energies

Article
Assessing the Cost of Biomass and Bioenergy Production in
Agroindustrial Processes
Elias Martinez-Hernandez *, Myriam A. Amezcua-Allieri * and Jorge Aburto *

Biomass Conversion Division, Mexican Institute of Petroleum, Mexico City 07730, Mexico
* Correspondence: emartinez@imp.mx (E.M-H.); mamezcua@imp.mx (M.A.A-A.); jaburto@imp.mx (J.A.)

Abstract: This paper presents bioenergy value chain modelling to estimate the biomass and bioenergy
cost of production and biomass netback in combined heat and power (CHP) systems. Modelling
compares biomass cost and netback to analyse the feasibility of CHP systems, as well as the internal
rate of return (IRR) and payback period (PBP). Models are implemented into the IMP Bio2Energy®
software (Instituto Mexicano del Petróleo, Mexico City, Mexico) for practical application and demon-
strated for bioenergy generation in the agroindustrial processes of tequila production, coffee and
orange processing using as biomass the agave bagasse, coffee pulp and orange peels coproducts,
respectively. Results show that the CHP systems are economically feasible, i.e., biomass cost of
production is lower than netback, PBP between 3 and 4 years and IRR > 20%. The cost of bioenergy
is lower than the cost of fuel oil and grid electricity being replaced. The sensitivity analysis for
boiler steam pressure showed that there is an optimal pressure for coffee pulp (40 bar), a threshold
pressure for orange (60 bar) and agave bagasse (70 bar). Sensitivity to biomass input indicated a



maximum capacity where economy of scale does not produce any improvement in the indicators.


Results demonstrate the usefulness of the modelling approach and IMP Bio2Energy® in analysing
Citation: Martinez-Hernandez, E.;
biomass CHP systems.
Amezcua-Allieri, M.A.; Aburto, J.
Assessing the Cost of Biomass and
Keywords: biomass CHP; biomass cost; bioenergy cost; biomass netback; agave bagasse; coffee pulp;
Bioenergy Production in
Agroindustrial Processes. Energies
orange peels
2021, 14, 4181. https://doi.org/
10.3390/en14144181

Academic Editors: Alberto-Jesus 1. Introduction

Perea-Moreno and Adam Smoliński Biomass has become an important resource for energy generation worldwide as a
sustainable alternative to fossil fuels. The most widely spread modern use of biomass is
Received: 19 April 2021 in combined heat and power (CHP) generation systems either by direct combustion or
Accepted: 21 June 2021 through biomass gasification [1]. The techno-economics of biomass CHP has been studied
Published: 11 July 2021
for various biomass sources, with wood and agricultural biomass being the most widely
studied [2,3], with a few focusing on residues from the agroindustry [4–7]. Assessing the
Publisher’s Note: MDPI stays neutral
cost of biomass and bioenergy generation to establish competitiveness with current fossil
with regard to jurisdictional claims in
fuel systems is a crucial step for the establishment of successful bioenergy projects [3,8].
published maps and institutional affil-
Biomass feedstock is usually a major cost component in biomass conversion to energy
iations.
and biofuels. Furthermore, biomass cost is a source of uncertainty for bioenergy projects,
and the nature of biomass and the design parameters of the bioenergy system should also
be considered [8,9]. For example, biomass moisture, ash content, elemental compositions,
as well as system operating pressure affect both efficiency and economic viability. Therefore,
Copyright: © 2021 by the authors. models that allow costing biomass generation and the impact of biomass nature on the cost
Licensee MDPI, Basel, Switzerland.
of bioenergy are required. Such models also determine cost savings, due to fossil energy
This article is an open access article
replacements and operating and capital costs of bioenergy so that biomass netback, i.e.,
distributed under the terms and
the price that results in economic break-even, can be calculated. The netback indicates the
conditions of the Creative Commons
net value of a feedstock from its products selling, and it is also a cost threshold that indicates
Attribution (CC BY) license (https://
the value at which bioenergy generation from given biomass is no longer economically
creativecommons.org/licenses/by/
feasible [10].
4.0/).

Energies 2021, 14, 4181. https://doi.org/10.3390/en14144181 https://www.mdpi.com/journal/energies

Energies 2021, 14, 4181 2 of 17

Modelling biomass and bioenergy cost is generally complex in the case of dedicated
crops, or biomass from forest or agricultural fields, due to their variety, widespread and
temporal availability. Several efforts have been performed, especially in the case of dedi-
cated crops, forest and agricultural residues going from value chain costing approaches to
regional and global economic equilibrium models [11–14]. Case studies for the economic
assessment of lignocellulosic biomass pellets have been recently reported [15]. Biomass
CHP systems have also evolved to include carbon capture and storage (CCS). For example,
the techno-economic assessment of bioenergy with CCS in a sugarcane mill has been stud-
ied [16]. The cost-effectiveness of such CHP systems in carbon credit markets has also been
shown [17]. Biomass CHP systems are also recently reviewed as a key for flexible power
generation from biomass and cost-effective electricity supply [18]. Other works compare
different bioenergy technologies, including gasification and anaerobic digestion, and direct
combustion of pelletised biomass for large scale CHP systems [19]. A wider review of
biomass CHP systems is available in the literature [20].
The economic potential of alternative biomass sources for solid biofuels, such as
digestate and sawdust [21], and beetle-killed trees [22], have also been evaluated. Another
alternative source of biomass currently underutilised is the agroindustrial processing of
major crops, which generates large amounts of residual biomass that is already collected,
avoiding several logistics complexities. CHP systems are especially attractive in co-location
with processes demanding both electricity and heat in the form of steam, as shown for sugar
cane [23], sago [24,25], wheat [26], sunflower [27] and Jatropha processing [28]. Therefore,
it would be advantageous to use such biomass in bioenergy systems for self-supply of
energy in their parent agroindustrial processes [29]. The techno-economic modelling for
biomass and bioenergy value chain in agroindustrial processes has not been addressed in
the literature. Furthermore, the combined modelling from both biomass generation and
biomass utilisation perspectives has not been reported, as studies generally, focus on one
side of the bioenergy value chain, i.e., only the biomass generation and logistics side [30]
or the bioenergy generation side [31,32].
The agroindustry is one of the most important economic activities in Mexico, which gen-
erates about 28 million tonnes of solid residues [33]. Only a fraction of them is utilised
in animal feed, composting, or other uses and the rest is disposed of. The characterisa-
tion and technical potential of several biomass resources available in Mexico have been
widely reported [34,35]. Such underutilised agroindustrial biomass has the potential for
bioenergy generation in combined heat and power systems to supply energetic demands
of the processes that generate them, thus promoting a circular economy and avoiding
greenhouse gas emissions and other environmental problems. Bioenergy is becoming
relevant in the Mexican energy sector contributing to 8% of final energy consumption in
the country [36]. In this case, sugarcane bagasse is one of the main agroindustrial biomass
used to generate combined heat and power in sugar mills for the sugar production pro-
cess [37,38]. Another case study reported using orange peels to generate steam in the citrus
processing industry [39]. However, the wider adoption and deployment of bioenergy in
agroindustrial requires more capacity building and tools for aiding in the decision-making
processes, especially economic evaluation tools. This type of tools would also support
the development of energy services companies (ESCO) which business includes financing
projects switching fossil fuels to biomass [32,40]. The models and concomitant software
tool are demonstrated in this work to assess biomass and bioenergy costs for representative
agroindustry in Mexico, including tequila, coffee and orange processing. Such industries
generate agave bagasse, coffee pulp and orange peels as biomass. To the best of the authors’
knowledge, the techno-economic feasibility of CHP systems using such biomass feedstocks
is presented here for the first time.
Based on the hypothesis that comparing the upstream cost and the downstream net-
back value of biomass provides the basis for assessing a bioenergy system, this paper
proposes a biomass and bioenergy modelling framework for techno-economic assessment
to estimate the production cost of biomass and bioenergy, as well as biomass netback.
Energies 2021, 14, x FOR PEER REVIEW 3 of 17

Based on the hypothesis that comparing the upstream cost and the downstream net-
Energies 2021, 14, 4181
back value of biomass provides the basis for assessing a bioenergy system, this paper3pro- of 17

poses a biomass and bioenergy modelling framework for techno-economic assessment to

estimate the production cost of biomass and bioenergy, as well as biomass netback. The
modelling
The modelling is separated into an
is separated intoupstream (to estimate
an upstream the cost
(to estimate the of producing
cost biomass)
of producing and
biomass)
aand
downstream model (to estimate the cost of producing bioenergy
a downstream model (to estimate the cost of producing bioenergy and biomass net-and biomass netback).
This compares
back). the cost the
This compares of biomass and the netback
cost of biomass and theto establish
netback to the viability
establish theofviability
a bioenergy
of a
value chain.
bioenergy Furthermore,
value the economic
chain. Furthermore, theviability
economic indicators,
viability such as payback
indicators, period
such as and
payback
internal
period andrateinternal
of return, can
rate ofbe estimated
return, can bein estimated
the downstream model using model
in the downstream the biomass
usingcost
the
estimated from the upstream model. The upstream model parametrisation
biomass cost estimated from the upstream model. The upstream model parametrisation is detailed
enough for enough
is detailed capturing fordifferent
capturing agroindustrial processes generating
different agroindustrial processes biomass.
generatingThe down-
biomass.
stream model captures
The downstream modelthe thermodynamics
captures of the steam
the thermodynamics turbine
of the steamcycle andcycle
turbine usesand
the uses
bio-
mass composition
the biomass to capture
composition the effect
to capture of moisture,
the effect ash and
of moisture, ash elemental
and elementalcomponents
components to es-
to
timate
estimatethethesteam
steam generation
generationininboilers
boilersand
andthe
thesubsequent
subsequentelectricity
electricitygeneration
generation in
in steam
turbines.
turbines. Such
Such integrated
integrated modelling
modelling framework
framework for for biomass
biomass andand bioenergy value chain
used in the techno-economic
used techno-economic assessment
assessment of of bioenergy
bioenergy systems
systems is presented and applied
to agroindustrial
to agroindustrial settings here for the first time. The models have been implemented into
IMP Bio2Energy® (Instituto
IMP Bio2Energy® Mexicano
(Instituto Mexicano deldel Petróleo,
Petróleo, Mexico
Mexico City,City, Mexico),
Mexico) a user-friendly
, a user-friendly soft-
software
ware application
application programmed
programmed asExcel
as an an Excel add-in.
add-in.

2. Materials
2. Materials and
and Methods
Methods
The modelling
The modelling framework,
framework,including
includingthe two
the twomajor models
major implemented
models implemented into into
the IMP
the
Bio2Energy ® software tool, is shown in Figure 1. The upstream model considers all stages
IMP Bio2Energy® software tool, is shown in Figure 1. The upstream model considers all
of biomass
stages production
of biomass from cultivation
production field to conditioning
from cultivation for use in
field to conditioning forbioenergy systems,
use in bioenergy
with the calculation of biomass cost of production. The downstream model
systems, with the calculation of biomass cost of production. The downstream model con- considers the
bioenergy generation system to calculate biomass netback, bioenergy costs and
siders the bioenergy generation system to calculate biomass netback, bioenergy costs and economic
feasibility indicators. The modules included are briefly described as follows. Details on the
economic feasibility indicators. The modules included are briefly described as follows.
models and Equations can be found in the Supplementary Material.
Details on the models and Equations can be found in the Supplementary Material.

Figure 1. Overview
Overviewof
ofthe
themodels
modelsininIMP
IMPBio2Energy ® used
Bio2Energy® used
for for techno-economic
techno-economic assessment
assessment of biomass
of biomass and bioenergy
and bioenergy costs.
costs.
2.1. Biomass Production Cost (Upstream) Model
This model is intended to establish the cost allocated to biomass generated before
being utilised in a bioenergy system. This model follows a value chain approach with
stages considered depending on the system boundary and the origin of the biomass used
for bioenergy. In the case of dedicated energy crops and agricultural coproducts, the value
chain starts with the cultivation stage. In the case of agroindustrial biomass, the value
chain starts with the crop processing stage and may include a conditioning stage (in the
Energies 2021, 14, 4181 4 of 17

case of pellets, dried chips or other final form of biomass ready for utilisation in bioenergy
systems). The modules capturing the aforementioned stages in this model include:

2.1.1. Cultivation
Here the cost for cultivation is determined considering agricultural inputs from es-
tablishment, maintenance, harvesting and other costs, and for both irrigation and rain-fed
systems, similar to previous works [41]. For a given amount of crop, the mix from irrigation
and rainfed systems can be specified. A simplified model option is provided if only total
costs are already known, and itemised costs are, thus not provided as inputs. Several
products may be harvested from the cultivation field, such as the main crop, or any other
biomass coproduct. An economical allocation is then performed for the total costs.

2.1.2. Transportation
Here the costs for a given amount of transported material and an average distance is
determined from the mix of transportation means by road and rail. The rail transportation
model and two road transportation models are options using the transport Equation with
fixed and variable costs terms [13]. For the case of road transportation, there is a third
option with a detailed model disaggregating the operational (driver salary, fuel, insurance,
etc.) and fixed costs per journey.

2.1.3. Crop Processing

In this module, the mass and energy balance of the agroindustrial process generating
biomass is simulated based on information provided on the flows or input-output factors
(taking the amount of crop processed as a basis). Based on the simulation results, the mod-
ule calculates the operating and capital costs and allocates the total among the various
products, including any stream specified as a biomass product. Allocation can be done
using the mass or economic value approach, and if cultivation and transportation costs
are calculated, it can take into account the calculated cost of production of the crop or the
market price paid by the processing facility.

2.1.4. Biomass Conditioning

In this module, the mass and energy balance of biomass conditioning is simulated
based on information provided on the flows or input and output factors taking the amount
of raw biomass processed. Pelletising and drying are two conditioning processes provided
by default, but any other processes can be simulated. Pelletising costs are based on the
previous model [13], while drying is based on the calculation of the heat required for water
vaporisation for specified initial and final desired moisture. Based on the simulation results,
the module calculates the operating and capital costs and allocates the total costs to the
conditioned biomass product.
The modules can be run independently to calculate the cost of each stage. In addition,
total costs can be added in a final report. In this work, only the crop processing, and in
some cases, the biomass conditioning modules are run as the bioenergy is produced from
agroindustrial coproducts. However, when using dedicated crops or agricultural biomass
collected from the field, all the modules can be used for a full analysis.

2.2. Biomass Netback and Energy Production Cost (Downstream) Model

In this model, the total cost of producing bioenergy is determined using thermo-
dynamic modelling and simulation of a steam cycle. The energy production systems
include options for electricity only, heat only or combined heat and power systems. The
model includes:

2.2.1. Bioenergy Simulation Module

This module starts with the biomass amount and composition. Correlations to es-
timate high heating value are provided, or value can be user-specified. The elemental
 In this model, the total cost of producing bioenergy is determined using thermody-
namic modelling and simulation of a steam cycle. The energy production systems include
options for electricity only, heat only or combined heat and power systems. The model
includes:
Energies 2021, 14, 4181 5 of 17
2.2.1. Bioenergy Simulation Module
This module starts with the biomass amount and composition. Correlations to esti-
mate high heating value are provided, or value can be user-specified. The elemental com-
composition
position is used
is used to determine
to determine thethe stoichiometric
stoichiometric combustionrelationships
combustion relationshipsfor
formass
mass and
energy balance in the biomass boiler. Net heat available for steam production
energy balance in the biomass boiler. Net heat available for steam production is calculated is calculated
from an
from an enthalpy
enthalpy balance
balance considering
considering heating
heating value
value minus
minus energy
energy losses
losses through
through the the
boiler wall, the flue gas and the ash outputs. The parameters that can
boiler wall, the flue gas and the ash outputs. The parameters that can be explored in thebe explored in the
boiler model are the steam pressure and temperature, the efficiency (as
boiler model are the steam pressure and temperature, the efficiency (as 100 minus losses 100 minus losses
through the
through the boiler
boiler walls),
walls), the
the boiler
boiler feedwater
feedwater andand air
air input
input temperatures,
temperatures, the the flue
flue gas
gas
outlet temperature and the air excess ratio. Steam can be specified as saturated
outlet temperature and the air excess ratio. Steam can be specified as saturated steam or steam or
superheated. The steam properties are calculated based on the IAPWS
superheated. The steam properties are calculated based on the IAPWS formulation [42]. formulation [42].
In the
In the CHP
CHP system
system option,
option, the
the turbine
turbine parameters
parameters include
include the
the outlet
outlet pressure,
pressure, isentropic
isentropic
and generator efficiencies. With this flexibility, several operating conditions can be
and generator efficiencies. With this flexibility, several operating conditions can be anal-
ana-
lysed for any particular biomass-based energy system. The system calculates the turbine
ysed for any particular biomass-based energy system. The system calculates the turbine
power, net
power, net heat
heat andand power
power generation, percentage of
generation, percentage of electricity
electricity and
and steam
steam supply
supply forfor
given biomass
given biomass input
input andand demands
demands specified,
specified, the
the outlet
outlet steam
steam conditions,
conditions, and
and the
the system’s
system’s
efficiency. Figure 2 summarises the bioenergy CHP system simulated in a screenshot of the
efficiency. Figure® 2 summarises the bioenergy CHP system simulated in a screenshot of
IMP Bio2Energy software. The CHP system consists of combustion in a biomass boiler to
the IMP Bio2Energy® software. The CHP system consists of combustion in a biomass
generate steam, the steam is then passed on to a backpressure turbine to generate electricity,
boiler to generate steam, the steam is then passed on to a backpressure turbine to generate
and the resulting steam is sent to the corresponding agroindustrial process.
electricity, and the resulting steam is sent to the corresponding agroindustrial process.

Figure 2. Screenshot of IMP Bio2Energy®® software for the bioenergy module showing the flowsheet of the general CHP
Figure 2. Screenshot of IMP Bio2Energy software for the bioenergy module showing the flowsheet of the general CHP
system simulated.
system simulated.

The basic modelling equations for the bioenergy simulation are presented as follows.
The net amount of usable energy in the boiler to produce steam is estimated as:

netQ = totalQ(e f f boiler /100) − m ash Cp ash ( Tout − 25) −m f luegas Cp f luegas ( Tout − 25) (1)

where e f f boiler is the boiler efficiency, m ash is the ash mass flow, Cp are the corresponding
heat capacities, Tout is the outlet temperature, m f luegas is the flow of flue gases coming out
of the boiler. totalQ is the total energy that enters the boiler in biomass and air, estimated as:

totalQ = HV (1 − moisture/100) + m air Cp air ( Tair − 25) (2)

Energies 2021, 14, 4181 6 of 17

where HV is the heating value of biomass, moisture is the percentage of moisture in biomass,
m air is the airflow that enters for combustion, Cpair is the heat capacity of the air. The higher
heating value can be specified by the user or estimated from the elemental analysis of
biomass. The lower heating value is calculated from the higher heating value.
The amount of air and flue gases is calculated based on the biomass elemental compo-
sition assuming full combustion. The stoichiometric reactions assumed include:

C + O2 CO2

1
2H + O2 H2 O
2
2O O2
N + O2 NO2
S + O2 SO2
Then, a percentage of excess air is a parameter specified by the user to determine the
total air inlet from the theoretical oxygen flow:
 
f
mO2theo ∗ 1 + excess
100
m air = (3)
fO2air /100

where mO2theo is the stoichiometric mass flow required for complete combustion, f excess is
the percentage of excess air, fO2air is the percentage of O2 in the air.
Equations for calculation of steam properties according to IAPWS are used to deter-
mine the energy change (enthalpy) required to generate steam at the boiler operating T
and P conditions:
dHw = ( Hsteam − H f eedwater ) (4)
Then, the amount of steam generated is calculated as:

netQ
mvapor = (5)
dHw
This will be equal to the amount of water to be fed to the boiler, and will be used for
boiler sizing and cost estimation purposes.
From the calculated steam flow, the specified turbine outlet discharge pressure and
turbine efficiency, the amount of electricity generated (in kW) is calculated as:

Eel = Wtheo × e f f /1000 (6)

The total amount in kWh/year is obtained by multiplying Eel by the annual operating
time of the plant. eff is the specified efficiency factor. Wtheo is the theoretical expansion
work calculated by:

Wtheo = mvapor ( f e/( f e − 1)) ∗ p1 ∗ v1 ∗ (1 − ( p2/p1)ˆ(( f e − 1)/ f e)) (7)

where fe is the steam expansion factor, p1 is the vapor inlet pressure, p2 is the discharge
pressure of the turbine and v1 is the specific volume of steam (m3 /kg) at turbine inlet
conditions. mvapor should be in kg/s and pressure in Pa, to get the result in Watts.
Once the amounts of steam and electricity are determined, the overall efficiency of
the bioenergy generation process and the percentage of demand for steam and electricity
supplied is calculated.
Energies 2021, 14, 4181 7 of 17

2.2.2. Economic Assessment Module

This part of the model calculates first a biomass netback using the following Equation:

Energy sales + Savings − total costs

NB = (8)
m
where NB is the biomass netback in $US/ton and m is the biomass input flow rate
(ton/year). The netback considers the annual sales of any excess energy produced ($US/year),
fuel and electricity bill savings ($US/year) by the co-located process (agroindustrial crop
processing in this case), minus the capital and operating costs of the bioenergy system
($US/year). In this case, the cost of biomass is not considered, as the objective is to deter-
mine what biomass cost allows for economic break-even. The result can then be used to
establish whether the cost of biomass production or the purchasing biomass price is below
the netback, for the system to be economically feasible.
The bioenergy production costs are also calculated, but using a user-specified biomass
reference price. In this case, the net present value, the payback period and internal rate of
return are calculated. In this calculation, the biomass production cost determined by the
upstream model can be used to perform an economic feasibility evaluation for utilising
available agroindustrial biomass in a facility that is advantageous for replacing another
fuel and electricity from the grid. Further details of the economic modelling Equations can
be found in the Supplementary Material and in authoritative textbooks on bioenergy and
biorefinery systems [10].
Although the application of Bio2Energy® for agroindustrial biomass is presented here,
the software and models are flexible enough to analyse any type of biomass and bioenergy
process conditions. The user interface allows for sensitivity analysis of relevant variables
in each model. These capabilities are demonstrated in the following case studies.

3. Case Studies
Three agroindustrial value chains have been studied in this paper, including tequila
production, gold coffee production and orange juice production. Figure 3 provide a simpli-
fied diagram showing the major inputs and outputs for these processes. The biomasses
generated from these processes are agave bagasse, coffee pulp and orange peels, respec-
tively. Mass balances and utility demands were obtained from IMP Bio2Energy® using
the Crop processing module. In the case of agave bagasse and coffee pulp, it is assumed
that these biomasses are used with 50% moisture directly in the CHP system. In the case
of orange peels, these are dried from 80 to 30% moisture before used in the CHP system.
The drying process was simulated using the Conditioning module in IMP Bio2Energy® .
The main parameters for simulation of the agroindustrial processes are reported in Table 1,
including the amount of crop processed and biomass used in the CHP system. All the
mass balances and utility demands are reported in the Supplementary Material. The oper-
ational data was obtained from various literature sources for tequila production [43,44],
coffee [45,46] and orange processing [39,47,48].
The bioenergy generation process in a CHP system was simulated using the down-
stream model in IMP Bio2Energy® . The biomass compositions are summarised in Table 2.
The operational parameters specified for the boiler and steam turbine are reported in
Table 3. The parameters used for the economic assessment are indicated in Table 4.
Energies 2021, 14, x FOR PEER REVIEW 8 of 18

Energies 2021, 14, 4181 8 of 17

mass balances and utility demands are reported in the Supplementary Material. The op-
erational data was obtained from various literature sources for tequila production [43,44],
coffee [45,46] and orange processing [39,47,48].

Agave Bagasse Coffe pulp

Tequila Coffee beans Gold coffe
Water Tequila production Gold coffee
production
Yeast Methanol

Evaporated water
Vinasse
Other residues
Tails
(a) (b)
Orange
Orange peels 30%
Drying moisture
peels 80%
Orange moisture
Orange
Water
juice Concentrated orange juice
CaO production Essential oil

Wastewater
Press liquor
Other residues
(c)
Figure 3. Figure
Overview of the main
3. Overview of inputs
the mainand outputs
inputs andinoutputs
the agroindustrial processes processes
in the agroindustrial for: (a) Tequila
for: (a)production, (b) coffee (b) coffee
Tequila production,
production and (c) orange
production juice
and (c) production.
orange juice production.

Table 1. Agroindustrial operatingoperating

Table 1. Agroindustrial parameters for agave,for
parameters coffee andcoffee
agave, orange processing.
and orange processing.
Agroindustry Value Chain Tequila Coffee Orange
Agroindustry Value Chain Tequila Coffee Orange
Main crop Agave Coffee Orange
Main
Crop processing input crop
(ton/year) 19,500 Agave 47,175 Coffee 366,000 Orange
Operating time (h/year)
Crop processing input (ton/year) 8400 19,500 7200 47,175 4652 366,000
Steam demand (ton/ton
Operating input)
time (h/year) 2.087 8400 0.68 7200 0.125 4652
Electricity demand (kWh/ton input) 70.51 29.68 20.12
Steam demand (ton/ton input) 2.087 0.68 0.125
Main crop price ($US/ton) 1100 2753 125
Electricity demand (kWh/ton input) 70.51 29.68 Orange 20.12
Biomass generated Agave bagasse Coffee pulp
Main crop price ($US/ton) 1100 2753 peels 125
Biomass to CHP (ton/year)
Biomass generated 16,409 19,333
Agave bagasse 23,493
Coffee pulp Orange peels
Biomass to CHP (ton/year) 16,409 19,333 23,493
The bioenergy generation process in a CHP system was simulated using the down-
stream model in IMP Bio2Energy® . The biomass compositions are summarised in Table
2. The operational parameters
Table 2. Composition specified for biomass
of agroindustrial the boiler and bagasse,
(agave steam turbine are and
coffee pulp reported
orangein
peels).
Table 3. The parameters used for the economic assessment are indicated in Table 4.
Component Agave Bagasse Coffee Pulp Orange Peels
Table 2. CompositionCof agroindustrial biomass
17.57(agave bagasse, coffee
20.21 28.89
pulp and orange peels).
H 2.24 Coffee 2.65 4.05
Component Agave Bagasse Orange Peels
N 0.79 Pulp 1.17 0.38
C S 17.57 0.13 20.21 0.16 28.89 0.03
H 2.24 2.65 4.05
O 23.40 22.44 23.13
N 0.79 1.17 0.38
S Ash 0.13 5.86 0.16 3.37 0.03 13.51
O Moisture 23.40 50.00 22.44 50.00 23.13 30.00
Ash Reference 5.86 [35] 3.37 [35] 13.51 [39]
Moisture 50.00 50.00 30.00
Energies 2021, 14, 4181 9 of 17

Table 3. Operation parameters for simulation of the biomass CHP systems.

Parameter Value Unit

Boiler steam pressure 20 bar
Boiler steam temperature 212.4 ◦C

Boiler air excess ratio 50 %

Flue gas exit temperature 150 ◦C

Air inlet temperature 25 ◦C

Water inlet temperature 25 ◦C

Turbine isentropic efficiency 75 %

Turbine mechanical efficiency 90 %
Turbine outlet pressure 2 bar
Outlet steam temperature 120.2 ◦C

Table 4. General economic evaluation parameters.

Economic Parameter Agroindustrial Processing Bioenergy Unit

Tequila: 72,186 Agave bagasse: 35,414
Labour costs Coffee: 100,000O Coffee pulp: 48,341 US$/year
range: 325,000 Orange peels: 85,575
Working capital 10 - % of operating costs
Plant cost - Estimated US$
Project lifetime 20 20 years
Maintenance 0 0.5 % of annual capital cost
Annual inflation rate 3 3 %
Biomass value as animal feed 5 - US$/ton
Grid electricity cost 0.0858 US$/kWh
Current steam cost 14.87945 US$/ton
Current fuel used Fuel oil -
Fuel price 6 US$/GJ

For the agroindustrial crop processing stage, it is considered that all processing facil-
ities are in place, and investment has been already recovered. Therefore, no investment
and only working capital was considered. For the case of the orange peels, the investment
needed for drying equipment was estimated and included in the cost calculations. The cost
allocation in the agroindustrial crop processing stage was carried out using the economic
value method and using the average value of biomass as animal feed. Economic assessment
is performed in a Mexican context using $US as currency and based on 2020 as the year of
analysis. Prices for the main crop were specified and include transportation costs, thus the
cultivation and transportation modules were not used in the present case studies. In the
sensitivity analysis, we performed a two-way ANOVA, using Origin® v.8.5 to test whether
the independent variables (biomass type and biomass input, or fossil fuel price or boiler
steam pressure) have an effect on the dependent variable (unit cost, netback and PBP)
with an α = 0.05; i.e., Ho = biomass type = biomass input = fossil fuel price = boiler steam
pressure. All other economic data used in the assessment, as well as ANOVA results,
are presented in the Supplementary Materials.
Energies 2021, 14, 4181 10 of 17

4. Results and Discussion

4.1. Biomass Production Cost vs. Biomass Netback
The advantage of modelling the upstream and downstream sides of a bioenergy
system is that we can compare the cost of producing the biomass and the biomass netback
(the biomass cost threshold for the economic feasibility of bioenergy). Figure 4 shows the
results for the three case studies for the cost of biomass production on a wet basis. In all
cases, biomass production cost is lower than the netback, meaning that the bioenergy value
chain is economically feasible. The difference between these values is the net economic
benefit obtained by implementing the biomass-based CHP systems to supply energy in
Energies 2021, 14, x FOR PEER REVIEW 11 of 17
each of the agroindustrial processes. This demonstrates the advantage of the whole value
chain modelling and the usefulness of the approach for rapid assessment of the feasibility
of a CHP system as stated in our hypothesis.

Figure 4. Biomass production cost vs biomass netback for orange peels, coffee pulp and
Figure 4. Biomass production cost vs biomass netback for orange peels, coffee pulp and agave
agave bagasse.
bagasse.
On a wet basis, the production cost of biomass is lower for agave bagasse, followed
4.2. Bioenergy Production and Demand Satisfaction
by coffee pulp, while the cost of dried orange peels is much higher. On a dry basis,
Figure 5a
the highest costshows
is 9.8 the resultsfor
US$/ton oforange
the netpeels,
bioenergy produced
followed by coffee from pulpthe(7.24
CHPUS$/ton)
systems
using the corresponding
and agave bagasse (6.16biomass
US$/ton). in each
Thisagroindustrial
is mainly due process
to the costin Table 1. The
required toresulting
produce
CHP
orange electrical
peels withcapacities are in the
30% moisture. small size
However, range:
this results0.261
in aMW,
higher 0.414
biomassMW netback
and 1.134 forMWthe
for the agave bagasse, coffee pulp and orange peels, respectively.
orange peels case, mainly due to the economy of scale and higher bioenergy production, These are in line with
the varying
as shown inamounts of biomass
the following sections.available
Moreover, at each
the facility
higher thestudied.
fossil Figure 5b shows the
fuel replacement for
percentage of energy
steam generation, thedemand
higher the satisfaction
biomass for each agroindustrial
netback. This is because process. It cansavings
the higher be ob-
served that in the
allow higher agaveaccording
netback, bagasse case, the whole8.electricity
to Equation demand
In the cases is satisfied
of coffee pulp withand 55.5%
agave
of electricity
bagasse, it is being exportedexport
the electricity (aboutsales
763,806
that kWh/year),
contributes while
to their the steam demand
netback. Although is direct
satis-
fied only by 80.8%.
comparisons are notThis is because
possible, due tothe tequila process
a difference is intensive
in country conditions, in the steamtypes
biomass require-
and
ment for the distillation unit. In the coffee pulp case, both the
model assumptions, the costs obtained here are lower than costs reported for dedicated steam and electricity de-
mands
crops orare satisfied,
on-field with 107%
agricultural electricity
biomass ($10 being
to more exported.
than 100This is because
US$/ton) [1,12].the
Oncoffee pro-
an energy
cessing
basis, theinto goldwere
costs coffee2.7beans is relatively
US$/GJ, 2.6 US$/GJ less complex and requires
and 2.3 US$/GJ lowerbagasse,
for agave energy. Using coffee
pulp
an and orange
average peels,
electricity respectively.inThese
consumption costs are
rural houses inin line with
Mexico the low
of 1135 range [49],
kWh/year biomassthe
costs between
agave bagasse 2.25CHPUS$/GJ
and coffeeandpulp
4 US$/GJ reportedcould
CHP systems elsewhere
supply [1].electricity
The costs to for673
pellets
and from
1324
woody
rural biomass
houses. Thiswere
shows reported in the range
that significant of 8.4 and 9.6
socio-economic euros/GJ
benefits can be [11], and infrom
obtained the case
the
of Mexico, a pellet cost range between 6.3 and 12.8 US$/GJ has
tequila and coffee agroindustries using their biomass for bioenergy. In the orange peelsbeen reported [13]. It can
be observed
case, while the that thesedemand
steam biomassfor costs
juiceare significantlyishigher,
concentration due to logistics
fully satisfied, and pellet
the electricity de-
processing
mand costs involved,
is satisfied only by 40%. thusThis
showing the advantage
is because part of the of directly
dried orange using
peels agroindustrial
is burned to
biomassheat
provide for cost effective
to the dryingCHP generation.
units, thus reducing Studies on theavailability
biomass netback value are CHP
for the rathersystem.
scarce.
One study determined
Furthermore, the value
juice extractors and of 48 US$/dry
centrifuges forton as an upperare
concentration limit for the economic
electricity intensive
unit operations in orange agroindustrial processing.
A comparison with similar systems shows different demand satisfaction percentages,
due to differences in agroindustrial processing systems and biomass characteristics. For
example, sugarcane bagasse was able to supply 100% of heat and generated a 30% of elec-
 Energies 2021, 14, 4181 11 of 17

feasibility of biomass CHP [32], which is lower than the results obtained in this work for
coffee pulp (59 US$/dry ton) and agave bagasse (62 US$/dry ton).

4.2. Bioenergy Production and Demand Satisfaction

Figure 5a shows the results of the net bioenergy produced from the CHP systems using
the corresponding biomass in each agroindustrial process in Table 1. The resulting CHP
electrical capacities are in the small size range: 0.261 MW, 0.414 MW and 1.134 MW for the
agave bagasse, coffee pulp and orange peels, respectively. These are in line with the varying
amounts of biomass available at each facility studied. Figure 5b shows the percentage
of energy demand satisfaction for each agroindustrial process. It can be observed that in
the agave bagasse case, the whole electricity demand is satisfied with 55.5% of electricity
being exported (about 763,806 kWh/year), while the steam demand is satisfied only by
80.8%. This is because the tequila process is intensive in the steam requirement for the
distillation unit. In the coffee pulp case, both the steam and electricity demands are
satisfied, with 107% electricity being exported. This is because the coffee processing into
gold coffee beans is relatively less complex and requires lower energy. Using an average
electricity consumption in rural houses in Mexico of 1135 kWh/year [49], the agave bagasse
CHP and coffee pulp CHP systems could supply electricity to 673 and 1324 rural houses.
This shows that significant socio-economic benefits can be obtained from the tequila and
coffee agroindustries using their biomass for bioenergy. In the orange peels case, while the
steam demand for juice concentration is fully satisfied, the electricity demand is satisfied
only by 40%. This is because part of the dried orange peels is burned to provide heat to
Energies 2021, 14, x FOR PEER REVIEW the drying units, thus reducing biomass availability for the CHP system. Furthermore,
12 of 17
juice extractors and centrifuges for concentration are electricity intensive unit operations in
orange agroindustrial processing.

(a) (b)
Figure Figure
5. Results of the bioenergy
5. Results simulation
of the bioenergy modulemodule
simulation for: (a)for:
Bioenergy production
(a) Bioenergy and (b)and
production de-(b) demand
mand satisfaction of steam and electricity in the tequila, coffee and orange processes using agave
satisfaction of steam and electricity in the tequila, coffee and orange processes using agave bagasse,
bagasse, coffee pulp and orange peels, respectively.
coffee pulp and orange peels, respectively.

4.3. BioenergyA Cost of Production

comparison with and Othersystems
similar Economic Indicators
shows different demand satisfaction percent-
ages, due to differences in agroindustrial processing
Figure 6a shows the results of the cost of bioenergy production systems and biomass characteristics.
considering capital
For example, sugarcane bagasse was able to supply 100%
and operating costs. The cost is lower in the orange peels case, due to the benefit of heat and generated a 30% of
of econ-
omies electricity
of scales asexcess
the CHPfor acapacity
case study in a sugarcane
is higher than in themill [38],cases.
other whileThethecost
use of orange peels can
bioenergy
can besupply
compared65%to ofthe
thecost
heatof required
heat from by orange
fuel oil juice processing
and grid [39].In the case of heat,
electricity.
the cost of production from fuel oil is (6 US$/GJ/0.8) = 7.5 US$/GJ. This means that in all
4.3. Bioenergy Cost of Production and Other Economic Indicators
cases, the cost of bioenergy is lower. The cost per kWh (0.0097, 0.0094 and 0.0083 US$/kWh
Figure 6a
for agave bagasse, shows
coffee the and
pulp results of thepeels,
orange cost ofrespectively)
bioenergy production
is also lowerconsidering
than the capital
grid and
operating costs. The cost is lower in the orange peels case, due to
electricity cost (in Table 4) in all cases. These results showed the significant advantage of the benefit of economies
of scales as
using biomass fromtheagroindustrial
CHP capacity isprocesses
higher than
for in the own
their othersupply
cases. The costcollection
as the of bioenergy
andcan be
compared
transport costs aretoavoided,
the cost resulting
of heat from fuel oil
in lower and grid
biomass andelectricity.
bioenergyIncoststhe case of heat, the cost
of production.
of production from fuel oil is (6 US$/GJ/0.8) = 7.5 US$/GJ.
However, the cost is dependent on the cost allocated to biomass, and therefore, the cost This means that in all
of cases,
the cost of bioenergy is lower. The
the main crop in the upstream agroindustrial process. cost per kWh (0.0097, 0.0094 and 0.0083 US$/kWh
for agave bagasse, coffee pulp and orange peels, respectively) is also lower than the grid
electricity cost (in electricity
Biomass-generated Table 4) incan all cases.
be veryThese results showed
competitive the significant
where low-cost advantage
feedstocks are of
available onsite at industrial, forestry or agricultural processing plants. In such cases, elec-
tricity costs have been reported with a range of between 0.03 US$/kWh and 0.06 US$/kWh
(lower costs are obtained in developing countries using bulk agroindustrial biomass) [1].
Thus, the bioenergy costs obtained in our results are competitive mainly due to the high
 electricity cost (in Table 4) in all cases. These results showed the significant advantage of
using biomass from agroindustrial processes for their own supply as the collection and
transport costs are avoided, resulting in lower biomass and bioenergy costs of production.
However, the cost is dependent on the cost allocated to biomass, and therefore, the cost of
Energies 2021, 14, 4181 the main crop in the upstream agroindustrial process. 12 of 17

Biomass-generated electricity can be very competitive where low-cost feedstocks are

available onsite at industrial, forestry or agricultural processing plants. In such cases, elec-
using biomass
tricity frombeen
costs have agroindustrial processes
reported with a rangefor their own
of between supply
0.03 as theand
US$/kWh collection and
0.06 US$/kWh
transport
(lower costs are avoided,
obtained resulting in lower
in developing biomass
countries and bulk
using bioenergy costs of production.
agroindustrial biomass) [1].
However,
Thus, thethebioenergy
cost is dependent on the in
costs obtained cost allocated
our to biomass,
results are and mainly
competitive therefore,
duethe
tocost
the of
high
thebenefit
main crop in the upstream agroindustrial process.
from using the steam for agroindustrial processing.

(a) (b)
Figure
Figure 6. Results
6. Results of the
of the Bioenergy
Bioenergy economic
economic assessment
assessment module
module for:Cost
for: (a) (a) Cost of bioenergy
of bioenergy produc-
production
andtion
(b)and (b) payback
payback periodperiod
(PBP) (PBP) and internal
and internal rate ofrate of return
return (IRR)(IRR)
of the ofCHP
the CHP systems
systems using
using agave
agave bagasse, coffee pulp and orange peels.
bagasse, coffee pulp and orange peels.

The investment required

Biomass-generated forcan
electricity thebeagave
very bagasse CHPwhere
competitive system was estimated
low-cost at 2are
feedstocks mil-
lion USD,
available with
onsite at aindustrial,
corresponding payback
forestry period of
or agricultural 3.5 years plants.
processing and IRRInofsuch
22%cases,
(Figure 6b).
elec-
tricity costs have been reported with a range of between 0.03 US$/kWh and 0.06 US$/kWh a
The investment for the coffee pulp CHP system was estimated at 2.5 million USD, with
corresponding
(lower paybackinperiod
costs are obtained of 4 years
developing and IRR
countries of 20%
using bulk(Figure 6b). Finally,
agroindustrial for the
biomass) [1].or-
Thus, the bioenergy costs obtained in our results are competitive mainly due to the high
benefit from using the steam for agroindustrial processing.
The investment required for the agave bagasse CHP system was estimated at 2 million
USD, with a corresponding payback period of 3.5 years and IRR of 22% (Figure 6b).
The investment for the coffee pulp CHP system was estimated at 2.5 million USD, with a
corresponding payback period of 4 years and IRR of 20% (Figure 6b). Finally, for the
orange peel case study, the investment is 4.6 million USD plus 2 million for the drying
equipment, with a payback period of 3.5 years and IRR of 22%. (Figure 6b) This latter
case required higher investment, but has the benefit of the economy of scale. In all cases,
the investment showed positive indicators confirming the attractiveness and benefits of
agroindustrial biomass CHP systems. For comparison, the PBP of 4.3 years have been
obtained for sugarcane bagasse [23], and between 3 and 15 years with IRR values between
13 and 23 % for various biomass CHP case studies [40].
It is well known that the techno-economic results are sensitive to the biomass and
fossil energy prices, as well as the economy of scale. Furthermore, CHP systems can be
designed with different operating conditions, such as the steam pressure in the boiler.
Therefore, the sensitivity to these variables was explored in the following section using the
Bio2Energy® software.

4.4. Sensitivity
Sensitivity analyses were carried out for the biomass cost of production, bioenergy
unit cost of production, biomass netback and PBP. For the sensitivity of biomass cost of
production, the crop price was varied. For the agave case, the price was varied from
750 to 1500 US$/ton, for coffee beans from 250 to 500 US$/ton and for oranges from
75 to 150 US$/ton. The resulting range for biomass cost was 2.2–4.1 US$/ton for agave
bagasse, 2.62–5.1 US$/ton for coffee pulp and 6.3–7.1 US$/ton for orange peels. In all
cases, these costs are lower than the corresponding biomass netback obtained from the
downstream model. From ANOVA results, we observed that the dependent variable, i.e.,
the unit cost of production ($US/ton) is significantly affected at α = 0.05 by all independent
factors except by boiler steam pressure (see Supplementary information for ANOVA data).
This is mainly because in the current model, the effect of boiler steam pressure is more
Energies 2021, 14, 4181 13 of 17

directly related to the amount of energy produced and the resulting savings or sales rather
than costs. It is also acknowledged that the pressure needs to be captured in the boiler cost
estimation in new versions of the model.
For the sensitivity of biomass netback, bioenergy unit cost of production and PBP
to variation in boiler steam pressure, fossil fuel price and biomass input, the results are
shown in Figure 7. It can be observed how the operating boiler steam pressure has an
interesting impact on the biomass netback. Optimum pressure is found at around 40 bar for
coffee pulp. For the case of agave bagasse, the netback becomes practically constant after
70 bar, and after 60 bar for the orange peels case. The biomass netback and PBP showed a
linear influence by the fuel oil price, improving when increasing the price. Sensitivity to
electricity price was also performed, but the impact was less important (see Supplementary
Material). Finally, the sensitivity to biomass input to the CHP system was also analysed.
The asymptotic trend in Figure 7 indicated that there is a maximum capacity towards which
the economy of scale does not produce any improvement in the indicators. This happens at
around 100 t/d for the agave bagasse and coffee pulp cases, and at around 130 t/d for the
orange peels case. The ANOVA results presented in the Supplementary Material indicate
that all independent factors (biomass input, biomass type and boiler steam pressure) have
Energies 2021, 14, x FOR PEER REVIEW a significant effect at a α = 0.05 on biomass netback and PBP. In all cases, unit cost14
isofnot
17

greatly affected. Correlations for the significant effects were developed and presented
in Table 5.

Figure 7.
7. Sensitivity
Sensitivity results
results for
for bioenergy
bioenergy cost
cost of
of production,
production, payback
payback period
period (PBP)
(PBP) and
and biomass
biomass netback
netback for
for agave
agave bagasse
bagasse
(left column), coffee pulp (central column) and orange peels (right column).

Table 5. Correlation parameters for the sensitivity results.

Independent Variable (x) Dependent Variable (y) Equation R2

Orange peels
Fuel price Netback y = 8.3486x + 3.0406 1
Fuel price PBP y = 16.596x−0.875 0.9993
Energies 2021, 14, 4181 14 of 17

Table 5. Correlation parameters for the sensitivity results.

Independent Variable (x) Dependent Variable (y) Equation R2

Orange peels
Fuel price Netback y = 8.3486x + 3.0406 1
Fuel price PBP y= 16.596x−0.875 0.9993
Biomass input Netback y = 13.259ln(x) − 9.959 0.9612
Biomass input PBP y= 54.416x−0.575 0.9895
Biomass input Unit cost y= 21.672x−0.432 0.9935
Agave bagasse
Fuel price Netback y = 4.9725x + 1.2961 1
Fuel price PBP y= 16.326x−0.866 0.9988
Biomass input Netback y = 5.1375ln(x) + 10.995 0.9785
Biomass input PBP y= 27.052x−0.53 0.9974
Biomass input Unit cost y= 12.369x−0.397 0.9959
Coffee pulp
Fuel price Netback y = 4.6086x + 1.9011 1
Fuel price PBP y= 17.459x−0.829 0.999
Biomass input Netback y = 7.0032ln(x) − 0.0185 0.9716
Biomass input PBP y= 42.304x−0.562 0.9863
Biomass input Unit cost y= 15.11x−0.42 0.9912

5. Conclusions
This paper presented the bioenergy value chain modelling to estimate the biomass
cost of production using an upstream model, and the biomass netback and bioenergy
cost of production in CHP systems using a downstream model. Such models were im-
plemented into the IMP Bio2Energy® software for practical application to various case
studies. In specific, it allowed comparing biomass cost of production and biomass netback
to analyse CHP systems using agroindustrial biomass for energy supply to the industrial
processes generating such biomass and considering the sales of excess electricity. The three
agroindustries studied included tequila production, coffee and orange processing, us-
ing the agave bagasse, coffee pulp and orange peels, respectively, as biomass. Results
showed that the CHP systems are in all cases economically feasible as the biomass cost
of production is lower than the netback, while the cost of bioenergy was lower than the
fuel oil and grid electricity being replaced. IMP Bio2Energy® carried out a sensitivity
analysis to technical (boiler steam pressure) and economic variables (fuel oil price and
biomass input). All independent factors (biomass input, biomass type and boiler steam
pressure) had a significant effect on dependent variables (unit cost, netback and PBP) at
α = 0.05. The only exception was for boiler steam pressure which effect was not significant
on biomass unit cost. Capturing the effect of pressure on the capital cost of the boiler
in the deterministic modelling is recommended. From sensitivity analysis, the optimal
design pressure, and system scale thresholds were identified. These results demonstrate
the usefulness of the modelling approaches and the IMP Bio2Energy® software for decision
makers in analysing biomass CHP systems from a whole biomass and bioenergy value
chain perspective.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/10

.3390/en14144181/s1, Economic and Upstream Modelling Description. Agroindustrial modelling
results (Table S1 to Table S4), sensitivity to electricity prices (Figure S1 to Figure S2) and ANOVA
results (Table S5 to Table S12).
Energies 2021, 14, 4181 15 of 17

Author Contributions: Conceptualization, E.M.-H., M.A.A.-A. and J.A.; Formal analysis, E.M.-H.;
Funding acquisition, M.A.A.-A. and J.A.; Methodology, E.M.-H.; Project administration, M.A.A.-A.
and J.A.; Software, E.M.-H.; ANOVA analysis, J.A.; Writing—original draft, E.M.-H.; Writing—review
& editing, E.M.-H., M.A.A.-A. and J.A. All authors have read and agreed to the published version of
the manuscript.
Funding: This research was funded by SENER CONACyT Energy Sustainability Fund, grant number
246911 and IMP, grant number Y.61025, and the APC was funded by Instituto Mexicano del Petróleo.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: Authors thank SENER CONACyT Energy Sustainability Fund (Project 246911,
IMP Y. 61025) for financial support. Authors thank Juan A. Zermeño Eguía-Lis for his participation
in software testing, and Diana Dominguillo Ramírez for support in data collection and software logo.
Conflicts of Interest: The authors declare no conflict of interest.

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