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Advanced HRT-Controller Aimed at Optimising Nitrogen Recovery by Microalgae: Application in an Outdoor Flat-Panel Membrane Photobioreactor

ChemEngineering 2022, 6(2), 24; https://doi.org/10.3390/chemengineering6020024

by Juan Francisco Mora-Sánchez^1,2, Josué González-Camejo^1,3,*

, Aurora Seco¹ and María Victoria Ruano¹

Reviewer 1: Anonymous

Reviewer 2: Anonymous

Reviewer 3:

Vincenzo A. Riggio

ChemEngineering 2022, 6(2), 24; https://doi.org/10.3390/chemengineering6020024

Submission received: 20 December 2021 / Revised: 5 March 2022 / Accepted: 14 March 2022 / Published: 16 March 2022

(This article belongs to the Special Issue Industrial Cultivation of Microalgae: Technologies, Applications and Challenges)

Round 1

Reviewer 1 Report

The paper “Advanced HRT-controller aimed at optimising nutrient recovery by microalgae. Application in an outdoor flat-panel membrane photobioreactor” by Mora-Sánchez et al. is a very interesting work about the control of microalgae-based wastewater treatment systems by using advanced control techniques.

The article is well structured. The introduction addresses in a clear way the problem that the authors are facing, i.e. the lack of control strategies in microalgae cultivation units, aimed at treating municipal and industrial wastewaters, by maximizing nutrient removal/recovery. Materials and methods concisely explain the relevant characteristics of the pilot plant and all the necessary information is provided. The results are novel, well presented and discussed.

Very minor adjustments to improve the quality of the work can be found below.

L 21, 23→ Although being known in the field, the abbreviations SS, NLR and NRR should be clearly explained
L 147→ I suggest using “similarly to” instead of “similarly than”
L 164-167→ The authors should explain how the presence of bacteria was assessed, and how the interaction with heterotrophic organisms was faced. Also, it should be stressed that in case of scaling up the pilot plant, using ATU may not be a feasible strategy (due to associated costs and environmental impacts), therefore the side-effects of nitrification could represent a problem to be addressed at the full scale
L 169→ The total duration and the season in which the experiment was performed should be added
L 184→ The meaning of the parameter DO25 should be explained. As the parameter is not commonly used in microalgae studies, readers with little expertise in the field may find it hard to follow this concept
L 189-190→ The same considerations reported in comment #6 also apply to the parameter HRT_I1. How was equation 7 built?
L 202-203→ What is the length of the defined period of time for ΔPAR?
L 206-213→ The auxiliary meteorological model is not clearly explained. If based on previous studies, references should be provided. Otherwise, a detailed description of the model should be added
L 289→ Typing error: HTR should be HRT
L 310-311→ The correlation among the laboratory SS and Solitax data should be provided
L 320-321→ As for comment #5, an explanation of the meaning of the DO25 could be reported to help the readers
Figure 1 → The abbreviation OD25 is not consistent with previous uses of DO25
L 335 → The term “lineal” should be “linear”
L 375-376 → How were the three days per month chosen for calibration? According to what criteria?
Figure 5 → As the cloud cover cannot be negative, it is suggested to set the secondary Y-axis from 0 to 100%
L 434 → Typing error: “y”

Author Response

Dear Editor and Reviewer 1:

Thank you very much for your e-mail with the revision comments of our paper entitled “Advanced HRT-controller aimed at optimising nitrogen recovery by microalgae. Application in an outdoor flat-panel membrane photobioreactor”, reference number 1541327.

We really appreciate the reviewer comments as we think they have helped us to improve the manuscript. Below you will find our considerations explaining specifically how and where each point has been incorporated to the manuscript and the text that has been added. Please bear in mind that the numbers of lines are referred to the manuscript with the track changes shown.

We hope that after the effort made to improve the paper according to the reviewer suggestions, you would consider the paper for publication in ChemEngineering.

To facilitate the location of our responses and changes made in the original version of the manuscript, the following colour code has been used:

Black: Reviewer comments
Blue: Authors response to the reviewer comments
Blue and Italics: The new text incorporated in the final version of the paper

We would also like to inform you that one Figure and six new references have been added to comply with the reviewers´ requirements. We hope there is no inconvenience with that.

Thank you very much for your attention.

Kind regards,

Josué González-Camejo

********************************************************************

English language and style are fine/minor spell check required.

The text has been fully revised to correct the typos, errors and with the goal to make the text clearer.

Very minor adjustments to improve the quality of the work can be found below.

L 21, 23→ Although being known in the field, the abbreviations SS, NLR and NRR should be clearly explained

Authors agree with the reviewer and apologise for not clarifying these terms before. The explanation of those acronyms has been included in lines 21-26:

“Different indexes based on suspended solids (SS), DO25, predicted PAR and real PAR served as input variables, and the initial HRT of the operation day (HRT0) and the variation of HRT (DHRT) were the output variables. The nitrogen recovery efficiency, measured as nitrogen recovery rate (NRR) per nitrogen loading rate (NLR)”

L 147→ I suggest using “similarly to” instead of “similarly than”

Sorry for the mistake. The expression has been changed as suggested.

L 164-167→ The authors should explain how the presence of bacteria was assessed, and how the interaction with heterotrophic organisms was faced. Also, it should be stressed that in case of scaling up the pilot plant, using ATU may not be a feasible strategy (due to associated costs and environmental impacts), therefore the side-effects of nitrification could represent a problem to be addressed at the full scale

Following the advice of the reviewer, the text was modified as indicated below (Lines 230-241):

“The microalgae culture was obtained from a previous experiment [18]. Originally, microalgae proceeded from the walls of the secondary clarifiers of the Carraixet WWTP [61]. As a consequence, the culture was composed by a mix of green microalgae (mainly Coelastrella and Scenedesmus) and heterotrophic and nitrifying bacteria. To favour microalgae growth with respect to their competitors, the start-up procedure described in González-Camejo et al. [16] was followed.

To avoid the side-effects of nitrification to the process, allylthiourea (ATU) was added to the culture to inhibit nitrifier activity [44]. This practice would not be feasible at industrial scale (due to its associated costs and environmental impacts), but it was considered necessary for the comparison with previous results.”

L 169→ The total duration and the season in which the experiment was performed should be added

The total duration and the season were added in the text (line 246):

“The experiment was performed for 55 days in spring.”

L 184→ The meaning of the parameter DO25 should be explained. As the parameter is not commonly used in microalgae studies, readers with little expertise in the field may find it hard to follow this concept

The explanation was added in the text (lines 286-294):

“2.2.1.1. Dissolved oxygen standardised to 25 ºC (DO25).

In this closed MPBR system, DO concentration would depend on air flow rate and mixing, temperature and microalgae photosynthetic activity [30]. Air flow and mixing were maintained constant, so were their effects over DO. To directly correlate DO to microalgae activity, the temperature effect must be neglected by standardising to a reference temperature (25 ºC), obtaining the DO25 parameter. This standardisation was achieved by subtracting the difference between the saturation concentration of dissolved oxygen at the microalgae culture temperature and the saturation concentration of dissolved oxygen at 25ºC.”

L 189-190→ The same considerations reported in comment #6 also apply to the parameter HRT_I1. How was equation 7 built?

An explanation of the HRT_I1 has been added (lines 295-305):

“2.2.1.2. HRT_I1 index.

NRR is related to the culture photosynthetic activity (indirectly measured by DO25sl), biomass concentration (SS) and light conditions (PAR). A normalised multivariable correlation was made with historical database daily average values, using SS, PAR and DO25sl. to correlate them with NRR (R2=0.4955; p-value<0.05). The resulted weights of the parameters were 18%, 34%and 48%, for SS, PAR and DO25sl, respectively, which is very close to 1:2:3 relation. To simplify, it was decided to combine SS and PAR in a single parameter, i.e., HRT_I1, defined in (7), by adjusting the weight of SS at half of its value. This parameter was used to calculate an appropriate initial HRT (HRT0) (Section 2.2.3) before the daytime operation started, using SS_YD_AV and predicted PAR_TDA_AV as the most accurate values related to biomass and light irradiance, respectively.”

L 202-203→ What is the length of the defined period of time for ΔPAR?

The length of the defined period of time for ΔPAR has been added (lines 280-281):

“ΔPAR is the variation of PAR in a defined period of time (one day in this case)”

L 206-213→ The auxiliary meteorological model is not clearly explained. If based on previous studies, references should be provided. Otherwise, a detailed description of the model should be added

Authors agree with the reviewer and apologise for not clarifying the auxiliary meteorological model before. Its description has been clarified (lines 314-338):

“The first step of the model for the PAR prediction was based on establishing the PAR_MAX in cloudless conditions as a correlation was found between experimental data and the cosine of maximum daily solar altitude angle (g_s_MAX) (Section 3.2), which was easily obtained using the methodology from Page [54]

Latitude (j) and longitude (l) positions together with the day number of the year allowed the astronomical calculation of some variables of interest for each moment of the daytime, using the methodology of Page [54]: declination (d), hour angle (w), solar altitude angle (g_s), azimuth angle of the sun (a_s), sunrise hour, and culmination and sunset hours. As already said, the PBR was vertical, which implied a slope angle (b) of 90º, and oriented to the south. By convention in North Hemisphere, the azimuth angle of the surface (a) was thus 0º.

The astronomical variables showed, for a full sunny day, a Gaussian-like distribution of PAR between PAR=0 at sunrise time and PAR=0 at sunset, i.e., increasing PAR values to PAR_MAX at culmination hour, and decreasing PAR values after that to zero values. That distribution was directly proportional to cos n, where n was the angle of incidence between the sun and normal to the PBR’s surface. It was calculated by the methodology from Page [54] adapted to a vertical surface (9):

(9)

Cos n (9) had a minimum value at sunrise and sunset hours, and a maximum at culmination hour. A normalisation was therefore made to establish the values between 0 (minimum) and 1 (maximum) along daytime (10):

(10)

PAR for full sunny days (cloudless) was calculated at any moment of the daytime (11):

(11)

The cloudiness factor was included using a Haurwitz-type equation (12):

PAR(Cloudiness)/PAR(Cloudless) = (1-a*(% TCC)/100)b

(12)

As the model is semi-empirical, a calibration stage was needed (Section 3.2).”

The text in Section 3.2 (See lines 494-496), Calibration of PAR prediction, has consequently been adjusted in line with the changes showed above:

“First, maximum daily solar altitude angle (g_s _MAX) was obtained using the methodology from Page [54]. A significant correlation between cosine of g_s _MAX and experimental data for PAR_MAX was found, as shown in Figure 4.”

Text in lines 507-509 has been adjusted as well:

“As explained in Section 2.2.2, PAR (Cloudless) can be easily calculated for every moment of the day using (11). This model performance is shown in Figure 5, which compares it to real PAR data for a full sunny day.”

L 289→ Typing error: HTR should be HRT

Sorry for the mistake. The word has been changed to “HRT” in line 390 (head text of Table 3). In addition, thanks to the reviewer, we realised we made the same mistake in line 416 (head text of Table 3). It has been also corrected.

L 310-311→ The correlation among the laboratory SS and Solitax data should be provided

The correlation values have been added to the text (lines 436-439):

“Suspended solids of the microalgae culture (from grab samples) were analysed according to Standard Methods[47]: method 2540 E. These values were used to correlate with the SS monitored with the SOLITAX probe (R²=0.9903; p-value < 0.05; n =17).”

L 320-321→ As for comment #5, an explanation of the meaning of the DO25 could be reported to help the readers

A complete explanation of the meaning of the DO25 was added to the text (lines 286-294):

“2.2.1.1. Dissolved oxygen standardised to 25 ºC (DO25).

Figure 1 → The abbreviation OD25 is not consistent with previous uses of DO25

Sorry for the mistake. The word has been changed to DO25 in the caption of Figure 1 (now Figure 2 in the revised version, lines 456-457):

“Figure 2: Daily evolution of dissolved oxygen (DO) concentration, dissolved oxygen concentration standardised at 25ºC (DO25) and PAR during: (a) a sunny day (operating day 12); and (b) a cloudy day (operating day 17).”

L 335 → The term “lineal” should be “linear”

Sorry for the error. The word has been changed to “linear” in line 467.

L 375-376 → How were the three days per month chosen for calibration? According to what criteria?

The criteria was cloudiness: one of the three days per month was fully cloudy; one of them had medium-cloudiness; while the other one was little cloudy, with the goal to ensure enough accuracy in every condition and season. This has been incorporated to the text (lines 512-515):

“To calibrate (12), three days a month during the previous four years of the operation were chosen: one fully cloudy, another medium cloudy and the other one little cloudy.”

Figure 5 → As the cloud cover cannot be negative, it is suggested to set the secondary Y-axis from 0 to 100%

Thank you for the suggestion. Figure 5 (now called Figure 6) has been modified as indicated (line 521):


(a)	(b)

(c)	(d)

Figure 6. PAR TCC model performance for several days: (a) winter; (b) spring; (c) summer; and (d) autumn.

In addition, following the same advice of the reviewer, we decided to modify also Figure 3a to avoid showing negative values in the Y-axis (lines 486-487):


(a)	(b)

Figure 3a: Correlation between NRR:SS and BP:SS related to DO25sl:SS. All variables are calculated as daily average values. Figure 3b: Continuous distribution of DO25’:SS and NRR:SS.

L 434 → Typing error: “y”

Sorry for the error. The word has been changed to “and”.

Author Response File: Author Response.docx

Reviewer 2 Report

The paper is not novel, I mean the authors didn't show how the study is new and not studied before. How the fuzzy can optimize the condition and what will happen if we use other tools? I suggest thinking about the objectives again and resubmitting.

Author Response

Dear Editor and Reviewer 2:

We hope that after the effort made to improve the paper according to the reviewer suggestions, you would consider the paper for publication in ChemEngineering.

To facilitate the location of our responses and changes made in the original version of the manuscript, the following colour code has been used:

Black: Reviewer comments
Blue: Authors response to the reviewer comments
Blue and Italics: The new text incorporated in the final version of the paper

We would also like to inform you that one Figure and six new references have been added to comply with the reviewers´ requirements. We hope there is no inconvenience with that.

Thank you very much for your attention.

Kind regards,

Josué González-Camejo

********************************************************************

Moderate English changes required.

Authors appreciate the reviewer’s comments as we think they can help us to improve the manuscript quality.

Authors would also like to say that they do not agree with the fact that the manuscript is not novel. We would like to clarify that the main topic of this manuscript is related to the optimisation of the microalgae-based wastewater treatment system under study, where the research group that sign the manuscript has shown relevant experience (see for instance publications of González-Camejo et al., Robles et al., or Viruela et al.). In most of studies developed so far in microalgae systems (from the cited research group or others), microalgae are commonly operated with fixed conditions, independently of outdoor variations, which probably reduces their nutrient recovery potential. The goal of this study lies on proving the improvement that can be achieved if an automation tool such as fuzzy logic is used in a microalgae-based system.

Authors would also like to note that the comparison between automation tools is out of the scope of this study. Fuzzy logic was selected since it has appeared to be a useful tool to control wastewater treatment operations, according to our previous experiences with other microbiological processes. It must be considered that biological systems do not behave the same way than physical, mechanical or chemical processes. Microbial activity (in this case microalgae) not only depends on external conditions but also on the culture inertia. Culture performance was non-linear and the same environmental and feed conditions in different days not necessarily led to same results due to the relevance of previous conditions (stressing or favourable) on culture inertia. A classical approach based on linear modelling was not thus possible. On the contrary, a knowledge-based fuzzy logic approach was feasible and not widely implemented for microalgae-based systems.

To clarify in the manuscript all the stated above, the introduction, and the conclusion sections have been modified:

Introduction (lines 73-168)

“All these drawbacks could be improved by using monitoring tools and instrumentation, control and automation (ICA) systems that can help to improve the efficiency and robustness of the system.

There are some examples of control strategies based on dynamic modelling of data to predict microalgae behaviour. For instance, Pawlowski et al. [31] described a model-based control to regulate pH by CO2 addition in open microalgae cultivation ponds. Robles et al. [32] used pH and dissolved oxygen (DO) sensors to assess microalgae performance during the start-up of a raceway pond. Also, Foladori et al. [28] evaluated the nitrogen and phosphorus removal of a lab-scale microalgae-bacteria culture using pH, DO and oxidation-reduction potential (ORP) sensors, while Hossain et al. [58] developed empirical models to predict nitrogen and phosphorus removal from a microalgae-based municipal wastewater treatment system. Other authors have used artificial neural networks (ANN) to model and predict the microalgae biological activity according to the input data [33]. These control systems obtained valuable information about microalgae performance. However, they cannot predict accurately how the variability of ambient parameters and the biochemical state of the culture can affect microalgae in the next hours or days, which can have significant consequences on the process robustness. In this respect, approaches using weather forecast coupled to detailed predictive models of microalgae productivity have been used by other authors [34,35]. The relation between microalgal growth, nitrogen uptake and storage, and dissolved oxygen production in a polyculture cultivated in open algal ponds has been also stablished [36]. All these authors have faced with the complexity of modelling variable environmental conditions and their effects on biological cultures. Further research on this topic is therefore needed to obtain proficient ICA controllers that can improve the performance of microalgae-based wastewater treatment systems.

Fuzzy logic has appeared to be a useful tool to control wastewater treatment operations. In this kind of processes, conditions not only depend on physical conditions but also on microbiological activities that are usually related to culture inertia (apart from the variability in physical and chemical conditions) [30]. There are plenty of successful examples of optimised wastewater complex processes by this kind of advanced controllers [37,38,39]. However, little information can be found regarding advanced control in microalgae cultivation systems, the majority of them being used to increase the biofuel production from microalgae biomass instead of improving microalgae-based wastewater treatment performance [40-42]. Consequently, there is a huge potential for advanced controlling in microalgae-based wastewater treatment technology. Proficient ICA systems require appropriate monitoring to gather all the relevant information about the process. With respect to microalgae-based wastewater treatment processes basic parameters such as photosynthetically active radiation (PAR), temperature, pH and dissolved oxygen are usually monitored on-line since they are widely known to affect microalgae [13,20-24,59]. But to have a comprehensive knowledge of these systems and to provide relevant information to operators about microalgae biological activity (and that of their competitors), nutrient consumption, etc., other parameters are needed [25-27]. Off-line lab-based measurements (for instance, suspended solids (SS) and nutrient concentrations) are often employed to gather this information, but the data obtained from them is limiting since punctual performance variations and decays due to limiting and inhibitory conditions cannot be assessed. Moreover, they entail expensive and time-consuming analyses that require certain delay [28,29]. Finding performance indicators (PI) of microalgae activity (that overcome those hurdles) can help to develop models and tools which in turn serve to monitor and control microalgae-based systems. By way of example, González-Camejo et al. [30] developed an on-line monitoring system based on data obtained from pH data dynamics. They showed that the first derivate of pH data variations (pH’) could be correlated (at both short- and long-term) with microalgae photosynthetic activity. However, this pH’ depends on several factors. The model has thus to be adapted to each cultivation system and treatment conditions, limiting its applicability.

The aim of this work is using an ICA system based on knowledge-based fuzzy logic approach to increase the performance of a pilot-scale MPBR system in terms of nitrogen recovery from sewage by hourly modifying the operating HRT according to variations in ambient conditions with the goal to improve the feasibility and competitiveness of microalgae-based wastewater treatment systems”.

Conclusions (lines 611-622)

“This work proposed a fuzzy logic knowledge-based controller based on the application of DO, SS and PAR sensors combined with an astronomic plus cloud cover forecast meteorological model to control nitrogen recovery from microalgae in an outdoor MPBR, adjusting the hourly HRT of the plant to improve the instantaneous recovery capacity of the system. Results showed that this HRT control improved by 45% the ratio of nitrogen biologically removed to nitrogen fed to the system when it is compared to fixed HRT. Moreover, when the fuzzy logic HRT control was used, a reduction of nitrogen losses with the wastewater effluent of 47% was achieved. Overall, the results obtained show a promising application of an ICA system to improve microalgae-based wastewater treatment as a side nitrogen recovery process in a WRRF scheme”.

About English language, the text has been fully revised to correct the typos, errors and with the goal to make the text correct and clearer.

Author Response File: Author Response.docx

Reviewer 3 Report

The paper “Advanced HRT-controller aimed at optimizing nutrient recovery by microalgae. Application in an outdoor flat-panel membrane photobioreactor” deals with an interesting and not easy subject concerning the possible utilization of a fuzzy controller to predict the best HRT in a membrane photobioreactor with the main scope to reduce Nitrogen in the effluent. The authors investigate the use a self-made device tested with a fuzzy logic controller to reach their thesis. The paper is well written, and all the relevant aspects are clearly treated and discussed. Although the subject is very complex to be deeply tackled with the number of experiences described. For instance, it would be really interesting to test the system under strong stress conditions. An aspect that was reported by the authors only regarding some cloudy scenario. Also, I have the following minor issues that need to be solve before publication:

In the title I suggest the author to change “nutrient recovery” with “nitrogen recovery” or “total soluble nitrogen recovery”
The manuscript it is quite detailed in terms of methods description, but I feel that a short description of the reactor used, together with a schematic figure could really help the reader to understand the experimental scenario adopted. Also, it is mandatory to specify the specific point where all the sensors are measuring.
There are a couple of technical passages that are not so clear to me: first the system is completely open to the atmosphere? If yes, this aspect must be cited in the manuscript. Also, why the authors used artificial lights? Even this aspect must be deeply described, as it is not clear to the reader how the management of the artificial light source is working.
In the discussion I’ll appreciate if the authors could be report if they encountered some problems with the probes used. I asked this as all the method is strongly dependent on SS and ammonium – nitrate sensors and based on my experience optical correlation with microalgae biomass dry weight becomes very unstable over 0,8 g/L (biomass data are completely missing), same aspect for the ammonium – nitrate sensor that could be faulty with dirty, or rich in SS, liquid.

I’m not an English mother tongue so I do not suggest language correction, I found the text well written and clear to read there are some typos all over the manuscript that can be easily correct. I really appreciate the authors work and I think that this manuscript it is very interesting under a scientific point of view.

In my humble opinion the manuscript could be published with minor revision if the authors can agree to the suggestions provided.

Author Response

Dear Editor and Reviewer 3:

We hope that after the effort made to improve the paper according to the reviewer suggestions, you would consider the paper for publication in ChemEngineering.

To facilitate the location of our responses and changes made in the original version of the manuscript, the following colour code has been used:

Black: Reviewer comments
Blue: Authors response to the reviewer comments
Blue and Italics: The new text incorporated in the final version of the paper

We would also like to inform you that one Figure and six new references have been added to comply with the reviewers´ requirements. We hope there is no inconvenience with that.

Thank you very much for your attention.

Kind regards,

Josué González-Camejo

********************************************************************

The authors are very thankful to the reviewer and appreciate their comments.

About testing strong stress conditions, we would like to clarify that this study was a preliminary work related to the automation of this microalgae-based treatment system, which showed promising results. Further research would be needed to go in deep in this topic and to develop a more consistent control system. A clarification has been added in lines 576-579:

“Further investigation is needed to design a SRT controller and couple it to the HRT controller of this study to improve the MPBR performance in extreme cloudy days and other possible stress conditions.”

In the title I suggest the author to change “nutrient recovery” with “nitrogen recovery” or “total soluble nitrogen recovery”

Authors agree with the reviewer. The title has been changed:

“Advanced HRT-controller aimed at optimising nitrogen recovery by microalgae. Application in an outdoor flat-panel membrane photobioreactor”

The manuscript is quite detailed in terms of methods description, but I feel that a short description of the reactor used, together with a schematic figure could really help the reader to understand the experimental scenario adopted. Also, it is mandatory to specify the specific point where all the sensors are measuring.

Thank you for your comment. A new Figure was added to show the scheme of the microalgae-based treatment system and the specific points where the sensors were put (Figure 1, lines 194-195):


(a)	(b)

Figure 1: (a) Outdoor MPBR system scheme. (b) Detail of probes location.

With respect to the plant description, a piece of text was added to clarify how the probes were connected (see lines 192-195):

“Probes i), ii), iv) and v) were installed submerged into the left side of the PBR, 20 to 40 cm from top surface. Figure 1 shows a scheme of the system including probe locations.”

On the other hand, it was considered unnecessary to include more information about the description of the pilot plant, since this system has been already described in several previous publications:

[16] González-Camejo, J., Jiménez-Benítez, A., Ruano, M.V., Robles, A., Barat, R., Ferrer, J. Optimising an outdoor membrane photobioreactor for tertiary sewage treatment. J. Environ. Manag. 2019, 245, 76-85. https://doi.org/10.1016/j.jenvman.2019.05.010

[18] González-Camejo, J., Aparicio, S., Jiménez-Benítez, A., Pachés, M., Ruano, M.V., Borrás, L., Barat, R., Seco, A. Improving membrane photobioreactor performance by reducing light path: operating conditions and key performance indicators. Water Res. 2020, 172, 115518. https://doi.org/10.1016/j.watres.2020.115518

[24] Viruela A., Robles A., Durán F., Ruano M.V., Barat R., Ferrer J., Seco A. Performance of an outdoor membrane photobioreactor for resource recovery from anaerobically treated sewage. J. Clean. Prod. 2018, 178, 665-674

There are a couple of technical passages that are not so clear to me: first the system is completely open to the atmosphere? If yes, this aspect must be cited in the manuscript. Also, why the authors used artificial lights? Even this aspect must be deeply described, as it is not clear to the reader how the management of the artificial light source is working.

Authors would like to apologise for not making those aspects clear from the beginning. First, the system was closed to the atmosphere, as it is now in the text in lines 175-176:

“The PBR was closed to the atmosphere and perfectly mixed by air at 0.2-0.25 vvm.”

With respect to the artificial lights, authors are aware that this source of light would not be feasible at industrial scale. However, they have been used for scientific purposes in previous studies to avoid possible limitations that could mislead the real effect of the factor(s) that had been studied in each case.

In this study, since one of its goals was to compare the results with those obtained at fixed HRT [18] (where artificial lights were used), it was necessary to include them in order to avoid introducing new factors affecting microalgae performance. This has been clarified in the text (lines 178-182):

“Twelve white LED lamps (Unique Led IP65 WS-TP4S-40W-ME) were installed at the dark surface of the PBR, offering a continuous irradiance of 300 μmol·m^-2·s^-1 with the goal to avoid possible side effects and to make results comparable to those obtained (at fixed HRT) in previous study [18]”

In the discussion I’ll appreciate if the authors could be report if they encountered some problems with the probes used. I asked this as all the method is strongly dependent on SS and ammonium – nitrate sensors and based on my experience optical correlation with microalgae biomass dry weight becomes very unstable over 0,8 g/L (biomass data are completely missing), same aspect for the ammonium – nitrate sensor that could be faulty with dirty, or rich in SS, liquid.

Regarding SS probe, authors are pleased to clarify that the SOLITAX ts-line sc LXV423.99.00100 is not the regular probe (LXV423.99.10100). This one was specifically recommended by the distributor due to its improved colour correction. It has and automatic wiper, too. The average SS concentration was 532±78 mg/L (%VSS=92.5±1.1), so the operating range of work was quite below 0.8 g/L.

Our wide previous experience using that probe, and also the ANISE probe for ammonium-nitrate concentrations, was taken into account to establish an exhaustive cleaning protocol. We found out that both probes worked better with a deviation of 30 degrees from the regular perpendicular angle.

A new paragraph has been added in lines 205-212 to clarify these aspects:

“To achieve representative values of SS an total soluble nitrogen (TSN), some considerations were taken from previous experiences in the use of SOLITAX probes for SS concentration measurements and ANISE probes to monitor NH4+ and NO2-+NO3- concentrations. The SOLITAX probe was equipped with colour correction and automatic wiper, which improved probe accuracy. An exhaustive cleaning protocol was established for both probes, and they were both installed with a 30-degrees deviation from the regular perpendicular angle to the PBR surface (Figure 1b) as this was found to prevent probe fouling and to obtain more stable data”

Also, a clarification related to the use of SS data has been added in lines 463-466:

“To normalise DO production with biomass, SS concentration values (532±78 mg·L-1 in average) were used as a proxy of microalgae biomass. This was considered as good approximation since previous study showed a high correlation between suspended solids and microalgae cell concentrations [26]”.

In addition, calibration of these measurements from probes was done with standard methods for the analysis of grab samples in the lab, showing good correlation for both probes. In the case of SOLITAX, high correlation was found, as now explained in the text (lines 433-439):

“To calibrate these sensors, these nitrogen compounds were analysed in a Smartchem 200 analyser (WestcoScientific Instruments, Westco) according to methods 4500-NH3-G, 4500-NO2-B, and 4500-NO3-H of Standard Methods [47]. Suspended solids of the microalgae culture (from grab samples) were analysed according to Standard Methods[47]: method 2540 E. These values were used to correlate with the SS monitored with the SOLITAX probe (R2=0.9903; p-value < 0.05; n =17)”.

With respect to ANISE, calibration of NH4 was done once a week, always showing good correlation. From our experiences, the most controversial measurement is that of NO3 concentration. However, since nitrification was limited by ATU addition (lines 238-241), NO3 remained always at negligible values (check also in grab samples), so NO3 was not considered a relevant factor in this study.

“To avoid the side-effects of nitrification to the process, allylthiourea (ATU) was added to the culture to inhibit nitrifier activity [44]. This practice would not be feasible at industrial scale (due to its associated costs and environmental impacts), but it was considered necessary for the comparison with previous results”.

From all the stated above, authors think that results obtained from the probes regarding SS and NH4 concentrations were consistent.

In my humble opinion the manuscript could be published with minor revision if the authors can agree to the suggestions provided.

Thank you for your appreciation!

The text has been fully revised to correct the typos, errors and with the goal to make the text clearer.

Round 2

Reviewer 2 Report

The paper is acceptable.

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Advanced HRT-Controller Aimed at Optimising Nitrogen Recovery by Microalgae: Application in an Outdoor Flat-Panel Membrane Photobioreactor

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