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Journal of Taibah University for Science
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Process optimization and techno-economic
analysis for the production of lipase from Bacillus
sp.
Muthu Kumar S., Pooja C. Asani, Hrithik Baradia & Soham Chattopadhyay
To cite this article: Muthu Kumar S., Pooja C. Asani, Hrithik Baradia & Soham Chattopadhyay
(2023) Process optimization and techno-economic analysis for the production of
lipase from Bacillus sp., Journal of Taibah University for Science, 17:1, 2198925, DOI:
10.1080/16583655.2023.2198925
To link to this article: https://doi.org/10.1080/16583655.2023.2198925
© 2023 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
Published online: 10 Apr 2023.
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JOURNAL OF TAIBAH UNIVERSITY FOR SCIENCE
2023, VOL. 17, NO. 1, 2198925
https://doi.org/10.1080/16583655.2023.2198925
Process optimization and techno-economic analysis for the production of
lipase from Bacillus sp.
S. Muthu Kumar, Pooja C. Asani, Hrithik Baradia and Soham Chattopadhyay
Department of Bioengineering and Biotechnology, Birla Institute of Technology, Ranchi, Jharkhand, India
ABSTRACT
Lipases are widely used in many biochemical industries. Media optimization is considered one
of the best ways to produce a large quantity of lipase economically. This study used Response
Surface Methodology (D - Optimal design in MODDE 13 to comprehend the effect of different
media on the enzyme activity. The enzyme activity was highest under the following conditions:
10.1 g/L of peptone, 7.5 g/L of Yeast Extract, and 13.9 mL/L of Olive oil. A feasibility analysis of
lipase production from the microbes using the optimized data was performed, and at optimized
conditions, lipase activity increased from 0.757 µmol/min to 0.959 µmol/min. A simulation study
for large-scale batch production for lipase was conducted using SuperPro Designer (v10), and
techno-economic data was analyzed. The expected cost per unit volume of lipase was estimated
to be 4393.96 $. Thus, process optimization with technoeconomic analysis could be helpful for
industrial-scale lipase production.
ARTICLE HISTORY
Received 6 November 2022
Revised 23 February 2023
Accepted 29 March 2023
KEYWORDS
Lipase; techno-economic
analysis; optimization;
enzyme activity; RSM
1. Introduction
Lipases (Triglycerol Acylhydrolases, EC 3.1.1.3) fall under
specialized hydrolases responsible for the hydrolysis of
triacylglycerols to generate free fatty acids, mono and
diacylglycerols, and glycerol. Esterification and trans-
esterification reactions can also be catalyzed by lipase
[1].
Lipases are the most crucial biocatalyst due to
their ability to conduct reactions in aqueous and non-
aqueous media [2]. Lipases are profusely found in ani-
mals, plants, bacteria, and fungi. The enzyme has come
to notice due to its flexible activity towards extreme
temperatures, pH, and organic solvents [3]. Microbial
lipases are most prominently studied due to their wide
range of industrial applications, and soil is the most
abundant, natural source for isolating these organ-
isms [4]. Microbial lipases can also be separated from
marine environments, human skin, silkworm intestines,
and agro-industrial wastes [5]. Lipases have achieved
a prominent position in the world market and con-
tribute to 10% of the global industrial enzymes; they are
the 3rd most selling enzyme, following proteases and
amylases [1,6]. The specificities needed from enzymes
are evolving rapidly, thus mandating their ongoing
study and optimization while considering the economic
aspects of production [7]. Lipase has various appli-
cations in industries, including pharmaceuticals, cos-
metics, leather, and agriculture [8–11]. Owing to its
non-toxic and eco-friendly nature, lipase is preferred
over other synthetic chemicals and is widely used in the
food industry and biodiesel manufacturing [3,12].
The extracellular nature of bacterial lipases allows
nutritional and physio-chemical factors like nitrogen
source, carbon source, pH, temperature, aeration, agi-
tation, etc., to influence the enzyme activity and speci-
ficity; therefore, it becomes essential to optimize these
factors to achieve maximum enzyme activity and speci-
ficity [2,13]. Lipase activity can be optimized using the
traditional one-factor variation approach while keep-
ing all other factors constant. However, this method
cannot account for the interaction between the ele-
ments. Alternatively, a statistical approach like response
surface methodology (RSM) efficiently reveals the inter-
action between different factors and the optimum
value for each factor [14]. MODDE 13 is software that
aims to optimize a response that the user defines well
by recording a minimum number of experiments to
achieve the result. D – optimal designs determine the
number of experiments that can be parallelly performed
on a microtiter plate. These designs allow better flexibil-
ity in the specification of a given problem and provide
the best-fit model based on the chosen criteria for opti-
mization [15]. The sum of all factors in a D – optimal
design remains constant (i.e. 100%), and any factor can
be modified as per need [16]. The experimental data can
further be analyzed through specific plots and values.
Standard probability plots can be used to identify sta-
tistical errors, and coefficient plots are used to interpret
CONTACT Soham Chattopadhyay soham@bitmesra.ac.in Department of Bioengineering and Biotechnology, Birla Institute of Technology,
Mesra-835215, Ranchi, Jharkhand, India
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor& Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided the original work is properlycited. The terms on which this article has been published allow the posting of the Accepted
Manuscript in a repository by the author(s) or with their consent.
2S. MUTHU KUMAR ET AL.
and analyze interactions of the factors. R2, Q2, model
validity, and reproducibility are parameters helpful in
estimating the model quality. The observed versus pre-
dicted plot can be consulted for further evaluation.
Cost plays an essential role in the industrial applica-
tion and development of enzymes. Lowering the pro-
duction cost is critical to promoting novel industrial
applications of lipase [17]. SuperPro Designer software
performs Techno-Economic analysis to check the over-
all economic feasibility of the production and accounts
for raw materials, equipment, labour, and other util-
ity costs [18,19]. The total investment in the produc-
tion plant can be divided into direct fixed costs and
operation costs. Direct fixed cost comprises equipment,
installation, building, electricity, and additional facility
charges. Raw material, waste treatment, labour, trans-
port, marketing, and other costs fall under operating
expenses. Process optimization is necessary to minimize
operating expenses that improve returns [20].
Although process optimization for lipase produc-
tion was reported in the literature, the economic via-
bility and technical feasibility on a large scale were
not described. A few reports are available that address
these two combined aspects in detail. Thus, this study
aims to determine the optimum concentration of most
essential media constituents and to analyze their inter-
action to enhance enzyme activity. This optimized data
obtained from the experiments were used to conduct
the Techno-economic analysis to determine the feasi-
bility of the process on an industrial scale.
2. Materials and methods
2.1. Microorganisms
The bacterial culture was taken from a departmental
culture collection and was characterized as Bacillus sp.
2.2. Production media compositions
Yeast extract (5 g/L), peptone (10 g/L), NaCl (1 g/L),
Na2HPO4 (8.63 g/L), NaH2PO4 (6.08 g/L), MgSO4, 7H2O
(0.5 g/L), and olive oil (10 ml/L).
2.3. Lipase assay
Enzyme Assay was performed using the titrimetry
method [21]. In summary, one mL of olive oil was mixed
with 4.5 mL of Tris-HCl buffer (pH 7.0, 0.05 M) and 0.5
mL of CaCl2. After 10 min of incubation at 40 °C, 0.6 mL
of crude lipase was added to the reaction mixture, fol-
lowed by re-incubation at 40°C in an incubator shaker
at 120 rpm for 30 min. The addition of 10 ml of 95%
acetone-ethanol (1:1) led to the termination of the reac-
tion. 0.05 N NaOH was used to titrate the free fatty
acids produced by the reaction using phenolphthalein
solution as an indicator. A blank sample with 0.6 mL of
distilled water was also run using the same method. The
enzyme activity was calculated based on Equation (1)
Enzyme activity (µmol/mL)
=
NaOH required for titration of (sample −blank)
(mL)×normality of NaOH(N)×1000
vol.of enzyme(mL)×reaction time(min)
(1)
2.4. Design of experiment
Response Surface Methodology was performed via the
MODDE 13 software to study the changes in enzyme
activity by different factor combinations. The RSM
design is developed using factors that include A) Pep-
tone concentration (varied between 8 to 12 g/L), B)
Yeast Extract concentration (varied between 5 to 10
g/L), and C) Amount of Olive oil (varied between 10 to 18
mL/L). These factors are varied as per the combinations
suggested by the software using the D-optimal model
to determine the suitable combination for increased
response (Enzyme Activity). The minimum and maxi-
mum values for each factor are shown in Table 1.The
factors were evaluated based on the experiments gen-
erated by the D-Optimal design model in the software,
which is given in Table 2.
2.5. Techno-economic analysis of lipase
production
SuperPro Designer software (V. 10) was used to perform
the Techno-economic Analysis of lipase production
from Bacillus sp. The optimized media parameters for
increased enzyme activity obtained from the MODDE 13
software through lab-scale experiments were used to
stimulate the techno-economic analysis for lipase pro-
duction. The production process was carried out in a
continuously stirred tank batch reactor of 40 L capacity,
with a working volume of 30 L per batch.
Tab le 1 . Description of factors investigated for the develop-
ment of RSM design in the optimization process.
Factor Low Level High Level
Peptone (g/L) 8 12
Yea st E xtr ac t (g /L) 5 10
Olive Oil (mL/L) 10 18
Tab le 2 . Quantity of raw materials required (in Kg) for lipase
production per year and per batch.
Raw Materials Kg/ Year Kg/ batch
Acetic Acid 19,570 182.90
Media 484 4.52
Olive Oil 193 1.81
Peptone 161 1.51
Phosphate Buffer 200,590 1,874.67
Sodium Acetate 176,134 1,646.12
Water 2,683 25.07
Yeast Extract 129 1.21
TOTAL 399,975 3,738.08
JOURNAL OF TAIBAH UNIVERSITY FOR SCIENCE 3
2.5.1. Process design
The plant was designed to be set up in India to pro-
duce 604.97 kg/year of lipase. Optimized parameters
obtained from lab scale were explored for simulation
process. Seed fermentor, production fermentor, filtra-
tion unit and extraction unit were the unit operations
involved in the simulation process [22]. The cultured
microorganism was transferred to the fermenter along
with sterilized media. Fermentation was carried out for
146 h. The broth was centrifuged, ultra-filtered, and
purified using packed bed adsorption chromatography
techniques.
2.5.2. Economic performance
The techno-economic analysis for the production of
lipase using the optimized concentration of raw materi-
als was undertaken using the Super Pro Designer soft-
ware to check the project’s economic feasibility. The
economics of the production plant accounts for various
factors, including raw material, equipment, building,
utility, and labour cost. The input raw material includes
acetic acid (0.73 $/kg), olive oil (8.00 $/kg), phosphate
buffer (5.00 $/kg), sodium acetate (5.00$/kg), and yeast
extract (2.30 $/kg). The prices for the raw materials are
given as per their cost in the Indian market. The US dol-
lar exchange rate for the reference year 2022 is 78.14
INR. The raw material quantities required per batch
and annually are given in Table 2. The overall produc-
tion cost, operating cost, fixed capital, and revenue was
calculated. The cost analysis provides an overview of
the investment, annual operating cost, annual revenue,
return on investment, payback period, IRR (Internal Rate
of Return), and NPV (Net Present Value) [23,24].
3. Results and discussion
3.1. Optimization using D- Optimal design
MODDE 13 was used to set up a D- optimal experiment
design; Table 3shows the experimental trials generated
by the software and their responses after performing
the experiments. The lowest enzyme activity obtained
was 0.13 μmol/min with a peptone concentration of 8
g/L, yeast extract concentration of 10 g/L, and olive oil
concentration of 18 mL/L. The highest enzyme activity
obtained was 1.05 μmol/min with a peptone concen-
tration of 10g/L, yeast extract concentration of 7.5g/L,
and olive oil concentration of 14mL. The significance
of the generated model was statistically determined by
calculating the R2, Q2, model validity score, and repro-
ducibility value. The R2value obtained for the model
was 0.96, indicating a significant model and the Q2
value of 0.86 which indicates model precision and excel-
lent model fit [25]. A model validity score of 0.65 was
obtained; this confirms the absence of non-substantial
factors, and a reproducibility score of 0.98 was obtained
which means that the replicate values of the response
at the centre point are identical under the same con-
ditions [15]. Table 4summarizes the coefficient table
for lipase activity. It can be used to study each vari-
able’s linear and square effect. The p<0.05 for media
components revealed that they significantly controlled
enzyme activity. The ANOVA result for the response is
Tab le 3 . D-Optimal design layout generated through MODDE 13 along with the response after the experiment run.
Standard Run Factor A: Peptone (g/L) Factor B: Yeast Extract(g/L) Factor C: Olive Oil (mL) Response 1: Enzyme Activity (μmol/mL)
1 1 8 5 10 0.31
2212 5 10 0.90
3 3 12 10 10 0.75
4 4 8 5 18 0.19
5512 5 18 0.39
668 10 18 0.13
7 7 12 10 18 0.34
8 8 8 10 12.6667 0.25
9 9 8 10 15.3333 0.30
10 10 8 8.33333 10 0.52
11 11 9.33333 10 10 0.83
12 12 10.6667 10 10 0.95
13 13 12 7.5 14 0.50
14 14 10 5 14 0.85
15 15 10 7.5 18 0.75
16 16 10 7.5 14 0.96
17 17 10 7.5 14 0.98
18 18 10 7.5 14 1.05
Tab le 4 . Table for the coefficient of variance of individual factors for lipase activity.
Lipase Activity Coefficient Score Standard Error P– value Conf. int(±)
Constant 0.951172 0.0370469 3.61386e-11 0.0815386
Peptone 0.140057 0.0246654 0.000142708 0.0542876
Yea st E xtr ac t −0.026175 0.0241335 0.301301 0.0531168
Olive Oil −0.156361 0.0247881 5.77351e-05 0.0545577
Pep∗Pep −0.43312 0.0460498 1.35922e-06 0.101354
Yea ∗Yea −0.104512 0.0477273 0.0509891 0.105046
4S. MUTHU KUMAR ET AL.
Tab le 5 . Analysis of variance (ANOVA) for the model of lipase activity of Bacillus sp., considering 95% confidence level (p-value <
0.05).
Source Sum of Squares Degree of freedom Mean Square F – value P-value Standard Deviation
Total 8.3231 18 0.4624
Constant 6.6613 1 6.6613
Total Corrected 1.6619 17 0.0978 0.3127
Regression 1.5884 6 0.2647 39.6510 0.0001 (Significant) 0.5145
Residual 0.0734 11 0.0067 3.4316 0.2460 0.0817
Lack of fit 0.0690 9 0.0077 0.0875
Pure Error 0.0045 2 0.0022 0.0473
provided in Table 5. The calculated f-value and p-value
were 39.65 and <0.001, respectively which imply that
the model was highly significant in representing the
effect of the factors studied on lipase activity.
To further study the model, a graphical overview was
generated. Figure 1(a) shows the variation of the experi-
ments in a raw format, followed by Figure 1(b) depicting
a summary of the fit plot generated using the statisti-
cal data related to the model, including the R2value, Q2
value, model validity, and reproducibility value. Figure 1
(c) gives the coefficient plotted using the values in Table
4and Figure 1(d) provides the Residual with vs. Normal
Probability plot, which can be compared to Figure 2,to
show the observed vs. predicted plot, check for normal
values distribution, and identify the outliers or deviants.
The model is able to predict results in the selected vari-
able range. The deviation appears to be relatively higher
in a few points but in most range of the variables the
model has accurate predictability [26]. On further ana-
lyzing the coefficient plot, we learn that olive oil appears
to play a substantial role in controlling enzyme activity,
followed by peptone. Yeast extract does not have a
significant role in improving enzyme activity. The coef-
ficient plot and the values of coefficient of variance
indicate that squaring peptone concentration does not
improve the model and may lead to a negative effect,
i.e. decreased enzyme activity.
Sifour et al. [27] evaluated 19 variables to determine
their effect on lipase productivity and found olive oil
played a positive role in enzyme productivity. In con-
trast, yeast extract had little impact on productivity [27].
Wang et al. [28] found peptone to be the most signif-
icant factor in enhancing lipase activity, followed by
olive oil [28]. The effect of peptone on enzyme activ-
ity was studied by Xiang et al. [29], and they found that
moderate concentrations of peptone improved enzyme
activity, whereas higher concentrations reduced it. This
study also found that olive oil significantly influences
lipase activity [29]. Similar results for peptone were also
reported by Veerapagu et al. [30].
Figure 3(a) represents the interaction between olive
oil and peptone by keeping yeast extract at a constant
value (7 g/L). Figure 3(b) shows the interaction between
yeast extract and olive oil while keeping peptone at
Figure 1. Graphical overview of a) Experiment data and replicates, d) Summary of fit, c) Coefficient plot for factors affecting enzyme
activity and d) Normal Probability curve for Lipase Activity.
JOURNAL OF TAIBAH UNIVERSITY FOR SCIENCE 5
Figure 2. Observed vs. Predicted experimental values for lipase enzyme activity.
Figure 3. Contour Plot on a) Effect of Peptone and Olive Oil, b) Effect of Olive Oil and Yeast Extract and c) Effect of Peptone and Yeast
Extract on lipase enzyme activity.
a constant value of (10.4g/L). Figure 3(c) depicts the
contour plot for the interaction between yeast extract
and peptone when olive oil is kept at a value of 13.2
ml. On analyzing these contour plots, olive oil syner-
gistically interacts with peptone and yeast extract to
yield a high enzyme activity that can be seen as dark
red elliptical regions in the graph. At constant olive oil
concentration, the impact is less significant, implying
the importance of olive oil in enhancing lipase activ-
ity. The source of lipase could be responsible for the
impact of media components already reported in the
literature.
Figure 4shows a dynamic profile of each factor and
provides optimized set point values for each aspect.
It predicts a value for enzyme activity under the opti-
mized conditions with minimal error. The predicted
value lies between the low and high levels set for each
factor; between 8 g/L to 12 g/L for peptone, 5 g/L
to 10 g/L for yeast extract and 10 mL/L to 18 mL/L
for olive oil. The optimized values for the factors were
10.095 g/L of peptone, 7.5 g/L of Yeast Extract, and
13.936 mL/L of Olive Oil. Under the optimized condi-
tion, the predicted enzyme activity was 0.959 μmol/min.
The expected value was checked by performing an
6S. MUTHU KUMAR ET AL.
Figure 4. Desirability plot representing optimized set point values for each factor based on minimal failure.
experimental run for which enzyme activity was obtained
1.021 μmol/min. The observed value is close to the pre-
dicted one and indicates model validity.
3.2. Process design and techno-economic analysis
of lipase production
A techno-economic analysis for the pilot scale pro-
duction of lipase was carried out using the SuperPro
designer software at an annual output level of 604.97
Kg of lipase enzyme to determine the feasibility of
producing lipase on an industrial scale. The improved
process data gathered fermentation metrics such as
substrate concentration, enzyme activity, yield, pro-
ductivity, and fermentation time. The costs of raw
materials and energy were determined using market
prices in the area. Medium preparation, fermentation,
and purification are all part of the process. SuperPro
Figure 5. SuperPro process design of lipase enzyme production.
JOURNAL OF TAIBAH UNIVERSITY FOR SCIENCE 7
Tab le 6 . Techno-economic analysis of Lipase production.
Total capital investment 264,000 $
Capital investment charged to this project 264,000 $
Operating cost 2,658,000 $/yr
Main revenue 3,025,000 $/yr
Batchsize 5.65kgMP
Cost basis annual rate 604.97 kg MP/yr
Unit production cost 4393.96 $/kg MP
Net unit production cost 4393.96 $/kg MP
Unit production revenue 5000.00 $/kg MP
Gross margin 12.12%
Return on investment 85.26%
Payback time 1.17 years
IRR (After taxes) 41.33%
NPV (at 7.0% Interest) 1,188,000 $
Designer’s economic evaluation module automatically
calculated the financial data for this procedure. Figure
5depicts the lipase enzyme production process from
microorganisms. Data on a plant’s economic perfor-
mance can be used to evaluate its profitability and long-
term viability before it is built. Table 6shows the plant’s
overall financial data, including total plant investment,
annual operating costs and revenue, net profit cost, ROI,
payback period, IRR, and NPV. The project’s total capital
expenditure is 264,000 $, with a process operating cost
of 2,658,000 $ a year. The primary source of income from
lipase production was 3,025,000 $ per year.
The unit production cost of lipase was 4393.96
$/kg, with an 85.26 percent return on investment.
The industrial-scale lipase production shows a payback
period of 1.17 years, an IRR of 41.33 percent, and an
NPV of 1,188,000 $ at 7.0 percent interest. Khootama
et al. [31] performed a similar study for lipase produc-
tion using solid state fermentation from bacteria. They
reported a payback period of 2.98 years, an IRR of 34.99
percent, and an NPV of 3,711,31.41$ at 7.0 percent inter-
est [31].
4. Conclusion
The main objective of this work is to optimize the lipase
activity and perform a techno-economic performance
for its large-scale production. From the results obtained,
it can be concluded that enzyme activity was improved,
and the techno-economic analysis was performed. The
concentrations of yeast extract, peptone, and olive oil
were optimized. It was observed that olive oil and pep-
tone play a significant role in enhancing enzyme activ-
ity. After optimization, a 21% increase in enzyme activity
was observed. The optimum conditions were used to
evaluate the large-scale production of lipase enzymes.
The techno-economic analysis revealed that the oper-
ating cost was higher than the capital investment cost,
indicating the process’s economic feasibility. A produc-
tion cost of 4393.96 $ per kg of enzyme and a payback
period of 1.1 years were achieved based on the analy-
sis of a 604.97 annual output level production plant of
lipase.
Acknowledgments
The authors would like to thank the Department of Bioengi-
neering & Biotechnology, Birla Institute of Technology, Mesra,
Ranchi, Jharkhand, for providing infrastructure facilities for
performing the experiments.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Statements and declaration
Declaration of competing interest
The authors declare that they have no known com-
peting financial interests or personal relationships that
could have appeared to influence the work reported in
this paper.
Author contributions
SC and MK conceived and designed the project. SC and
MK acquired the data. PA and HB analyzed and inter-
preted the data and wrote the paper. All authors read
and approved the manuscript.
ORCID
Soham Chattopadhyay http://orcid.org/0000-0002-3797-
5333
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