Evaluation of Biomass, Lipid and Chlorophyll Production of a Microalgal Consortium Cultured in Dairy Wastewater

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

section 84-93 can be removed

please describe better the methods for chlorophyll content, nitrate consumption, COD removal, lipid production 

there is no statistical analysis. A one-way ANOVA can be enough to compare the three studied cases.

figures 2-4 are a result and must be on the results section. also the quality of the figures is too low. must be increased

if the data obtained experimentally comes from a batch method, how authors can support a "continuous" simulation?

Methane as solvent in line 132??

Please describe the conditions for chlorophyll and lipid extraction better. Please add the cell lysis method, mixing process, and separation method.

Why did the authors use an air lift rather than other types of reactors? Maybe a raceway could be better to simulate, since it has lower requirements and it's cheaper to operate.

please add the One-way ANOVA to figures 5, 6 and 7

table 3 has the title in spanish

about the operation cost, please discuss your results with the information already available in the literature. you can find papers of simulated algal cultures on MDPI 

 

 

 

 

 

Author Response

We have taken into consideration the reviewer comments and we have modified the paper accordingly. It is clear that the reviewers know the area and read the paper very carefully; most of the comments were highly insightful and enabled us to greatly improve the quality of our manuscript. The following is the list of the changes made to the manuscript in response to the reviewer’s comments.

 

ITEMIZED LIST OF CHANGES

 

Reviewer #1:

General comments:

Point 1:

(1). “section 84–93 can be removed”.

Response:

In accordance with the recommendation, the section corresponding to lines 84–93 (introductory paragraph describing the dairy industry and the production context in Mexico) was removed, as the information is considered contextual and not essential for a direct understanding of the study's objectives and methods. This will reduce redundancy and improve conciseness.

Point 2:

(2). “Please describe better the methods for chlorophyll content, nitrate consumption, COD removal, lipid production”.

Response:

In response to the comment, the following modification was made:

“….chlorophyll content [17] (the total chlorophyll (a + b) is evaluated by measuring the absorbance of the methanol extract at 652 nm and 665 nm, with UV/Vis spectrophotometer), nitrate consumption [18] (the NitraVerX reagent kit distributed by HACH was used, along with the N method, Nitrate RA TNT, preloaded in the HACH DR3900 spectrophotometer. This method measures absorbance at 410 nm), and COD removal [18] (the COD HR reagent kit distributed by HACH was used, along with the COD 1500 method preloaded in the HACH DR3900 spectrophotometer. This method measures absorbance at 435 nm), were determined daily. In addition, the accumulated lipid production at the end of the growth phase was estimated [19] (Bligh-Dyer method, using a mixture of chloroform-methanol 1:2 as solvents, respectively. Extraction temperature at 60°C and drying time of 15 min)”

Point 3:

(3). “There is no statistical analysis. A one-way ANOVA can be enough to compare the three studied cases”.

Response:

We would like to clarify that the main focus of this manuscript is the simulation and techno-economic evaluation of the proposed process using SuperPro Designer. The results shown in the tables and figures correspond to deterministic outputs of the process simulator, which are calculated based on mass and energy balances and equipment design equations. These values are not experimental replicates but model-based estimations; therefore, statistical analysis such as ANOVA cannot be applied to these simulated data. However, we acknowledge the importance of statistical analysis in the experimental section. We would like to clarify that the experimental part of this study was performed in duplicate to provide input data for the simulation stage. Although we agree that statistical analysis such as ANOVA is useful for comparing multiple treatments, this approach requires at least three replicates per treatment to be valid, which is not the case in our experimental design. For this reason, we presented the average values of the duplicates as descriptive statistics.

Point 4:

(4). “Figures 2-4 are a result and must be on the results section. Also, the quality of the figures is too low. must be increased”.

Response:

 The figures are part of the simulation methodology and we consider it more appropriate to present them in the methodology section. As in this paper, “https://doi.org/10.3390/biology11091359.”

The quality of the images in the figures has been improved.

Point 5:

(5). “If the data obtained experimentally comes from a batch method, how authors can support a 'continuous' simulation?”.

Response:

In response to the comment, the following modification was made:

“Continuous mode simulation was used as an industrial-scale projection scenario to leverage average productivity data obtained experimentally. The limitations of extrapolating batch data (experimental data) to continuous operation lie in the dynamics of operation, control of conditions, and the behavior of the biological system. In a real industrial case, these must be taken into account, but for the simulation, they are not considered determining factors.

The validation of kinetic parameters in continuous pilot tests is a critical step when seeking to design, scale, or control a bioprocess that will operate continuously. Although kinetic parameters can be estimated in batches, there are specific needs for which it is essential to validate them in continuous pilot tests, such as reproducibility, refining simulation models, control and optimization, and errors in design and scaling.”

Point 6:

(6). “Methane as solvent in line 132??”.

Response:

In accordance with the recommendation, the solvent was corrected from “Methane” to “Methanol.”

Point 7:

(7). “Please describe the conditions for chlorophyll and lipid extraction better. Please add the cell lysis method, mixing process, and separation method”.

Response:

In response to the comment, the following modification was made:

“…The process includes the cultivation stage, primary harvesting of biomass through floccu-lation with chitosan, secondary harvesting through filtration, chlorophyll extraction with methanol as a solvent (prior mechanical maceration and exposure to cold solvent), and subsequent evaporation of the solvent to 60°C, Figure 2 (flowsheet). On the other hand, the wet and residual biomass from the chlorophyll extraction stage is combined with a mixture of solvents, chloroform:methanol 1:2 v/v, for lipid extraction. This last stage of the process is very similar to the chlorophyll extraction stage.”

Point 8:

(8). “Why did the authors use an air lift rather than other types of reactors? Maybe a raceway could be better to simulate, since it has lower requirements and it's cheaper to operate”.

Response:

The selection of the airlift photobioreactor model in SuperPro Designer was based on the nature of our experimental setup. The laboratory-scale experiments were performed in closed systems under controlled conditions (sealed flasks with regulated parameters), which corresponds to the concept of closed photobioreactors at a larger scale. Although raceway ponds are widely recognized as a cost-effective alternative for large-scale cultivation, they represent an open system, which significantly differs from our experimental conditions in terms of contamination risk and operational control. For consistency between the experimental data and the industrial simulation, we selected the airlift configuration as the most representative option for a closed cultivation system, which allows maintaining similar control strategies at scale. Future work could explore open systems such as raceways for comparative economic assessments; however, this was beyond the scope of the current study.

Point 9:

(9). “Please add the One-way ANOVA to figures 5, 6 and 7”.

Response:

We agree that statistical analysis is an important tool to evaluate the significance of differences between treatments. However, in the present study, the experiments were performed in duplicate, as stated in lines 103–105. Performing a one-way ANOVA requires a minimum of three replicates per treatment to properly estimate variability and ensure the validity of the test. Although we cannot apply ANOVA under these conditions, we have reported the average values and included the standard deviation in the experimental results to reflect variability between duplicates. In future work, we plan to increase the number of replicates to allow for more robust statistical comparisons.

Point 10:

(10). “Table 3 has the title in Spanish”.

Response:

In response to the comment, the following modification was made:

“Table 3. Total production cost, values calculated using the simulator.”

Point 11:

(11). “About the operation cost, please discuss your results with the information already available in the literature. you can find papers of simulated algal cultures on MDPI”.

Response:

In response to the comment, lines 399-418 were added.

“…In this study, the complete process simulation indicated that operating costs are dominated by the cultivation stage, which accounted for 99% of the total costs. Approximately 98% of the operating costs correspond to facility-dependent expenses, which include equipment maintenance, fixed capital depreciation, and other costs such as insurance and local taxes, all calculated as a percentage of the direct fixed capital cost, which is itself de-rived from the estimated equipment purchase cost. The simulator’s database was used to determine equipment costs, applying the Chemical Engineering Cost Index for inflation adjustments. The cultivation stage alone represents 99% of the total equipment cost, ex-plaining its high contribution to facility-dependent expenses. This trend is consistent with Cabrera et al. (2023) [31], who reported that operating closed photobioreactors entails high energy requirements to maintain mixing and controlled conditions, with energy consumption exceeding 170 kWh per kg of biomass, making cultivation the most impact-ful stage in processes involving microalgae. Similarly, Valdovinos et al. (2024) [35], re-ported production costs ranging from 836.9 to 1131.5 USD/kg, noting that the integration of wastewater reduces these costs by 10%, an observation aligned with our findings in Case 3, where resource consumption and operating costs were reduced without compro-mising productivity. Although the evaluated process is not competitive compared to open pond systems, it offers advantages in terms of cultivation control and product quality, as highlighted in recent studies on algal biorefineries…”

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The paper proposed seems interesting, but many improvements are required. Below are the issues that, in my view, must be addressed before this manuscript is ready for publication.

• In the biomass discussion you attribute Case 2’s lower yield to “this treatment incorporated BBM-3N… if not only diluted wastewater,” which contradicts your own Case 2 definition (no BBM-3N; 60% freshwater + 40% WWDI). This reads as a copy/edit error and undermines the interpretation of Figure 5. Also, The study says cultures ran 9 days, yet the caption for Figure 5 says “up to 7 days of retention,” while the x-axis shows 0–10 days. This makes it impossible to know what time points were truly analyzed and when peaks/declines were measured.
• Nitrate and COD numbers don’t square with the wastewater characterization. Raw WWDI is reported at NO₃⁻ = 287 mg/L and COD = 2111 mg/L; with 40% WWDI in Case 2, first-principles mixing would suggest initial NO₃⁻ ≈ 115 mg/L and COD ≈ 844 mg/L. Yet you report NO₃⁻ = 20.4 → 0 mg/L and COD = 1321 → 196 mg/L for Case 2. For Case 3 (60% BBM-3N + 40% WWDI), initial NO₃⁻ is 68.9 mg/L, not obviously derivable from the earlier WWDI characterization or added BBM-3N, and COD again starts ~1330 mg/L. This suggests an arithmetic/typographical mistakes. Clarify with a full mass balance and sampling chronology.
• From two cases you generalize a “20–22 mg/L nitrate uptake” constant. You infer a “total” nitrate consumption of ~20–22 mg/L by the consortium from Case 2 and Case 3 end-point differences, despite different starting concentrations. This is not supported: uptake must be reported relative to initial concentration and biomass produced (e.g., mg NO₃⁻ consumed per g biomass), not as a single concentration decrement applied across cases.
• You repeatedly state that responses “showed significant differences,” but the figures lack error bars; n is duplicates only (explicitly stated), and there are no tests (ANOVA, post-hoc) or p-values reported.
• Table 1 reports maximum biomass/chlorophyll values, not means ± SD. Comparing treatments via maxima risks cherry-picking outliers and does not support the narrative claims; re-analyze with appropriate central tendency and variance.
• Porra coefficients depend on solvent and path length; these are not clearly specified for the measurements (only a literature citation), yet the TEA later assumes methanol extraction conditions.
• Given the sustainability framing of this study, it would be useful to mention greener extraction routes (e.g., NaDES/ionic liquids, process-intensified schemes) highlighted in a recent review on Limnospira/Spirulina pigment recovery (https://doi.org/10.1016/j.crfs.2025.101141)
• In the SuperPro model you fix biomass composition at 7.03% lipids and 1.52% chlorophyll (values matching Case 1), then later claim higher lipid output for Case 3 in Table 2. If lipid fraction is fixed at 7.03% in the model, a 2× increase in annual lipids for Case 3 implies an implicit, unreported change elsewhere (e.g., biomass throughput). Either (i) use case-specific compositions in the model, or (ii) don’t attribute modeled lipid gains to composition effects in Case 3. 
• You state COD reductions of 85% (Case 2) and 71% (Case 3) and imply “proper disposal,” yet you do not compare final COD to discharge limits (e.g., the relevant NOM). 
• The process description says chlorophyll extraction uses methane as solvent, but i thinl you mean methanol?
• The SuperPro feed includes glucose, implying mixotrophic operation at scale, whereas the experimental section uses phototrophic conditions with BBM-3N/WWDI and no added organic carbon. Please explain this.
• The lipid section explicitly assumes 100% extraction of the lipids measured experimentally. That is not realistic at scale (particularly with wet biomass and methanol:chloroform), and materially biases costs/throughputs. Maybe you should add a sensitivity analysis.
• You claim Case 3 has “lower operating costs,” but Table 3 shows Operating (USD/yr): Case 2 = 445.209M, Case 1 = 445.873M, Case 3 = 446.264M. Case 3 is actually the highest of the three.
• You attribute 62.68% (Case 1) and 56.46% (Case 3) of raw-material costs to “EDTA (nitrogen source)”. EDTA is a chelating agent, not a nitrogen nutrient. Please revise.

Comments on the Quality of English Language

Minor revision of English

Author Response

We have taken into consideration the reviewer comments and we have modified the paper accordingly. It is clear that the reviewers know the area and read the paper very carefully; most of the comments were highly insightful and enabled us to greatly improve the quality of our manuscript. The following is the list of the changes made to the manuscript in response to the reviewer’s comments.

 

ITEMIZED LIST OF CHANGES

Reviewer #2:

General comments:

Point 1:

(1). “In the biomass discussion you attribute Case 2’s lower yield to “this treatment incorporated BBM-3N… if not only diluted wastewater,” which contradicts your own Case 2 definition (no BBM-3N; 60% freshwater + 40% WWDI). This reads as a copy/edit error and undermines the interpretation of Figure 5. Also, The study says cultures ran 9 days, yet the caption for Figure 5 says “up to 7 days of retention,” while the x-axis shows 0–10 days. This makes it impossible to know what time points were truly analyzed and when peaks/declines were measured.”.

Response:

In response to the comment, the following modification was made:

“…This trend can be attributed to a limitation in nutrient availability, given that this treatment does not contain BBM-3N culture medium, but only diluted wastewater.”

Time (Days) to 9, to avoid confusion with the text.

“Figure 5. Biomass production for up to 9 days of retention on an experimental basis for different crop cases.”

 

Point 2:

(2). “Nitrate and COD numbers don’t square with the wastewater characterization. Raw WWDI is reported at NO₃⁻ = 287 mg/L and COD = 2111 mg/L; with 40% WWDI in Case 2, first-principles mixing would suggest initial NO₃⁻ ≈ 115 mg/L and COD ≈ 844 mg/L. Yet you report NO₃⁻ = 20.4 → 0 mg/L and COD = 1321 → 196 mg/L for Case 2. For Case 3 (60% BBM-3N + 40% WWDI), initial NO₃⁻ is 68.9 mg/L, not obviously derivable from the earlier WWDI characterization or added BBM-3N, and COD again starts ~1330 mg/L. This suggests an arithmetic/typographical mistakes. Clarify with a full mass balance and sampling chronology.”.

Response:

We greatly appreciate your insightful comments. The experimental values are correct, and what we have modified is the characterization of WWDI. We have a database of WWDI characterization from 2018 to date. The characterization corresponding to this research was not correct. The modification is as follows.

“…The wastewater was collected from the dairy industry, GRUPO CUADRITOS®, located near the city of Celaya (20°37'56.5“ N 100°47'04.8” W), Guanajuato, Mexico. The WWDI was filtered using 25 µm membranes to remove the largest suspended solids. The WWDI was characterized, yielding values of pH 7.6, chemical oxygen demand (COD) 3,015.34 mg/L, phosphates (PO4) 40.02 mg/L, nitrates (NO3) 50.98 mg/L, ammonium (NH4) 7.2 mg/L, dissolved oxygen (DO) 5.64 ppm, proteins 121.6 mg/L and TKN 66.57 mg/L (total Kjeldahl nitrogen)…”

Point 3:

(3). “From two cases you generalize a “20–22 mg/L nitrate uptake” constant. You infer a “total” nitrate consumption of ~20–22 mg/L by the consortium from Case 2 and Case 3 end-point differences, despite different starting concentrations. This is not supported: uptake must be reported relative to initial concentration and biomass produced (e.g., mg NO₃⁻ consumed per g biomass), not as a single concentration decrement applied across cases.”.

Response:

In response to the comment, the following modification was made:

“…The first was nitrogen in its inorganic form (nitrates), which represented nitrogen consumption by the microalgae consortium. Initially, in the experiment for case 2, we had a value of 20.4 mg/L, and at the end, we obtained 0 mg/L of nitrates, which represents 100% consumption of this nutrient. For case 3, the initial estimated value was 68.9 mg/L higher due to the contribution from the addition of BBM-3N medium and the final value was 46.7 mg/L, representing 32.22% consumption. In both cases, the microalgae consumed 20 to 22 mg/L of nitrates. In this sense, this value can be taken as an indicator of the savings of this nutrient. Which indicates that 35.22 mg NO₃⁻ and 61.95 mg NO₃⁻ consumed per g biomass are required for case 3 and case 2, respectively…..”

Point 4:

(4). “You repeatedly state that responses “showed significant differences,” but the figures lack error bars; n is duplicates only (explicitly stated), and there are no tests (ANOVA, post-hoc) or p-values reported.”.

Response:

We agree that the term “significant differences” is inappropriate without statistical validation. The experimental part was performed in duplicate, and therefore it was not possible to apply ANOVA or other statistical tests. We have revised the manuscript to remove references to “significant differences” and replaced them with descriptive terms such as “differences observed” based on the trends in the data. Additionally, the text was adjusted to clarify that these comparisons are qualitative rather than statistically validated.

Point 5:

(5). “Table 1 reports maximum biomass/chlorophyll values, not means ± SD. Comparing treatments via maxima risks cherry-picking outliers and does not support the narrative claims; re-analyze with appropriate central tendency and variance.”.

Response:

In this case, the table contains the values we used in the simulation, obtained experimentally. These are the maximum production values for biomass, chlorophyll, and lipids. The values shown are the average values of the measurements, as they were measured in duplicate.

Considering the observation, we decided to leave the average values, without considering the standard deviation.

Table 1 was modified. The title was also modified and a footnote was added.

“Table 1. Summary of the values obtained for biomass, total chlorophyll, and lipid content for the three experimental treatments. Values used for the simulations.

 

Case	Composition of the medium	Biomass (g/L)	Total Chlorophyll (µg/mL)	% Lipids
1	100% BBM-3N (control)	0.7470	8.6062	7.03
2	60% Fresh water + 40% WWDI	0.4493	2.0731	8.72
3	60% BBM-3N + 40% WWDI	0.7543	10.6890	14.63

¹ Average production values, values measured in duplicate.”

Point 6:

(6). “Porra coefficients depend on solvent and path length; these are not clearly specified for the measurements (only a literature citation), yet the TEA later assumes methanol extraction conditions.”.

Response:

In response to the comment, the following modification was made:

“….chlorophyll content [17] (the total chlorophyll (a + b) is evaluated by measuring the absorbance of the methanol extract at 652 nm and 665 nm, with UV/Vis spectrophotometer), nitrate consumption [18] (the NitraVerX reagent kit distributed by HACH was used, along with the N method, Nitrate RA TNT, preloaded in the HACH DR3900 spectrophotometer. This method measures absorbance at 410 nm), and COD removal [18] (the COD HR reagent kit distributed by HACH was used, along with the COD 1500 method preloaded in the HACH DR3900 spectrophotometer. This method measures absorbance at 435 nm), were determined daily. In addition, the accumulated lipid production at the end of the growth phase was estimated [19] (Bligh-Dyer method, using a mixture of chloroform-methanol 1:2 as solvents, respectively. Extraction temperature at 60°C and drying time of 15 min)”

Point 7:

(7). “Given the sustainability framing of this study, it would be useful to mention greener extraction routes (e.g., NaDES/ionic liquids, process-intensified schemes) highlighted in a recent review on Limnospira/Spirulina pigment recovery (https://doi.org/10.1016/j.crfs.2025.101141)”.

Response:

The following lines, 438–453, were added:

“…Regarding the cost associated with solvents for the extraction of compounds of interest, in case 2, these represent almost 5% of the total cost of raw materials. Although they are not the most significant cost, it is desirable to analyze alternatives for use in order to improve extraction yield, reduce operating costs, and lessen the environmental impact of the process. In terms of extraction sustainability, recent advances offer promising alternatives to conventional solvent-based methods. For instance, ultrasound-assisted extraction using natural deep eutectic solvents (NaDES) has proven both effective and scalable for pigment recovery from Spirulina [36]. Green extraction techniques, as reviewed by Kopp (2025), demonstrate yields comparable or even superior to traditional approaches, while improving energy efficiency [37]. Comprehensive reviews, such as Fatima et al. (2023), outline a range of advanced methods (including NaDES, microwave-assisted, and ultra-sound-assisted extraction) that outperform conventional methods in terms of efficiency and solvent use [38] . These greener extraction routes represent viable directions for future improvements of the evaluated process, aiming to reduce solvent toxicity, operational costs, and overall environmental impact….”

Point 8:

(8). “In the SuperPro model you fix biomass composition at 7.03% lipids and 1.52% chlorophyll (values matching Case 1), then later claim higher lipid output for Case 3 in Table 2. If lipid fraction is fixed at 7.03% in the model, a 2× increase in annual lipids for Case 3 implies an implicit, unreported change elsewhere (e.g., biomass throughput). Either (i) use case-specific compositions in the model, or (ii) don’t attribute modeled lipid gains to composition effects in Case 3.”.

Response:

This paper uses specific compositions for each simulation. Table 1 shows the results in the experimental stage (used for the simulation of the three process scenarios, cases 1, 2, and 3), and Table 2 shows the results obtained from the simulation of each simulated case (mass balance results using the results obtained experimentally).

Point 9:

(9). “You state COD reductions of 85% (Case 2) and 71% (Case 3) and imply “proper disposal,” yet you do not compare final COD to discharge limits (e.g., the relevant NOM).”.

Response:

Since Mexican standards have not been updated in two decades, we have decided to omit the comparison with those standards. The text is left as follows:

”…The second value measured was COD is important for determining the reduction in or-ganic load and thus disposing of the effluent correctly. For case 2, initial values of 1321 mg/L were obtained in the experiment, and at the end, 196 mg/L of COD. and for case 3, the initial value was 1330 mg/L and the final value was 382 mg/L of COD, representing a decrease of 85.16% and 71.27%, respectively. In this regard, a clear reduction in key pollution indicators was recorded, which supports the use of the microalgae consortium as a bioremediation agent, in addition to its use for obtaining value-added compounds.”

Point 10:

(10). “The process description says chlorophyll extraction uses methane as solvent, but i thinl you mean methanol?”.

Response:

In accordance with the recommendation, the solvent was corrected from “Methane” to “Methanol.”

Point 11:

(11). “The SuperPro feed includes glucose, implying mixotrophic operation at scale, whereas the experimental section uses phototrophic conditions with BBM-3N/WWDI and no added organic carbon. Please explain this.”.

Response:

In both cases (experimental and simulation), the type of culture is mixotrophic, but this had not been specified.

In response to the comment, the following modification was made:

“Three cases were considered for the experimental evaluation of biomass, lipid, and chlorophyll production using WWDI and BBM-3N, as described above. The amount of each component in the mixture is described in Figure 1, and each experiment was performed in duplicate. The culture type was mixotrophic, using glucose as the organic carbon source at 15 g/L in all cases.”

Point 12:

(12). “The lipid section explicitly assumes 100% extraction of the lipids measured experimentally. That is not realistic at scale (particularly with wet biomass and methanol:chloroform), and materially biases costs/throughputs. Maybe you should add a sensitivity analysis.”.

Response:

You are right, although we believe it was better to use the experimentally extracted value, as this is the value we assume we could achieve. On the other hand, performing a sensitivity analysis with lower lipid extraction efficiencies would yield results where energy consumption and process operating costs would be even higher. For now, these results are a good starting point for analyzing what is happening in the process and making adjustments to the different stages we are considering in this design. For example, under current conditions, the operating cost is still very high, with the cultivation stage contributing the most to this. We can therefore analyze the use of other cultivation technologies that reduce operating costs. We also observe that nutrient consumption is reduced when we use wastewater and that the impact on biomass production is practically nil, which is very good for the proposal as it reduces operating costs. During the harvesting stage, other flocculants should be tested that seek to increase collection efficiency and are more economical than chitosan. In the case of the extraction stage, the use of solvents increases production costs, impacting the cost of raw materials. With these results, we now know that we must seek other alternatives that, like flocculants, increase extraction efficiency, are more economical, and facilitate the product purification process.

Point 13:

(13). “You claim Case 3 has “lower operating costs,” but Table 3 shows Operating (USD/yr): Case 2 = 445.209M, Case 1 = 445.873M, Case 3 = 446.264M. Case 3 is actually the highest of the three.”.

Response:

The observation is correct, we appreciate the comments.

In response to the comment, the following modification was made:

“Case 3 showed the highest operating costs, 446,264,000 USD/yr, mainly attributable to the intensive use of nutrients.”

Point 14:

(14). “You attribute 62.68% (Case 1) and 56.46% (Case 3) of raw-material costs to “EDTA (nitrogen source)”. EDTA is a chelating agent, not a nitrogen nutrient. Please revise.”.

Response:

That's right, EDTA (ethylenediaminetetraacetic acid) is not a nutrient, it is a chelating agent that plays a key role in the bioavailability of nutrients, especially in culture media for microorganisms and cells.

In response to the comment, the following modification was made:

“…Regarding the cost of raw materials, in case 1, 62.68% is due to the consumption of EDTA (chelating agent), followed by chitosan (9.41%), which is used as a flocculant, and acetic acid (6.36%), which is also used to prepare the flocculant…..”

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

authors have made all the requested modifications

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have extensively answered to all my queries.

The paper just need some adjustments for the English language.