrothermal carbonization. The temperature
NER net energy ratio
for hydrothermal carbonization (HTC) is in the range of 180–250 °C and
HHV higher heating value
the solid product of the process is usually called hydrochar. The most
APEA Aspen Process Economic Analyzer
important advantage of HTC over dry torrefaction is its ability to pro-
TPEC total purchased equipment cost
cess wet feedstock without the need for the energy-intensive drying
HTC hydrothermal carbonization
stage to reduce the feedstock's moisture content to below 15%, which is
necessary in dry torrefaction. This creates the opportunity to convert a
wide variety of feedstocks including municipal solid waste (MSW),
biomass in terms of the risk of self-ignition during grinding (Mody et al.,
sewage sludge, animal manures, aquaculture residues and algae, agri-
2014). Moreover, unlike raw biomass, bio-coal can be stored outside
cultural residues, and energy crops to bio-coal (Libra et al., 2011).
easily for a long time, thereby eliminating the cost of building a storage
During HTC, the inorganics in biomass are dissolved in the liquid phase,
facility (Rautiainen et al., 2012). Apart from the word bio-coal, many
which decreases the ash content of hydrochar. Biochar produced in the
other terms have been used in the literature for the coal-like solid
dry torrefaction process, on the other hand, has higher ash content than
product of coalification processes: char, torrefied wood, green coal,
raw biomass because in this process only volatiles can be released
biochar, charcoal, and hydrochar (Wang et al., 2014).
(Stemann et al., 2013). Furthermore, hydrochar is more like natural
In coalification processes, biomass is heated in an inert environment
coal than biochar in terms of hydrogen to carbon (H/C) and O/C ratios
and bio-coal, oil, and gases are generated as three main products
(Libra et al., 2011).
(Rautiainen et al., 2012). Depending on the reaction conditions (such as
Many experimental studies have been conducted on the HTC of
temperature and residence time), product properties such as bio-coal
various feedstocks including food waste (Li et al., 2013), moist agro-
yield and calorific value will vary (Libra et al., 2011; Rautiainen et al.,
industrial waste (Benavente et al., 2015), empty palm fruit bunches
2012). If the temperature ranges from 180 °C or 200 °C to 300 °C, the
(Jamari and Howse, 2012; Parshetti et al., 2013), forest residues (Bach
coalification process is called torrefaction, which is a partial pyrolysis
et al., 2014), fecal biomass (Danso-Boateng et al., 2013), sludge
process (Libra et al., 2011; Nhuchhen et al., 2014; Rautiainen et al.,
(Makela et al., 2016; Makela et al., 2015; Zhang et al., 2013), municipal

Fig. 1. Two plant configurations (A and B) for bio-coal production from biomass.

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M. Akbari, et al. Bioresource Technology Reports 7 (2019) 100210

waste streams (Berge et al., 2011), wheat straw digestate (Reza et al., 2. Method
2015), corncob residues (Zhang et al., 2015), distiller's grains
(Heilmann et al., 2011), black liquor (Kang et al., 2012), macroalgae 2.1. Process description and simulation
(Xu et al., 2013), and different types of wood (Bach et al., 2015;
Hoekman et al., 2011; Jatzwauck and Schumpe, 2015; Kambo and Two configurations for the HTC plant presented in an earlier study
Dutta, 2015; Reza et al., 2013; Sermyagina et al., 2015). There are few (Erlach, 2014) were investigated in this paper with yard waste as the
techno-economic assessments of the HTC process. Stemann et al. (2013) feedstock. 160 t/d of yard waste, with 70% moisture, corresponding to
investigated the economics of the HTC of empty fruit bunches as 48 t/d dry yard waste, is processed in the plants. The elemental com-
feedstock for a proposed HTC plant in Malaysia. The authors estimated position of yard waste is as follows: C 42.54 wt% H 5.94 wt% N 1.14 wt
investment costs from the literature; they did not include the land cost. % S 0.16 wt% O 44.62 wt% Ash 5.6 wt% (Erlach, 2014).
They also estimated labor cost using data for a pyrolysis plant. Erlach A simple schematic of plant configurations A and B are shown in
et al. (2011) studied the techno-economics of the HTC of poplar chips. Fig. 1. In configuration A, biomass slurry is prepared through direct
They estimated investment costs based on data from the literature. heating with steam; heat exchangers are not used for biomass pre-
There are a few published studies on the economics of the HTC heating for temperatures above 100 °C because of condensing tar-like
process using yard waste as feedstock. Those studies are not as com- substances and fouling problems in peat upgrading plants for tem-
prehensive as this one. Yard waste, the feedstock of the HTC plants peratures above 100 °C. In DeLaval's process, a peat hydrothermal
considered in this paper, refers to leaves, grass, branches, trimmings, treatment plant, the indirect heat is used to preheat biomass slurry
etc. In Calgary, Alberta, landfilling yard wastes will be banned starting through special heat exchangers (Erlach, 2014), which is configuration
in 2019 (Bell, 2015). 500,000 t/y of leaf and yard waste (or park and B. The two configurations differ mainly in terms of heat scheme and
gardening waste) are produced in Alberta by the residential, industrial, equipment used.
and commercial and institutional sectors (Government of Alberta, In configuration A, a mixture of biomass and recycled process water
2012), 95% of which are currently landfilled (Bell, 2015). The Leaf and from the filter press enters the reactor after the pressure and tem-
Yard Waste Diversion Strategy office of Alberta Environment and Sus- perature have been increased in several stages. Biomass is mixed with
tainable Resource Development has requested new policies and ap- recycled water to make a pumpable slurry. Mixing yard waste with
proaches for diverting yard wastes from landfills to value-added pro- recycled water that contains chemicals from the previous cycle in-
ducts (Government of Alberta, 2014). In the USA, 24 states and creases bio-coal production efficiency (Reza et al., 2014). Slurry is
hundreds of municipalities have already banned landfilling yard waste preheated by mixing it with steam recovered in the next processing
(Planet natural research center, 2018). Converting yard waste to bio- units from flash separators. The reactor temperature and pressure are
coal would not only eliminate the environmental pollutions associated 220 °C and 25 bar. Bio-coal slurry leaving the reactor is depressurized
with conventional yard waste management and coal use approaches but and cooled in flash separators at different pressures and temperatures.
would also diversify Alberta's economy by localizing a novel conversion The pressures of the flash separators are set such that all of the vapor
technology. produced in each step can be used in a corresponding preheating step.
HTC is a new technology; no commercial full-scale plant has yet Following depressurizing and cooling, bio-coal slurry enters a filter
been built (Stemann et al., 2013). For HTC to become economically press, where it is mechanically dewatered to 40% moisture content.
feasible, a detailed techno-economic model that comprehensively in- Part of the liquid phase leaving the filter press is recycled in the process
vestigates the economic feasibility of the process is essential. None of and mixed with biomass to make a slurry. Finally, the bio-coal is dried
the previous HTC techno-economics studies is specific or detailed en- with hot air to 10% dry matter (Erlach, 2014).
ough for a HTC plant. More importantly, the net energy ratio (NER), In configuration B, the “simplified configuration” in this study, in-
which is the ratio of output energy to input energy of the HTC process, stead of using flash separators to produce steam and heat recovery to
has not been studied previously, and there is no study discussing the preheat the slurry, heat transfer from steam is used to increase the
effect of capacity on the production cost of bio-coal or scale factor es- slurry temperature. Water acts as a thermal fluid to transfer heat from
timation for an HTC plant. In this study, two HTC plant configurations bio-coal to the biomass slurry. Heat is transferred through two special
are compared and the superior one (in terms of economics and energy) heat exchangers that can operate at temperatures higher than 100 °C
is selected based on the COP and NER. Configuration A uses the steam without risk of fouling (Erlach, 2014).
from flash separators while configuration B uses special heat ex- The two processing plant configurations are simulated in Aspen Plus
changers that can operate at higher temperatures without fouling. In V8.8 software (Aspen Technology Inc., Cambridge, Massachusetts,
addition, the effect of carbon tax on bio-coal price was investigated. USA). The reactor is simulated through the RYield reactor with the
The key objectives of this study are: component yield calculated in an earlier study (Erlach, 2014) (see
Table 1). Dissolved organics are considered as a single component that
• To develop detailed techno-economic models for bio-coal produc- is simulated using its aggregated elemental composition (Erlach, 2014).
tion from the HTC of yard waste for two different plant configura- The liquids considered separately in the simulation are acetic acid and
tions. formic acid. Dissolved ash includes minerals that are in dissolved form
• To calculate mass and energy yields and compare the NER of the two in the reactor outlet stream. Nonconventional parameters such as bio-
configurations. coal are defined by their elemental composition. The results of the si-
• To comparatively analyze the economic results of the two config- mulation were validated with Erlach's (2014) in terms of the char-
urations by assessing the COP of bio-coal associated with each acteristics of different process streams and bio-coal amount and com-
configuration. position. The difference in the yield of bio-coal between the model and
• To explore the effects of production plant capacity on bio-coal COP experimental results is less than 1%.
and develop scale factors for a hydrothermal carbonization plant
and to study the sensitivity of the COP to different cost parameters 2.2. Mass and energy yields and NER estimation
and uncertainty of the COP.
• To investigate the impact of carbon offset credits for clean bio-coal The mass and energy yields of the HTC process were calculated
fuel on the final COP. using Eqs. (1) and (2) obtained from Yan et al.'s (2009) work:
• To conduct a case study for Alberta, a western province in Canada.
Mass of dried biocoal
Mass yield (%) = × 100
Mass of dried biomass (1)

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M. Akbari, et al. Bioresource Technology Reports 7 (2019) 100210

Table 1 Table 2
Mass yields (%) in the HTC reactor outlet (Erlach, 2014). Input data and factors considered in techno-economic assessment.
Weight % Parameters Value

Components Base year 2015

CO2 9.14 Currency USD
CO 0.42 Internal rate of return 10%
CH4 0.02 Plant characteristics
H2 0.04 Lifetime 15 years
H2S 0.06 Location Alberta
C2H4O2 3.78 Operating hours 8000 h/y
CH2O2 0.27 Capacity 48 t/d (dry solid)
Dissolved organics 3.33 Plant construction factora
H2O 22.96 Year 1 20%
Ash (dissolved) 3.00 Year 2 35%
Bio-coal 56.98 Year 3 45%
Bio-coal composition Plant operating factora
C 63.00 Year 1 70%
H 4.69 Year 2 80%
N 2.01 Year 3 and onward 85%
S 0.18 Capital cost estimationb
O 25.56 Total purchased equipment 100%
cost (TPEC)
Total installed cost (TIC) Installation factor (3.02) × TPEC
HHV of biocoal Indirect cost (IC) 89% TPEC
Energy yield (%) = Mass yield (%) × Total indirect cost (TDIC) TIC + IC
HHV of biomass (2)
Contingency 20% TDIC
The higher heating value (HHV) of bio-coal was estimated from the Fixed capital investment TDIC + contingency
(FCI)
elemental analysis using Eq. (3) obtained from studies by Guo et al. Location factor (LF)c 10% FCI
(2016) and Channiwala and Parikh (2002). Total project investment FCI + LF
(TPI)
HHV (MJ/kg) = 0.3491 C + 1.1783 H + 0.1005 S 0.1034 O 0.0015 N Operating cost componentsd
(3) Maintenance cost 3% TPI
0.0211 Ash
Operating charges 25% labor cost
where C, H, S, O, and N = the carbon, hydrogen, sulphur, oxygen, and Plant overhead 50% (labor cost+ maintenance cost)
Subtotal operating cost Plant overhead + operating charges + labor
nitrogen content of bio-coal. According to Kieseler et al.'s comparison of
cost + maintenance cost + utilities cost + raw
different correlations for predicting the HHV of solids products of hy- materials cost
drothermal processes, Eq. (3) presents the lowest error for different G&A cost 8% subtotal operating cost
feedstocks, including grass, in the HTC process (Kieseler et al., 2013). Total operating cost G&A cost + subtotal operating cost
Escalation ratesd
NER is the ratio energy produced or generated to energy consumed
G&A 3.50%
or input (Prueksakorn and Gheewala, 2008). The NER is a crucial factor Project capital 5%
for renewable systems' evaluation and is calculated using Eq. (4) from Products 5%
Kabir and Kumar (2011): Raw materials 3.50%
Operating and maintenance 3%
Energyout labors
NER =
Energyin (4) Utilities 3%
Market prices specifications
where Energyout is equivalent renewable energy produced and Energyin Cooling watere 0.66 $/m3
Electricitye 0.05 $/kWh
is the primary non-renewable energy requirement (Kabir and Kumar,
Operator labor wage ratef 27.45 $/h
2011). Because there are many interpretations in the literature for the Supervisor labor wage ratef 35.29 $/h
terms input and output energy, it is essential to clearly define the terms. Landfill rate (yard waste unit 27.30 $/t
We define energy input as the primary input energy required for the price)g
system and energy output as the HHV-based energy of bio-coal pro- a
Distribution of plant construction over the 3 years before operation (Kumar
duced. The NER is an indication of how efficient a technology is in
et al., 2003; Sultana et al., 2010).
terms of energy and an NER of 1 is a thermodynamic breakeven point b
Method from Peters et al. (2003).
(Razon and Tan, 2011). c
Kumar et al. (2003).
d
Taken from APEA.
e
2.3. Development of techno-economic models EPCOR (2018).
f
Canada Salary Calculator (2016).
g
The simulated process models of the two configurations were City of Calgary (2016).
transferred from Aspen Plus to the Aspen Process Economic Analyzer
(APEA) to estimate the equipment cost for different unit operations, and calculated the total capital investment (capital cost) of the project from
a discounted cash flow sheet was later used to assess the production the TPEC evaluated in the APEA, as shown in Table 2. The labor cost
cost of the hydro-char process. The plant is assumed to be located in estimated by the APEA was verified with the Ulrich method (Brown,
Alberta given the vast potential to reduce the waste and pollution lo- 2006).
cally. First, process equipment is mapped, their sizes are calculated, and The input data considered in the techno-economic model was ob-
finally, the economics of the whole process are evaluated. Then the tained from the literature (City of Calgary, 2016; Kumar et al., 2003;
results of the APEA evaluation, including the total purchased equip- Peters et al., 2003; Sultana et al., 2010; EPCOR, 2018; Canada Salary
ment cost (TPEC), supervising and operating laborers required, and Calculator, 2016) and is summarized in Table 2.
utility rate, are used as input to develop a discounted cash flow sheet. Labor wages and water unit cost are considered in the calculations
Using the installation factors defined by Peters et al. (2003), we based on current market price in Alberta. To have a more reliable and

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M. Akbari, et al. Bioresource Technology Reports 7 (2019) 100210

realistic assessment, this study used the average market price of elec- Table 3
tricity over the past 11 years in Alberta because of the sharp drop in the H/C vs O/C atomic ratios of the solid products of HTC of yard waste, the tor-
price of electricity in the province in the last few years. refaction of yard waste, and different ranks of coal.
Fig. 2 shows the summary of techno-economic approach taken in Yard waste Biochar Hydrochar Lignite Bituminous
this study for calculation of production cost of bio-coal.
O/C 0.79 0.49 0.3 0.25 0.2
H/C 1.68 1.2 0.89 0.9 0.8
3. Results and discussion

3.1. Comparison of the bio-coal of hydrothermal carbonization and 3.3. Effect of capacity on unit capital cost and estimation of scale factor
torrefaction with different ranks of coal
Fig. 3A shows unit capital cost ranges. To study the effect of capa-
To define different ranks of coal and the suitability of other solid city on unit capital cost, capacity was changed by changing the input
fuels for co-combustion, co-pyrolysis, and co-gasification with coal, a amount of yard waste in Aspen Plus, and then the simulated process in
proximate analysis is commonly used. Table 3 shows O/C and H/C Aspen Plus was run for the new capacity. Larger capacities require
ratios of different ranks of coal and hydrothermally carbonized and larger equipment sizes. Individual equipment costs for the larger ca-
torrefied yard waste. The elemental composition of biochar is taken pacities were estimated using the APEA. Finally, the associated capital
from Verhoeff et al. (2011a, 2011b). As shown in the table, bio-coal cost was calculated. The graph showing capital cost versus capacity is
produced in the HTC process (hydrochar) has properties similar to coal, presented in Fig. 3A. As capacity increases to 100 t/d, the unit capital
while bio-coal produced in the torrefaction process (biochar) does not. cost drops sharply and then remains nearly constant at 150,000 $/t/d.
Dehydration and decarboxylation reactions in both processes cause H2O This same pattern was found in another study (Jenkins, 1997). There is
and CO2 to be released (Libra et al., 2011) and, as a result, the ratio of a slight increase (from 100 t/d to 200 t/d) because the number of
O/C and H/C decreases in solid products compared with raw yard identical items for some unit operations, like the reactor and the mixer,
waste. The carbon content (wt%) increases in both processes but more is doubled (this situation occurred for the first time at 200 t/d), and
drastically in HTC. This is in agreement with the outcome of studies thus economies of scale are ineffective for those unit operations. It
using loblolly pine as the feedstock (Yan et al., 2009). should be noted that at capacity of 200 t/d, there are 2 identical but
smaller pieces of these equipment sizes, but when the capacity doubles
(400 t/d), there are 3 identical, larger pieces of these equipment, not 4.
3.2. Mass and energy yields and net energy ratio Hence, economies of scale are ineffective only at this capacity for these
equipment types. As a result, unit capital cost increases at this capacity
The HHV of bio-coal is 24.8 MJ/kg (dry basis), which is in the same (200 t/d).
range as bituminous coal (Illinois #6) (Rubin et al., 2007). The mass Plant scaling-up is expected to decrease capital cost as relatively
and energy yields of the HTC process are 57.5% and 83.2%, respec- fewer equipment units are required for a larger capacity. Since there is
tively, showing that about 60% of mass of input yard waste is preserved no commercial wet torrefaction plant available, no scale factor is re-
in the form of bio-coal through the HTC process, while bio-coal includes ported for such a plant. In this study, using basic principles behind
about 85% of yard waste energy. scaling, scale factor was estimated using the following formula:
The NERs for configurations A and B are 5.2 and 1.4, respectively,
indicating a higher net energy ratio in configuration A than in B. The Capital costcapacity 2 Capacity2
=
system output's renewable energy is similar for both cases while the Capital costCapacity 1 Capacity1 (5)
primary input energy required for configuration B is 4 times higher,
mostly due to the extra coolers and heat exchangers. As explained be- Fig. 3A shows that for capacities above 100 t/d, increasing the plant
fore, in configuration A, instead of using special heat exchangers as in capacity does not change the capital cost per unit output. Therefore,
configuration B, the heat is recovered and reused from steam generated there is a nearly linear relation between capacity and capital cost per
in the flash separators. The design results in large energy savings and unit output above 100 t/d. This means the scale factor is approximately
smaller input energy required, as the NERs clearly indicate. equal to 1, and economies of scale have already been achieved at

Fig. 2. Summary of the techno-economic method used in this study.

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M. Akbari, et al. Bioresource Technology Reports 7 (2019) 100210

Fig. 3. (A) Unit capital cost versus capacity for configuration B (simplified configuration); (B) estimation of scale factor for configuration B (simplified configuration).

capacities below 100 t/d. 3.5. Effect of capacity on the COP

For capacities below 100 t/d, the effect of capacity on capital cost
per unit output results in a scale factor of 0.74 (see Fig. 3B). To develop Since configuration B comes with a lower COP than A, it is the
this graph, the capital cost for different capacities was calculated by preferred configuration in terms of cost, and so the results in the fol-
changing the input yard waste amount. As explained before, for capa- lowing sections describe only this configuration. Fig. 5 shows the bio-
cities below 100 t/d, unlike at higher capacities, economies of scale are coal production cost as a function of HTC plant capacity for config-
remarkable and effective. uration B. To calculate the COP at different capacities, the amount of
input yard waste was varied from the base capacity and a simulated
3.4. The cost of production (COP) of bio-coal process was run for that capacity. Then the associated cost components
were estimated and finally the COP was calculated through a new cash
Fig. 4 shows the components contributing to the final production flow analysis. An increase in feedstock capacity processed in the plant
cost of bio-coal in the first year for both configurations. There is a from 9.6 t/d to 960 t/d decreases the production cost of bio-coal sig-
difference in capital and labor cost for the two configurations because nificantly (to 4 $/GJ, a 92% drop from the 47.2 $/GJ, due to economies
configuration A has considerably more equipment, leading to a greater of scale for larger plants). For capacities higher than 960 t/d, the COP
labor requirement to operate the process equipment. Overall, the COP does not show a substantial change with capacity variations.
breakdown shows that capital cost contributes most to the COP for both
configurations. The reactor, filter press, and dryer are the most ex- 3.6. Effect of carbon offset credit on bio-coal COP
pensive equipment and make up 33.7%, 11.8%, 9.5% and 39.9%,
20.9%, 8.2% of the capital cost of configurations A and B, respectively. The Government of Alberta has taken steps to reduce carbon pol-
In configuration B, 19% of the capital cost is associated with the cooling lution by charging a carbon levy of 16 $/t for the year 2017 and 24 $/t/
system. Raw material includes water; only a small quantity is needed y from January 2018 onward (Government of Alberta, 2016b). As-
per year in both cases, as most of the water is recycled back to the suming 0.97 kg (2.16 pounds) of CO2 emitted per kWh electricity
processes. The landfilling fee (27.30 $/t of yard waste) is considered a generated from fossil coal (US EIA, 2016), substituting coal with the
source of revenue in this study because yard waste producers pay to CO2-neutral bio-coal produced in the plants studied here is projected to
dispose their yard waste (City of Calgary, 2016). This fee can vary from result in a 1.9 $/GJ carbon offset credit. This means that if the carbon
one jurisdiction to another. Since both configurations process the same offset credit cost is factored in, the COP of bio-coal will drop by 1.9
amount of yard waste, this amount of revenue is the same. The COPs of $/GJ.
bio-coal from configurations A and B are 16.4 $/GJ and 13.1 $/GJ. This approach can be used to calculate the carbon credit for any

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Fig. 4. Bio-coal production cost components for the two configurations.

other jurisdiction. The only difference with the Alberta case studied et al., 2015). Therefore, 24 years was considered the longest plant
here is the amount of carbon tax, which brings a different amount for lifetime here, corresponding to a change of +60% from the 15 years
carbon credit. Therefore, the amount that reduces the final COP differs assumed in this study. Capital cost and internal rate of return were
according to carbon tax imposed. changed by ± 40% and ± 100% based on data for a hydrothermal li-
quefaction plant (Jones et al., 2014); there is no published data for a
HTC plant. As explained before, consumers pay for yard waste disposal
4. Sensitivity analysis in many jurisdictions around the world in the form of a tipping fee, gate
fee, landfilling fee, etc. (California Department of Resources Recycling
The sensitivity of bio-coal COP to change in the economic para- and Recovery, 2015; Hogg, 2002; City of Calgary, 2016; Porter, 2010).
meters was investigated when each parameter was changed in- Yard waste disposal prices differ considerably throughout the year and
dependently, and the results are shown in Fig. 6A. For most parameters, by geographical location. It is especially beneficial to explore the cases
a ± 20% variation is relatively large and takes into account all un- of zero revenue from feedstock or incurring feedstock costs instead of
certainties related to the parameters. In the case of electricity price, a making revenue. In the case of yard waste feedstock, producers must
greater change ( ± 50%) was used here because this price can be sub- pay the landfilling fee, which is a source of revenue for the landfill and
ject to much larger uncertainty than ± 20% (Saari et al., 2016). Other lowers the final production cost of bio-coal. For cases when some bio-
exceptions to this ± 20% range are capital cost, internal rate of return, mass feedstock needs to be purchased or has transportation costs, bio-
the unit price of the yard waste, plant lifetime, and the annual oper- mass is not a source of revenue, and all costs associated with it will
ating hours of the plant. increase the final cost of bio-coal. If, as an example, feedstock waste
Obviously, there is a limit to plant operating hours per year, so the costs 11 $/t, which corresponds to −140% of the base unit price of
upper range is considered to be +10% of the base value. HTC is a new yard waste in this study, the COP will increase to 15.6 $/GJ. Therefore,
technology, moreover, so a conservative approach to plant lifetime is paying 11 $/t for biomass increases the bio-coal COP by about 2.5 $/GJ
considered, only 15 years, as is reported in other studies (Erlach et al., from the case in which biomass earns a revenue of 27.3 $/t.
2011; Stemann et al., 2013); mature technologies such as pyrolysis or The sensitivity of the COP to the yard waste unit price suggests that
hydrothermal liquefaction have higher plant lifetimes, i.e., 20 years 0 $/t for yard waste cost increases the COP significantly (to 14.9 $/GJ).
(Anex et al., 2010; Zhu et al., 2014) to 30 years (Jones et al., 2014; Ou

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M. Akbari, et al. Bioresource Technology Reports 7 (2019) 100210

Fig. 5. Effect of capacity on the production cost of bio-coal in configuration B (simplified configuration).

The increase in COP is due to the cut in the revenue coming from yard parameters. Uncertainties in process variables result in less precise
waste landfilling fee. The greatest impact on the COP of bio-coal is seen predictions. Therefore, it is necessary to evaluate how the amount of
with a 100% change in internal rate of return followed by a 40% change uncertainty in each parameter can affect model accuracy and results. To
in capital cost and a 140% decrease in yard waste unit price. A 60% analyze the uncertainty, a Monte Carlo simulation was conducted in
increase in plant lifetime decreases the cost of bio-coal to 11.2 $/GJ. this study by considering a range of probable volatilities for the influ-
The results also indicate that the COP is highly sensitive to annual ential parameters and the bio-coal cost. The uncertainty analysis was
operating hours. A 20% decrease in annual operating hours will raise carried out using Model Risk software with 10,000 iterations
the COP to 14.8 $/GJ. The COP is slightly less sensitive to labor cost (VoseSoftware, 2015). Of the many different distributions in risk ana-
than to annual operating hours. Plant lifetime shows the same trend as lysis, one of the most common is a triangle distribution, which uses
annual operating hours although to a smaller degree. The plant over- three input values (base, minimum, and maximum) to form a triangle
head cost, maintenance cost, yard waste unit price, operating charge using straight lines between these points (VoseSoftware, 2018). This
cost, general and administrative (G&A) cost, and electricity cost are distribution leads to a conservative distribution, and here a con-
low-sensitivity parameters; a 20% change changes the COP by less than servative approach seems necessary given the lack of data.
4%. Wastewater treatment cost and water cost are small; the effects of The results of the Monte Carlo simulation that are probability dis-
changes of ± 20% on the COP are not identifiable in the sensitivity tributions are shown in Fig. 6B. According to the distribution of prob-
graph. able results, the bio-coal COP is 13.14 ± 2.4 $/GJ with 95% con-
fidence.
5. Comparing the COP with fossil coal price
7. Conclusion
Larger hydrothermal carbonization plant capacities lower COPs. A
960 t/d yard waste plant produces bio-coal at 4 $/GJ (taking into ac- Hydrochar mass and energy efficiencies of the HTC process are
count yard waste revenue). As shown in Table 4, if yard waste provides 57.5% and 83.2%, respectively. The NERs are 5.2 and 1.4 for config-
neither cost nor revenue, then the bio-coal COP increases from 1.7 $/GJ urations A and B, which show that both plant configurations are ac-
to 5.7 $/GJ. Adding a carbon offset credit to this number reduces the ceptable with respect to energy. In terms of costs, the bio-coal cost of
COP to 3.8 $/GJ. The price of coal in Alberta was 2.1 $/GJ (2015 production (COP) for configuration B is 3.3 $/GJ less than for config-
dollars) (All-Canadian Coal-Fired Heaters, 2015) in the scenario in uration A. The two configurations differ mainly in terms of heat scheme
which biomass feedstock is paid for (and provides revenue) and when a and equipment used. Increasing the plant capacity from 9.6 t/d to
carbon offset credit is considered (see Table 4). 960 t/d of feedstock decreases the COP from 47.2 $/GJ to 4 $/GJ for
configuration B. The scale factor is 0.74 for configuration B.
6. Uncertainty analysis
Declaration of interest
Process models are usually associated with uncertainties resulting
from the assumptions and estimates of the input data and model None.

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M. Akbari, et al. Bioresource Technology Reports 7 (2019) 100210

Fig. 6. (A) Sensitivity analysis of bio-coal COP to key process parameters for configuration B (simplified configuration); (B) uncertainty analysis of bio-coal COP for
configuration B (simplified configuration).

Acknowledgements
Table 4
Bio-coal COP considering different scenarios for a 960 t/d plant capacity. The authors are grateful to the NSERC/Cenovus/Alberta Innovates
Biomass unit price ($/t)
Associate Industrial Research Chair Program in Energy and
Environmental Systems Engineering and the Cenovus Energy Endowed
−27.3 0 11 Chair in Environmental Engineering at the University of Alberta for
financial support for this research. As part of the University of Alberta's
Carbon credit Yes 2.1 3.8 4.5
Future Energy Systems (FES) research initiative, this research was made
No 4 5.7 6.4
possible in part thanks to funding from the Canada First Research
Excellence (CFREF). Astrid Blodgett is thanked for editing.

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M. Akbari, et al. Bioresource Technology Reports 7 (2019) 100210

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