See discussions, stats, and author profiles for this publication at: https://www.researchgate.

net/publication/347558756

Exploration of Biomass for the Production of Bioethanol: "A Process

Modelling and Simulation Study" - Renewable Energy Research and
Application Journal (RERA)

Preprint · January 2021

DOI: 10.22044/rera.2020.10287.1042

CITATIONS READS

0 163

4 authors, including:

Toyese Oyegoke Obadiah Emo

Ahmadu Bello University Ahmadu Bello University
50 PUBLICATIONS   48 CITATIONS    10 PUBLICATIONS   0 CITATIONS   

SEE PROFILE SEE PROFILE

Some of the authors of this publication are also working on these related projects:

Propane & LPG Value Addition View project

Renewable Energy & Power Generation View project

All content following this page was uploaded by Toyese Oyegoke on 24 June 2021.

The user has requested enhancement of the downloaded file.

Vol 2. No 1, 2021, 51-69 DOI: 10.22044/RERA.2020.10287.1042

Exploration of Biomass for the Production of Bioethanol: “A Process

Modelling and Simulation Study”

T.Oyegoke1,2*, M.Y.Sardauna1, H.A.Abubakar1, E.Obadiah1

1. Ahmadu Bello University, Department of Chemical Engineering, Zaria, Nigeria.

Laboratoire de Chimie, ENS yon, l’Universite de Lyon, 69007, Lyon, France.

Received Date 20 November 2020; Revised Data 04 December 2020; Accepted Date 14 December 2020
*Corresponding author: OyegokeToyese@gmail.com (T.Oyegoke)

Abstract
Bio-ethanol is a clean and renewable fuel that is gaining a significant attention mainly due to its major
environmental benefits and its production from diverse resources. The campaign for establishment of bio-
refineries and encouragement of fossil fuels is gradually gaining a greater attention. In this research work, we
seek to comparatively investigate the material requirement, production yield, and total equipment cost
involved in the rice-husk and maize-cob transformation into the bio-ethanol fuel for a large-scale production
using a process modeling and simulation study in order to promote the potential investors' interest. This
analysis is carried out using a simulator (Aspen HYSYS) and a computational package (MATLAB). The
evaluation entails modeling, simulating, and material and energy analysis including the process equipment
sizing and cost for the plants. The comparative material analysis of the yield from the model process for the
use of biomasses reveals that 9.94 kg and 7.32 kg of fuel-grade bio-ethanol is obtained using 0.03 kg and
0.02 kg of enzymes for every 1 kg of rice-husk and maize-cob charge in the plant, respectively, per hour.
Analysis of the plants' energy flow shows that the maize-cob transformation into the bio-ethanol fuel requires
more energy than the rice-husk-based plant, confirming that the maize-cob conversion is more energy-
intensive than the rice-husk conversion. Moreover, the equipment cost analysis indicates that it costs
$4739.87 and $1757.36 in order to process 1 kg of biomass (rice-husk and maize-cob) into fuel-grade bio-
ethanol, respectively, per hour. Ultimately, the findings of this work identify the rice-husk's use to be of high
yield, while maize-cob makes the production less capital-intensive.

Keywords: Bio-fuels, Biomass, Process Modeling, Fermentation, Hydrolysis.

1. Introduction
The growing environmental problems in the containing starch, sugar or the lignocellulosic
recent years and the need to reduce oil materials such as potatoe, corn, corn cobs and
dependency have necessitated an increased stalks, grains, and wood that mainly comprise
interest in producing bio-ethanol as an alternative cellulose (a glucose polymer), hemicellulose, a
to the vehicle fuel. These are in addition to the mixture of polysaccharides mainly composed of
octane boosting capacity and potential reduction glucose, mannose, xylose, arabinose, and lignin
in carbon monoxide (CO) emissions [1]. An [2, 3].
increase in the world‘s energy demand and the Due to the rising demand for energy and the
progressive depletion of oil reserves motivate the continuous depletion of oil reserves coupled with
search for alternative energy resources, especially greenhouse emissions from non-renewable energy
for those derived from renewable materials such sources, the need for alternative clean energy fuels
as biomass. Biomass is one of the most promising such as bio-ethanol is indispensable. Besides, the
renewable resources used in order to generate disposal of lignocellulose wastes causes
different types of bio-fuels such as bio-diesel [2]. environmental pollution in our surroundings—this
The global concern about climate change and the improper management of solid wastes affects the
consequent need to diminish greenhouse gas human and animal health. Lignocellulose is
emissions have encouraged the use of bio-ethanol considered as an attractive feedstock for fuel
as a gasoline replacement or additive. Bio-ethanol ethanol production due to its availability in large
can be obtained from renewable sources quantities, relatively low cost, and significant
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

reduction in the competition with food but not comparatively assessed the utilization of maize-
necessarily with feed [4, 5]. cob (A) and rice-husk (B) for the bio-ethanol
A survey of the literature indicates that several production using a process simulation approach.
investigations have been carried out in order to This goal was achieved via the execution of the
provide a possible solution to get this challenge following tasks: (1) collection of the relevant
addressed. For instance, Sasser et al. [6] have experimental data, (2) modeling and simulation of
evaluated the feasibility of using spruce the process plant using the relevant laboratory-
(softwood), salix (hardwood), and corn stover verified data in order to analyze the material and
(agricultural residue), and demonstrates the energy flow across the modeled process plant, (3)
importance of a high ethanol yield and the the equipment modeled was sized with the aid of
necessity of utilizing the pentose fraction for the Aspen HYSYS process simulator, and (4) the
ethanol production to obtain a good process sized equipment cost was used in order to
economy, especially when salix or corn stover is determine the total plant equipment cost involved
used [7]. Christiana and Eric [8] have been able to in the transformation of rice-husk and maize-cob
identify that the production of bio-ethanol from in the plants A and B, respectively. This work
cassava is only feasible in Nigeria, provided that reveals the plant yield, total equipment cost for
the plant is a site next to the plant. Oyegoke et al. processing 1 kg of biomass (rice-husk and maize-
[9] have indicated that 143 million liters of bio- cob), and material and energy requirements for a
ethanol per annum can be obtained using 402 biomass conversion into bio-ethanol. This
metric tonnes of sugarcane bagasse. That is 2.8 information would provide the preliminary
metric tonnes of sugarcane bagasse can always guidance for the potential investors, especially on
yield 1 million liters of bio-ethanol. Some works feedstocks' choice considering their yield, energy,
are related to the bio-ethanol production from and cost implications in terms of the equipment
molasses [10], combine sugarcane-bagasse-juice cost.
[11, 12], and sorghum bagasse [13]. In other
research works, the researchers have examined the 2. Materials and Methods
potential of converting wastes into power instead In this work, we employed a PC coupled with
of bio-fuels. In some of these research works, some application software like Aspen HYSYS
Sobamowo and Ojolo [14], Oyegoke et al. [15], (for modeling and simulation of the process) and
Abbas et al. [16], and Mataji and Shahin [17] have Microsoft Excel/MATLAB (for computational
explored the use of municipal wastes, sugarcane use). The mass basis (for the feedstock) used in
bagasse, other biomass resources, and wind this work was obtained as a fraction of what was
energy, respectively, to generate power. reported by the Nigeria Bureau of Statistics for the
In addressing the challenge of solid waste annual total mass of maize (for cob) and rice (for
management and the promotion of a green fuel husk) produced nationally, averaged for a series of
and cleaner air campaign, in this work, we recent years.

Table 1. Components used for the bio-ethanol simulation

Component Formula Use in the process
Cellulose* C6H10O5 Feedstock
Hemicellulose* C5H8O4 Feedstock
Sulphuric acid H2SO4 Acid catalyst
Furfural C5H4O2 Hemicellulose hydrolysis by-product
Acetic acid C2H4O2 Hydrolysis and fermentation by-product
Acetate* C2H4O2 Acetate groups present in hemicellulose
Glucose* C6H12O6 From cellulose hydrolysis and saccharification
Cellobiose* C12H22O11 From cellulose hydrolysis and saccharification
Xylose* C5H10O5 Coming from hydrolysis and saccharification
Water H2O Product moisture, washing, and the reaction product
Ethanol C2H6O Desired product
Carbon dioxide CO2 Fermentation product
Z. mobilis* CH1.8O0.5N0.2 Fermentation bacteria
Glycerol C3H8O3 Fermentation by-product
Lignin* C10H13.9O1.3 Feedstock
Xylitol* C5H12O5 Fermentation by-product

52
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

Note: Some of the components listed above (The Oyegoke & Dabai [12] to be useful due to the
asterisk ones*) are not available in the nature of the components. Any component not
components database. These components are available in the Aspen database was modeled
added as hypotheticals. using the PubChem database information data.
The set of the components involved in this work is
2.1 Modelling conditions, method, and components presented in table 1. In contrast, the details for the
In this work, we employed Aspen HYSYS in the modeled components, commonly called the
modeling of the process in order to produce bio- hypothetical components, are presented in tables 2
ethanol from rice-husk (Plant A) and maize-cob and 3 for the liquid and solid components.
(Plant B). In the modeling of the plants (Plants A Moreover, the approach employed in the
and B), a non-random two-liquid (NRTL) modeling and simulation of the process plant was
thermodynamic model was selected in order to carried out using the approach step-wisely
predict the thermodynamic and physical displayed in figure 1 adopted from the literature
properties of the components involved in this [12]. The process simulation was done using the
work. Aspen HYSYS simulator.
The NRTL selection has been reported by

Table 2. Liquid hypotheticals and their properties used in the HYSYS process simulator.
Properties Unit Acetate Glucose Cellobiose Xylose Hemicellulose Xylitol
Molecular weight g/mol 60.05 180.16 342.3 150.1 132.1 152.1
o
Normal boiling point C 118 343.9 626.9 -382.9 421.2 421.2
Ideal liq. density kg/m3 1052 1269 1514 1288 894.7 894.7
o
Critical temperature C 319.6 737.9 961.9 642.4 759.8 759.8
Critical pressure kPa 5770 6200 3921 6588 6320 6320
Critical vol. m3/kgmol 0.1710 0.4165 0.778 0.388 0.3990 0.3990
Accentricity 0.4470 2.567 0.8442 0.706 0.5651 0.5651
Ht. of formation kJ/kgmol -435079 -1256903 -625070 -1040020 -241287.3 kJ/kgmol

Ht. of combustion kJ/kgmol -786425 2817760 - 0.002352 - kJ/kgmol

Table 3. Solid hypotheticals and their properties used in HYSIS process simulator.
Properties Unit Cellulose Lignin Z-mobilis
Molecular weight g/mol 162.1406 122.49 24.63
Density kg/m3 1500 1500 1500
Heat of formation (25 oC ) kJ/kgmol -963000 -1592659 -130500
Heat of combustion (25 oC ) kJ/kgmol -2828000 3265480 520125

Figure 1. Flow chart for simulating a process in Aspen HYSYS [12].

53
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

2.2 Feedstocks and reaction set involved in this both obtained by averaging the feedstock's two
work cited compositions.
The chemical composition of the feeds charged
into the two plants is presented in tables 4 and 5,

Table 4. Cellulose, hemicellulose, and lignin content in rice-husk.

Cellulose Hemicellulose Lignin Reference
15–36 12 – 35 8–16 Saha & Cotta [18]; Saha & Cotta [19]
25-35 18-21 26-31 Rabemanolontsoa & Saka [20]
27.8 21.5 20.3 Average

Table 5. Cellulose, hemicellulose, and lignin content in maize-cob.

Cellulose Hemicellulose Lignin Reference
45 35 15 Sun & Cheng [21]
42-45 35-39 14-15 Rabemanolontsoa & Saka [20]
44 36.7 13.2 Average

In this work, the bio-ethanol production steps presence of sulfuric acid. The pH adjustment
employed were as follow: pre-treatment, reaction set (3) was modeled in order to initiate
hydrolysis, pH adjustment/neutralization, and the neutralization reaction used to neutralize the
fermentation. The reaction sets characterizing acid content present in the simple sugar produced
these steps are present in table 6. These reaction in the hydrolyzer. Another reaction set is
sets present the series of reactions modeled in a fermentation reactions (4), which entail a set of
reactor. First was the pre-treatment hydrolyzer reactions that experimentally occur within a
reaction set (1), which was modeled in the pre- fermenter during the sugar conversion into bio-
treatment unit. Moreover, the hydrolysis reaction ethanol. All the reaction sets occur in separate
set (2) was modeled within the hydrolyzer, reactors except for the process of simultaneous
converting the polysaccharide into the saccharification and fermentation (SSF), which
monosaccharides like xylose and glucose in the combines the reaction sets (2) and (4).

Table 6. The reaction sets employed in this work.

Reaction sets Reaction expressions/equations
(1) Pre-treatment hydrolyzer reaction(s) Cellulose + H2O  Glucose
Cellulose + 0.5 H2O  0.5 Cellobiose
Hemicellulose + H2O  Xylose
Hemicellulose  Furfural + 2 H2O
Acetate  Acetic acid
(2) Hydrolysis reaction(s) Sucrose + H2O  2 Glucose
Cellulose + H2O  90 Glucose
Cellulose + 0.5 H2O  0.5 Cellobiose
Cellobiose + H2O  90 Glucose
Cellulose + H2O  90 Glucose
Cellulose + 0.5 H2O  Cellobiose
Hemicellulose + H2O  64 Xylose
Hemicellulose  Furfural + 47 H2O
(3) pH adjustment reaction 2 + H2SO4  + 2 H2O
(4) Fermentation reaction(s) Glucose  3 Ethanol + CO2
3 Xylose  2 Ethanol + CO2
Glucose + H2O  0.2 Glycerol + O2
Xylose + 5 H2O  Glycerol + 4.6 O2

During the pre-treatment hydrolyzer reaction(s), to the barest minimum. Finally, the fermentation
there is some partial hydrolysis of the feed in reaction(s) convert glucose and xylose to ethanol
which a significant fraction of hemicellulose is and carbon dioxide in the presence of enzymes at
hydrolyzed. In contrast, the hydrolysis reaction(s) a temperature of 394 K.
involve the breaking down of sucrose,
hemicellulose, and cellulose into glucose and 2.3 Process flow development
xylose in the presence of water at a temperature of In a literature review [22–27], two different routes
394 K. In a neutralization reaction, all the acidic were identified for bio-ethanol production from
content of the hydrolyzed products is neutralized lignocellulosic biomass (for the use of rice-husk

54
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

and maize-cob). The first route was a The products from the acid pre-treatment reactor
simultaneous saccharification and fermentation, were first heated to 121 oC, and then fed into the
which was chosen for bio-ethanol production from acid hydrolysis (Reaction set 2 in table 6) and
rice-husk due to the reported high bio-ethanol fermentation (Reaction set 4 in table 6) reactor
yield, low quantity of enzyme requirement, together with dilute sulfuric acid at 90 kg/h, 25oC,
reduced contamination, low inhibition, and low and 101.3 kPa, and enzyme (91.68% water) at
cost. The second route was a separate hydrolysis 5.94 kg/h, 121oC, and 101.3 kPa.
and fermentation, which was chosen to produce The products from the dilute acid hydrolysis and
bio-ethanol from maize-cob because the fermentation (SSF) reactor were fed into a filter,
hydrolysis and fermentation processes occurred where the products were filtered into a solid
under the optimum conditions. fraction and a liquid fraction. The liquid fraction,
i.e. the filtrate, was sent to the pH adjustment
2.4 Process Description reactor (Reaction set 3 in table 6) using NaOH in
Plant A: The rice-husk with the composition order to neutralize the acidity. The pH adjustment
shown in table 4 at 180 kg/h, 25oC, and 101.3 kPa, reactor effluent was cooled to 30 oC and sent to
and water at 90 kg/h, 25 oC, and 101.3 kPa were the purification section. Figure 2 shows the block
mixed in a mixer and heated to 121 oC at 1605 flow diagram for producing bio-ethanol from rice-
kg/h and 101.3 kPa, and were fed together into the husk using the selected routes.
acid pre-treatment reactor, where dilute sulfuric
acid at 90 kg/h, 25 oC, and 101.3 kPa was fed
(Reaction set 1 in table 6).

Figure 2. Production of bio-ethanol in plant A.

Plant B: The feedstock, corn cob whose hydrolysis reactor, where the saccharification
composition is presented in table 5 at 567.3 kg/h, reaction (Reaction set 2 in table 6) took place.
25 oC, 101.3 kPa, and water at 1021 kg/h, 25 oC, The dilute acid hydrolysis reactor products were
and 101.3 kPa, was mixed in a mixer and heated fed together with an enzyme (91.68 % water) at
to 121 oC at 1080 kg/h, 101.3 kPa; these were fed 11.35 kg/h, 121oC, and 101.3 kPa into the
together into the pre-treatment reactor (Reaction fermentation reactor, where the fermentation took
set 1 in table 6), where sulfuric acid at 17.02 kg/h, place (Reaction set 4 in table 6). The fermentation
25 oC, and 101.3kPa was fed. The products from reactor products were fed into a filter, where it
the pre-treatment reactor were cooled to 98oC and filtered the products into a solid fraction and a
fed together with water at 1021 kg/h, 101.3 kPa, liquid fraction. The liquid fraction, i.e. the filtrate,
and 25 oC and dilute sulphuric acid at 51.34 kg/h, was sent to the pH adjustment reactor (Reaction
25oC, and 101.3 kPa into the dilute acid set 3 in table 6) using NaOH in order to neutralize
its acidity. The pH adjustment reactor effluent was

55
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

cooled to 30 oC and sent to the purification produce bio-ethanol from maize-cob using the
section. Figure 3 shows the block flow diagram to selected routes.

Figure 3. Production of bio-ethanol in plant B.

In the purification section, a separator was used in were evaluated in order to ensure that the energy
order to separate CO2 from the beer. CO2 was sent and mass were conserved [28, 29]. Overall, the
to a 10-stage absorption column with the feed plant material and energy analysis helped to
entering at stage 4 to wash CO2 before releasing identify the material and energy required to keep
into the atmosphere, and the beer was channeled the plant running, while the overall process
to a different absorption column with steam in equipment balance aided in designing the process
order to remove the stillage (waste). Two products equipment. The general material balance equation
were obtained from the beer refinement; the light is given as:
one was sent to a refluxed absorber, while the Material in= Material out + Generation -
concentrated one was sent to the distillation (1)
Consumption - Accumulation
column. The condenser pressure was set to 101.3
kPa. Two specifications were made: an overhead In this work, the material and energy balance for
vapor rate 69 kg/h and an ethanol component the two plants was carried out with the aid of the
mass fraction. The liquid was fed into a 29-stage process simulator (Aspen HYSYS), and overall,
distillation column with the feed entering at stage the plant-wide energy balance flow was collected
12. The condenser and reboiler pressures were for both plants.
kPa 172.3 and 202.6 kPa, respectively. At the full
reflux condenser, two specifications were added. 2.6 Process equipment sizing and costing
The specification included setting the reflux ratio The equipment sizing was done using Aspen
to be 1.241 and its flow rate to be 38940 kg/h (in HYSYS, and subsequently, the cost of the plant
the process plant modeling) to give a 95% purity equipment was estimated. Each equipment was
(for bio-ethanol) during the distillation process. It costed via the use of the cost relations presented
is noteworthy that the same purification procedure in Sinnot [28] and Seider & Seader [29], which
used for the bioethanol production in plant A could be written as follow:
(rice-husk) was adopted here for plant B (maize- C0=a+bSn (2)
cob). C0=aSb (3)
2.5 Material and energy balance analysis where S is the equipment design parameter, n is
The material and energy flow stream across the the exponent for the type of equipment, a and b
process equipment and the entire plant modeled are the cost constants, and C0 is the base cost (at a

56
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

specified year). The estimated base cost, C0, was from 567.3 kg/h pre-treated, washed, and crushed
updated to the current year via the relation feed of maize-cob using 5.94 kg enzyme/h and
presented in Equation 4 using the cost index 11.35 kg enzyme/h, respectively.
table/chart. Furthermore, in this work, indicates that a
kilogram of rice-husk and maize-cob would yield
(4) 9.94 kg (1789.45 kg/180 kg) and 7.32 kg (4154.94
kg/567.30 kg) of fuel-grade bio-ethanol using 5.94
where and are the base and current chemical kg and 11.35 kg of enzymes, respectively, in
engineering plant cost index and is the current every hour. These findings indicate that the
cost for the year of study. Summing the cost of all amount of yield obtained for the use of rice-husk
equipment, the total plant equipment cost was and maize-cob is higher than the values reported
evaluated. The details of the computations are for the use of rice-hull as 0.27 kg (347.25 L/t) by
shown in Appendix B. Quintero and Cardona [30], cassava as 0.34 kg by
Christiana and Eric [8], rice-husk as 0.20 kg by
3. Research Findings and Discussions Quintero et al. [31], sugarcane bagasse as 0.28 kg
by Oyegoke et al. [9], molasses as 0.12 kg by
3.1 Process Flow Sheet Model Abemi et al. [10], combined use of sugarcane
The process flow diagrams modeled for the bagasse-juice as 0.29 kg by Oyegoke & Dabai
process information presented in the block flow [11], and sorghum as 0.27 kg by Ajayi et al. [13].
diagrams in figures 2 and 3 are shown in figures 4 The yield obtained for the use of rice-husk in
and 5, respectively. these studies show a higher yield compared to the
ones obtained by Quintero and Cardona [30] and
3.2 Material Balance Analysis Quintero et al. [31], that have used the same rice
The material balance analysis of the proposed hull and husk but a different technique of using
plants is shown in table 7. It can be seen that the SHF approach, unlike what was adopted for
1,789.45 kg/h of 99% pure bio-ethanol can be this work. However, this work implies that both
produced from 180 kg/h pre-treated, washed, and maize-cob and rice-husk display a high yield
crushed feed of rice-husk. Similarly, 4,154.94 based on the conditions employed.
kg/h of 99 % pure bio-ethanol could be produced

Figure 4. Process flow diagram for bio-ethanol production from rice-husk (plant A).

57
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

Figure 5. Process flow diagram for bio-ethanol production from maize-cob (plant B).

Table 7. Overall material balance across plants.

Plant A Plant B
Flow Flow Flow
Inlet material Outlet material Inlet material Outlet material Flow (kg/h)
(kg/h) (kg/h) (kg/h)
(1) Sulfuric acid (1) Other streams (1) Sulfuric acid (1) Other streams
Acid feed 23.50 Bottom 3 285.00 Acid feed 17.02 Solids 120.92
Acid feed 2 66.50 Stillage-A 698.6 Acid feed 2 51.34 Bottom 571.37
(2) Biomass Stillage B 583.26 (2) Biomass Stillage- A 995.9625
Rice husk 180.00 Fusel 3.00 Maize-cob 567.30 Stillage B 966.5075
(3) Water Rect_Dist 2.00 (3) Water Rect_Dist 2.00
Water 810.00 (1) Light gases Water 1021.00 Fusel 3.00
Wash_H2O 13.10 CO2_stream 23.89 Wash_H2O 28.17 (2) Light gases
Steam A 2270.23 Light_Vent 43.91 Steam A 5515.94 CO2_stream 39.89
(4) Neutralizer Rect_Vap 4.29 (4) Neutralizer Rect_Vap 4.31
NaOH 90.000 (1) Bio-ethanol NaOH 36.67 Light_Vent 68.89
(5) Enzyme 2ndEtOH(< 99%) 25.87 (5) Enzyme (3) Bio-ethanol
Z. Mobilis 5.94 1st Prod (99%) 1789.45 Z. mobilis 11.35 1st Prod (99%) 4154.94
Total 3459.30 Total 3459.30 Total 7248.79 Total 7248.79

3.3 Energy Balance Analysis sucrose is an endothermic reaction process that

Table 8 shows the energy balance analysis for requires 498 thousand kJ/h and 551 thousand kJ/h
fuel-grade bio-ethanol production from rice-husk of energy (i.e. ―Heatadded‖) for the use of rice-
and maize-cob in plants A and B. It can be seen husk and maize-cob, respectively.
that the hydrolysis reaction(s) of cellulose and The overall plant energy balance deduces that the
hemicellulose are highly exothermic i.e. excess process ‗energy flow in,‘ which represents the
heat is giving off. The heat(s) released (i.e. total quantity of heat that flows into the plants, is
―Heatremoved1‖ and ―Heatremoved2‖) are 7.29 worth 1.11 billion kJ/h and 1.23 billion kJ/h for
million kJ/h and 99.8 million kJ/h for the use of the use of rice-husk (plant A) and maize-cob
rice-husk and 87.2 million kJ/h and 1.053 million (plant B), respectively. Moreover, the study
kJ/h for the use of maize-cob. The indicated that the overall energy flow of 1.11
monosaccharide fermentation reaction is also an billion kJ/h and 1.23 billion kJ/h computed for
exothermic reaction, which releases heat (i.e. rice-husk and maize-cob, respectively, is higher
―Heatremoved3‖) of 11.2 kJ/h million and 13.6 than the values reported in the literature. Oyegoke
million kJ/h for the use of rice-husk and maize- et al. [9], Abemi et al. [10], Oyegoke & Dabai
cob, respectively. However, the hydrolysis of (2018), and Ajayi et al. [13] obtained values of

58
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

1.02 billion, 909.5 million, 1.08 billion, and 624 level obtained was higher than that reported by
million kJ/h using sugarcane bagasse, molasses, Oyegoke & Dabai [11] and Oyegoke et al. [9] as
combined use of sugarcane-bagasse-juice, and 0.01 % for the combined-use of sugarcane-
sorghum bagasse, respectively. bagasse-juice and sugarcane bagasse, respectively.
An error of 0.03% was found for plant A, and In contrast, the error obtained for this study was
0.02% for plant B was obtained for the energy found to be less than that obtained for the use of
analysis, which could be traced to the hypothetical sorghum bagasse (0.06 %), as reported by Ajayi et
components (modeled) during the simulation in al. [13] in the analysis of the energy flow in the
line with previous reports [10, 13], which plant network.
associate it to the same factor. However, the error

Table 8. A plant-wide energy balance analysis across the plants.

Plant A Plant B
Inlet stream Flow (J/h) Outlet stream Flow (J/h) Inlet stream Flow (J/h) Outlet stream Flow (J/h)
(1) Energy of material in (1) Energy of material out (1) Energy of material in (1) Energy of material out
Acid feed -7.33E+03 Bottom 3 -9.50E+05 NaOH -7.65E+04 Solids -1.19E+06
Feedstock -1.22E+03 CO2_Stream -6.31E+05 Acid feed -1.39E+05 Bottom -5.69E+04
Water -1.28E+05 Stillage_A -4.05E+07 Feedstock -2.99E+06 CO2_Stream -2.31E+07
NaOH -1.88E+05 2ndEtOH -1.85E+06 Water -1.61E+07 Stillage_A -3.84E+08
Wash_H2O -3.70E+05 Light_Vent -5.69E+05 Wash_H2O -3.70E+07 2ndEtOH -1.85E+06
SteamA -1.45E+07 Rect_Vap -2.31E+04 SteamA -2.45E+08 Light_Vent -5.69E+05
Z. Mobilis -3.13E+04 StillageB -6.77E+07 Z. mobilis -5.99E+04 StillageB -5.19E+07
(2) Heating duties 1stProd -2.32E+08 Acid feed 2 -2.46E+08 1stProd -3.99E+07
QH1 5.26E+08 Fusel -2.02E+08 (2) Heating duties Fuel -2.20E+04
QH2 3.90E+08 (2) Cooling duties Heatadded 5.51E+05 Rect_Vap -2.33E+04
Rect_RebQ 3.27E+08 CondDuty 4.57E+08 QH1 6.73E+08 Rect_Dist -1.26E+04
Heatadded 4.98E+05 Rect_CondQ 4.40E+08 Rect_RebQ 4.27E+08 (2) Cooling duties
(3) Cooling duties Rect_Dist 8.99E+08 QA 7.81E+08 QC1 2.66E+08
Heatremoved1 -7.29E+06 (3)Cooling duties QC2 8.15E+08
Heatremoved2 -9.98E+07 QM2 -9.27E+05 CondDuty 6.55E+08
Heatremoved3 -1.12E+07 Heatremoved1 -8.72E+07 Rect_CondQ 4.04E+07
Heatremoved3 -1.36E+07
Heatremoved2 -1.05E+06
Total 1.11E+09 Total 1.11E+09 Total 1.23E+09 1.23E+09
Error (%) 0.03 Error (%) 0.02

3.4 Estimation of process plant equipment cost for processing of 1 kg of biomass into the bio-
The result of the process plant equipment cost ethanol fuel. In contrast, the total cost estimated
estimated via cost relations and index for updating for producing a kilogram of bio-ethanol was far
the price of equipment to the current year is greater than stated as 0.01626, 0.0612, and
summarized in table 9, displaying the plant 179.1829 $/kg in the report of molasses,
purchased equipment cost before and after the sugarcane (bagasse-juice), and sorghum bagasse,
update process. respectively.
The estimation indicated that the bio-ethanol
produced via rice-husk and maize-cob as their 4. Conclusions
feedstock would require a total cost of $853 In the present work, we showed that agricultural
thousand and $997 thousand to transform the waste such as rice-husk and maize-cob could be
biomass into the fuel-grade bio-ethanol (99% used as a feedstock or substrate for the bio-ethanol
purity). The findings imply that maize-cob (plant production using two different routes. Moreover,
B) could require more funds to buy equipment to the findings from the material balance analysis
set up than rice-husk (plant A). indicate that the use of 180 kg/h of rice-husk
However, evaluating the result based on produces 1,789 kg/h of fuel-grade bio-ethanol
processing a kilogram of the biomass (rice-husk using 5.94 kg/h of enzyme. In contrast, 567.3 kg/h
and maize-cob) into bio-ethanol indicated that it of maize-cob produces 4,154.94 kg/h of fuel-
would cost $4,739.87 ($853176.20/180.00 kg) and grade bio-ethanol using 11.35 kg/h of enzyme.
$1,757.36 ($996950.10/567.30 kg) to purchase the Furthermore, the findings of this work reveal that
process equipment for the establishment of a plant a kilogram of rice-husk and maize-cob yields 9.94

59
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

kg and 7.32 kg of fuel-grade bio-ethanol using Although the use of rice-husk shows a higher
0.03 kg and 0.02 kg of enzymes, respectively, in yield (i.e. 9.94 kg bio-ethanol/kg biomass) and a
every hour. The overall plant energy balance lower energy input (1.11 billion kJ/h), it requires
analysis shows that plant B (maize-cob) requires more funds ($4,739.87/kg biomass) to purchase
more energy than plant A (rice-husk); that is, the the start-up equipment when compared to the use
maize-cob transformation into bio-ethanol require of maize-cob that required $1,757.36/kg biomass
more energy during the plant operation than that to get started. Also this study's findings identified
of the rice-husk plant. The equipment cost the rice husk's use to be of high yield, while
analysis shows that it costs $4,739.87 and maize-cob makes the production less capital-
$1,757.36 to process a kilogram of biomass (rice- intensive.
husk and maize-cob) into fuel-grade bio-ethanol,
respectively, in an hour.
Table 9. Purchased equipment cost summary for different plants.
Plant A B
Description ($) ($) ($) ($)
Mixer 450790.50 484977.40 348594.50 375031.10
Heater 11223.20 14304.50 12525.00 15962.50
Reactor 160602.90 205469.30 385882.00 493682.50
Column 1382.80 1769.20 1382.80 1769.20
Separator 75329.00 96373.10 75329.00 96373.10
Molecular Sieve 1138.61 1225.00 257.55 277.10
Condenser 5911.66 6327.70 5911.66 6327.70
Reboiler 6996.28 7526.90 6996.28 7526.90
Cooler 27620.00 35203.10 - -
Total cost 734694.95 853176.20 836878.79 996950.10
Total cost per kg of bio-ethanol - 4739.87 - 1757.36

5. Recommendations a&b Cost constants

Further studies can look into the thermodynamic A Area required
analysis (energy, exergy, and pinch analysis) of
the process in order to understand the plants‘ A Maize-cob for plant A
energy efficiency, identify the potential units AC Absorption column
mainly contributing to the plant's loss in energy, B Rice-husk for plant B
and better ways to resolve it. Also future research
works can be carried out in order to investigate CD Condenser
the use of algae for bio-ethanol production, CL Cooler
assessing its energy efficiency, economic D Vessel diameter
viability, production yield, and other issues related
DC Distillation column
to the plant scale-up.
H Heat load
6. Nomenclature HT Heat exchanger
Base cost (at a specified year)
L Length
Current cost for the year of
Molecular sieve/Filters like M2,
study M1
M3
Diameter of the mixer MX Mixer
Height of the mixer Exponent for the type of
Base chemical engineering plant n
equipment
cost index N Number of tubes
Current chemical engineering Non-random two-liquid
plant cost index NRTL
thermodynamic model
Total mass flow rate of the P Vessel pressure
mixture
Q Duty
Required size of the mixer
R1 Reactors like R2, R3

60
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

Rectification/Absorption Tv Tray volume

RAC
column v Liquid volume
RB Reboiler Volumetric flow rate of the
V
S Rhe equipment design parameter mixture/Vessel volume
S1 Separator Overall heat transfer coefficient
Tm Log mean temperature
Ts Tray spacing

7. Appendix

Equipment specification results for plants A and B

Mixer sizing and specification for plants A and B

Table A1. Summary of mixer 1 specifications

S/N Parameters A B
Values Values
1 Total mass flow rate of the mixture 1080 kg/h 1605 kg/h
2 Volumetric flow rate of the mixture 0.9941 𝑚3/h 1.528 𝑚3/h
3 Required size of the mixer 3.7 m3 5.7 m3
4 Diameter of the mixer 1.6 𝑚 1.8 𝑚
5 Height of the mixer 1.9 𝑚 2.2 𝑚

Table A2. Summary of heater 1 specifications

S/N Parameters A B
Values Values
1 Heat load H 598 kW 790 kW
2 log mean temperature Tm 80 0C 80 0C
3 Overall heat transfer coefficient ⁄𝑚 ⁄𝑚
4 Area required A 10.5 m2 13.9
5 Length L 4.83 m
6 Number of tubes N 28
7 Duty Q 2.153 2.843

Table A3. Summary of heater 1 specifications

S/N Parameters A B
Values Values
1 Heat load H 35.1944 kW -
2 log mean temperature Tm 38 0C -
3 Overall heat transfer coefficient ⁄𝑚 -
4 Area required A 1.0 m2 -
5 Length L 1.12 m -
6 Number of tubes N 24 -
7 Duty Q 1.267 -

Pre-treater sizing and specifications for plants A and B

Table A4. Summary of pre-treater specifications

S/N Parameters A B
Values Values
2 Vessel volume V 2.982 m3 1.146 m3
3 Vessel diameter D 1.363 m 0.9908 m
4 Liquid volume v 1.494 m3 0.764 m3
5 Vessel pressure P 101.3 kPa 101.3 kPa
6 Height H 2.044 m 1.486 m

Hydrolysis/Fermentation reactor sizing and specifications for A and hydrolysis reactor for B

61
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

Table A5. Summary of pre-treater specifications

S/N Parameters A B
Values Values
2 Vessel volume V 72.72 m3 3.5 m3
3 Vessel diameter D 3.952 m 1,428 m
4 Liquid volume v 36.36 m3 1.750m3
5 Vessel pressure P 101.3 kPa 101.3 kPa
6 Height H 5.928 m 2.156 m

pH adjuster sizing and specifications for plants A and B

Table A6. Summary of pH adjuster specifications

S/N Parameters A B
Values Values
2 Vessel volume V 0.773 m3 1.030 m3
3 Vessel diameter D 0.8690 m 0.9561 m
4 Liquid volume v 0.3865 m3 0.5149 m3
5 Vessel pressure P 101.3 kPa 101.3 kPa
6 Height H 1.303 m 1.434 m

Fermenter for plant B sizing and specifications

Table A7. Summary of pH adjuster specifications

S/N Parameters A B
Values Values
2 Vessel volume V - 50 m3
3 Vessel diameter D - 3.488 m
4 Liquid volume v - 25 m3
5 Vessel pressure P - 101.3 kPa
6 Height H - 5.232 m

Separator sizing and specifications for plants A and B

Table A8. Summary of separator specifications

S/N Parameters A B
Values Values
2 Vessel volume V 2.00 m3 2.00 m3
3 Vessel diameter D 1.193 m 1.193 m
4 Liquid volume v 1.00 m3 1.00 m3
5 Vessel pressure P 101.3 kPa 101.3 kPa
6 Height H 1.789 m 1.789 m

Absorber sizing and specifications for plants A and B

Table A9. Summary of absorber specifications

S/N Parameters A B
Values Values
2 Vessel volume V m3 m3
3 Vessel diameter D 1.5 m 1.5 m
4 Liquid volume v 101.3 kPa 101.3 kPa
5 Vessel pressure P 10.5 m 10.5 m
6 Height H 0.5 m 0.5 m
7 Tray spacing Ts 0.8836 m3 0.8836 m3
8 Tray volume Tv m3 m3

62
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

Table A10. Summary of Absorber 2 specifications

S/N Parameter A B
Values Values
2 Vessel volume V m3 m3
3 Vessel diameter D 1.5 m 1.5 m
4 Liquid volume v 101.3 kPa 101.3 kPa
5 Vessel pressure P 10.5 m 10.5 m
6 Height H 0.5 m 0.5 m
7 Tray spacing Ts 0.8836 m3 0.8836 m3
8 Tray volume Tv m3 m3

Reflux absorber 1 sizing and specification for plant A and plant B

Table A11. Summary of reflux absorber 1 specifications

S/N Parameter A B
Values Values
2 Vessel volume V m3 m3
3 Vessel diameter D 1.5 m 1.5 m
4 Liquid volume v 101.3 kPa 101.3 kPa
5 Vessel pressure P 10.5 m 10.5 m
6 Height H 0.5 m 0.5 m
7 Tray spacing Ts m3 m3
8 Tray volume Tv m3 m3

Condenser sizing and specifications for plants A and B

Table A12. Summary of condenser 1 specifications

S/N Parameter A B
Values Values
1 Vessel volume V 2.00 m3 2.00 m3
2 Vessel diameter D 1.00 m3 1.00 m3
3 Liquid volume v 1.193 m 1.193 m
4 Vessel pressure P 101.3 kPa 101.3 kPa
5 Height H 1.789 m 1.789 m
6 Tray spacing 0.8836 m3 0.8836 m3
7 Tray volume m3 m3

Distillation sizing and specifications for plants A and B

Table A13. Summary of distillation column specifications

S/N Parameter A B
Values Values
1 Vessel volume V m3 m3
2 Vessel diameter D 1.5 m 1.5 m
3 Liquid volume v 101.3 kPa 101.3 kPa
4 Vessel pressure P 10.5 m 10.5 m
5 Height H 0.5 m 0.5 m
6 Tray spacing 0.8836 m3 0.8836 m3
7 Tray volume m3 m3

Condenser sizing and specifications for plants A and B

Table A14. Summary of condenser specifications

S/N Parameter A B
Values Values
1 Vessel volume V 2.00 m3 2.00 m3
2 Vessel diameter D 1.00 m3 1.00 m3
3 Liquid volume v 1.193 m 1.193 m
4 Vessel pressure P 101.3 kPa 101.3 kPa
5 Height H 1.789 m 1.789 m

63
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

Reboiler sizing and specifications for plants A and B

Table A15. Summary of reboiler specifications

S/N Parameter B
Values
1 Vessel volume V 2.00 m3
2 Vessel diameter D 1.00 m3
3 Liquid volume v 1.193 m
4 Vessel pressure P 101.3 kPa
5 Height H 1.789 m
6 Area 6.71

Cooler sizing and specifications for plant B

Table A16. Summary of cooler specifications

S/N Parameter Cooler 1 Cooler 2
Values Values
1 Volume V 2.02 m3 2.46 m3
2 Duty D 4.153 3.643
3 Area A 7.6 m2 16.6 m2

Molecular sieve sizing and specifications for plants A and B

Table A17. Summary of molecular sieve 1 specifications

S/N Parameter A B
Values Values
1 Bottom pressure kPa 101.3 1013
2 Overhead pressure kPa 101.3 1013
3 Mass flow rate kg/h 1086 1605
4 Volumetric flow rate 𝑚 1.01 1.528
5 Volume 3.03 4.584

Table A18. Summary of molecular sieve 2 (plant B) specifications

S/N Parameter
2
1 Bottom pressure kPa 100
2 Overhead pressure kPa 100
3 Mass flow rate kg/h 1650
4 Volumetric flow rate 𝑚 1.556
5 Volume 4.668

Equipment Cost for all Plants

Cost Escalation
All the cost-estimating methods use the historical cost data makes use of the published cost indices.
data, and are themselves forecasts of the future These relate the present costs to the one-time
costs. The prices of the materials of construction costs, and are based on labor, material, and energy
and the costs of labor are subject to inflation. costs published in the government statistical
Some methods have to be used to update old cost digests.
data for use in estimating the design stage and
forecasting the plant's future construction cost.
The method usually used to update the historical

64
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

700
Chemical Engineering cost index againts Year
600

500

400
CE

300

y = 0.0015x5 - 14.67x4 + 58859x3 - 1E+08x2 + 1E+11x - 5E+13

200
R² = 0.9691

100

0
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
Year
Figure B1. Graph of chemical engineering cost index against year

Table B1. Chemical engineering cost index for different years

CE cost index Year Reference
382 1996 Richard et al. [32]
387 1997 Richard et al. [32]
390 1998 Richard et al. [32]
391 1999 Richard et al. [32]
394 2000 Richard et al. [32]
394 2001 Richard et al. [32]
396 2002 Richard et al. [32]
402 2003 Richard et al. [32]
444 2004 Richard et al. [32]
468 2005 Richard et al. [32]
500 2006 Richard et al. [32]
525 2007 Richard et al. [32]
575 2008 Richard et al. [32]
521 2009 Richard et al. [32]
551 2010 Richard et al. [32]
586 2011 Richard et al. [32]
585 2012 Richard et al. [32]
567 2013 Richard et al. [32]
576 2014 Richard et al. [32]
557 2015 Richard et al. [32]
542 2016 Richard et al. [32]
550 2017 Richard et al. [32]
570 2018 Extrapolation
610 2019 Extrapolation

Table B2. Cost of a mixer for plant A

Label Type V( ) n
MX 1 Kneader, sigma double arm 131 18.64 0.6 348594.5 375031.1
Total 348594.5 375031.1
Source: Seider & Seader [29]

65
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

Table B3. Cost of a mixer for plant B

Label Type V( ) n
MX 1 Kneader, sigma double arm 201 24.10 0.6 450790.5 484977.4
Total 450790.5 484977.4
Source: Seider & Seader [29]

Table B4. Cost of a reactor for plant A

Label Type S( ) A b n
R1 Jacketed, agitated 2.0 14000 15400 0.7 1.62 38948 49828.6
R2 Jacketed, agitated 72.7 14000 15400 0.7 20.09 323386 413727.9
R2 Jacketed, agitated 0.5 14000 15400 0.7 0.62 23548 30126
Total 385882 493682.5
Source: Sinnott [28]

Table B5. Cost of a reactor for plant B

Label Type S( ) A b n
R1 Jacketed, agitated 1.146 14000 15400 0.7 1.62 30941.42 39585.3
R2 Jacketed, agitated 3.5 14000 15400 0.7 20.09 51014.20 65265.7
R3 Jacketed, agitated 1.03 14000 15400 0.7 0.62 27721.96 35466.4
R4 Jacketed, agitated 3.488 14000 15400 0.7 0.62 50925.32 65151.9
Total 160602.9 205469.3
Source: Sinnott [28]

Table B6. Cost of a column for plant A

Label Type S (m) A b n
DC1 Sieve tray 1.5 100 120 2 2.25 370 473.4
AC1 Sieve tray 1.5 100 120 2 2.25 370 473.4
AC2 Sieve tray 1.5 100 120 2 2.25 370 473.4
RAC2 Sieve tray 1.2 100 120 2 1.44 272.8 349.0
Total 1382.8 1769.2
Source: Sinnott [28]

Table B7. Cost of a column for plant B

Label Type S (m) A b n
DC1 Sieve tray 1.5 100 120 2 2.25 370 473.4
AC1 Sieve tray 1.5 100 120 2 2.25 370 473.4
AC2 Sieve tray 1.5 100 120 2 2.25 370 473.4
RAC2 Sieve tray 1.2 100 120 2 1.44 272.8 349.0
Total 1382.8 1769.2
Source: Sinnott [28]

Table B8. Cost of a separator for plant A

Label Type S (kg) a b n
S1 Vertical, carbon steel 15700 -400 230 0.6 329.26 75329 96373.1
Total 75329 96373.1
Source: Sinnott [28]

Table B9. Cost of a separator for plant B

Label Type S (kg) a b n
S1 Vertical, carbon steel 15700 -400 230 0.6 329.26 75329 96373.1
Total 75329 96373.1
Source: Sinnott [28]

Table B10. Cost of a heater for plant A

Equipment Type S( ) a b n
HT1 U-tube shell and tube 10.5 10000 88 1 10.5 10924 13923.2
HT2 Double pipe 1.0 500 1100 1 1.0 1600 2039.3
Total 12525 15962.5
Source: Sinnott [28]

66
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

Table B.11. Cost of a heater for plant B

Equipment Type S( ) a b n
HT2 U-tube shell and tube 13.9 10000 88 1 13.9 11223.2 14304.5
Total 11223.2 14304.5
Source: Sinnott [28]
Table B12. Cost of a cooler for plant B
Equipment Type S 𝑚 ) a b n
CL1 Double pipe 7.6 500 1100 1 7.6 8860 11292.5
CL2 Double pipe 16.6 500 1100 1 16.6 18760 23910.6
Total 27620 35203.1
Source: Sinnott [28]

Table B13. Cost of a molecular sieve (modelled as a component splitter) for plant A
Equipment Type S( )
M1 Molecular sieve 3,03 257.55 277.1
Total 257.55 277.1
Source: Seider & Seader [29]

Table B14. Cost of a molecular sieve (modelled as a component splitter) for plant B
Equipment Type S( )
M1 Molecular sieve 4.584 389.674 419.2
M2 Molecular sieve 4,668 396.78 426.9
M3 Molecular sieve 4.143 352.155 378.9
Total 1138.61 1225
Source: Seider & Seader [29]

Table B15. Cost of a condenser for plant A

Equipment Type S (lb/h.torr) n
CD1 Cooling water 0.8859 0.9515 1.6 2915.40 3136.5
CD2 Cooling water 0.9239 0.9681 1.6 2966.26 3191.2
Total 5911.66 6327.7
Source: Seider & Seader [29]

Table B16. Cost of a condenser for plant B

Equipment Type S (lb/h.torr) n
CD1 Cooling water 0.8859 0.9515 1.6 2915.40 3136.5
CD2 Cooling water 0.9239 0.9681 1.6 2966.26 3191.2
Total 5911.66 6327.7
Source: Seider & Seader [29]

Table B17. Cost of a reboiler for plant A

Equipment Type Q (million Btu/h)
RB1 Fired heater 26.38 6996.28 7526.9
Total 6996.28 7526.9
Source: Seider & Seader [29]

Table B18 Cost of a reboiler for plant B

Equipment Type Q (million Btu/h)
RB1 Fired heater 26.38 6996.28 7526.9
Total 6996.28 7526.9
Source: Seider & Seader [29]

8. Acknowledgment 9. Conflicts of Interest

The authors wish to acknowledge the support of The authors declare no conflict of interests.
other research team members and the entire
Chemical Engineering Department, Ahmadu 10. References
Bello University Zaria, in order to make the [1] J. M. Hernández-Salas et al., ―Comparative
research work successful. hydrolysis and fermentation of sugarcane and agave
bagasse,‖ Bioresource Technology, Vol. 100, No. 3,

67
T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

pp. 1238–1245, 2009, doi: Evaluation,‖ Nigerian Journal of Technology, Vol. 37,
10.1016/j.biortech.2006.09.062. No. 4, pp. 913–920, 2018, doi: 10.4314/njt.v37i4.9.
[2] R. C. Saxena, D. K. Adhikari, and H. B. Goyal, [13] O. O. Ajayi, K. R. Onifade, A. Onadeji, and T.
―Biomass-based energy fuel through biochemical Oyegoke, ―Techno-economic Assessment of
routes: A review,‖ Renewable and Sustainable Energy Transforming Sorghum Bagasse into Bio-ethanol Fuel
Reviews, Vol. 13, No. 1, pp. 167–178, 2009, doi: in Nigeria : 1 - Process Modeling, Simulation, and Cost
10.1016/j.rser.2007.07.011. Estimation.,‖ Journal of Engineering Studies and
Research, Vol. 26, No. 3, pp. 154–164, 2020.
[3] Q. Kang, L. Appels, T. Tan, and R. Dewil, ―Bio-
ethanol from lignocellulosic biomass: Current findings [14]G. M. Sobamowo and S. J. Ojolo, ―Techno-
determine research priorities,‖ Scientific World Economic Analysis of Biomass Energy Utilization
Journal, Vol. 2014, No. Ci, 2014, doi: through Gasification Technology for Sustainable
10.1155/2014/298153. Energy Production and Economic Development in
Nigeria,‖ Journal of Energy, No. Article ID 4860252,
[4] C. A. Cardona and Ó. J. Sánchez, ―Fuel ethanol
p. 16 pages, 2020.
production: Process design trends and integration
opportunities,‖ Bioresource Technology, Vol. 98, No. [15] T. Oyegoke and B. E. Jibril, ―Design and
12, pp. 2415–2457, 2007, doi: Feasibility Study of a 5 MW Bio-Power Plant in
10.1016/j.biortech.2007.01.002. Nigeria,‖ International Journal of Renewable Energy
Research-IJRER, Vol. 6, No. 4, pp. 1498–1505, 2016.
[5] J. R. Mielenz, ―Ethanol production from biomass:
Technology and commercialization status,‖ Current [16] M. D. Abbas, A. M. Falih, and K. G. M. Al-
Opinion in Microbiology, Vol. 4, No. 3, pp. 324–329, Mutawki, ―A Comparative Study Between Municipal
2001, doi: 10.1016/S1369-5274(00)00211-3. Solid Wastes Management Options in Processing Stage
of Al-Diwaniyah city / Iraq,‖ Journal of Physics:
[6] P. Sassner, M. Galbe, and G. Zacchi, ―Techno-
Conference Series, Vol. 1664, No. 012131, pp. 1–10,
economic evaluation of bioethanol production from
2020.
three different lignocellulosic materials,‖ Biomass and
Bioenergy, Vol. 32, No. 5, pp. 422–430, 2008, doi: [17] M. M. Amirrud and M. Shahin, ―Sensitivity
10.1016/j.biombioe.2007.10.014. and Uncertainty Analysis of Economic Feasibility of
Establishing Wind Power Plant in Kerman, Iran,‖
[7] K. Belkacemi and S. Hamoudi, ―Enzymatic
Renewable Energy Research and Application, Vol. 1,
hydrolysis of dissolved corn stalk hemicelluloses:
No. 2, pp. 247–260, 2020.
Reaction kinetics and modeling,‖ Journal of Chemical
Technology and Biotechnology, Vol. 78, No. 7, pp. [18] B. C. Saha and M. A. Cotta, ―Enzymatic
802–808, 2003, doi: 10.1002/jctb.865. saccharification and fermentation of alkaline peroxide
pretreated rice hulls to ethanol,‖ Enzyme and Microbial
[8] C. N. Ogbonna and O. C. Eric, ―Economic
Technology, Vol. 41, No. 4, pp. 528–532, 2007, doi:
feasibility of on-farm fuel ethanol production from
10.1016/j.enzmictec.2007.04.006.
cassava tubers in rural communities,‖ African Journal
of Biotechnology, Vol. 12, No. 37, pp. 5618–5626, [19] B. C. Saha and M. A. Cotta, ―Lime
2013, doi: 10.5897/AJB2013.12855. pretreatment, enzymatic saccharification and
fermentation of rice hulls to ethanol,‖ Biomass and
[9] T. Oyegoke, F. N. Dabai, M. Jaju, and B. Y.
Bioenergy, Vol. 32, No. 10, pp. 971–977, 2008, doi:
Jibril, ―Process Modelling and Economic Analysis for
10.1016/j.biombioe.2008.01.014.
Cellulosic Bioethanol Production in Nigeria,‖ in First
NCCT, 2017, No. NCCT, pp. 125–129. [20] H. Rabemanolontsoa and S. Saka,
―Comparative study on chemical composition of
[10] A. Abemi, T. Oyegoke, F. N. Dabai, and B. Y.
various biomass species,‖ RSC Advances, Vol. 3, No.
Jibril, ―Technical and Economic Feasibility of
12, pp. 3946–3956, 2013, doi: 10.1039/c3ra22958k.
Transforming Molasses into Bioethanol in Nigeria,‖ in
Faculty of Engineering National Engineering [21] Y. Sun and J. Cheng, ―Hydrolysis of
Conference, 2018, No. November 2018, p. 145. lignocellulosic materials for ethanol production: A
review,‖ Bioresource Technology, Vol. 83, No. 1, pp.
[11] T. Oyegoke and F. Dabai, ―Techno-economic
1–11, 2002, doi: 10.1016/S0960-8524(01)00212-7.
feasibility study of bio-ethanol production from a
combined cellulose and sugar feedstock in Nigeria: 2- [22] J. O. Madu and B. O. Agboola, ―Bio-ethanol
economic analysis,‖ Nigerian Journal of Technology, production from rice-husk using different pretreatments
Vol. 37, No. 4, pp. 921–926, 2018, doi: and fermentation conditions,‖ Biotech, Vol. 8, No. 1,
10.4314/njt.v37i4.9. 2017, doi: 10.1007/s13205-017-1033-x.
[12] T. Oyegoke and F. N. Dabai, ―Techno- [23] R. Kaur and H. Singh, ―Bio-ethanol
economic feasibility study of bio-ethanol production Production from Rice-Husk using Simultaneous
from a combined cellulose and sugar feedstock in Saccharification and Fermentation and Optimization of
Nigeria: 1-Modeling, Simulation, and Cost Pretreatment Methods,‖ Der Pharma Chemica, Vol. 9,
No. 7, pp. 1–7, 2017.

68
 T.Oyegoke , et al./ Renewable Energy Research and Application, Vol 2. No 1, 2021, 51-69

[24] W. Nachaiwie, S. Lumyong, R. P. Hon, K. [28] R. K. Sinnott, Coulson & Richardson‘s

Yoshioka, and C. Khanongnuch, ―Potentialin bio- Chemical Engineering: ―Chemical Engineering
ethanol production from various ethanol fermenting Design,‖ Second. Jordan Hill, Oxford OX2: Elsevier
microorganisms using rice-husk as substrate,‖ Butterworth-Heinemann, 2005.
BIODIVERSITAS, Vol. 16, No. 2, pp. 320–326, 2015,
[29] W. D. Seider, J. D. Seader, and D. R. Lewin,
doi: 10.13057/biodiv/d160229.
Product and Process Design Principles: Synthesis,
[25] J. Orji et al., ―Bio-ethanol Production from Analysis and Design, Internatio. NY: John Wiley and
Corncob Hydrolyzed by Cellulase of Aspergillus niger Sons Inc., 2018.
using Zymomonas mobilis and Saccharomyces
[30] J. A. Quintero and C. A. Cardona, ―Process
cerevisiae Isolated from Palm Wine,‖ International
simulation of fuel ethanol production from
Journal of Current Research in Biosciences and Plant
lignocellulosic using aspen plus,‖ Industrial and
Biology, Vol. 3, No. 1, pp. 39–45, 2016, doi:
Engineering Chemistry Research, Vol. 50, No. 10, pp.
10.20546/ijcrbp.2016.301.004.
6205–6212, 2011, doi: 10.1021/ie101767x.
[26] A. D. Tambuwal, A. S. Baki, and A. Bello,
[31] J. A. Quintero, J. Moncada, and C. A.
―Bio-ethanol Production from Corn Cobs Wastes as
Cardona, ―Techno-economic analysis of bio-ethanol
Bio-fuel,‖ Direct Research Journal of Biology &
production from lignocellulosic residues in Colombia:
Biotechnology Sci, Vol. 4, No. 2, pp. 22–36, 2018, doi:
A process simulation approach,‖ Bio-resource
10.26765/DRJBB.2018.5701.
Technology, Vol. 139, pp. 300–307, 2013, doi:
[27] U. G. Akpan, A. S. Kovo, M. Abdullahi, and 10.1016/j.biortech.2013.04.048.
J. J. Ijah, ―The Production of Ethanol from Maize Cobs
[32] T. Richard, J. A. Bhattacharyya, and D.
and Groundnut Shells,‖ Assumption University (AU)
Shaeiwitz, Analysis, Synthesis, and Design of
Journal of Technology, Vol. 9, No. 2, pp. 106–110,
Chemical Processes, Internatio. Boston: Prentice-Hall,
2005.
2018

69

View publication stats