Process optimization and techno-economic analysis for the production of lipase from Bacillus sp.

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.

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 transesterification 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 animals, 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 organisms [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 contribute 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 applications in industries, including pharmaceuticals, cosmetics, 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, agitation, etc., to influence the enzyme activity and specificity; therefore, it becomes essential to optimize these factors to achieve maximum enzyme activity and specificity [2,13]. Lipase activity can be optimized using the traditional one-factor variation approach while keeping all other factors constant. However, this method cannot account for the interaction between the elements. Alternatively, a statistical approach like response surface methodology (RSM) efficiently reveals the interaction 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 flexibility in the specification of a given problem and provide the best-fit model based on the chosen criteria for optimization [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 statistical errors, and coefficient plots are used to interpret and analyze interactions of the factors. R², Q2, model validity, and reproducibility are parameters helpful in estimating the model quality. The observed versus predicted plot can be consulted for further evaluation.

Cost plays an essential role in the industrial application and development of enzymes. Lowering the production cost is critical to promoting novel industrial applications of lipase [17]. SuperPro Designer software performs Techno-Economic analysis to check the overall economic feasibility of the production and accounts for raw materials, equipment, labour, and other utility costs [18,19]. The total investment in the production 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, transport, 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 production was reported in the literature, the economic viability 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 interaction to enhance enzyme activity. This optimized data obtained from the experiments were used to conduct the Techno-economic analysis to determine the feasibility 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, followed 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 reaction. 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) (1) $\begin{array}{rl}& \mathrm{Enzyme}\mathrm{activity}(\mathrm{\mu mol}/\mathrm{mL})\\ & =\frac{\begin{array}{c}\mathrm{NaOH}\mathrm{required}\mathrm{for}\mathrm{titration}\mathrm{of}(\mathrm{sample}-\mathrm{blank})\\ (\mathrm{mL})\times \mathrm{normality}\mathrm{of}\mathrm{NaOH}(N)\times 1000\end{array}}{\mathrm{vol}.\mathrm{of}\mathrm{enzyme}(\mathrm{mL})\times \mathrm{reaction}\mathrm{time}(\min )}\end{array}$(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) Peptone 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 maximum values for each factor are shown in Table 1. The factors were evaluated based on the experiments generated by the D-Optimal design model in the software, which is given in Table 2.

Table 1. Description of factors investigated for the development of RSM design in the optimization process.

Factor	Low Level	High Level
Peptone (g/L)	8	12
Yeast Extract (g/L)	5	10
Olive Oil (mL/L)	10	18

Table 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

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 production. 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.

2.5.1. Process design

The plant was designed to be set up in India to produce 604.97 kg/year of lipase. Optimized parameters obtained from lab scale were explored for simulation process. Seed fermentor, production fermentor, filtration 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 materials was undertaken using the Super Pro Designer software 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 dollar 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 production 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 3 shows 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 concentration 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 reproducibility value. The R² value obtained for the model was 0.96, indicating a significant model and the Q2 value of 0.86 which indicates model precision and excellent 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 conditions [15]. Table 4 summarizes the coefficient table for lipase activity. It can be used to study each variable’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 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.

Table 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
2	2	12	5	10	0.90
3	3	12	10	10	0.75
4	4	8	5	18	0.19
5	5	12	5	18	0.39
6	6	8	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

Table 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
Yeast Extract	−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

Table 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

To further study the model, a graphical overview was generated. Figure 1 (a) shows the variation of the experiments in a raw format, followed by Figure 1 (b) depicting a summary of the fit plot generated using the statistical data related to the model, including the R² value, Q2 value, model validity, and reproducibility value. Figure 1 (c) gives the coefficient plotted using the values in Table 4 and 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 variable 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 analyzing 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 coefficient 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.

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.

Figure 2. Observed vs. Predicted experimental values for lipase 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 contrast, yeast extract had little impact on productivity [27]. Wang et al. [28] found peptone to be the most significant factor in enhancing lipase activity, followed by olive oil [28]. The effect of peptone on enzyme activity 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 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 synergistically 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 activity. The source of lipase could be responsible for the impact of media components already reported in the literature.

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.

Figure 4 shows a dynamic profile of each factor and provides optimized set point values for each aspect. It predicts a value for enzyme activity under the optimized 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 condition, the predicted enzyme activity was 0.959 µmol/min. The expected value was checked by performing an experimental run for which enzyme activity was obtained 1.021 µmol/min. The observed value is close to the predicted one and indicates model validity.

Figure 4. Desirability plot representing optimized set point values for each factor based on minimal failure.

3.2. Process design and techno-economic analysis of lipase production

A techno-economic analysis for the pilot scale production 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, productivity, 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 Designer’s economic evaluation module automatically calculated the financial data for this procedure. Figure 5 depicts the lipase enzyme production process from microorganisms. Data on a plant’s economic performance can be used to evaluate its profitability and long-term viability before it is built. Table 6 shows 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.

Figure 5. SuperPro process design of lipase enzyme production.

Table 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
Batch size	5.65 kg MP
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 $

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 production 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 interest [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 peptone play a significant role in enhancing enzyme activity. 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 operating cost was higher than the capital investment cost, indicating the process’s economic feasibility. A production cost of 4393.96 $ per kg of enzyme and a payback period of 1.1 years were achieved based on the analysis of a 604.97 annual output level production plant of lipase.

Statements and declaration

Declaration of competing interest

The authors declare that they have no known competing 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 interpreted the data and wrote the paper. All authors read and approved the manuscript.

Acknowledgments

The authors would like to thank the Department of Bioengineering & 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).