Process Optimization and Techno-Economic Analysis for the Production of Phycocyanobilin from Arthrospira maxima-Derived C-Phycocyanin

Abstract

C-Phycocyanin (C-PC) is a photosynthetic pigment found in cyanobacteria, notably in Arthrospira species. The extraction of phycocyanobilin (PCB), the chromophore of C-PC, is a common approach to address the instability of C-PC under light, heat, and acidic conditions. Methanol is typically used for PCB extraction. However, its use poses challenges for industrial applications owing to the need for solvent removal, extensive purification, and safety validation. Therefore, this study proposes ethanol as an alternative to methanol, optimizing the ethanol extraction conditions through response surface methodology (RSM) using a central composite design (CCD) and techno-economic analysis. The parameters evaluated were the extraction temperature, time, and C-PC/solvent ratio. Optimal conditions—68.81 °C, 14.91 h, and a C-PC to solvent ratio of 1:95 (w/v)— yielded a predicted PCB yield of 29.18%, closely aligning with the actual value of 29.67 ± 1.33%. A techno-economic analysis for pilot-scale PCB production showed that optimized ethanol extraction could yield 147.13 kg/year with 506 batches, compared with 84.31 kg/year standard methanol extraction with 317 batches. Furthermore, it was evaluated to have a unit production cost of USD 1,413,588/kg, an internal rate of return (IRR) of 53.36%, and a payback time of 1.6 years with increased yields and reduced toxic solvent disposal costs. This study supports scalable PCB production with a natural blue pigment suitable for the food, beverage, and cosmetics industries.

1. Introduction

Arthrospira species, commonly known as spirulina, are blue-green photosynthetic microorganisms classified as non-toxic edible cyanobacteria [1]. Known for their rich nutrient profile, including proteins, vitamins, and minerals, Arthrospira-derived products have attracted significant attention as a healthy and functional food ingredient [2]. In particular, the water-soluble photosynthetic pigment C-phycocyanin (C-PC) in Arthrospira cells is a major bioactive component that has been extensively studied for its diverse physiological activities, including antioxidant, anti-inflammatory, and immune-enhancing effects [3,4]. Moreover, its excellent biocompatibility, non-toxicity, and unique color make it widely used in the food, cosmetics, and biomedical industries [3,4]. Among naturally occurring blue pigments, C-PC is considered the most suitable for application in the food industry [5]. In recent years, the US Food and Drug Administration granted GRAS (Generally Recognized As Safe) status to C-PC extracted from Arthrospira spp. as a blue pigment in confectionery and chewing gum. However, as a protein-based compound, C-PC exhibits low stability under light, heat, and acidic conditions because of protein denaturation, limiting its industrial applicability [5,6]. C-PC is composed of α and β subunits joined together to form the minimum building block, a monomer, which further assembles into (αβ)₃ trimers and then (αβ)₆ hexamers to form higher-order structures [7]. Generally, food-grade purity C-PC exists as a hexamer [8], but under conditions that do not allow it to maintain a stable structure, it dissociates into trimers, monomers, and monomeric subunits or enters into protein aggregation and easily loses its color [9]. Among the many factors that affect C-PC stability, heat is known to have the greatest impact [8,10]. Many studies have focused on the addition of various additives to increase stability against heat [11]. Although treating C-PC with additives can be a cheap and attractive option, it is essential to use additives with low toxicity, and they sometimes impart unacceptable flavors to the food product [11]. To address this limitation, previous studies have proposed extracting the chromophore phycocyanobilin (PCB), which is responsible for the blue color [12]. PCB can be obtained by cleaving molecules from phycobiliproteins present in Arthrospira spp. PCB, an open-chain tetrapyrrole prosthetic group, which is separated from the apoprotein of C-PC and can serve as a stable blue pigment even under strong light, acidity, and high temperatures [13]. In addition to its enhanced stability, PCB exhibits physiological activities, including antitumor, antioxidant, and anti-inflammatory effects, which confer significant commercial potential for applications in the pharmaceutical, cosmetic, food, and healthcare industries [14,15,16]. Methanol is commonly used to obtain this appealing natural blue pigment from C-PC [17]. Although methanol extraction may be effective in maximizing the yield of PCB [18], its use in industrial applications is limited because of the need for the complete removal of residual solvents, extensive purification processes, and safety validation. According to the Toxic Release Inventory (TRI) of the US Environmental Protection Agency (EPA), methanol is classified as a solvent that is generally harmful to both the environment and human health, necessitating measures to maximize recovery or minimize emissions in industrial settings [19]. In contrast, ethanol, a ‘green solvent,’ is excluded from the TRI. It is a non-toxic, food-grade, and environmentally friendly solvent, making it a suitable alternative to methanol for industrial applications [20].

Efforts to use ethanol for PCB extraction have been discussed in several studies [12,13]. However, to the best of our knowledge, no studies have simultaneously optimized the extraction conditions for PCB using ethanol or conducted large-scale economic evaluations through simulations. PCB extraction can be significantly influenced by factors such as solvent type, extraction temperature, extraction time, and solvent-to-solute ratio [12]. The traditional approach to optimizing extraction conditions typically involves the one-variable-at-a-time method, in which one independent variable is altered while the others remain constant. However, this unidimensional optimization model often requires extensive experiments and considerable time and may be ineffective for evaluating the interactive effects among process variables [21]. Response surface methodology (RSM) was proposed as a statistical experimental design technique to overcome these limitations. It is a mathematical and statistical approach for modeling and analyzing the conditions influenced by multiple variables [21]. Among the RSM techniques, central composite design (CCD), first developed by Box and Wilson [22], allows for the placement of experimental points at five levels for each factor [23]. The application of RSM in science and engineering has been shown to significantly reduce the number of experiments, cost, and time required; thus, it is often employed for the efficient extraction of valuable compounds derived from microalgae [24].

In the manufacturing of expensive materials such as PCB, optimizing processes can significantly reduce industrial losses. In industrial systems, competitive advantage is achieved through a process called optimization, which minimizes waste and ensures the effective use of time, cost, and materials. It is necessary to refine the optimization of conditions using reliable statistical techniques such as RSM rather than the traditional optimization approach of selecting a few single factors. Furthermore, the feasibility and economic feasibility of this strategy in industrial terms should be evaluated through simulation. Therefore, this study aimed to evaluate the effects of various extraction parameters (C-PC to ethanol ratio, extraction time, and temperature) on the extraction efficiency of PCB from C-PC using ethanol, and to determine the optimal extraction conditions through CCD. Furthermore, this is the first study to perform a techno-economic analysis required for industrial applications based on process simulation under optimized conditions.

2. Materials and Methods

2.1. Microalgae Cultivation and Crude Phycocyanin Production

A. maxima (LIMS-PS-3470), isolated and identified by the Korea Institute of Ocean Science and Technology Jeju Bio Research Center (Jeju, Republic of Korea), was cultivated for 21 d in a 200 L photobioreactor [25] to obtain Arthrospira biomass containing C-PC. The cultivation of A. maxima was conducted using Zarrouk’s medium, which contained 13.61 g/L NaHCO₃, 4.03 g/L Na₂CO₃, 2.50 g/L NaNO₃, 1.00 g/L K₂SO₄, 1.00 g/L NaCl, 0.50 g/L K₂HPO₄, 0.20 g/L MgSO₄·7H₂O, 0.04 g/L CaCl₂·2H₂O, 0.01 g/L FeSO₄·7H₂O, and 0.08 g/L Na₂EDTA [26]. On the final day of cultivation, A. maxima biomass was harvested by centrifugation at 11,000 rpm for 30 min using a large-capacity centrifuge (GQLY series; Hanil S.M.E, Anyang, Republic of Korea). The harvested wet biomass was frozen at –50 °C for 24 h and then freeze-dried for 2 d using a freeze dryer (FDTA-45; Operon, Gimpo, Republic of Korea). Crude phycocyanin was extracted from dried A. maxima powder according to the method proposed in a previous study [27]. The powder was suspended in 10 mL of 0.1 M sodium phosphate buffer (pH 7.0) at a 1:100 w/v ratio and homogenized for 15 min using an ultrasonic disruptor. The homogenate was subjected to three freeze–thaw cycles. Crude phycocyanin was collected via centrifugation at 6000 rpm for 20 min, and the supernatant was freeze-dried for further experiments.

2.2. Purification of C-PC from Crude Phycocyanin

Crude phycocyanin was purified using the methodology described by Kumar et al. [28]. Briefly, crude phycocyanin was subjected to single-step precipitation using 65% (NH₄)₂SO₄ and left to stand overnight at 4 °C. The pellet was recovered via centrifugation at 6000 rpm and 4 °C for 20 min and dissolved in 100 mL of the same extraction buffer. The resulting 100 mL extract was dialyzed twice against 1,000 mL of extraction buffer at 4 °C in the dark for 24 h. The dialysate was recovered from the dialysis membrane and filtered through a 0.45 µm syringe filter, yielding purified C-PC that was then used for PCB extraction. The purity of phycocyanin was confirmed to be A620/A280 using a spectrophotometer (OPTIZEN POP-BIO; Mecasys, Daejeon, Republic of Korea), confirming a C-PC purity of 1.7, meeting food-grade levels (>0.7) [29].

2.3. Extraction of PCB from C-PC

Purified C-PC powder was washed three times with 99% ethanol to remove residual chlorophyll completely, and PCB was extracted from the C-PC through heat extraction. The parameters for the heat extraction comprised an extraction temperature (50–90 °C), extraction time (8–24 h), and C-PC to solvent ratio (1:20–1:100, w/v). After heat extraction, the mixture was centrifuged at 7000 rpm for 20 min to separate the crude extract pellet from the PCB-containing supernatant. The supernatant was subsequently concentrated under reduced pressure at 500–700 mmHg and 45 °C to obtain the final PCB powder. The yield (%) of PCB was calculated by comparing the weight of the purified C-PC powder to the weight of the final PCB powder obtained after extraction (Equation (1)).

PCB yield (%) = PCB powder (mg)/Purified C-PC powder (mg) × 100

(1)

2.4. Experimental Design

To model the effects of extraction temperature, extraction time, and C-PC/solvent ratio on PCB yield from Arthrospira-derived C-PC, a CCD with three variables at five levels (–2, –1, 0, 1, and 2) was implemented using Design Expert software (version 8; Stat-Ease Inc., Minneapolis, MN, USA). The experiments were conducted in a randomized manner, and the ranges of the three selected parameters (extraction temperature, extraction time, and C-PC to solvent ratio) are depicted in

Table 1. Experimental design for PCB extraction.

Factor	Level
	–2	–1	0	1	2
A: Extraction temperature (°C)	50	60	70	80	90
B: Extraction time (h)	8	12	16	20	24
C: C-PC to solvent ratio (w/v)	20 (1:20)	40 (1:40)	60 (1:60)	80 (1:80)	100 (1:100)

. The quality of the generated model was evaluated using the coefficient of determination (R²). One-way analysis of variance (ANOVA) was applied to estimate the significance of the models (p < 0.05). For clarity, the interactive effects of the parameters were illustrated using 3D surface plots, where one variable is held constant at the middle value while the other two are varied.

2.5. High-Performance Liquid Chromatography of PCB

PCB was characterized using a high-performance liquid chromatography (HPLC) system (Waters, CO., Milford, MA, USA) with a photodiode array detector (Waters 2998 PDA detector) and a COSMOSIL C18 column (5 μm, 150 × 4.6 mm). The mobile phase consisted of two solvents: A (deionized H₂O containing 0.1% trifluoroacetic acid) and B (acetonitrile containing 0.1% trifluoroacetic acid). A linear gradient of 40–55% (v/v) aqueous acetonitrile was applied for 15 min at a flow rate of 0.8 mL/min [12]. The detection wavelength was 666 nm, and the injection volume was 10 μL [12]. The PCB standard used in this study was obtained from Santa Cruz Biotechnology (sc-396921B; Santa Cruz, TX, USA).

2.6. Techno-Economical Analysis

To optimize the extraction method, large-scale production modeling was conducted using SuperPro Designer (v10.0; Intelligen, Inc., Scotch Plains, NJ, USA). Calculations were performed in batch mode, simulating a production plant operating for 1 year. The design for the PCB production process using C-PC included a 100 L scale reactor, decanter centrifuge, and batch distillation vessel. The prices of C-PC (USD 400,000/kg) and PCB (USD 1,600,000/kg) used in the simulation were referenced from the prototype prices provided by Santa Cruz Biotechnology, respectively. The significant price difference between C-PC and PCB can be attributed to the fact that the conventional synthesis of PCB through plant extraction and chemical methods results in low yields and high production costs, leading to significantly higher prices for PCB [30].

2.7. Schematic Diagram of Experiments

The overall flow of the experiment is shown in Figure 1. It consisted of three main steps: Arthrospira maxima cultivation, C-PC extraction, and PCB extraction. The main objective of this study was to explore the optimal conditions for PCB extraction from C-PC through condition optimization and techno-economic analysis, as depicted in the section to the right of Figure 1.

3. Results and Discussion

3.1. Statistical Analysis of the Quadratic Model

A CCD with three variables and five levels was applied to evaluate the effects of key process variables, including extraction temperature (50–90 °C), extraction time (8–24 h), and C-PC to solvent ratio (1:20–1:100), on PCB extraction. The actual PCB yields obtained from the 20 experimental groups are listed in

Table 2. Central composite design (CCD) runs and corresponding PCB yields.

Run	A: Temperature (°C)	B: Time (h)	C: C-PC to Solvent Ratio (w/v)	Yield (%)
1	60	12	40 (1:40)	3.8
2	80	12	40 (1:40)	9.21
3	70	16	60 (1:60)	26.28
4	70	16	60 (1:60)	24.91
5	70	16	60 (1:60)	24.01
6	70	24	60 (1:60)	22.45
7	60	12	80 (1:80)	22.33
8	70	16	20 (1:20)	1.42
9	70	16	60 (1:60)	24.61
10	70	16	60 (1:60)	23.25
11	50	16	60 (1:60)	4.68
12	60	20	80 (1:80)	18.29
13	70	16	60 (1:60)	24.36
14	60	20	40 (1:40)	5.14
15	80	12	80 (1:80)	23.34
16	80	20	80 (1:80)	27.92
17	70	8	60 (1:60)	17.58
18	80	20	40 (1:40)	15.54
19	90	16	60 (1:60)	16.77
20	70	16	100 (1:100)	29.89

. The results from these 20 experimental sets were used to generate a regression equation (Equation (2)).

Expected PCB yield (%) = 24.20 + 3.16A + 1.12B + 7.20C + 1.70AB − 0.6462AC − 0.8912BC − 3.65A² − 1.32B² − 2.41C²

(2)

Based on the experimental results, a second-order polynomial regression equation and its mathematical expression in coded terms were automatically generated using Design Expert software. The PCB yields obtained from the 20 experimental groups ranged from 3.8 to 29.89%, with the lowest and highest yields recorded in runs 1 and 20, respectively. The fit summary analysis of the PCB extraction suggested that the quadratic model was the best fit for the system, with a p value of <0.0001 and a high coefficient of determination (R² = 0.9821). Additionally, the non-significant p value in the lack-of-fit analysis further validated the suitability of the model for predicting PCB yield. According to the model equation, PCB yield is influenced not only by linear and quadratic terms, but also by interactive effects among the factors. The extraction temperature (A, A²), extraction time (B, B²), C-PC to solvent ratio (C, C²), and interaction term AB were found to have significant effects on the PCB yield (p < 0.05). Among the parameters, the linear term C made the largest contribution to the PCB yield, with the highest coefficient value of 7.20, followed by A² (3.65), A (3.16), and C² (2.41).

3.2. Process Parameter Interaction and PCB Yield Optimization

The interactions between pairs of independent variables are illustrated in the 3D response surface plots (Figure 2). As shown in Figure 2a,b, the yield of PCB increased as the extraction temperature increased from 50 to 70 °C, but decreased between 70 °C and 90 °C. A decline in the PCB yield beyond a certain temperature threshold has been reported in previous studies [12]. While the extraction time showed a slight increase and decrease as it was extended from 8–24 h, there was a significant interaction between the extraction time and temperature, as indicated in

Table 3. Analysis of variance for the quadratic model.

Source	Sum of Squares	Df	Mean Squares	F Value	p Value
Model	1448.13	9	160.90	62.84	<0.0001 a
A: Extraction temperature	160.21	1	160.21	62.57	<0.0001 a
B: Extraction time	20.14	1	20.14	7.87	0.0186 a
C: C-PC to solvent ratio	828.43	1	828.43	323.56	<0.0001 a
AB	23.15	1	23.15	9.04	0.0132 a
AC	3.34	1	3.34	1.30	0.2799 b
BC	6.35	1	6.35	2.48	0.1462 b
A2	334.44	1	334.44	130.63	<0.0001 a
B2	44.12	1	44.12	17.23	0.0020 a
C2	146.60	1	146.60	57.26	<0.0001 a
Residual	25.60	10	2.56
Lack-of-Fit	20.46	5	4.09	3.98	0.0779 b
Pure Error	5.14	5	1.03
Cor Total	1473.73	19
R2	0.9826

^a Significant term. ^b Non-significant term.

. Longer extraction times at higher temperatures resulted in a considerable reduction in yield. The C-PC to solvent ratio, which had the most significant effect on the extraction yield, increased as the yield increased, as shown in Figure 2b,c. However, from an economic perspective for industrial applications, an excessive increase in solvent use should be avoided [31].

Sixteen solutions with a desirability of 0.8 or higher were generated by setting each parameter within the specified range (extraction temperature, 50–90 °C; extraction time, 8–24 h; and C-PC to solvent ratio, 1:20–1:100, w/v) and conducting a numerical optimization analysis to maximize the PCB yield. Among these, the solution closest to a desirability of 1.0 had a desirability of 0.905, with a predicted yield of 29.18%. Considering the minimum unit of the extraction equipment parameters, the first decimal place was rounded off for the validation experiment. The actual PCB yield of 29.67 ± 1.33% was found to have no significant difference from the theoretical value (

Table 4. Results of the numerical optimization and actual verification run.

	Variable			Yield
	Extraction Temperature (°C)	Extraction Time (h)	C-PC to Solvent Ratio (w/v)	(%)
CCD (theoretical)	68.81	14.91	1:95	29.18
Validation (actual)	69	14.91	1:95	29.67 ± 1.33

). These optimized extraction conditions with ethanol require a higher extraction temperature than conventional extraction with methanol, and the extraction time can be reduced by more than 1 h [10].

3.3. Characterization of PCB Extracted via Optimized Conditions

PCB was characterized at 666 nm via HPLC, as shown in Figure 3. A comparison of standard PCB (PCB STD in Figure 3) and Arthrospira-derived PCB extracts (PCB Sample in Figure 3) showed retention times of ~4.77 min and ~4.66 min, respectively, consistent with a previous PCB study [12]. The peak of ~4.1 min retention time observed in PCB STD, although not identified in PCB Sample, is likely to be an adduct of methanol during methanol extraction, which may reduce the purity of the PCB extract [12,32]. Thus, nuclear magnetic resonance analysis is essential for determining the exact purity of the PCB extract [12].

3.4. Techno-Economical Analysis

In this study, the total capital investment, cost estimation, and profitability of the optimized method were evaluated (Figure 4).

The total capital investment, cost estimation, and profitability analysis of the optimized method were thoroughly evaluated. The total capital investment comprised equipment, direct fixed capital, working capital, and start-up costs. Among the cost components, material costs constituted the largest proportion at 97.33%, with the inputs used in this study estimated at USD 400/g for C-PC and USD 0.75/kg for 99% ethanol. Labor costs were the second highest, accounting for 1.50% of the total. Facility-dependent costs, including equipment maintenance, fixed capital cost depreciation, and other related expenses, represented 0.95% of the total costs. Lastly, laboratory, quality control, and quality assurance (QC/QA) costs accounted for 0.23%. Based on the simulation results, the process allows for 506 batches to be operated annually, with an expected PCB production volume of 147.13 kg/year. The price of the PCB product was set at USD 1,600,000/kg, which was determined by referencing the product price from Santa Cruz Biotechnology, Inc. Therefore, the expected revenue from producing PCB using this process for 1 year is estimated to be USD 235,414,870/year, and the payback time is estimated to be 1.60 years. This model was compared with the well-known PCB methanol extraction method to evaluate its economic viability. In the methanol extraction method, C-PC at a concentration of 1:100 (w/v) was extracted using methanol at 60 °C for 16 h, yielding an efficiency of 27.12%. By operating 317 batches/year, this method produced 84.31 kg/year with a payback time of 7.84 years. The difference in payback time between methanol and ethanol was attributed to the higher yield of ethanol and the absence of toxic waste handling costs. Methanol is listed in the TRI of the US EPA and is known to have significant adverse effects on human health and the environment [33]. Referring to previous studies on methanol disposal costs through incineration (USD 1.05/kg) [19], applying this to our model resulted in an additional annual disposal cost of USD 26,527. The extraction solvent and its optimization are crucial for large-scale extraction processes considering time, cost, and disposal expenses. The financial indicators for ethanol and methanol extraction methods were compared (

Table 5. Comparison of economic parameters for methanol and ethanol extraction methods.

Parameter	Methanol Extraction	Ethanol Extraction
Investment charged to this project	USD 21,633,680	USD 27,867,186
Annual operating cost	USD 131,959,686/year	USD 207,987,258/year
Annual revenues	USD 134,902,668/year	USD 235,414,870/year
Unit production ref. rate	84.31 kg MP 1/year	147.13 kg MP 1/year
Unit production cost	USD 1,565,095/kg MP 1	USD 1,413,588/kg MP 1
Unit production revenue	USD 1,600,000/kg MP 1	USD 1,600,000/kg MP 1
Gross margin	2.18%	11.65%
Return on investment (ROI)	12.75%	62.61%
Payback time	7.84 years	1.60 years
Internal rate of return (IRR)	8.20%	53.36%
Net present value (NPV, at 7.00%)	USD 1,749,872	USD 103,815,276

¹ Main product, PCB.

).

Ethanol and methanol extraction processes show differences in economic performance due to variations in production efficiency, cost structures, and output capacity. Methanol extraction requires a lower initial investment (USD 21,633,680) and operating costs (USD 131,959,686/year), which makes it attractive for projects with budget constraints. However, its profitability is limited, with a gross margin of 2.18%, an ROI of 12.75%, and an IRR of 8.20%. These modest returns are primarily attributed to its lower production efficiency (84.31 kg/year) and higher unit production cost (USD 1,565,095/kg). Furthermore, its longer payback time (7.84 years) and marginal NPV (USD 1,749,872) highlight the process’s inability to generate significant value over time. In contrast, ethanol extraction demonstrates higher initial investment (USD 27,867,186) and operating costs (USD 207,987,258/year), but achieves superior profitability due to its greater production efficiency (147.13 kg/year), lower unit production cost (USD 1,413,588/kg), and higher annual revenues (USD 235,414,870/year). These factors contribute to its stronger financial indicators, including a gross margin of 11.65%, an ROI of 62.61%, and an IRR of 53.36%, which reflect the process’s ability to convert investments into substantial returns. The faster payback time (1.60 years) and significantly higher NPV (USD 103,815,276) indicate that ethanol extraction not only recovers its costs quickly but also generates long-term value. The disparity in performance arises from ethanol extraction’s ability to handle larger production volumes, achieve better cost efficiencies, and capitalize on economies of scale, which offset its higher capital and operating costs.

Therefore, the large-scale production of PCB using ethanol extraction demonstrates substantial profitability relative to the investment and the ability to generate immense added value even after recovering the initial investment, strongly supporting the economic viability of this approach; moreover, the optimized PCB extraction method using ethanol developed in this study proved to be more productive than the conventional methanol extraction method, while also eliminating additional disposal costs, making it a viable model for industrial-scale applications.

4. Conclusions

This study proposed a method for extracting PCB from C-PCs using ethanol instead of methanol. The optimal conditions for the heating extraction of PCB were statistically determined to include an extraction time of 14.91 h, an extraction temperature of 68.81 °C, and a C-PC/solvent ratio of 1:95 (w/v). Under these conditions, there was no significant difference between the statistically predicted yield (29.18%) and the actual yield (29.67 ± 1.33%). The techno-economic analysis showed that, compared to the standard methanol extraction method, the optimized ethanol extraction method eliminates the cost of handling toxic solvents and has a higher PCB yield, which significantly reduces the production unit cost by USD 151,507/kg and the payback time by 6.2 years. It has also been shown to increase ROI and IRR by 4.9 times and 6.5 times, respectively. This study is the first to demonstrate the efficient use of ethanol as a solvent for PCB production. Furthermore, the statistical methodology used to obtain the results suggests that it is a robust tool for scaling up from the lab to industrial production.