Prospective technical and technological insights into
microalgae production using aquaculture wastewater
effluents

Ira-Adeline Simionov, Marian Barbu, Iulian Vasiliev, Larisa Condrachi,
Mariana Titica, George Ifrim, Dragos Cristea, Florian Marcel Nuță,
Ștefan-Mihai Petrea

► To cite this version:

Ira-Adeline Simionov, Marian Barbu, Iulian Vasiliev, Larisa Condrachi, Mariana Titica, et al. Prospective technical and technological insights into microalgae production using aquaculture wastewater effluents. Journal of Environmental Management, 2025, 377, pp.124537. 10.1016/j.jenvman.2025.124537. hal-05244519

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https://hal.science/hal-05244519v1

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Review

Prospective technical and technological insights into microalgae
production using aquaculture wastewater effluents

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Ira-Adeline Simionov<sup>a,b,c</sup>, Marian Barbu<sup>b</sup>, Iulian Vasiliev<sup>b</sup>, Larisa Condrachi<sup>b</sup>, Mariana Titica<sup>d</sup>, George Ifrim<sup>b</sup>, Dragos Cristea<sup>e</sup>, Florian Marcel Nuță<sup>f,*</sup>, Ștefan-Mihai Petrea<sup>a,c,e,**</sup>

ARTICLE INFO

Keywords:

Microalgae

PBR

Control strategy

Aquaculture

Wastewater

A B S T R A C T

Microalgae biomass is a promising resource addressing climate change and play a role in energy transition for generating biofuels. Due to their ability to produce higher yield per year, biofuels obtained from microalgae are considered 3rd generation-advanced biofuels. The industrial production of microalgae mitigates the effects of CO2 emissions and can be used for wastewater bioremediation since most effluents are rich in nutrients. Using wastewater as growth media for microalgae promotes the principles of circular economy and nutrient recovery. The aquaculture wastewater effluent contains high levels of nitrogenous compounds, as well as phosphates and dissolved organic carbon. The current review aims to identify, centralize, and provide extensive information on the decisive technological and technical factors involved in the growth process of different microalgae species in aquaculture wastewater. The study focuses on technological growth performance indicators, as well as specific control strategies applied to achieve pH control, since it has been highlighted to be one of the most important growth-related cofactors. A bibliometric framework was developed to identify future trends in integrated microalgae production. The scientific literature analysis highlighted the great potential of aquaculture wastewater effluents to be used as growth media for microalgae biomass production, due to superior performance in lipid and carbohydrate productivity. Most control strategies developed for microalgae production systems found in the literature aim at controlling the pH in the bioreactor by injecting CO2, while few other papers consider manipulating the dissolved oxygen. The need for higher-level control arises to not only track pH or DO references but also to maximize the treatment efficiency of the bioreactor.

1. Introduction

The world population has reached 8 billion in the year 2022 and
according to predictions, it will reach 10 billion by the year 2100
(United Nations, Department of Economic and Social Affairs, 2022).
Considering the fast population growth, as well as intense urbanization
generated by the expansion of urban areas with 150% in the last four
decades, energy consumption by the human population has reached a
peak level (Muzayanah et al., 2022; Tian et al., 2022). It is forecasted
that by the year 2030, the use of global energy will increase by

approximately 28% compared to the year 2003 (Ganesh Saratale et al., 2022). Due to non-renewable resource depletion, the renewable energy demand is increasing (Takabe et al., 2016). Moreover, one of the sustainable development goals assumed by the United Nations in 2022 targets to increase the share of renewable energy in the global energy mix by 2030 (United Nations, 2022). New financial prospects related to alternative renewable energy sources arise in the context of the Russia-Ukraine war, which generated the significant increase of stock values within this sector (Umar et al., 2022). The use of biotic components such as biomass for generating environmental friendly and

<footnote><sup>a</sup> Department of Food Science, Food Engineering, Biotechnologies and Aquaculture, "Dunarea de Jos" University Galati, 800008, Galati, Romania</footnote>

<footnote>b Department of Automatic Control and Electrical Engineering, "Dunarea de Jos" University Galati, 800008, Galati, Romania</footnote>

<footnote>c Rexdan Research Infrastructure, "Dunarea de Jos" University Galati, 800008, Galati, Romania</footnote>

<footnote>d GEPEA, CNRS-UMR 6144, Nantes University Saint-Nazaire, France</footnote>

<footnote>e Department of Business Administration, "Dunarea de Jos" University Galati, 800008, Galati, Romania</footnote>

<footnote>Human and Social Sciences Doctoral School, "Ștefan Cel Mare" University of Suceava, Suceava, Romania</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author. Department of Food Science, Food Engineering, Biotechnologies and Aquaculture, "Dunarea de Jos" University Galati, 800008, Galati, Romania.</footnote>

<footnote>E-mail addresses: florian.nuta@usm.ro (F.M. Nuță), stefan.petrea@ugal.ro (Ș.-M. Petrea).</footnote>
replenishing biofuels can solve the stringent challenge related to the utilization of non-renewable fossil fuels that society faces in the Anthropocene epoch (Priya et al., 2022). There is a high potential for microalgae innovative cultivation on aquaculture wastewater effluents, topic which still requires an exhaustive and in-depth exploration. Thus, the current review aims to identify, centralize, and provide extensive information on the decisive technological and technical factors involved in the growth process of different microalgae species in aquaculture wastewater, to advance a step-by-step guideline for a continuing growing industry. The study focuses on technological growth performance indicators (biomass production and productivity, lipid, protein, and carbohydrate productivity, and nutrient removal rate of biomass from wastewater) as well as specific control strategies applied to achieve pH control since it has been highlighted to be one of the most important growth-related cofactors. At the same time, a bibliometric framework was developed to identify future trends in integrated microalgae production. Moreover, an analytical framework was defined for the future development of decision support tools in the management of research activity, by using AI-based forecasting algorithms that can reveal future trends in various knowledge dimensions related to integrated multi-trophic aquaculture, microalgae production, renewable energy sources based on microalgae production and automation in microalgae production, considering the historical data found in both WoS and SCOPUS database and the type of publications.

## 2. Literature overview on microalgae production

Microalgae biomass is a promising resource for generating biofuels in the future (Peter et al., 2022; Shuba and Kifle, 2018). Due to their ability to produce higher yield per year compared to other types of biomasses, biofuels obtained from microalgae are considered 3rd generation-advanced biofuels (Bórawski et al., 2019; Mahapatra et al., 2021). In the year 2020, the global market of microalgae was estimated to have a net worth of approximately 980 million dollars and it is projected to reach 1.14 billion dollars by the year 2024 [24, 26]. The associated global production is represented by 35.82 million tonnes of wet biomass (estimated in 2019), of which autotrophic microalgae account for approximately 20,000 tons of dry weight (estimated in 2018) (Abreu et al., 2022; Allied Marked Research, n.d.; Food and Agriculture Organization of the United Nations, 2021). Microalgae cells produce bioactive compounds such as lipids, proteins, and carbohydrates, which can be extracted and transformed into biodiesel, bioethanol, bio-methane, and biohydrogen through thermochemical, fermentation, anaerobic digestion, and photo-fermentation processes respectively (Peter et al., 2022). Even further, through their metabolism (high photosynthesis rates), microalgae play a crucial role in the carbon cycle and contribute to reducing CO₂ emissions (Abo et al., 2019). According to Razzak et al., in the year 2019, CO₂ emissions reached 37 Gtons, of which 29 Gtons were generated from fossil fuel burning (Razzak et al., 2022). Previously, it has been highlighted that 1 kg of microalgae biomass can remove approximately 1.8 kg of CO₂, thus, the exploitation of microalgae provides an environmentally sustainable option for generating biofuels (Peter et al., 2022; Razzak et al., 2022). Based on their trophic metabolism, microalgae can be produced under autotrophic conditions (light is used as energy source to transform inorganic carbon into organic matter), heterotrophic conditions (organic carbon compounds are used in the absence of light as both energy and carbon sources), photoheterotrophic conditions (light is used as energy source to transform organic carbon into organic matter) and mixotrophic conditions (both light and organics are used as energy sources and both inorganic and organic carbon are used as carbon sources) (Abreu et al., 2022; Castillo et al., 2021; Cecchin et al., 2018; Chang et al., 2022; Ruiz et al., 2022; Vale et al., 2020; Vidotti et al., 2020; L. Yu et al., 2022; Zhan et al., 2017).

In the case of autotrophic microalgae, the production of biomass is directly influenced by the photosynthesis rate (Xie et al., 2022). Through

the photosynthesis process, CO₂, solar energy, and H₂O are transformed into carbohydrates and O₂ by the algal cells, a process which can be described by Eq. (1)

$$CO_2 + H_2O + Sunlight \rightarrow [CH_2O] + O_2 + waste heat \quad (1)$$

The industrial production of microalgae not only mitigates the effects of CO₂ emissions but also can be used for wastewater bioremediation since most effluents are rich in nutrients such as carbon (C), nitrogen (N) and phosphorus (P), which are the main growth promoters in microalgae cultivation (Ganesh Saratale et al., 2022). The ratio between C, N, and P in microalgae biomass was defined by Redfield in 1934 and is referred to today as the Redfield ratio 106C: 16N: 1P (They et al., 2017).

To produce 1 L of biodiesel from microalgae, between 591 and 3000 L of freshwater is consumed and approximately 121,000 L of biodiesel can be generated from one cultivated surface hectare (Enwereuzoh et al., 2021; Nazloo et al., 2022; Rehman et al., 2022). Therefore, using wastewater as growth media increases the economic sustainability of microalgae production systems and promotes the principles of circular economy, nutrient recovery, and waste recovery. According to the scientific literature, a suitable wastewater effluent that can be used for microalgae production is aquaculture effluent (Abdul Hamid et al., 2014; Calderini et al., 2021; Han et al., 2019; Lananan et al., 2014; Li et al., 2022; Limrujiwat et al., 2022; Lugo et al., 2020; Meril et al., 2022; N. Mohd Nasir et al., 2019; Qian et al., 2022; Sfez et al., 2015; Sirohi et al., 2022; Sun et al., 2020; Wu et al., 2022). In the global food production sectors, the aquaculture sector registers major growth, with a mean annual increase of 3.2% (Ahmad et al., 2022; Tom et al., 2021). The productions resulting from aquaculture practices represent more than 60% of the total fisheries productions and according to the latest Food and Agriculture Organization (FAO) report, in the year 2020, these productions reached a record of 214 million tons (Food and Agriculture Organization, 2022; Nie et al., 2020). The intensification of aquaculture activities, determined by the high market demand for fish products and the decline of wild fish populations, raises concerns related to the environmental sustainability of intensive production systems such as recirculating aquaculture systems (RAS). The aquaculture wastewater effluent contains high levels of nitrogenous compounds such as ammonia (NH₄), nitrites (NO₂), and nitrates (NO₃) respectively, as well as phosphates and dissolved organic carbon, generated through fish metabolism (digestion and respiration) and feed decomposition (Lananan et al., 2014; Meril et al., 2022; Qian et al., 2022; Sun et al., 2020). Considering the reared fish species, stocking density, production cycle, and fish feed composition, the nutrient concentration in the effluent may vary as follows: 0.01–5.32 mg/L for NH₄-N, 0.05–40.67 mg/L for NO₃-N, 0–5.52 mg/L NO₂-N respectively (Li et al., 2022). Aquaculture production systems such as RAS are operated by applying a specific water exchange rate (5–10%) per day, thus, the discharge of aquaculture effluents can cause environmental water quality degradation through eutrophication (Tom et al., 2021). Using aquaculture wastewater as growth media for the production of microalgae not only provides a sustainable integrated solution for bioremediating the effluent but also optimizes the growth process of microalgae because it contains mixed sources of N such as NH₄ and NO₃, which can be uptake preferentially by the cells (Enwereuzoh et al., 2021). Some microalgae species, rich in lipids, are used as an ingredient to develop functional fish feeds, closing thus the circular economy principle (Sánchez-Zurano et al., 2021).

The production industry of microalgae is focused merely on a few species such as Arthrospira sp., Chlorella sp., Scenedesmus sp., Aphanizomenon sp., Haematococcus sp., respectively, which are produced in either open systems (raceway ponds) or enclosed systems (photobioreactors - PBR) (Abo et al., 2019; Saadaoui et al., 2021). By comparing the two types of production systems, it is needed to highlight that even though raceway systems are easy to construct and imply low investment costs (they rely on solar light and weather conditions), their biomass is significantly lower (below 1 g/L) compared to PBR (30–100 g/L)
systems (Abo et al., 2019; Jareonsin and Pumas, 2021; Saadaoui et al., 2021). In PBR it is possible to achieve higher yields due to the implementation of growth bioprocess control. This allows to identify and maintain the optimum growth conditions to generate the highest performances related to the growth and production of targeted bio-molecules (Ding et al., 2021a). The main cultivation parameters that are decisive to the growth performance of microalgae are pH, temperature, light intensity, nutrient matrix of media, system configuration, and mixing (Ding et al., 2021b; Lim et al., 2023). Nevertheless, pH is by far one of the most important cofactors influencing microalgae growth and productivity, because it governs cell metabolism and determines CO₂ availability (H. Yu et al., 2022a). To increase the production of biofuels, the growth process of microalgae needs to be optimized to enhance biomass productivity (Xie et al., 2022).

In concept of environmental sustainability, circular economy, and nutrient recovery, most studies are focused on the cultivation of microalgae in different types of wastewater effluents such as industrial effluent (*Chlorella* sp. and *Chlorococcum* sp.) (Yadav et al., 2019), synthetic effluent (*Chlorococcum* sp., *Chlorella* sp., *Coelastrum* sp. and *Desmodesmus* sp.) (Cao et al., 2022; Mehariya et al., 2023), municipal effluent (A. S. Mohd Nasir et al., 2023), domestic effluent (*Scenedesmus* sp. and *Limnothrix* sp.) (Debeni Devi et al., 2023), textile (*Chlorella* sp.) (Javed et al., 2022), treatment plant effluent (Qi et al., 2023), palm oil mill effluent (*Chlorella* sp.) (Kumaran et al., 2023), dairy effluent (*Chlorella* sp.) (Kusmayadi et al., 2023), livestock effluent (*Chlorella* sp.) (Lee et al., 2022), swine effluent (*Dictyosphaerium* sp.) (Zhang et al., 2022), distillery effluent (*Coelastrella* sp.) (Vasistha et al., 2023).

## 3. Bibliometric framework

The bibliometric framework was performed by assuming the

hypothesis diagram presented in Fig. 1. Thus, in accordance with the European Green Deal, which encourages the transition to a more sustainable blue economy, integrated aquaculture systems can be a solution (Fig. 1). However, since integrating two different trophic niches is considered a difficult scenario due to different technological requirements, automation can be a useful tool for overlapping this barrier. In the research and development field, the trend of dissemination activities is crucial, since it can be a determinant factor for future studies which can promote and develop knowledge to fill the existing gaps. Therefore, bibliometric analysis frameworks have the purpose of identifying the trend of publication in certain niched areas and work as a supporting tool for researchers, in order to optimize the planning and experimental design of future studies.

The hypothesis diagram (Fig. 1) reveals that the present bibliometric framework is based on the Facts – Solution – Supporting tools flow and targets to use this flow to analyze the literature and to reveal future trends in the integrated microalgae production niched sector.

A number of 4 dimensions were defined to perform the bibliometric study, as follows: D1: integrated multi-trophic aquaculture; D2: microalgae production; D3: renewable energy sources based on microalgae production; D4: automation in microalgae production. The 1st dimension (D1) is based on 3 key drivers (multi-trophic aquaculture, aquaculture effluent, aquaculture wastewater), the 2nd dimension (D2) is designed on 4 key drivers (microalgae production, microalgae photobioreactor, microalgae aquaculture, microalgae cultivation), the 3rd dimension (D3) on 3 key drivers (microalgae biodiesel, microalgae biorefinery, microalgae biofuel) and the 4th dimension (D4) on 2 key drivers (microalgae automation, photobioreactor automation).

Firstly, article metadata were extracted from SCOPUS and WoS on 25th – December 1, 2023 using the key drivers from each dimension in the article title, abstract, and keywords search condition criteria. Also,

<figure><img src="image_3.png" /><figcaption>Fig. 1. Hypothesis diagram of the bibliometric study.</figcaption></figure>
secondary criteria were related to language (English language exclu-
sively) and the publication year (between 2013 and 2022) – the last ten
years. The 3rd selection criteria targeted grouping the results into three
major sub-groups, as follows: review papers, research papers and books -
book chapters.

The results revealed that, in the case of D1, the publications involving aquaculture wastewater are the most popular among the research, while certain gaps may be revealed in the multi-trophic aquaculture area of study (Fig. 2). This situation confirms the initial hypothesis of the present bibliometric study, which assumes that using aquaculture effluents for microalgae growth can be a niche subject that requires more intensive research. Also, the review papers and books, along with book chapters, represent a low percentage of the total research output performed in the last ten years, within the D1 dimension (Fig. 2). However, it can be observed that the review papers recorded a slight increase in the last 4 years, a fact which confirms the utility and requirements for holistic studies within D1 (Fig. 2).

The D2 bibliometric analysis reveals that microalgae cultivation is a highly popular research subject, while the association of microalgae with aquaculture, as well as microalgae photobioreactor - based studies, have a much lower share in terms of popularity within the academic groups of researchers (Fig. 3). Thus, this confirms the need for these study areas to be supported by future studies. Also, the dynamics of the study categories reveal a highly increasing trend in review papers, in the last three years (Fig. 3). However, the gap between review papers and research papers is still highly consistent (Fig. 3), in the case of D2.

In the case of the D3 dimension, the microalgae biofuel research

subject is the most common, while microalgae biorefinery-focused
research studies are in the lowest percentage (Fig. 4). The research pa-
pers category covers most of the studies performed within D3, while
both review papers, books and book chapters categories reveal an
increasing trend in the last 3 years (Fig. 4). These findings support the
conclusion which revealed the need for a holistic approach within the
analysed research field.

The final dimension (D4) reveals that photobioreactors’ automation is a subject which needs to be future developed, especially since digitalization, AI and, therefore, automation are highly appealing buzzwords nowadays and represent an important direction in European Union development strategy (Fig. 5). Furthermore, it seems that holistic publications (review papers, books and chapters) suffer from a significant limitation in number (Fig. 5), while the category research paper dynamics is inconsistent. This makes D4 the most recommended dimension of those analysed within the present study.

Moreover, all 4 dimensions were integrated into keywords co-
occurrence networks, based on the results revealed by Web of Science
(WoS) platform, using an Abstract, title or keyword filter. As well, as in
the case of SCOPUS database, the secondary criteria were related to
language (English language exclusively) and the publication year (be-
tween 2013 and 2022) – the last ten years. However, the 3rd selection
criteria targeted only the review papers category, which enrichment was
concluded to be a priority in the near future. In order to elaborate the
keywords co-occurrence networks, the minimum co-occurrence was set
to 15, in order to highlight especially the close connections between the
new created clusters. The counting methods are set for counting co-

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Number and type of publications on multi-trophic aquaculture, aquaculture effluent and aquaculture wastewater, indexed by SCOPUS and WoS, respectively (in the last 10 years).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Number and type of publications on microalgae photobioreactors, microalgae aquaculture and microalgae cultivation, indexed by SCOPUS and WoS, respectively (in the last 10 years).</figcaption></figure>

occurrence analysis and binary counting is used to term co-occurrence analysis.

The 1st dimension (D1) reveals a number of 352 inputs and 4 clusters (Fig. 6A), individualized as follows: aquaculture, multi-trophic aquaculture (part of microalgae – most important component), wastewater treatment, aquaculture technology. The microalgae cluster is linked with 2 other clusters as follows: the aquaculture cluster and the wastewater cluster, according to the B network (Fig. 6B). It seems that in the recent papers, microalgae studies are connected with multi-trophic aquaculture studies within the same cluster, while they also present connections with wastewater (bioremediation) and nitrogen balance studies in older studies (published between years 2016–2018).

The keywords co-occurrence network associated with 2nd dimension (D2) reveals a number of 2152 publications and 8 clusters (Fig. 7A.). However, the recent publication did not emphasize new trends, other than the characterization of algae biomass in terms of carotenoids. It seems that most studies are focused on *Chlamydomonas* sp. and the development of lipid extraction methodologies can be considered an emerging subject (Fig. 7B.).

The 3rd dimension reveals 8 clusters and a number of 1330 publications. Most of the links indicate studies performed in microalgae-based biofuel, biodiesel, and microalgae biomass production (Fig. 8A.). Also, new trends reveal the inclusion of aspects related to nutrient removal, fatty-acid composition, and light intensity, among the older research directions which targeted biodiesel as the main output (Fig. 8B.).

The 4th dimension, which identifies 2 clusters, confirms the finding

previously recorded from the analysis of SCOPUS database (Fig. 9). Therefore, automation in the algae production industry is related to aspect as wastewater treatment, cell disruption and microalgae cultivation technologies (Fig. 9). Therefore, there is still a gap in terms of the automation of different processes in integrated algae biomass production technologies, a fact which must be strongly encouraged by the scientific decision-makers through new special numbers in scientific journals, on algae growth automation, as well as through new financing lines (project calls which should target this subject).

From a critical point of view, it can be stated that publications such as books and guidelines must be increased in future in order to ensure the upscale of the research towards development and, further on, to deployment. However, review papers' trend must be increased since they will create a clear view of the actual research situation, identifying the opportunities for development, as well as new research niches that can be explored to open new roads towards empowering the existing knowledge. A positive fact is revealed by the interdisciplinary character of microalgae-integrated technologies for biogas production since it creates a nexus between aquaculture – multi-trophic concepts – nitrogen balance and creates a basic framework for multidisciplinary studies. However, groundbreaking studies related to algae biomass characterization considering various technological and operational scenarios must be encouraged, as well as the diversification of algae species, since most research has been performed for *Chlamydomonas* sp. However, few studies that were performed on other algae species must be continued further on, with the lipid extraction, in order to defined the technologies and to start the deployment process.
<figure><img src="image_6.png" /><figcaption>Fig. 4. Number and type of publications on microalgae biodiesel, microalgae biorefinery and microalgae biofuel, indexed by SCOPUS and WoS, respectively (in the last 10 years).</figcaption></figure>

It is strongly recommended to increase the studies related to the use of AI-based algorithms, as well as automatic monitoring and control frameworks for optimizing the developed technologies considering various scenarios that can occur during the synthetic biological process. Thus, merging the AI with automatics can lead to economic feasible monitoring and control systems, based on soft sensors for water quality and biomass growth real-time determinations, using machine learning and computer vision algorithms.

## 4. Forecasting models based on historical bibliometric framework

The Autoregressive Integrated Moving Average (ARIMA) represents an analysis technique used for time series analysis. It is widely used in fields such as economics, finance, and meteorology to forecast future values based on past observations. The ARIMA model is based on several mathematical concepts such as time series, stationarity, and autocorrelation (Luo and Gong, 2023). Thus, the ARIMA model is a combination of autoregressive (AR), integrated (I), and moving average (MA) components (Chodakowska et al., 2023). The AR component captures the dependence of current observations on past observations, while the MA component captures the dependence of current observations on past errors. The I component is used to transform non-stationary time series into stationary time series. A time series is a sequence of observations over time. Stationarity refers to the property of a time series where the statistical properties such as mean and variance remain constant over time. Autocorrelation refers to the correlation between a time series and a lagged version of itself. In other words, it measures the degree of similarity between observations at different time points. The AR component of the ARIMA model captures the dependence of current

observations on past observations (Khan and Alghulaiakh, 2020). It is defined as a linear regression of the current observation on its past values. The mathematical formula for the AR component is presented in eq. (1).

$$X_t = \phi_1 X_{t-1} + \phi_2 X_{t-2} + \dots + \phi_p X_{t-p} + \epsilon_t \quad (1)$$

where $X_t$ is the current observation, $X_{t-1}$, $X_{t-2}$, ..., $X_{t-p}$ are the lagged values of $X$, $\phi_1$, $\phi_2$, $\phi_n$ are the AR coefficients and $\epsilon_t$ is the error term.

The AR component is useful in capturing the trend and seasonality in time series data. The coefficients $\phi_1$, $\phi_2$, ..., $\phi_p$ can be estimated using methods such as maximum likelihood estimation or least squares estimation. The order of the AR component, denoted by p, represents the number of lagged values used in the regression. The MA component of the ARIMA model captures the dependence of current observations on past errors. It is defined as a linear regression of the current observation on its past error terms. The mathematical formula for the MA component is presented in eq. (2).

$$X_t = \epsilon_t - \theta_1 \epsilon_{t-1} - \theta_2 \epsilon_{t-2} - \dots - \theta_q \epsilon_{t-q} \quad (2)$$

where $\theta_1$, $\theta_2$, ..., $\theta_q$ are the MA coefficients and $\epsilon_{t-1}$, $\epsilon_{t-2}$, ..., $\epsilon_{t-q}$ are the error terms. The MA component is useful in capturing the random fluctuations in time series data.

The Integration (I) component of the ARIMA model is used to transform a non-stationary time series into a stationary one. A non-stationary time series is one where the statistical properties such as mean and variance change over time. The I component achieves stationarity by taking the difference between consecutive observations. The mathematical formula for the I component (first order) is presented in eq. (3):
<figure><img src="image_7.png" /><figcaption>Fig. 5. Number and type of publications on microalgae automation and photobioreactor automation, indexed by SCOPUS and WoS, respectively (in the last 10 years).</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 6. Keywords co-occurrence network among the 4 resulted clusters (A) and links of microalgae cluster to other clusters (B), considering multi-trophic aquaculture, aquaculture effluent and aquaculture wastewater as key – drivers for review type publications, in the last 10 years.</figcaption></figure>

$$Y_t^{(1)} = X_t - X_{t-1} \quad (3)$$

where $Y_t^{(1)}$ is the first-order differenced series, and it is computed by subtracting each observation $X_t$ from its previous observation $X_{t-1}$.

The first difference is the most commonly used differencing method, but higher order differences can also be used. The I component is useful

in removing the trend and seasonality in time series data. By making the time series stationary, it becomes easier to model and forecast future values. The order of the I component, denoted by d, represents the number of differences needed to achieve stationarity. The ARIMA (p, d, q) model combines the AR, MA, and I components to model a time series (Sylaios and Iliadis, 2006). The mathematical formula for the ARIMA (p,
<figure><img src="image_9.png" /><figcaption>Fig. 7. Keywords co-occurrence network among the 8 resulted clusters (A) and links between microalgae biomass production – lipid extraction – biodiesel production *Chlamydomonas* sp. (B), considering microalgae production, microalgae photobioreactor, microalgae aquaculture, microalgae cultivation as key – drivers for review-type publications, in the last 10 years.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 8. Keywords co-occurrence network among the 8 resulted clusters (A) and links between microalgae production – lipid production - biodiesel clusters (B), considering microalgae biodiesel, microalgae biorefinery and microalgae biofuel as key – drivers for review-type publications, in the last 10 years.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 9. Keywords co-occurrence network among the 2 resulted clusters, considering microalgae automation and photobioreactor automation as key – drivers for review-type publications, in the last 10 years.</figcaption></figure>

$d,q)$ model is presented as eq. (4).

$$X_t = c + \phi_1 X_{t-1} + \phi_2 X_{t-2} + \dots + \phi_p X_{t-p} - \theta_1 \epsilon_{t-1} - \theta_2 \epsilon_{t-2} - \dots - \theta_q \epsilon_{t-q} + \epsilon_{t,1} \quad (4)$$

where $X_t$ represents the value of the time series at time t, c is a constant (or drift) term that can be included in the model, $\phi_1, \phi_2, \dots, \phi_p$ are the autoregressive coefficients corresponding to the past p values of the time series, $X_{t-1}, X_{t-2}, \dots, X_{t-p}$ are the lagged values of the time series, $\theta_1, \theta_2, \dots, \theta_q$ are the moving average coefficients corresponding to the past, q white noise error terms, $\epsilon_t$ represents the white noise error term at time t, which is assumed to be independently and identically distributed with mean 0 and constant variance, d is the order of differencing required to make the time series stationary.

Once the optimal values of p, d, and q for the ARIMA model are determined, the best model among the candidates should be identified. This can be done using statistical tests such as the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) (Zhao et al., 2022). The model with the lowest AIC or BIC value is considered the best fit. After identifying the best model, it is important to check the assumptions of the chosen model. This can be done using residual plots and the *Ljung-Box* test. Residual plots show the difference between the observed values and the predicted values of the model. The *Ljung-Box*
test checks whether the residuals are independent and identically distributed over time (Adedire et al., 2022). If the assumptions are not met, it may indicate that the model needs to be adjusted. Interpreting the results of model identification and diagnostic checking is crucial for the accuracy of future predictions. If the assumptions are met and the model is a good fit, we can use it to make future predictions. This can be done by estimating the model parameters using the available data and then using them to forecast future values. Point forecasts and prediction intervals can be calculated using the estimated model.

The current study tested various time series models like ARIMA, respectively XGBoost (eXtreme Gradient Boosting) and LSTM (Long Short-Term Memory), each with its own strengths and weaknesses (Deng et al., 2023). Still, for the dataset provided, ARIMA performed better, and these are some reasons justifying this: 1) ARIMA inherently deals with non-stationary data by using differencing which can be easily adjusted. In XGBoost and LSTM, the stationarity has to be enforced externally, which can be less effective, 2) ARIMA is generally effective for smaller datasets, while LSTM often requires large datasets to train effectively, 3) ARIMA assumes a linear relationship between variables, which might be closer to the actual relationship in many real-world scenarios, while XGBoost and LSTM do not make this assumption, which can be an advantage but also a drawback if the relationships are, in fact, linear, 4) If the dataset lacks complex patterns or seasonality, tree-based methods like Random Forest and gradient boosting algorithms like XGBoost may create overly complex models that can not generalize properly.

Therefore, by applying the methodology described above, the forecast models for all three dimensions (D1, D2, D3) were elaborated, focusing on 3 main variables, namely books (Figs. 10–12), research papers (Figs. 13–15) and review papers (Figs. 16–18). It can be observed that, in the case of book numbers, in the year 2024, a decrease is forecasted in the case of D3 and an upward evolution in the case of D1 and D2, compared to the year 2022 (real data). The situation indicates an upward trend during the year 2025, in the case of all three dimensions – however, D1 and D3 reveal the highest increasing rate of books published in 2025, compared to the year 2024. The ARIMA (2,1,2) recorded the best performance in the case of D1 published books forecasting, with a MAPE of 0.100, while in the case of D2 the ARIMA (1,2,1) revealed the best performance, with a MAPE value of 0.213 (Figs. 10 and 11). The D3 ARIMA (1,1,2) published books forecasting model had recorded the highest metrics accuracy, with a MAPE of 0.227 (Fig. 12). Therefore, it can be concluded that in the case of D1 published books forecasting two lagged values of the series are used, capturing a second-order autoregressive process; the series is differenced once to make it stationary and two moving average terms are included, which models the relationship

between an observation and the residual errors from two lagged observations. In the case of D2 published books forecasting one lagged value of the series is used in the model, capturing a first-order autoregressive process; the series is differenced twice, often necessary for series with a strong seasonal component and a single moving average term is included, which models the relationship between an observation and the residual error from a single lagged observation. In the case of D3 published books forecasting, one lagged value of the series is used in the model, capturing a first-order autoregressive process; the series is differenced once to make it stationary and two moving average terms are included, which models the relationship between an observation and the residual errors from two lagged observations.

In the case of research article numbers, all the forecasted values were superior compared to the results of the year 2022 (real data) (Figs. 13–15). The situation indicates a higher upward trend during the years 2023, 2024, 2025, in the case of D1 and D3, compared to D2. The ARIMA (2,1,2) recorded the best performance in the case of D1 published research articles forecasting, with a MAPE of 0.100, while in the case of D2 the ARIMA (1,1,0) revealed the best performance, with a MAPE value of 0.116 (Figs. 13 and 14). The D3 ARIMA (0,2,0) published research articles forecasting model had recorded the highest metrics accuracy, with a MAPE of 0.054 (Fig. 15). Therefore, it can be concluded that in the case of D1 published research articles forecasting two lagged values of the series are used, capturing a second-order autoregressive process; the series is differenced once to make it stationary and two moving average terms are included, which models the relationship between an observation and the residual errors from two lagged observations. In the case of D2 published research articles forecasting one lagged value of the series is used in the model, capturing a first-order autoregressive process; the series is differenced once to make it stationary - this is equivalent to using first differences and a single moving average term is included, which models the relationship between an observation and no moving average term is included, reducing the model to an Autoregressive Integrated (ARI) model. In the case of D3 published research articles forecasting, no autoregressive terms are included in the model - this simplifies the model to an Integrated Moving Average (IMA) model; the series is differenced twice, often necessary for series with a strong seasonal component and no moving average term is included, reducing the model to an Autoregressive Integrated (ARI) model.

In the case of review article numbers, all the forecasted values were highly superior to the results of 2022 (real data) (Figs. 16–18). The situation indicates an accentuated upward trend during the years 2023, 2024, and 2025, in the case of all three analysed dimensions. The ARIMA (2,2,0) recorded the best performance in the case of D1 published review

<figure><img src="image_12.png" /><figcaption>Fig. 10. ARIMA D1 books forecasting model for the years 2024 and 2025.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Fig. 11. ARIMA D2 books forecasting model for the years 2024 and 2025.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Fig. 12. ARIMA D3 books forecasting model for the years 2024 and 2025.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Fig. 13. ARIMA D1 research article forecasting model for the years 2024 and 2025.</figcaption></figure>

articles forecasting, with a MAPE of 0.260, while in the case of D2 the ARIMA (1,2,0) revealed the best performance, with a MAPE value of 0.179 (Figs. 16 and 17). The D3 ARIMA (0,2,1) published review articles

forecasting model had recorded the highest metrics accuracy, with a MAPE of 0.220 (Fig. 18). Therefore, it can be concluded that in the case of D1 published review articles forecasting two lagged values of the
<figure><img src="image_16.png" /><figcaption>Fig. 14. ARIMA D2 research article forecasting model for the years 2024 and 2025.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>Fig. 15. ARIMA D3 research article forecasting model for the years 2024 and 2025.</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Fig. 16. ARIMA D1 review article forecasting model for the years 2024 and 2025.</figcaption></figure>

series are used, capturing a second-order autoregressive process; the
series is differenced twice, often necessary for series with a strong sea-
sonal component and no moving average term is included, reducing the

model to an Autoregressive Integrated (ARI) model. In the case of D2-
published review articles forecasting one lagged value of the series is
used in the model, capturing a first-order autoregressive process; the
<figure><img src="image_19.png" /><figcaption>Fig. 17. ARIMA D2 review article forecasting model for the years 2024 and 2025.</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>Fig. 18. ARIMA D3 review article forecasting model for the years 2024 and 2025.</figcaption></figure>

series is differenced twice, often necessary for series with a strong seasonal component and no moving average term is included, reducing the model to an Autoregressive Integrated (ARI) model. In the case of D3 published review articles forecasting, no autoregressive terms are included in the model - this simplifies the model to an Integrated Moving Average (IMA) model; the series is differenced twice, often necessary for series with a strong seasonal component and a single moving average term is included, which models the relationship between an observation and the residual error from a single lagged observation.

Within Table 1 it can be observed that in terms of books, an increase of 5.93%, 17.14% and 8.35% respectively, is forecasted from the year 2023–2024, for each of the D1, D2 and D3 dimensions, while from the year 2024–2025, an 8.40%, -1.10% and 7.40% variation, respectively, will be recorded. In terms of research articles, an increase of 12.07%, 4.02% and 8.57% respectively, is forecasted from the year 2023–2024, for each of the D1, D2 and D3 dimensions, while from the year 2024–2025, 10.77%, 2.97% and 7.89% variation, respectively, will be recorded. The review articles are forecasted to increase by 12.57%, 19.86% and 15.87% respectively, from the year 2023–2024, for each of the D1, D2 and D3 dimensions, while from the year 2024–2025, an 11.14%, 16.56% and 13.69% variation, respectively, will be recorded.

## 5. Technological growth performance indicators of microalgae in aquaculture wastewater effluents

In Table 2, the most relevant information extracted from the scientific literature related to the technological aspects of microalgae growth processes in aquaculture wastewater effluents has been centralized, including data related to analysed species, wastewater type, and pretreatment, operating conditions applied and maintained in the growth system, instrumental analysis conducted during the experiments, as well as partial results. In this context, studies conducted by Guo et al. (2013), Enwereuzoh et al. (2021), Gao et al. (2016), Hawrot-Paw et al. (2020), Lugo et al. (2020), Ansari et al. (2017), Guerrero-Cabrera et al. (2014), Malibari et al. (2018), Tejido-Nuñez et al. (2020), Dourou et al. (2018), Tossavainen et al. (2019), Guldhe et al. [77], Khatoon et al. (2016), Liu et al. (2018), Andreotti et al. (2020) are presented in Table 2. The microalgae species most used in the experimental designs were *Chlorella* sp. and *Scenedesmus* sp. Given the fact that aquaculture wastewater effluents are rich in organic carbon, all centralized algae species were produced under mixotrophic conditions (Ansari et al., 2017). Yadav et al. (2019) highlighted in their study that lipid and carbohydrate productivity increased for *Chlorella* sp. (by 17% and 18.4% respectively) and *Chlorococcum* sp. (by 34% and 21% respectively) produced under mixotrophic conditions in PBR. This phenomenon was confirmed by Enwereuzoh et al. in their study, where the authors highlighted that the
<caption>**Table 1**</caption>

The forecasted values for each of the synthetic dimensions, considering each of the ARIMA analysed parameters (p, d, q).

<table><thead><tr><th>Parameter</th><th>Year 2023 forecasted value</th><th>Year 2024 forecasted value</th><th>Year 2025 forecasted value</th><th>p</th><th>d</th><th>q</th></tr></thead><tbody><tr><td>Books - D1</td><td>24.94</td><td>26.42</td><td>28.64</td><td>2.00</td><td>2.00</td><td>2.00</td></tr><tr><td>Books - D2</td><td>221.13</td><td>259.046</td><td>261.90</td><td>1.00</td><td>2.00</td><td>1.00</td></tr><tr><td>Books - D3</td><td>141.58</td><td>153.41</td><td>164.77</td><td>1.00</td><td>1.00</td><td>2.00</td></tr><tr><td>Research article - D1</td><td>570.92</td><td>639.84</td><td>708.76</td><td>0.00</td><td>2.00</td><td>1.00</td></tr><tr><td>Research article - D2</td><td>2035.19</td><td>2116.91</td><td>2179.79</td><td>1.00</td><td>1.00</td><td>0.00</td></tr><tr><td>Research article - D3</td><td>827.99</td><td>898.99</td><td>969.99</td><td>0.00</td><td>2.00</td><td>0.00</td></tr><tr><td>Review article - D1</td><td>81.19</td><td>91.40</td><td>101.59</td><td>0.00</td><td>2.00</td><td>1.00</td></tr><tr><td>Review article - D2</td><td>552.64</td><td>662.41</td><td>772.13</td><td>1.00</td><td>2.00</td><td>0.00</td></tr><tr><td>Review article - D3</td><td>293.59</td><td>340.20</td><td>386.79</td><td>0.00</td><td>2.00</td><td>1.00</td></tr></tbody></table>

lower nutrient concentrations (NH₄, NO₃, PO₄) in aquaculture wastewater lead to the accumulation of higher lipids and carbohydrate content in the cultivated microalgal species, compared to the ones cultivated in TAP media (Enwereuzoh et al., 2021). The microalgae growth systems preferred by the authors were equally divided between enclosed PBR (where medial conditions were fully controlled) and flasks such as Erlenmeyer (where medial conditions were partially controlled). Further on, the most used aquaculture effluent for microalgae growth media was the one generated by tilapia aquaculture, which is to be expected since tilapia is the third most produced fish species in the aquaculture sector (Jansen et al., 2019).

The lipid yield of the cultured microalgae is decisive considering the objective to produce biofuels. As can be observed in Table 2, the highest lipid productivity (140 mg/l/day) resulted in the case of the species *Chlorella sorokiniana* produced using tilapia RAS aquaculture effluent under heterotrophic conditions (complete dark phase), followed by *Ankistrodesmus falcatus* species (52.72 mg/l/day) cultured under mixotrophic conditions using also tilapia RAS aquaculture wastewater (Ansari et al., 2017; Guldhe et al., 2017). In both studies, the temperature was maintained at 25 °C. Another species that registered good results in terms of cell lipid concentration was *Navicula* sp. (14,855.7 pg/cell) cultured under mixotrophic conditions using a saline effluent from shrimp aquaculture (Malibari et al., 2018).

From a critical point of view, even though microalgae cultivation has registered good indicators in terms of growth performance at a laboratory and pilot scale, scaling up production at an industrial level remains a challenge (Novoveská et al., 2023; Phong Vo et al., 2019). From an economic point of view, the operating costs for 500 m² of PBR are approximately 30 dollars, while the amortization can be up to 13 years (Phong Vo et al., 2019). Furthermore, it has been highlighted that 1 kg of dry microalgae biomass can cost up to 570 euros (Novoveská et al., 2023). Besides operating costs of the production system, harvesting microalgae biomass by centrifugation or filtration implies energy-intensive and costly equipment, hindering economic feasibility (Cheng et al., 2023; Lam and Lee, 2012; Lu et al., 2020). Regarding technological challenges, the inconsistency of aquaculture effluent composition can negatively influence the production process. The molar ratios between C/N and N/P affect the capacity of microalgal cells to remove nutrients (Lu et al., 2020). The microalgae cells can be described as the following stoichiometric equation C₁₈H₁₈I₄O₄N₁₆P (Lu et al., 2020). Thus, the aquaculture effluent's chemical composition needs to

be constant and adjusted to the biological needs of microalgae biomass. Lastly, the lack of specialized human resources with expertise in microalgae production represents a challenge in scaling up production (Novoveská et al., 2023).

Control strategies applied for microalgae bioprocess.

The aim of the most control strategies developed and applied in photobioreactors found in the literature is to control the pH in the bioreactor by injecting CO₂ or flue gases (Table 3). This is usually done by using simple on/off controller, however, some studies started to propose more complex control strategies, accounting for system dynamics and disturbances, for the same purpose, but also to make efficient use of the CO₂ gases by minimizing losses (Table 3). Rodríguez-Torres et al. proposes using a Proportional-Integral (PI) controller (Fig. 19c) for regulating the pH in two types of production systems (raceway and thin-layer PBR), using, at the same time, two pH models, linearized around two functioning points: daytime and night time periods (Rodríguez-Torres et al., 2021). The authors compare the results obtained with the PI controller in both functioning points with the ones obtained by using a simple on/off controller, concluding that using PI controllers leads to better system performances in terms of CO₂ consumption and valve control effort. Ifrim et al. proposes an improved PI control, by tuning the controller parameters using a data driven method called fictitious reference iterative tuning (G. Ifrim et al., 2022). By using data from the real process to tune the controller, further performance improvements can be achieved. Isiram en et al. achieves a 60% reduction in terms of costs associated with CO₂ by using a control strategy involving a PI controller and dead-band (Fig. 19d) (Isiram en et al., 2022). This strategy improves the pH control and reduces the environmental impact associated with classical pH control (on/off controller). The efficient use of CO₂ in this control strategy results in a high reduction of the operating costs (60%), and, most important, in the reduction of the amount of CO₂, which is a greenhouse gas, lost to the atmosphere. Fernández et al. make use of a linear pH model, accounting for CO₂ injection input, but also for the solar radiation, to develop a control strategy that involves a PI controller for pH reference tracking and a feedforward (FF) compensator for reducing the perturbator effect of the solar radiation (Fig. 19g) (Fernández et al., 2010). The results show small improvements related to CO₂ injection time and operating cost, but a high reduction of the integrative absolute error between the pH reference and value. Pawlowski et al. uses an event-based Generalized Predictive Control (GPC) to compute a control sequence to minimize a cost function (Pawlowski et al., 2014). The cost function is given in quadratic function form resulting in a Quadratic Programming problem. Besides this, the GPC controller includes an actuator virtual deadband to reduce the actuation stress. Thus, the pH is controlled to a reference value, while reducing the required quantity of flue gases injected in the bioreactor. Rodríguez-Miranda et al. propose two control architectures (Fig. 19e and f) which couples an event-based method with a time-driven controller (a PI controller). The event-based method, a modified version of Send-On-Delta (SOD) technique called Symmetric-Send-On-Delta (SSOD) sampling, which allows a balance between the control effort and performances, improving the behaviour of the PI controller in terms of CO₂ consumption, actuator effort and Integrated Absolute Error in simulation (Rodríguez-Miranda et al., 2019). The results obtained with one of the architectures (Fig. 19e) in simulation have been validated in a real raceway bioreactor by Rodríguez-Miranda et al. in proving the method reliability (Rodríguez-Miranda et al., 2020).

Carreño-Zagarra et al. implement a control strategy using a robust PI controller improved by a linear active rejection method based on Generalized PI (GPI) observer (Carreño-Zagarra et al., 2019). The implemented control strategy is presented in Fig. 20. The linear active rejection approach aims at estimating dynamics of the system that are not modeled and various disturbances. The validation of the control strategy on a real raceway photobioreactor showed improvements in terms of reference tracking precision compared to a feedforward control
<table><caption>**Table 2**

Technological conditions applied in different microalgae growth processes and associated growth performances.</caption><thead><tr><th>Algal Specie</th><th>Wastewater</th><th>Operating parameters</th><th>Pre-treatment of wastewater</th><th>Analysed parameters</th><th>Partial Results</th><th>Reference</th></tr></thead><tbody><tr><td>Platymonas subcordiformis</td><td>Flounder aquaculture fisheries Saline</td><td>Batch cultivation 500 ml Erlenmeyer flasks<br>Temperature 25 °C pH 8.0<br>Light intensity (cool white fluorescent lamps)<br>50 µE/m²/sec<br>Light:dark 14hr:10hr</td><td>Wastewater was filtered (0.45 µm filter), autoclaved and supplemented with micronutrients</td><td>Nutrient removal (%)</td><td>87-95% efficiency for N removal and 98-99% efficiency for P removal</td><td>Guo et al. (2013)</td></tr><tr><td>Tetradesmus obliquus<sup>a</sup><br>Heterochlorella luteoviridis<sup>b</sup><br>Chlamydomonas reinhardtii<sup>c</sup></td><td>Tilapia RAS aquaculture Freshwater</td><td>Batch cultivation 10 L PBR, height 100 cm and interior diameter 14.5 cm pH 7.3<br>Light intensity (cool white fluorescent lamps)<br>2388 lx<br>Light:dark 12hr:12hr<br>Air continuous supply rate 7 ml/min<br>5% CO₂ supply rate for 1 h 2 ml/min</td><td>Wastewater was filtered with 509ws filter paper, sterilised in autoclave for 20 min at 120 °C<br>Wastewater was diluted (1:1) to induce nutrient stress</td><td>Biomass productivity = amount of biomass (mg/l)/(number of days)<br><sup>1</sup>Specific growth rate = 1/t<sub>m</sub>In(X<sub>f</sub>/X<sub>i</sub>)<br><sup>2</sup>Percentage nutrient removal = (X<sub>f</sub> - X<sub>i</sub>)/(X<sub>f</sub>) x 100<br>Lipid productivity = biomass productivity (mg/l/d) x (% lipid content)/100<br>Carbohydrate productivity = biomass productivity (mg/l/d) x (%carbohydrate content)/100</td><td>Biomass prod.<sup>a</sup> = 182.52 ± 1.67 mg/l<br>Biomass prod.<sup>b</sup> = 185.62 ± 1.34 mg/l<br>Biomass prod.<sup>c</sup> = 118.71 ± 1.35 mg/l<br>µ<sup>a</sup> = 0.14 ± 0.01<br>µ<sup>b</sup> = 0.17 ± 0.01<br>µ<sup>c</sup> = 0.15 ± 0.02<br>92% efficiency for PO₄ removal and 98% for NO₃ removal for all species</td><td>Enwereuzoh et al. (2021)</td></tr><tr><td>Chlorella vulgaris<sup>d</sup><br>Scenedesmus obliquus<sup>e</sup></td><td>Whiteleg shrimp aquaculture rearing tank Saline (2.8%)</td><td>Batch cultivation 1000 ml flasks with 800 ml wastewater<br>Inoculation with 0.025 g/l biomass<br>Flasks were maintained at 25 °C, continuous light (white fluorescent - 12,000 lux) and shacked at 100 rpm pH 7.78<br>Continuous cultivation 5 L cylindrical MPBR with the internal diameter 7.2 cm<br>Inoculation with 0.41 g/l biomass<br>Light regime: 2 led lamps (9 w) with a red/blue light ratio 4:1, at 2 cm from the surface of the reactor<br>HRT 1 day<br>Continuous air supply rate 0.5 l/min<br>Temperature 25 ±2 °C pH 6.8-7.2</td><td>Wastewater was settled overnight</td><td>Biomass productivity (mg/l/day) was determined gravimetrically<br>Nutrient removal (%)</td><td>Biomass prod.<sup>d</sup> = 7.3 mg/l/day<br>Biomass prod.<sup>e</sup> = 6.2 mg/l/day<br><br>Biomass prod.<sup>d</sup> = 42.6 mg/l/day<br>TN removal<sup>d</sup> = 86.1%<br>TP removal<sup>d</sup> = 82.7%</td><td>Gao et al. (2016)</td></tr><tr><td>Chlorella minutissima</td><td>Salmon RAS aquaculture Saline (10 ‰)</td><td>Batch cultivation 14 L tubular PBR<br>Inoculation with 1.3 g/l biomass and 1.2 L of inoculum was added in PBR<br>CO₂+air supply rate 14 l/min pH 7.98-8.54</td><td>Wastewater was filtered (1.2 µm)</td><td>Biomass concentration (g/l) was determined gravimetrically<br><sup>4</sup>Biomass productivity = <sup>B<sub>f</sub>-B<sub>o</sub></sup>/<sub>d</sub><br><sup>5</sup>Lipid content = (<sup>m<sub>L</sub></sup>/<sub>m<sub>DAB</sub></sub>) x 100<br>Nutrient removal (%)</td><td><sup>6</sup>Biomass concentration = 4.69 g/l<br><sup>7</sup>TN removal = 87.9%<br><sup>7</sup>TP removal = 99.1%</td><td>Hawrot-Paw et al. (2020)</td></tr></tbody></table>

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<table><caption>Table 2 (continued)</caption><thead><tr><th>Algal Specie</th><th>Wastewater</th><th>Operating parameters</th><th>Pre-treatment of wastewater</th><th>Analysed parameters</th><th>Partial Results</th><th>Reference</th></tr></thead><tbody><tr><td>Chlorella sorokiniana</td><td>Tilapia RAS aquaculture Freshwater</td><td>Batch cultivation 12 x 70 L column PBR Light regime: 5 m of LED strips, with red and blue colours (3:1 ratio) 24/0 h light/dark cycle Temperature 28 ±2 °C pH maintained in the range 6–8, with 0.5 M KOH and 0.5 M HCl Air-lifting (99% air + 1 % CO2) between 2 and 8 l/min</td><td>Wastewater was treated with UV-C lamp at a flow rate of 4.6 l/min</td><td>Biomass productivity (mg/l/day) Nutrient removal (%)</td><td>Biomass prod. = 140 mg/l/day N removal = 78% P removal = 77% Mg removal = 70% Zn removal = 90% Ni removal = 99% NH4 removal = 99.9% NO3 removal = 75.2 %</td><td>Lugo et al. (2020)</td></tr><tr><td>Chlorella sorokiniana<sup>f</sup> Scenedesmus obliquus<sup>g</sup> Ankistrodesmus falcatus<sup>h</sup></td><td>Tilapia RAS aquaculture Freshwater</td><td>Batch cultivation 1 L conical flasks with 500 ml working volume Inoculation:<sup>f</sup> 0.22 g/l,<sup>g</sup> 0.19 g/l,<sup>h</sup> 0.2 g/l Light intensity 120 mol/m²/s, 16:8 light dark cycle Continuous orbital shaker (100 rpm) Temperature 25 °C pH 7.28</td><td>Wastewater was filtered (0.45 µm paper filter), followed by autoclaving</td><td>Biomass yield (g/l) Biomass productivity (mg/l/d) = <i>Biomass yield (mg/l)</i> Number of days Nutrient percentage removal (%) = <i>Initial concentration – Final concentration</i> x 100 Lipid productivity (mg/l/d) = biomass productivity (mg/l/d) x <i>lipid content (%)</i> Protein productivity (mg/l/d) = biomass productivity (mg/l/d) x <i>protein content (%)</i> Carbohydrate productivity (mg/l/d) = biomass productivity (mg/l/d) x <i>protein content (%)</i></td><td>Biomass yield<sup>f</sup> = 1.51 ± 0.001 (g/l) Biomass yield<sup>g</sup> = 1.25 ± 0.01 (g/l) Biomass yield<sup>h</sup> = 2.25 ± 0.001 (g/l) Biomass prod.<sup>f</sup> = 107.85 ± 0.2 (mg/l/d) Biomass prod.<sup>g</sup> = 89.61 ± 0.76 (mg/l/d) Biomass prod.<sup>h</sup> = 160.79 ± 0.2 (mg/l/d) NO3 removal<sup>f</sup> = 75.76% NO2 removal<sup>f</sup> = 81.79% NH4 removal<sup>f</sup> = 98.91% NO3 removal<sup>g</sup> = 77.7% NO2 removal<sup>g</sup> = 73.83% NH4 removal<sup>g</sup> = 68.09% NO3 removal<sup>h</sup> = 80.85% NO2 removal<sup>h</sup> = 99.73% NH4 removal<sup>h</sup> = 86.45% PO4 removal<sup>f</sup> = ~100% PO4 removal<sup>g</sup> = ~100% PO4 removal<sup>h</sup> = 98.52% Lipid prod.<sup>f</sup> = 34.35 (mg/l/d) Carbohydrate prod.<sup>f</sup> = 38.21 (mg/l/d) Protein prod.<sup>f</sup> = 31.07 (mg/l/d) Lipid prod.<sup>g</sup> = 27.65 (mg/l/d) Carbohydrate prod.<sup>g</sup> = 31.41 (mg/l/d) Protein prod.<sup>g</sup> = 17.50 (mg/l/d) Lipid prod.<sup>h</sup> = 52.72 (mg/l/d) Carbohydrate</td><td>Ansari et al. (2017)</td></tr></tbody></table>

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<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Algal Specie</th><th>Wastewater</th><th>Operating parameters</th><th>Pre-treatment of wastewater</th><th>Analysed parameters</th><th>Partial Results</th><th>Reference</th></tr></thead><tbody><tr><td>Chlorella sp.<sup>i</sup><br>Scenedesmus sp.<sup>j</sup><br>Monoraphidium sp.<sup>k</sup></td><td>Tilapia<br>aquaculture<br>Freshwater</td><td>Batch cultivation<br>Flat wall PET<br>bottles (4 l) as PBR<br>Light regime: 60 W<br>fluorescent<br>Philips® T12<br>lamps (6000 lm)<br>Air supply without<br>CO<sub>2</sub> 2.9 l/min pH<br>adjusted to 7.5<br>with 1 M KOH and<br>1 M HCl<br>Temperature 25 °C</td><td>Wastewater was<br>filtered (55 µm<br>filter) and<br>sterilised by<br>boiling for 10 min</td><td>μ<sup>8</sup> = μ<sub>max(N)</sub> S<sub>N</sub> / (K<sub>N</sub>+S<sub>N</sub>)<br>Q = biomass concentration g/l<br>Volumetric productivity (Q<sub>V</sub>) = μ' (g/l/d)<br>Lipid content (%)<br>Protein content (%)<br>Lipid productivity (Q<sub>VL</sub>) = (Q<sub>V</sub>) (percentage of<br>lipid content)/100<br>Protein productivity (Q<sub>VP</sub>) = (Q<sub>V</sub>) (percentage of<br>protein content)/100<br>Nutrient removal % (NH<sub>4</sub>, PO<sub>4</sub>)</td><td>prod.<sup>h</sup> = 54.47<br>(mg/l/d)<br>Protein prod.<sup>h</sup> =<br>49.18 (mg/l/d)<br>μ<sup>i</sup> = 0.018<br>μ<sup>j</sup> = 0.014<br>μ<sup>k</sup> = 0.013<br>Q<sup>i</sup> = 0.070 g/l<br>Q<sup>j</sup> = 0.344 g/l<br>Q<sup>k</sup> = 0.162 g/l<br>Q<sup>l</sup><sub>V</sub> = 0.047 g/l/d<br>Q<sup>l</sup><sub>V</sub> = 0.026 g/l/d<br>Q<sup>k</sup><sub>V</sub> = 0.045 g/l/d<br>Lipid content<sup>l</sup> =<br>9.2%<br>Lipid content<sup>j</sup> =<br>10.3%<br>Lipid content<sup>k</sup> =<br>10.4%<br>Protein content<sup>l</sup> =<br>61.9%<br>Protein content<sup>j</sup> =<br>50.4%<br>Protein content<sup>k</sup> =<br>45.4%<br>Q<sup>l</sup><sub>VL</sub> = 0.004 g/l/d<br>Q<sup>l</sup><sub>VL</sub> = 0.003 g/l/d<br>Q<sup>k</sup><sub>VL</sub> = 0.002 g/l/d<br>Q<sup>l</sup><sub>VP</sub> = 0.029 g/l/d<br>Q<sup>k</sup><sub>VP</sub> = 0.008 g/l/d<br>Q<sup>l</sup><sub>VP</sub> = 0.013 g/l/d<br>NH<sub>4</sub> removal<sup>l</sup> =<br>82.2%<br>NH<sub>4</sub> removal<sup>j</sup> =<br>92.3%<br>NH<sub>4</sub> removal<sup>k</sup> =<br>98%<br>PO<sub>4</sub> removal<sup>l</sup> =<br>66.2%<br>PO<sub>4</sub> removal<sup>j</sup> =<br>80%<br>PO<sub>4</sub> removal<sup>k</sup> =<br>90%<br>μ<sup>i</sup> = 0.18 ± 0.03<br>μ<sup>j</sup> = 0.18 ± 0.05<br>μ<sup>k</sup> = 0.16 ± 0.12<br>μ<sup>o</sup> = 0.49 ± 0.09<br>μ<sup>p</sup> = 0.22 ± 0.05<br>CDW<sup>l</sup> = 58.8 ±<br>0.1 mg/L<br>CDW<sup>m</sup> = 61.7 ±<br>0.0 mg/L<br>CDW<sup>n</sup> = 73.0 ±<br>0.4 mg/L<br>CDW<sup>o</sup> = 86.3 ±<br>0.2 mg/L<br>CDW<sup>p</sup> = 81.3 ±<br>0.0 mg/L<br>L<sup>l</sup> = 968.0 ±<br>495.6 pg/cell<br>L<sup>m</sup> = 238.3 ±<br>15.5 pg/cell<br>L<sup>n</sup> = 17.0 ± 5.6<br>pg/cell<br>L<sup>o</sup> = 14,855.7 ±<br>7704.7 pg/cell<br>L<sup>p</sup> = 4667.7 ±<br>1826.8 pg/cell<br>CDW<sup>r</sup> = 5.09 ±<br>0.08 g/L<br>CDW<sup>s</sup> = 7.52 ±<br>0.11 g/L<br>NO<sub>3</sub> removal<sup>r,s</sup> =<br>98.73 ± 0.06%</td><td>Guerrero-Cabrera<br>et al. (2014)</td></tr><tr><td>Chlorella sp.<sup>i</sup><br>Dunaliella sp.<sup>m</sup><br>Nannochloropsis<br>sp.<sup>n</sup><br>Navicula sp.<sup>o</sup><br>Tetraselmis sp.<sup>p</sup></td><td>Shrimp<br>aquaculture<br>Saline</td><td>Batch cultivation<br>1000 ml<br>Erlenmeyer conical<br>flasks with 900 ml<br>working volume<br>Temperature 22.5<br>± 0.5 °C<br>Light regime:<br>extreme cool<br>daylight tubes<br>Philips TL-D, light<br>intensity 3360 lux<br>at 20 cm<br>Aeration rate<br>0.1-0.3 vvm pH =<br>7.9 ± 0.3</td><td>Wastewater was<br>filtered (1.2 µm),<br>sterilised at 121 °C<br>for 2 h and stored<br>at 4±2 °C until use</td><td>μ<sup>8</sup> = (ln N<sub>2</sub> - ln N<sub>1</sub>) / (t<sub>2</sub> - t<sub>1</sub>)<br>Cell dry weight (CDW) mg/L<br>Total lipids (L) pg/cell</td><td>μ<sup>i</sup> = 0.18 ± 0.03<br>μ<sup>j</sup> = 0.18 ± 0.05<br>μ<sup>k</sup> = 0.16 ± 0.12<br>μ<sup>o</sup> = 0.49 ± 0.09<br>μ<sup>p</sup> = 0.22 ± 0.05<br>CDW<sup>l</sup> = 58.8 ±<br>0.1 mg/L<br>CDW<sup>m</sup> = 61.7 ±<br>0.0 mg/L<br>CDW<sup>n</sup> = 73.0 ±<br>0.4 mg/L<br>CDW<sup>o</sup> = 86.3 ±<br>0.2 mg/L<br>CDW<sup>p</sup> = 81.3 ±<br>0.0 mg/L<br>L<sup>l</sup> = 968.0 ±<br>495.6 pg/cell<br>L<sup>m</sup> = 238.3 ±<br>15.5 pg/cell<br>L<sup>n</sup> = 17.0 ± 5.6<br>pg/cell<br>L<sup>o</sup> = 14,855.7 ±<br>7704.7 pg/cell<br>L<sup>p</sup> = 4667.7 ±<br>1826.8 pg/cell<br>CDW<sup>r</sup> = 5.09 ±<br>0.08 g/L<br>CDW<sup>s</sup> = 7.52 ±<br>0.11 g/L<br>NO<sub>3</sub> removal<sup>r,s</sup> =<br>98.73 ± 0.06%</td><td>Malibari et al.<br>(2018)</td></tr><tr><td>Chlorella vulgaris<sup>r</sup><br>Tetradesmus<br>obliquus<sup>s</sup></td><td>RAS<br>aquaculture<br>Freshwater</td><td>Batch cultivation<br>100 ml Erlenmeyer<br>flasks with 60 ml<br>working volume,<br>covered with a<br>cotton stopper<br>Light regime:</td><td>Wastewater was<br>filtered (0.22 µm)</td><td>Dry weight (DW)<br>Average nutrient removal (%)</td><td>CDW<sup>r</sup> = 5.09 ±<br>0.08 g/L<br>CDW<sup>s</sup> = 7.52 ±<br>0.11 g/L<br>NO<sub>3</sub> removal<sup>r,s</sup> =<br>98.73 ± 0.06%</td><td>Tejido-Núñez et al.<br>(2020)</td></tr></tbody></table>

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<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Algal Specie</th><th>Wastewater</th><th>Operating parameters</th><th>Pre-treatment of wastewater</th><th>Analysed parameters</th><th>Partial Results</th><th>Reference</th></tr></thead><tbody><tr><td>Nannochloropsis gaditana</td><td>Sea bream and sea bass aquaculture Saline</td><td>constant illumination warm white LEDs, 3500 K, photon flux density 90 µmol/s/m² Temperature 25 °C Stirring 115 rpm Batch cultivation 3.7 L Stirred tank reactor (STR) with 2.0 L working volume Inoculation with 200 ml of pre-culture Light regime: fluorescent lamps (8 W), light intensity 1636 µmol/m²/s, placed at 10 cm around the culture unit Light:dark cycle 24h:0H Temperature 25 ±1 °C pH was automatically controlled (8.5 ± 0.5) Aeration rate 60 l/h air Agitation rate 100 rpm Batch co-cultivation 2 L borosilicate bottles Light:dark cycle 16h:8h, photon lux density 250 µmol photons/m²/sec Temperature 20 °C pH 7.05-7.50 2% CO₂ supply rate 0.5 l/min in moist air Air supply (10 l/min) and CO₂ supply (0.2 l/min) was injected in the water phase PTFE membrane filters were connected to the gassing tubes</td><td>Wastewater was filtered, sterilised at 121 °C for 20 min and stored at room temperature until use</td><td>Dry weight (DW) Lipid content (%) Protein content (%)</td><td>PO₄ removal<sup>r,s</sup> = 99.46 ± 0.04% DW = 551.3 ± 33.1 mg/l Lipid content = 24.7 ± 3.1% Protein content = 41.0 ± 8.9%</td><td>Dourou et al. (2018)</td></tr><tr><td>Euglena gracilis Selenastrum sp.</td><td>Pikeperch<sup>t</sup> and catfish<sup>u</sup> RAS aquaculture Freshwater</td><td>Batch co-cultivation 2 L borosilicate bottles Light:dark cycle 16h:8h, photon lux density 250 µmol photons/m²/sec Temperature 20 °C pH 7.05-7.50 2% CO₂ supply rate 0.5 l/min in moist air Air supply (10 l/min) and CO₂ supply (0.2 l/min) was injected in the water phase PTFE membrane filters were connected to the gassing tubes</td><td>Wastewater was sterilised by O₃ and UV treatment</td><td>Dry weight (DW) µ<sup>8</sup> = Ln (DW₁/DW₀)/(t₁ - t₀) Nutrient removal (%)</td><td>DW<sup>t</sup> = 0.47 g/l DW<sup>u</sup> = 0.08 g/l µ<sup>t</sup> = 0.27 ± 0.04 day TN removal<sup>t</sup> = 75.4% TN removal<sup>u</sup> = 80.7% NH₄-N removal<sup>t</sup> = 99% NH₄-N removal<sup>u</sup> = 99.4% TP removal<sup>t</sup> = 87.5% TP removal<sup>u</sup> = 92.7% PO₄-P removal<sup>t</sup> = 98.8% PO₄-P removal<sup>u</sup> = 99.8% COD removal<sup>t</sup> = 43.2% COD removal<sup>u</sup> = 48.4% Biomass concentration = 2.47 g/l NO₃ removal = 84.51% NO₂ removal = 96.38% NH₄ removal = 75.56% PO₄ removal = 73.35% COD removal = 71.88% Lipid productivity = 140 mg/l/d Protein</td><td>Tossavainen et al. (2019)</td></tr><tr><td>Chlorella sorokiniana</td><td>Tilapia RAS aquaculture Freshwater</td><td>Batch cultivation 1 L conical flasks with 500 ml working volume Heterotrophic conditions (complete dark phase) Temperature 25 °C Shaking 110 rpm</td><td>Wastewater was filtered and autoclaved</td><td>Biomass concentration (g/L) Nutrient removal = Initial concentration - final concentration / Initial concentration x 100 Lipid productivity = biomass productivity x lipid content / 100 Protein productivity = biomass productivity x protein content / 100 Carbohydrate productivity = biomass productivity x carbohydrate content / 100</td><td>DW<sup>t</sup> = 0.47 g/l DW<sup>u</sup> = 0.08 g/l µ<sup>t</sup> = 0.27 ± 0.04 day TN removal<sup>t</sup> = 75.4% TN removal<sup>u</sup> = 80.7% NH₄-N removal<sup>t</sup> = 99% NH₄-N removal<sup>u</sup> = 99.4% TP removal<sup>t</sup> = 87.5% TP removal<sup>u</sup> = 92.7% PO₄-P removal<sup>t</sup> = 98.8% PO₄-P removal<sup>u</sup> = 99.8% COD removal<sup>t</sup> = 43.2% COD removal<sup>u</sup> = 48.4% Biomass concentration = 2.47 g/l NO₃ removal = 84.51% NO₂ removal = 96.38% NH₄ removal = 75.56% PO₄ removal = 73.35% COD removal = 71.88% Lipid productivity = 140 mg/l/d Protein</td><td>Guldhe et al. (2017)</td></tr></tbody></table>

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<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Algal Specie</th><th>Wastewater</th><th>Operating parameters</th><th>Pre-treatment of wastewater</th><th>Analysed parameters</th><th>Partial Results</th><th>Reference</th></tr></thead><tbody><tr><td>Chaetoceros calcitrans<sup>y</sup><br/>Nannochloris maculate<sup>w</sup><br/>Tetraselmis chuii<sup>x</sup></td><td>Shrimp aquaculture ponds<br/>Saline (24 ppt)</td><td>Batch cultivation 15 L PBR<br/>Temperature 25 °C<br/>Light regime: cool fluorescent light, 24 h photoperiod, light intensity 50 µmol/m²/sec<br/>Air supply throughout the experiment</td><td>Wastewater was filtered (1.2 µm filter), treated with submerged UV for 48 h and stored at 4±2 °C until use</td><td>Biomass concentration (g/l)<br/>Volumetric productivity (VP)<sup>11</sup> = (X<sub>n</sub> - X<sub>0</sub>)/N (g/l/d)<br/>Areal productivity (AP)<sup>12</sup> = (VP x V)/A (g/m/d)<br/>Lipid productivity (LP) = VP x (% lipid/100) (mg/l/d)</td><td>productivity = 90 mg/l/d<br/>Carbohydrate productivity = 130 mg/l/d<br/>Biomass concentration<sup>y</sup> = 1.20 g/l<br/>Biomass concentration<sup>w</sup> = 1.10 g/l<br/>Biomass concentration<sup>x</sup> = 1.09 g/l<br/>VP<sup>y</sup> = 1.11 mg/l/d<br/>VP<sup>w</sup> = 0.82 mg/l/d<br/>VP<sup>x</sup> = 1.02 mg/l/d<br/>AP<sup>y</sup> = 1.51 mg/cm/d<br/>AP<sup>w</sup> = 1.09 mg/cm/d<br/>AP<sup>x</sup> = 1.35 mg/cm/d<br/>LP<sup>y</sup> = 0.18 mg/l/d<br/>LP<sup>w</sup> = 0.14 mg/l/d<br/>LP<sup>x</sup> = 0.19 mg/l/d</td><td>Khatoon et al. (2016)</td></tr><tr><td>Scenedesmus quadricauda<sup>y</sup><br/>Scenedesmus obliquus<sup>s</sup><br/>Chlorella vulgaris<br/>Parachlorella kessleri<sup>*</sup><br/>Chlorococcum sp.</td><td>Fishery aquaculture</td><td>Batch cultivation 1000 ml flasks with 500 ml working volume<br/>Initial inoculation 100 mg/l<br/>Temperature 25 °C<br/>Light regime: fluorescent light, light intensity 55.5 µmol photons/m²/sec, light:dark cycle 14h:10h<br/>Shaking 160 rpm<br/>pH 7.86</td><td>Wastewater was settled for 1 h and the suspended solids were removed, no sterilization was applied</td><td>Biomass concentration (mg/l)<br/>Specific growth rate (µ)<sup>8</sup> = (lnN<sub>a</sub> - lnN<sub>b</sub>)/(t<sub>a</sub> - t<sub>b</sub>)<br/>Doubling time (DT) = (T<sub>d</sub>, t) = ln2/µ<br/>Chlorophyll-a = 13.92 * A665 - 6.88 * A649<br/>Chlorophyll-b = 24.96 * A649 - 7.32 * A665<br/>Nutrient removal (%)</td><td>Biomass concentration<sup>y</sup> = 240 mg/l<br/>Biomass concentration<sup>x</sup> = 225 mg/l<br/>Biomass concentration = 235 mg/l<br/>Biomass concentration = 260 mg/l<br/>Biomass concentration = 255 mg/l<br/>µ* = 0.11 ± 0.009 (days)<br/>DT* = 6.53 ± 0.04 (days)<br/>Chlorophyll-a = 2.43 mg/l<br/>Chlorophyll-a = 2.75 mg/l<br/>Chlorophyll-a = 2.62 mg/l<br/>Chlorophyll-a = 4.32 mg/l<br/>Chlorophyll-a = 2.54 mg/l<br/>Chlorophyll-b = 0.87 mg/l<br/>Chlorophyll-b = 0.85 mg/l<br/>Chlorophyll-b = 0.89 mg/l<br/>Chlorophyll-b = 1.43 mg/l<br/>Chlorophyll-b = 1.06 mg/l<br/>COD* removal = 94.4%<br/>NH<sub>4</sub> removal* = 96.2%<br/>NO<sub>2</sub> removal* = 99%</td><td>Liu et al. (2018)</td></tr></tbody></table>

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<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Algal Specie</th><th>Wastewater</th><th>Operating parameters</th><th>Pre-treatment of wastewater</th><th>Analysed parameters</th><th>Partial Results</th><th>Reference</th></tr></thead><tbody><tr><td>Tetraselmis suecica</td><td>Sea bream RAS aquaculture Saline</td><td>Batch cultivation Column PBR with a working volume of 3.5 L, 10 cm diameter Mixing by magnetic mixer 150 rpm Air supply at 1.8 l/ min Temperature 27.5 ± 0.5 °C pH 8.2 ± 0.5 °C pH was controlled by CO<sub>2</sub> injection Light regime: 4 fluorescent lamps, light intensity 120 µmol/m<sup>2</sup>/sec Light:dark cycle 12h:12h Wastewater feeding rate 4.2 l/ day</td><td>Wastewater was collected and stored at -18 °C until use, without sterilization</td><td>Microalgal growth expressed as TSS (mg/l) Volumetric productivity of biomass (P)<sup>14</sup> = (Q * C)/V Dissolved inorganic nitrogen (DIN)<sup>15</sup> removal efficiency (%) Dissolved inorganic phosphorous (DIP)<sup>15</sup> removal efficiency (%) Lipid productivity (LP) = biomass productivity (mg/l/d) x (lipid content/100) Protein productivity (PP) = biomass productivity (mg/l/d) x (lipid content/100) Carbohydrate productivity (CP) = biomass productivity (mg/l/d) x (lipid content/100)</td><td>NO<sub>3</sub> removal* = 94.3% PO<sub>4</sub> removal* = 95.6% Maximum TSS = 900 mg/l P = 68.53 ± 3.69 mg/l/d DIN removal = 99.82 ± 0.03% DIP removal = 97.18 ± 0.01% LP = 17.17 mg/l/ d PP = 34.40 mg/l/ d CP = 7.28 mg/l/d</td><td>Andreotti et al. (2020)</td></tr></tbody></table>

strategy.

Besides aiming to control the pH in the reactor, some authors propose controlling other variables. Starting from the fact that raceway production systems have a low mass transfer capacity causing low system performances due to dissolved oxygen (DO) accumulation, Barcelo-Villalobos et al. propose a control strategy to overcome this (Barceló-Villalobos et al., 2022). The proposed strategy (Fig. 21) regulates the air valve opening through a PI controller whose reference is represented by the required airflow. The required airflow is not constant but is determined based on a DO setpoint and a mass transfer coefficient ($Kla$) estimated online based on the current $O_2$ production. Tests on a real photobioreactor proved that the proposed method increases the performance of the system, while, at the same time, reducing the air required to be injected in the reactor.

One distinct approach is presented by Pawlowski et al., where a selective pH and DO control strategy (Fig. 22) is proposed for a raceway system (Pawlowski et al., 2015). The selective controller prioritizes the pH controller when the error between the pH setpoint and value is unacceptable, thus selects the DO controller when the pH value is within the limits. The pH controller uses an event-based Generalized Predictive Control (GPC) to apply a control sequence that minimizes a quadratic cost function. To reduce the actuator stress, the GPC includes an actuator virtual deadband. On the other hand, the DO control is implemented

using a simple on/off controller. This approach provides dynamic adaptation to the photosynthesis rate, allowing switching between pH and DO controller depending on the state of the bioreactor. This way, the $CO_2$ resources are efficiently used and the biomass production rate increases.

Further on, a model predictive controller (MPC) (Fig. 23) was developed to optimize the growth of microalgae under natural disturbances by using flow balance analysis (flow of media, extra nitrates and $CO_2$) (Juneja and Murthy, 2018). The controller was designed to consider the economic and environmental constraints associated with the process by incorporating them into the cost function. The experiments were performed using two identical reactors, with 30 L volume each cultivated in Bristol medium (Juneja and Murthy, 2018).

When MPC was applied for microalgae cultivation in PBR systems, under mixotrophic conditions, the results emphasised a significant increase in biomass and lipid productivity (Yoo et al., 2016). In this case, the simulation results of the biomass and lipid from the open-loop optimization were selected as target trajectory for MPC tracking.

The experimental validation of a control strategy for microalgae cultivation in a continuous PBR by using experimental data was proposed by Tebbani et al. (2015). Regulation of biomass concentration was maintained with a linearization control law and a PI controller, to which anti-windup compensation has been added, in order to improve the

<footnote n="1">¹$t_m$ = cultivation time (days), $X_f$ = biomass concentration (mg/l) in last day of cultivation, $X_i$ = biomass concentration (mg/l) at initial cultivation.</footnote>

<footnote n="2">²$X_i$ = nutrient initial concentration (mg/ml), $X_f$ = nutrient final concentration (mg/ml).</footnote>

<footnote n="3">³$B_f$ = final biomass concentration (g), $B_0$ = Initial biomass concentration (g), d = cultivation time (day).</footnote>

<footnote n="4">⁴Biomass productivity of exponential phases in the first 6 days.</footnote>

<footnote n="5">⁵$m_L$ = lipid mass (g), $m_{DAB}$ = dry algal biomass (g).</footnote>

<footnote n="6">⁶Productivity after 10 days.</footnote>

<footnote n="7">⁷Removal after 10 days.</footnote>

<footnote n="8">⁸µ (day) = specific growth rate, $S_N$ = nitrogen concentration in the growth media (mg/l), $\mu_{max}(N)$ = maximal specific growth rate of microalgal cell when the nitrogen concentration is saturated, $K_N$ = half-saturation constant of nitrogen (mg/l).</footnote>

<footnote n="9">⁹$N_1$ = cell abundance (cell per mL of the culture media) at $t_1$, $N_2$ = cell abundance at $t_2$.</footnote>

<footnote n="10">¹⁰$DW_0$, $DW_1$, DW = dry weight biomass at the beggining ($t_0$) and end ($t_1$).</footnote>

<footnote n="11">¹¹$X_n$ = final biomass; $X_0$ = initial biomass and N = culture days.</footnote>

<footnote n="12">¹²VP = volumetric productivity; V = total volume of the culture; A = surface area.</footnote>

<footnote n="13">¹³$N_a$ and $N_b$ = microalgal biomass (mg/l) at times $t_a$ and $t_b$, respectively.</footnote>

<footnote n="14">¹⁴Q = daily flow rate (l/day); C = algae biomass concentration (g TSS/l) and V = volume of the PBR (l).</footnote>

<footnote n="15">¹⁵DIN = the sum of nitrite, nitrate and ammonia; DIP = total dissolved phosphate.</footnote>
<table><caption>**Table 3**
Control strategies applied for microalgae bioprocess.</caption><thead><tr><th>Control strategy</th><th>Application context</th><th>Effects on the process performance</th><th>Reference</th></tr></thead><tbody><tr><td>Simple On/Off controller (Fig. 19b)</td><td>Baseline strategy for regulating pH by injecting CO<sub>2</sub></td><td>Basic pH control; Significant CO<sub>2</sub> loss; Increased valve stress</td><td>Rodríguez-Torres et al. (2021)</td></tr><tr><td>PI Controller (Fig. 19c)</td><td>Regulating pH in two types of bioreactors: raceway and thin-layer systems; Separate day/night operation</td><td>Improved CO<sub>2</sub> consumption efficiency; Reduced valve effort under dynamic conditions</td><td>Rodríguez-Torres et al. (2021)</td></tr><tr><td>Data driven tunned PI Controller</td><td>Regulating pH in lab-scale airlift PBR</td><td>Improved pH reference tracking</td><td>(G. Ifrim et al., 2021)<br>(G. Ifrim et al., 2022)</td></tr><tr><td>PI Controller + Dead-zone Control (Fig. 19d)</td><td>Regulating pH in bioreactor</td><td>Reduced CO<sub>2</sub> loss; Reduced CO<sub>2</sub> associated costs by 60%; No negative effect on the productivity</td><td>Isiramen et al. (2022)</td></tr><tr><td>PI Controller + FF Compensator (Fig. 19g)</td><td>Regulating pH in bioreactor, rejecting disturbances induced by the solar radiation</td><td>Reduced CO<sub>2</sub> consumption and loss; Reduction of the Integrative Absolute Error (IAE) between the pH reference and value</td><td>Fernández et al. (2010)</td></tr><tr><td>Event-based GPC</td><td>Event-driven pH control in experimental raceway photobioreactor</td><td>Reduced actuator stress; Minimized flue gas injection while maintaining pH regulation</td><td>Pawlowski et al. (2014)</td></tr><tr><td>SSOD sampling with PI Controller (Fig. 19e and f)</td><td>Day/night pH control in experimental raceway photobioreactor</td><td>Balance between control effort and performances; Lower CO<sub>2</sub> consumption; improved IAE</td><td>Rodríguez-Miranda et al. (2019)<br>Rodríguez-Miranda et al. (2020)</td></tr><tr><td>Robust PI with linear active disturbance rejection (Fig. 20)</td><td>Regulating pH in raceway photobioreactors</td><td>Improved precision in reference tracking; Compensates for unmodeled system dynamics and disturbances</td><td>Carreño-Zagarra et al. (2019)</td></tr><tr><td>PI Controller of air valve (Fig. 21)</td><td>Regulating DO in systems affected by DO accumulation (raceway systems)</td><td>Increased system performances; Reduced air usage; Reduced production costs</td><td>Barceló-Villalobos et al. (2022)</td></tr><tr><td>Selective pH and DO control (Fig. 22)</td><td>pH and DO control in experimental raceway photobioreactor; Controlling pH during deviations</td><td>Improved biomass production and CO<sub>2</sub> utilization</td><td>Pawlowski et al. (2015)</td></tr></tbody></table>

<table><caption>**Table 3 (continued)**</caption><thead><tr><th>Control strategy</th><th>Application context</th><th>Effects on the process performance</th><th>Reference</th></tr></thead><tbody><tr><td>MPC (Fig. 23)</td><td>and DO under normal pH conditions<br>Improving biomass production by controlling influent dilution rate and substrate concentration in laboratory-scale PBR systems</td><td>Optimized growth conditions under natural disturbances; Increased biomass productivity</td><td>Juneja and Murthy (2018)</td></tr><tr><td>MPC with lipid concentration estimation using unscented Kalman filter</td><td>Improving biomass and lipid production by controlling various process variables in laboratory-scale PBR</td><td>Improved biomass and lipid production</td><td>Yoo et al. (2016)</td></tr><tr><td>PI Controller with biomass estimation using extended Kalman filter (Fig. 24)</td><td>Biomass concentration control in laboratory-scale PBR</td><td>Improved performance (accuracy and overshoot) under disturbances conditions applied to the system and mismatch of the model parameters</td><td>Tebbani et al. (2015)</td></tr><tr><td>Multivariable control</td><td>Regulating DO, CO<sub>2</sub> and pH in laboratory-scale PBR</td><td>Improved biomass productivity when regulating the dissolved CO<sub>2</sub></td><td>(G. A. Ifrim et al., 2019)</td></tr><tr><td>Robust NMPC</td><td>Regulating biomass concentration and dilution rate in simulated bioreactor</td><td>Improved reference tracking accuracy</td><td>Benattia et al. (2015)</td></tr><tr><td>NMPC</td><td>Regulating biomass concentration in laboratory-scale bioreactor</td><td>Maintaining the reactor at an optimal operating point by maximizing the CO<sub>2</sub> fixation</td><td>Tebbani et al. (2014)</td></tr></tbody></table>

system's closed-loop performance. Biomass concentration was estimated with an extended Kalman filter based on available online measurements (pH, incident light intensity and dissolved carbon dioxide concentration). The proposed control structure used is presented in Fig. 24.

A simplified virtual reference feedback tuning method applied to the photosynthetic growth process of microalgae in a PBR was addressed in the study of Ifrim et al. (G. Ifrim et al., 2021). To achieve the study objective, independent controllers were designed based on two control channels, namely dilution-biomass and CO₂ gas flow-pH. In another study conducted by Ifrim et al., it was demonstrated that controlling dissolved CO₂ has the potential to generate higher yields of microalgae biomass with using less culture medium (G. A. Ifrim et al., 2019).

The study conducted by Benattia et al. proposes a robust nonlinear model predictive control (NMPC) algorithm that can maintain optimal growth conditions for the microalgae culture by adjusting the light intensity, pH, and nutrient concentration of the medium in real-time (Benattia et al., 2015). The NMPC algorithm was designed to handle modelling uncertainties, disturbances, and measurement noise in the control system. Another study addressed the implementation of
<figure><img src="image_21.png" /><figcaption>**Fig. 19.** Feedback control of pH in bioreactors: (a) the general feedback control architecture with various controllers described by (b)–(g), (b) simple on/off controller, (c) PI controller used by Rodríguez-Torres et al., (2021); Ifrim et al., (2022), (d) control strategy involving a PI controller and dead-band proposed by Isiramen et al., (2022), (e) SSOD-PI strategy proposed by Rodríguez-Miranda et al., (2019) and validated by Rodríguez-Miranda et al., (2020), (f) PI-SSOD control strategy proposed by Rodríguez-Miranda et al., (2019), (g) PI controller + FF compensator proposed by Fernández et al., 2010.</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>**Fig. 20.** Linear active disturbance rejection control of a photobioreactor proposed by Carreño-Zagarra et al. (Carreño-Zagarra et al., 2019).</figcaption></figure>

Nonlinear Predictive Control combined with an on-line estimation of biomass concentration, using dissolved CO₂ concentration measurements (Tebbani et al., 2014).

It can be summarised that using microalgae bioreactors for biogas production is a complex biotechnological process that raises various issues related to microalgae stain selection, the identification of optimum production/cultivation technologies, finding an equilibrium between nutrients inputs and outputs, establishing and respecting biosecurity protocols that prevent/control the biological material contamination, as well as identifying of proper solutions for automatic monitoring and control of the entire biotechnological process –

including harvesting of the resulted microalgae biomass. Also, considering that most studies were performed in laboratory-based bioreactors, targeting a technological readiness level (TRL) between 1 and 6, scalability can be a limited factor for adopting the new emergent technologies in industrial production. Therefore, it is recommended that future research should focus on increasing the TRL of the existing technologies until the value of 9, therefore, empowering the maturity degree of the solutions and creating direct links with potential investors that focus on valorising start-up niched products. However, the scaling-up process can be challenging and more complex, compared to other biotechnologies, since it requires maintaining uniform production conditions in order to eliminate any possible "noise" that can appear during the trials that target the increase of technical and technological maturity of this synthetic biology-based technology.

It must be mentioned that most of the microalgae bioreactors use wastewater or create their own culture water by adding nutrients, respecting a certain chemical matrix. However, integrating fish-rearing systems with microalgae bioreactors creates added value for both economic activity, contributing to a better valorization of waste products into a second product. Also, it is known that intensive recirculating fish-rearing systems have high operational costs due to the use of electricity for water conditioning purposes – thus, implementing new sustainable methods for boosting profitability, based on circular economy principles, represents the key for economic growth in areas with aquaculture heritage, powered by blue-economic activities.

<figure><img src="image_23.png" /><figcaption>**Fig. 21.** The control strategy proposed by Barceló-Villalobos et al. (Barceló-Villalobos et al., 2022).</figcaption></figure>
<figure><img src="image_24.png" /><figcaption>**Fig. 22.** The selective pH and DO control strategy developed by Pawlowski et al. (Pawlowski et al., 2015).</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>**Fig. 23.** The control scheme and stepwise application of MPC according to Juneja et al. (Juneja and Murthy, 2018).</figcaption></figure>

As growing microalgae was proven to be a synthetic biology-based process, the technical-technological-biological factors nexus is imperative – therefore, automatic process and AI-based solutions are to be used in order to ensure optimum monitoring and control during the entire biological cycle. In terms of control, soft sensors for detecting water quality parameters, as well as image recognition for detecting the algae growth patterns are popular subjects that are pending to be updated from research (TRL 1–3) to development (TRL 4–6) and, further on, to deployment (TRL 7–9), in the near future. Therefore, data from

the present research emphasizes that control system integration for real-time monitoring of essential parameters such as temperature, pH, CO₂ and nitrogen are essential in order to build capacity among operators for dealing with peculiar scenarios that will occur during the production cycle. Since real-time data recording implies data management tools and can, also, be a cost-demanding process due to its dependency on hardware tools, creating a symbiotic relation between classical physical sensors-based monitoring procedures and AI-based soft-sensors methodologies, all connected to a user-friendly interface, can represent a future solution for building capacity for operators while empowering the economic sustainability of the process, respecting, in the same time, the environmental sustainability principles.

Since is a synthetic biology-based process, producing microalgae bioreactors for biogas purposes implies a wide multi-disciplinarity character, linking biologic knowledge to chemistry, aquatic engineering, as well as automatics and computer science. If merged in a synergic manner, this will create resilience to actual threats that characterize the operational management of these integrated systems, therefore preventing nutrient imbalance, contamination risks or critical solutions in terms of outranging parameters such as pH, CO₂ and temperature, that can lead to poor economic feasibility.

Public perception and acceptance is an important part of adopting and widening the concept of using fish-rearing systems wastewater in producing microalgae bioreactors for biogas purposes. Therefore, fish-rearing facilities must be aware of the environmental and financial benefits that this integrated technology is able to offer – therefore, firstly, social studies are recommended to be performed, in future, in order to map fish farm owners' perceptions and knowledge related to this solution, as well as possible barriers and drawbacks in adopting this integrated technology. The results of these studies should serve as support tools for decision-makers in their attempt to shift from a blue to a green economy, in the spirit of the European Green Deal. Therefore, support policies, at the national and regional level should include incentives such as financial subsidies and tax breaks, but should also be joined by regulatory frameworks, as well as quality and good practices standards. Also, campaigns for recognition of renewable energy credits, considering the actual global framework, are recommended to be performed. Adopting integrated technologies will strengthen the collaborative resource management between aquaculture and energy production sectors and policies for facilitating the access to technology for the new investors in this economic niche must developed, based on innovative hubs – this must include also programs for business incubators and accelerators development.

## 6. Conclusions

The use of aquaculture wastewater effluents for microalgae cultivation is a niche subject, however, the bibliometric analysis performed in the present study revealed that there is a constant upward trend in the research and development area related to the subject. This was attributed to the ongoing market demand for biofuels and the need for environmentally friendly technologies which rely on nutrient and waste recovery.

<figure><img src="image_26.png" /><figcaption>**Fig. 24.** The control scheme and stepwise application of MPC according to Tebbani et al. (Tebbani et al., 2015).</figcaption></figure>
The scientific literature analysis highlighted the great potential of aquaculture wastewater effluents to be used as growth media for microalgae biomass production, due to superior performance in lipid and carbohydrate productivity.

The advanced bibliometric forecasting models that reveal all analysed dimensions within the current publication, in relation to the algae-circular economy nexus, considering books, research and review articles, emphasize that there is a continuous high upward trend in the next years, as research communities' subjects of interest. However, it seems that dimension D2 for research articles and books could be less popular, compared to the rest of the analysed dimensions (D1 and D3). Also, ARIMA models manage to offer great accuracy in terms of synthetic forecasting, associated with each of the 3 analysed dimensions.

Most control strategies developed for microalgal production systems found in the literature aim at controlling the pH in the bioreactor by injecting CO₂ or flue gases, while few other papers consider manipulating the dissolved oxygen. However, even if the authors consider their control strategy suitable for wastewater treatment, none of them quantify the treatment efficiency nor use it in the control strategy. Thus, the need for higher level control arises to not only track pH or DO references, but to maximize the treatment efficiency of the bioreactor.

## CRediT authorship contribution statement

**Ira-Adeline Simonov:** Writing – original draft, Investigation, Formal analysis, Data curation, Conceptualization. **Marian Barbu:** Validation, Supervision, Investigation, Formal analysis, Conceptualization. **Iulian Vasiliev:** Writing – original draft, Formal analysis, Data curation. **Larisa Condrachi:** Writing – original draft, Investigation, Formal analysis, Data curation. **Mariana Titica:** Writing – original draft, Visualization, Formal analysis, Data curation. **George Ifrim:** Writing – original draft, Investigation, Data curation. **Dragos Cristea:** Writing – original draft, Methodology, Formal analysis, Data curation. **Florian Marcel Nuță:** Writing – review & editing, Visualization, Project administration, Investigation. **Ștefan-Mihai Petrea:** Validation, Investigation, Formal analysis, Data curation.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

The present research was supported by the project An Integrated System for the Complex Environmental Research and Monitoring in the Danube River Area, REXDAN, SMIS code 127065, co-financed by the European Regional Development Fund through the Competitiveness Operational Programme 2014–2020, contract no. 309/July 10, 2021.

## Data availability

Data will be made available on request.

## References

Abdul Hamid, S.H., Lananan, F., Din, W.N.S., Lam, S.S., Khatoon, H., Endut, A., Jusoh, A., 2014. Harvesting microalgae, Chlorella sp. by bio-flocculation of Moringa oleifera seed derivatives from aquaculture wastewater phytoremediation. Int. Biodeterior. Biodegrad. 95, 270–275. https://doi.org/10.1016/j.ibiod.2014.06.021.

Abo, B.O., Odey, E.A., Bakayoko, M., Kalakodio, L., 2019. Microalgae to biofuels production: a review on cultivation, application and renewable energy. Rev. Environ. Health 34 (1), 91–99. https://doi.org/10.1515/reveh-2018-0052.

Abreu, A.P., Morais, R.C., Teixeira, J.A., Nunes, J., 2022. A comparison between microalgal autotrophic growth and metabolite accumulation with heterotrophic, mixotrophic and photoheterotrophic cultivation modes. Renew. Sustain. Energy Rev. 159, 112247. https://doi.org/10.1016/j.rser.2022.112247.

Adedire, O., Sadiku, Y., Adedire, O.O., Collina, K., Oladejo, A.O., Sikiru, G.K., 2022. Data driven mathematical models for forecast of COVID-19 disease in Nigeria. Journal of Computing Research and Innovation 7 (1). https://doi.org/10.24191/jcrinn.v7i1.258. Article 1.

Ahmad, A.L., Chin, J.Y., Mohd Harun, M.H.Z., Low, S.C., 2022. Environmental impacts and imperative technologies towards sustainable treatment of aquaculture wastewater: a review. J. Water Proc. Eng. 46, 102553. https://doi.org/10.1016/j.jwpe.2021.102553.

Allied Marked Research. (n.d.). Microalgae Market Size, Share, Growth | Report Forecast – 2028. Allied Market Research. Retrieved 27 March 2023, from https://www.alliedmarketresearch.com/microalgae-market-A13419.

Andreotti, V., Solimeno, A., Rossi, S., Ficara, E., Marazzi, F., Mezzanotte, V., García, J., 2020. Bioremediation of aquaculture wastewater with the microalgae Tetraselmis suecica: semi-continuous experiments, simulation and photo-respirometric tests. Sci. Total Environ. 738, 139859. https://doi.org/10.1016/j.scitotenv.2020.139859.

Ansari, F.A., Singh, P., Guldhe, A., Bux, F., 2017. Microalgal cultivation using aquaculture wastewater: integrated biomass generation and nutrient remediation. Algal Res. 21, 169–177. https://doi.org/10.1016/j.algal.2016.11.015.

Barceló-Villalobos, M., Hoyo, À., Rodríguez-Miranda, E., Guzmán, J.L., Acién, F.G., 2022. A new control strategy to improve the mass transfer capacity and reduce air injection costs in raceway reactors. New Biotechnology 70, 49–56. https://doi.org/10.1016/j.nbt.2022.04.005.

Benattia, S.E., Tebbani, S., Dumur, D., 2015. Robust nonlinear model predictive control for regulation of microalgae culture in a continuous photobioreactor. IFAC-PapersOnLine 48 (11), 192–197. https://doi.org/10.1016/j.ifacol.2015.09.182.

Bórawski, P., Beldycka-Bórawska, A., Szymańska, E.J., Jankowski, K.J., Dubis, B., Dunn, J.W., 2019. Development of renewable energy sources market and biofuels in the European Union. J. Clean. Prod. 228, 467–484. https://doi.org/10.1016/j.jclepro.2019.04.242.

Calderini, M.L., Stevčić, C., Taipale, S., Pulkkinen, K., 2021. Filtration of Nordic recirculating aquaculture system wastewater: effects on microalgal growth, nutrient removal, and nutritional value. Algal Res. 60, 102486. https://doi.org/10.1016/j.algal.2021.102486.

Cao, S., Teng, F., Lv, J., Zhang, Q., Wang, T., Zhu, C., Li, X., Cai, Z., Xie, L., Tao, Y., 2022. Performance of an immobilized microalgae-based process for wastewater treatment and biomass production: nutrients removal, lipid induction, microalgae harvesting and dewatering. Bioresour. Technol. 356, 127298. https://doi.org/10.1016/j.biortech.2022.127298.

Carreno-Zagarra, J.J., Guzmán, J.L., Moreno, J.C., Villarizzari, R., 2019. Linear active disturbance rejection control for a raceway photobioreactor. Control Eng. Pract. 85, 271–279. https://doi.org/10.1016/j.conengprac.2019.02.007.

Castillo, T., Ramos, D., García-Beltrán, T., Brito-Bazan, M., Galindo, E., 2021. Mixotrophic cultivation of microalgae: an alternative to produce high-value metabolites. Biochem. Eng. J. 176, 108183. https://doi.org/10.1016/j.bej.2021.108183.

Cecchin, M., Benfatto, S., Griggio, F., Mori, A., Cazzaniga, S., Vitulo, N., Delledonne, M., Ballottari, M., 2018. Molecular basis of autotrophic vs mixotrophic growth in Chlorella sorokiniana. Sci. Rep. 8 (1). https://doi.org/10.1038/s41598-018-24979-8. Article 1.

Chang, W., Li, Y., Qu, Y., Liu, Y., Zhang, G., Zhao, Y., Liu, S., 2022. Mixotrophic cultivation of microalgae to enhance the biomass and lipid production with synergistic effect of red light and phytohormone IAA. Renew. Energy 187, 819–828. https://doi.org/10.1016/j.renene.2022.01.018.

Cheng, P., Shan, S., Zhu, Z., Liu, K., Namsaraev, Z., Dubovskyi, I., Xu, Q., 2023. The role of microalgae culture modes in aquaculture: a brief opinion. Front. Bioeng. Biotechnol. 11. https://doi.org/10.3389/fbioe.2023.1196948.

Chodakowska, E., Nazarko, J., Nazarko, L., Rabayah, H.S., Abendeh, R.M., Alawneh, R., 2023. ARIMA models in solar radiation forecasting in different geographic locations. Energies 16 (13). https://doi.org/10.3390/en16135029. Article 13.

Debeni Devi, N., Sun, X., Hu, B., Goud, V.V., 2023. Bioremediation of domestic wastewater with microalgae-cyanobacteria co-culture by nutritional balance approach and its feasibility for biodiesel and animal feed production. Chem. Eng. J. 454, 140197. https://doi.org/10.1016/j.cej.2022.140197.

Deng, Y., Fan, H., Wu, S., 2023. A hybrid ARIMA-LSTM model optimized by BP in the forecast of outpatient visits. J. Ambient Intell. Hum. Comput. 14 (5), 5517–5527. https://doi.org/10.1007/s12652-020-02602-x.

Ding, N., Li, C., Wang, T., Guo, M., Mohsin, A., Zhang, S., 2021a. Evaluation of an enclosed air-lift photobioreactor (ALPBR) for biomass and lipid biosynthesis of microalgal cells grown under fluid-induced shear stress. Biotechnol. Biotechnol. Equip. 35 (1), 139–149. https://doi.org/10.1080/13102818.2020.1856717.

Ding, N., Li, C., Wang, T., Guo, M., Mohsin, A., Zhang, S., 2021b. Evaluation of an enclosed air-lift photobioreactor (ALPBR) for biomass and lipid biosynthesis of microalgal cells grown under fluid-induced shear stress. Biotechnol. Biotechnol. Equip. 35 (1), 139–149. https://doi.org/10.1080/13102818.2020.1856717.

Dourou, M., Tsolcha, O., Tekerlekopoulou, A., Bokas, D., Aggelis, G., 2018. Fish farm effluents are suitable growth media for Nannochloropsis gaditana, a polyunsaturated fatty acid producing microalga. Eng. Life Sci. 18. https://doi.org/10.1002/elsc.201800064.

Enwereuzoh, U., Harding, K., Low, M., 2021. Microalgae cultivation using nutrients in fish farm effluent for biodiesel production. South Afr. J. Chem. Eng. 37, 46–52. https://doi.org/10.1016/j.sajce.2021.03.007.

Fernández, I., Peña, J., Guzman, J.L., Berenguel, M., Acién, F.G., 2010. Modelling and control issues of pH in tubular photobioreactors. IFAC Proc. Vol. 43 (6), 186–191. https://doi.org/10.3182/20100707-3-BE-2012.0046.

Food and Agriculture Organization, 2022. The State of World Fisheries and Aquaculture 2022. FAO. https://doi.org/10.4060/cc0461en.
Food and Agriculture Organization of the United Nations. 2021. Global Seaweeds and Microalgae Production, pp. 1–172.

Ganesh Saratale, R., Ponnusamy, V.K., Jeyakumar, R.B., Sirohi, R., Piechota, G., Shobana, S., Dharmaraja, J., Lay, C., Dattatraya Saratale, G., Seung Shin, H., Ashokkumar, V., 2022. Microalgae cultivation strategies using cost-effective nutrient sources: recent updates and progress towards biofuel production. *Bioresour. Technol.* 361, 127691. https://doi.org/10.1016/j.biortech.2022.127691.

Gao, F., Li, C., Yang, Z.-H., Zeng, G.-M., Feng, L.-J., Liu, J., Liu, M., Cai, H., 2016. Continuous microalgae cultivation in aquaculture wastewater by a membrane photobioreactor for biomass production and nutrients removal. Ecol. Ecol. 92, 55–61. https://doi.org/10.1016/j.ecoleng.2016.03.046.

Guerrero-Cabrera, L., Rueda, J.A., García-Lozano, H., Navarro, A.K., 2014. Cultivation of monoraphidium sp., Chlorella sp. and Scenedesmus sp. algae in batch culture using nile tilapia effluent. *Bioresour. Technol.* 161, 455–460. https://doi.org/10.1016/j.biortech.2014.03.127.

Guldhe, A., Ansari, F.A., Singh, P., Bux, F., 2017. Heterotrophic cultivation of microalgae using aquaculture wastewater: a biorefinery concept for biomass production and nutrient remediation. Ecol. Ecol. 99, 47–53. https://doi.org/10.1016/j.ecoleng.2016.11.013.

Guo, Z., Liu, Y., Guo, H., Yan, S., Mu, J., 2013. Microalgae cultivation using an aquaculture wastewater as growth medium for biomass and biofuel production. *J. Environ. Sci.* 25, 585–588. https://doi.org/10.1016/S1001-0742(14)60632-X.

Han, P., Lu, Q., Fan, L., Zhou, W., 2019. A review on the use of microalgae for sustainable aquaculture. *Appl. Sci.* 9 (11). https://doi.org/10.3390/app9112377. Article 11.

Hawrot-Paw, M., Koniuszy, A., Galczyńska, M., Zajac, G., Szyszlak-Barglowicz, J., 2020. Production of microalgae biomass using aquaculture wastewater as growth medium. *Water* 12 (1). https://doi.org/10.3390/w12010106. Article 1.

Ifrim, G.A., Titica, M., Deppe, S., Frahm, B., Barbu, M., Caraman, S., 2019. Multivariable control strategy for the photosynthetic cultures of microalgae. 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC), pp. 218–223. https://doi.org/10.1109/ICSTCC.2019.8886109.

Ifrim, G., Barbu, M., Horincar, G., Titica, M., 2021. Data-driven multivariable control of a microalgae growth process. 2021 25th International Conference on System Theory, Control and Computing (ICSTCC), pp. 321–326. https://doi.org/10.1109/ICSTCC52150.2021.9607111.

Ifrim, G., Horincar, G., Condrachi, L., Titica, M., Guzman, J.L., 2022. Fictitious reference iterative tuning control of the pH in a photobioreactor. 2022 26th International Conference on System Theory, Control and Computing (ICSTCC), pp. 519–523. https://doi.org/10.1109/ICSTCC54526.2022.9931867.

Isiramen, O.E., Bahri, P.A., Moheimani, N.R., Vadiveloo, A., Shayesteh, H., Parlevliet, D. A., 2022. Improving pH control and carbon dioxide utilisation efficiency in microalgae cultivation systems with the use of a Proportional-integral + dead-zone control strategy. *Bioresour. Technol. Rep.* 17, 100917. https://doi.org/10.1016/j.biteb.2021.100917.

Jansen, M.D., Dong, H.T., Mohan, C.V., 2019. Tilapia lake virus: a threat to the global tilapia industry? *Rev. Aquacult.* 11 (3), 725–739. https://doi.org/10.1111/raq.12254.

Jareonsin, S., Pumas, C., 2021. Advantages of heterotrophic microalgae as a host for phytochemicals production. *Front. Bioeng. Biotechnol.* 9. https://www.frontiersin.org/articles/10.3389/fbioe.2021.628597.

Javed, F., Rehman, F., Khan, A.U., Fazal, T., Hafeez, A., Rashid, N., 2022. Real textile industrial wastewater treatment and biodiesel production using microalgae. *Biomass Bioenergy* 165, 106559. https://doi.org/10.1016/j.biombioe.2022.106559.

Juneja, A., Murthy, G.S., 2018. Model predictive control coupled with economic and environmental constraints for optimum algal production. *Bioresour. Technol.* 250, 556–563. https://doi.org/10.1016/j.biortech.2017.11.047.

Khan, S., Alghulaiakh, H., 2020. ARIMA model for accurate time series stocks forecasting. *Int. J. Adv. Comput. Sci. Appl.* 11 (7). https://doi.org/10.14569/IJACSA.2020.0110765. Article 7.

Khatoon, H., Banerjee, S., Syakir Syahiran, M., Mat Noordin, N. Bt, Munafi Ambok Bolong, A., Endut, A., 2016. Re-use of aquaculture wastewater in cultivating microalgae as live feed for aquaculture organisms. Desalination Water Treat. 57 (60), 29295–29302. https://doi.org/10.1080/19443994.2016.1156030.

Kumaran, M., Palanisamy, K.M., Bhuyar, P., Maning, G.P., Rahim, M. H. Ab, Govindan, N., 2023. Agriculture of microalgae Chlorella vulgaris for polyunsaturated fatty acids (PUFAs) production employing palm oil mill effluents (POME) for future food, wastewater, and energy nexus. *Energy Nexus* 9, 100169. https://doi.org/10.1016/j.nexus.2022.100169.

Kusmayadi, A., Huang, C.-Y., Kit Leong, Y., Lu, P.-H., Yen, H.-W., Lee, D.-J., Chang, J.-S., 2023. Integration of microalgae cultivation and anaerobic co-digestion with dairy wastewater to enhance bioenergy and biochemicals production. *Bioresour. Technol.* 376, 128858. https://doi.org/10.1016/j.biortech.2023.128858.

Lam, M.K., Lee, K.T., 2012. Microalgae biofuels: a critical review of issues, problems and the way forward. *Biotechnol. Adv.* 30 (3), 673–690. https://doi.org/10.1016/j.biotechadv.2011.11.008.

Lananan, F., Abdul Hamid, S.H., Din, W.N.S., Ali, N., Khatoon, H., Jusoh, A., Endut, A., 2014. Symbiotic bioremediation of aquaculture wastewater in reducing ammonia and phosphorus utilizing Effective Microorganism (EM-1) and microalgae (Chlorella sp.). *Int. Biocenter. Biodegrad.* 95, 127–134. https://doi.org/10.1016/j.ibiod.2014.06.013.

Lee, J.-C., Joo, J.-H., Chun, B.H., Moon, K., Song, S.H., Kim, Y.J., Lee, S.M., Lee, A.H., 2022. Isolation and screening of indigenous microalgae species for domestic and livestock wastewater treatment, biodiesel production, and carbon sequestration. *J. Environ. Manag.* 318, 115648. https://doi.org/10.1016/j.jenvman.2022.115648.

Li, S., Mubashar, M., Qin, Y., Nie, X., Zhang, X., 2022. Aquaculture waste nutrients removal using microalgae with floating permeable nutrient uptake system (FPNUS). *Bioresour. Technol.* 347, 126338. https://doi.org/10.1016/j.biortech.2021.126338.

Lim, Y.A., Ilankoon, I.M.S.K., Chong, M.N., Foo, S.C., 2023. Improving microalgae growth and carbon capture through micro-size bubbles generation in flat-panel photobioreactors: impacts of different gas sparger designs on mixing performance. *Renew. Sustain. Energy Rev.* 171, 113001. https://doi.org/10.1016/j.rser.2022.113001.

Limrujiwat, K., Suphan, S., Sujarit, K., Lomthong, T., Khetkorn, W., 2022. Optimizing parameters for the stability of alginate encapsulation to support microalgae growth and nutrient removal in shrimp wastewater using response surface methodology. *Biocatal. Agric. Biotechnol.* 43, 102419. https://doi.org/10.1016/j.bcab.2022.102419.

Liu, Y., Lv, J., Feng, J., Liu, Q., Nan, F., Xie, S., 2018. Treatment of real aquaculture wastewater from a fishery utilizing phytoremediation with microalgae. *J. Appl. Chem. Biotechnol.* 94. https://doi.org/10.1002/jctb.5837.

Lu, W., Alam, Md A., Liu, S., Xu, J., Saldivar, R.P., 2020. Critical processes and variables in microalgae biomass production coupled with bioremediation of nutrients and CO2 from livestock farms: a review. *Sci. Total Environ.* 716, 135247. https://doi.org/10.1016/j.scitotenv.2019.135247.

Lugo, L.A., Thorarinasdottir, R.I., Bjornsson, S., Palsson, O.P., Skulason, H., Johannsson, S., Brynjolfsson, S., 2020. Remediation of aquaculture wastewater using the microalgae Chlorella sorokiniana. *Water* 12 (11). https://doi.org/10.3390/w12113144. Article 11.

Luo, J., Gong, Y., 2023. Air pollutant prediction based on ARIMA-WOA-LSTM model. *Atmos. Pollut. Res.* 14 (6), 101761. https://doi.org/10.1016/j.apr.2023.101761.

Nazalopra, S., Kumar, D., Singh, B., Sachan, P.K., 2021. Biofuels and their sources of production: a review on cleaner sustainable alternative against conventional fuel, in the framework of the food and energy nexus. *Energy Nexus* 4, 100036. https://doi.org/10.1016/j.nexus.2021.100036.

Malibari, R., Sayegh, F., Elazazy, A.M., Baeshen, M.N., Dourou, M., Aggelis, G., 2018. Reuse of shrimp farm wastewater as growth medium for marine microalgae isolated from Red Sea - jeddah. *J. Clean. Prod.* 198, 160–169. https://doi.org/10.1016/j.jclepro.2018.07.037.

Mehariya, S., Plöhn, M., Jablonski, P., Stagge, S., Jönsson, L.J., Funk, C., 2023. Biopolymer production from biomass produced by Nordic microalgae grown in wastewater. *Bioresour. Technol.* 376, 128901. https://doi.org/10.1016/j.biortech.2023.128901.

Meril, D., Piliyan, R., Perumal, S., Sundarraj, D.K., Binesh, A., 2022. Efficacy of alginate immobilized microalgae in the bioremediation of shrimp aquaculture wastewater. *Process Biochem.* 122, 196–202. https://doi.org/10.1016/j.procbio.2022.08.030.

Mohd Nasir, A.S., Mohamed, B., Li, L.Y., 2023. Comparative life cycle assessment of co-pyrolysing sewage sludge and wastewater-grown microalgae for biofuel production. *Resour. Conserv. Recycl.* 190, 106780. https://doi.org/10.1016/j.resconrec.2022.106780.

Mohd Nasir, N., Mohd Yunos, F.H., Wan Jusoh, H.H., Mohammad, A., Lam, S.S., Jusoh, A., 2019. Subtopic: advances in water and wastewater treatment harvesting of Chlorella sp. microalgae using Aspergillus Niger as bio-flocculant for aquaculture wastewater treatment. *J. Environ. Manag.* 249, 109373. https://doi.org/10.1016/j.jenvman.2019.109373.

Muzayanah, I.F.U., Lean, H.H., Hartono, D., Indraswari, K.D., Partama, R., 2022. Population density and energy consumption: a study in Indonesian provinces. *Heliyon* 8 (9), e10634. https://doi.org/10.1016/j.heliyon.2022.e10634.

Nazalopra, E.K., Moheimani, N.R., Ennaceri, H., 2022. Biodiesel production from wet microalgae: progress and challenges. *Algal Res.* 68, 102902. https://doi.org/10.1016/j.algal.2022.102902.

Nie, X., Mubashar, M., Zhang, S., Qin, Y., Zhang, X., 2020. Current progress, challenges and perspectives in microalgae-based nutrient removal for aquaculture waste: a comprehensive review. *J. Clean. Prod.* 277, 124209. https://doi.org/10.1016/j.jclepro.2020.124209.

Novoveská, L., Nielsen, S.L., Eroldoğan, O.T., Haznedaroglu, B.Z., Rinkevich, B., Fazi, S., Robbens, J., Vasquez, M., Einarsson, H., 2023. Overview and challenges of large-scale cultivation of photosynthetic microalgae and cyanobacteria. *Mar. Drugs* 21 (8), 445. https://doi.org/10.3390/md21080445.

Pawlowski, A., Mendoza, J.L., Guzmán, J.L., Berenguel, M., Acién, F.G., Dormido, S., 2014. Effective utilization of flue gases in raceway reactor with event-based pH control for microalgae culture. *Bioresour. Technol.* 170, 1–9. https://doi.org/10.1016/j.biortech.2014.07.088.

Pawlowski, A., Mendoza, J.L., Guzmán, J.L., Berenguel, M., Acién, F.G., Dormido, S., 2015. Selective pH and dissolved oxygen control strategy for a raceway reactor within an event-based approach. *Control Eng. Pract.* 44, 209–218. https://doi.org/10.1016/j.conengprac.2015.08.004.

Peter, A.P., Koyande, A.K., Chew, K.W., Ho, S.-H., Chen, W.-H., Chang, J.-S., Krishnamoorthy, R., Banat, F., Show, P.L., 2022. Continuous cultivation of microalgae in photobioreactors as a source of renewable energy: current status and future challenges. *Renew. Sustain. Energy Rev.* 154, 111852. https://doi.org/10.1016/j.rser.2021.111852.

Phong Vo, H.N., Ngo, H.H., Guo, W., Hong Nguyen, T.M., Liu, Y., Liu, Y., Nguyen, D.D., Chang, S.W., 2019. A critical review on designs and applications of microalgae-based photobioreactors for pollutants treatment. *Sci. Total Environ.* 651, 1549–1568. https://doi.org/10.1016/j.scitotenv.2018.09.282.

Priya, Deora, P.S., Verma, Y., Muhal, R.A., Goswami, C., Singh, T., 2022. Biofuels: an alternative to conventional fuel and energy source. *Mater. Today Proc.* 48, 1178–1184. https://doi.org/10.1016/j.matpr.2021.08.227.
Qi, S., Wang, Z., Hu, Y., Lei, J., Zhan, X., Stengel, D.B., 2023. Selective enrichment of auto-floating microalgae for wastewater bioremediation and biofuel/bioproduct production. Algal Res. 69, 102911. https://doi.org/10.1016/j.algal.2022.102911.

Qian, Z., Na, L., Bao-Long, W., Tao, Z., Peng-Fei, M., Wei-Xiao, Z., Sraboni, N.Z., Zheng, M., Ying-Qi, Z., Liu, Y., 2022. Capabilities and mechanisms of microalgae on nutrients and florfenicol removing from marine aquaculture wastewater. J. Environ. Manag. 320, 115673. https://doi.org/10.1016/j.jenvman.2022.115673.

Razzak, S.A., Lucky, R.A., Hossain, M.M., deLasa, H., 2022. Valorization of microalgae biomass to biofuel production: a review. Energy Nexus 7, 100139. https://doi.org/10.1016/j.nexus.2022.100139.

Rehman, M., Kesharvani, S., Dwivedi, G., Gidwani Suneja, K., 2022. Impact of cultivation conditions on microalgae biomass productivity and lipid content. Mater. Today Proc. 56, 282-290. https://doi.org/10.1016/j.matpr.2022.01.152.

Rodríguez-Miranda, E., Beschi, M., Guzmán, J.L., Berenguel, M., Visoli, A., 2019. Daytime/nighttime event-based PI control for the pH of a microalgae raceway reactor. Processes 7 (5). https://doi.org/10.3390/pr7050247. Article 5.

Rodríguez-Miranda, E., Guzmán, J.L., Berenguel, M., Acién, F.G., Visoli, A., 2020. Diurnal and nocturnal pH control in microalgae raceway reactors by combining classical and event-based control approaches. Water Sci. Technol. 82 (6), 1155-1165. https://doi.org/10.2166/wst.2020.260.

Rodríguez-Torres, M.J., Morillas-España, A., Guzmán, J.L., Acién, F.G., 2021. Modelling and pH control in raceway and thin-layer photobioreactors for wastewater treatment. Energies 14 (4). https://doi.org/10.3390/en14041099. Article 4.

Ruiz, J., Wijffels, R.H., Dominguez, M., Barbosa, M.J., 2022. Heterotrophic vs autotrophic production of microalgae: bringing some light into the everlasting cost controversy. Algal Res. 64, 102698. https://doi.org/10.1016/j.algal.2022.102698.

Saadaoui, I., Rasheed, R., Aguilar, A., Cherif, M., Aljabri, H., Sayadi, S., Manning, S., 2021. Microalgal-based feed: promising alternative feedstocks for livestock and poultry production. J. Anim. Sci. Biotechnol. 12, 76. https://doi.org/10.1186/s40104-021-00593-z.

Sánchez-Zurano, A., Guzmán, J.L., Acién, F.G., Fernández-Sevilla, J.M., 2021. An interactive tool for simulation of biological models into the wastewater treatment with microalgae. Front. Environ. Sci. 9. https://www.frontiersin.org/articles/10.3389/fenvs.2021.721324.

Sfez, S., Van Den Hende, S., Taelman, S.E., De Meester, S., Dewulf, J., 2015. Environmental sustainability assessment of a microalgae raceway pond treating aquaculture wastewater: from up-scaling to system integration. Bioresour. Technol. 190, 321-331. https://doi.org/10.1016/j.biortech.2015.04.088.

Shuba, E.S., Kifle, D., 2018. Microalgae to biofuels: 'Promising' alternative and renewable energy, review. Renew. Sustain. Energy Rev. 81, 743-755. https://doi.org/10.1016/j.rsr.2017.08.042.

Sirohi, R., Joun, J., Lee, J.Y., Yu, B.S., Sim, S.J., 2022. Waste mitigation and resource recovery from food industry wastewater employing microalgae-bacterial consortium. Bioresour. Technol. 352, 127129. https://doi.org/10.1016/j.biortech.2022.127129.

Sun, J., Li, N., Yang, P., Zhang, Y., Yuan, Y., Lu, X., Zhang, H., 2020. Simultaneous antibiotic degradation, nitrogen removal and power generation in a microalgae-bacteria powered biofuel cell designed for aquaculture wastewater treatment and energy recovery. Int. J. Hydrogen Energy 45 (18), 10871-10881. https://doi.org/10.1016/j.ijhydene.2020.02.029.

Sylaios, G., Iliadis, L., 2006. Time-series modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software. Environ. Model. Software. https://www.academia.edu/2634283/Time_series_modeling_of_fishery_landings_using_A_RIMA_models_and_Fuzzy_Expected_Intervals_software.

Takabe, Y., Hidaka, T., Tsumori, J., Minamiyama, M., 2016. Effects of hydraulic retention time on cultivation of indigenous microalgae as a renewable energy source using secondary effluent. Bioresour. Technol. 207, 399-408. https://doi.org/10.1016/j.biortech.2016.01.132.

Tebbani, S., Lopes, F., Becerra Celis, G., 2015. Nonlinear control of continuous cultures of Porphyridium purpureum in a photobioreactor. Chem. Eng. Sci. 123, 207-219. https://doi.org/10.1016/j.ces.2014.11.016.

Tebbani, S., Lopes, F., Filali, R., Dumur, D., Pareau, D., 2014. Nonlinear predictive control for maximization of CO2 bio-fixation by microalgae in a photobioreactor. Bioproc. Biosyst. Eng. 37 (1), 83-97. https://doi.org/10.1007/s00449-013-0928-0.

Tejido-Nunez, Y., Aymerich, E., Sancho, L., Refardt, D., 2020. Co-cultivation of microalgae in aquaculture water: interactions, growth and nutrient removal efficiency at laboratory- and pilot-scale. Algal Res. 49, 101940. https://doi.org/10.1016/j.algal.2020.101940.

They, N.H., Amado, A.M., Cotner, J.B., 2017. Redfield ratios in inland waters: higher biological control of C:N:P ratios in tropical semi-arid high water residence time lakes. Front. Microbiol. 8. https://www.frontiersin.org/articles/10.3389/fmicb.2017.01505.

Tian, J., Liu, Y., Zheng, W., Yin, L., 2022. Smog prediction based on the deep belief—BP neural network model (DBN-BP). Urban Clim. 41, 101078. https://doi.org/10.1016/j.jclim.2021.101078.

Tom, A.P., Jayakumar, J.S., Biju, M., Somarajan, J., Ibrahim, M.A., 2021. Aquaculture wastewater treatment technologies and their sustainability: a review. Energy Nexus 4, 100022. https://doi.org/10.1016/j.nexus.2021.100022.

Tossavainen, M., Lahti, K., Edelmann, M., Eskola, R., Lampi, A.-M., Piironen, V., Korvonen, P., Ojala, A., Romantschuk, M., 2019. Integrated utilization of microalgae cultured in aquaculture wastewater: wastewater treatment and production of valuable fatty acids and tocopherols. J. Appl. Phycol. 31 (3), 1753-1763. https://doi.org/10.1007/s10811-018-1689-6.

Umar, M., Riaz, Y., Yousaf, I., 2022. Impact of Russian-Ukraine war on clean energy, conventional energy, and metal markets: evidence from event study approach. Resour. Policy 79, 102966. https://doi.org/10.1016/j.resourpol.2022.102966.

United Nations, 2022. The Sustainable Development Goals Report.

United Nations Department of Economic and Social Affairs, 2022. World Population Prospects 2022: Summary of Results, vol. 52.

Vale, M.A., Ferreira, A., Pires, J.C.M., Gonçalves, A.L., 2020. Chapter 17—CO2 capture using microalgae. In: Rahimpour, M.R., Farsi, M., Makarem, M.A. (Eds.), Advances in Carbon Capture. Woodhead Publishing, pp. 381-405. https://doi.org/10.1016/B978-0-12-819657-1.00017-7.

Vasitha, S., Balakrishnan, D., Manivannan, A., Rai, M.P., 2023. Microalgae on distillery wastewater treatment for improved biodiesel production and cellulose nanofiber synthesis: a sustainable biorefinery approach. Chemosphere 315, 137666. https://doi.org/10.1016/j.chemosphere.2023.137666.

Vidotti, A.D.S., Riaño-Pachón, D.M., Mattiello, L., Giraldi, L.A., Winck, F.V., Franco, T.T., 2020. Analysis of autotrophic, mixotrophic and heterotrophic phenotypes in the microalgae Chlorella vulgaris using time-resolved proteomics and transcriptomics approaches. Algal Res. 51, 102060. https://doi.org/10.1016/j.algal.2020.102060.

Wu, X., Wu, H., Zhang, A., Sekou, K., Li, Z., Ye, J., 2022. Influence of polystyrene microplastics on levofloxacin removal by microalgae from freshwater aquaculture wastewater. J. Environ. Manag. 301, 113865. https://doi.org/10.1016/j.jenvman.2021.113865.

Xie, Y., Khoo, K.S., Chew, K.W., Devadas, V.V., Phang, S.J., Lim, H.R., Rajendran, S., Show, P.L., 2022. Advancement of renewable energy technologies via artificial and microalgae photosynthesis. Bioresour. Technol. 363, 127830. https://doi.org/10.1016/j.biortech.2022.127830.

Yadav, G., Dash, S.K., Sen, R., 2019. A biorefinery for valorization of industrial wastewater and flue gas by microalgae for waste mitigation, carbon-dioxide sequestration and algal biomass production. Sci. Total Environ. 688, 129-135. https://doi.org/10.1016/j.scitotenv.2019.06.024.

Yoo, S.J., Jeong, D.H., Kim, J.H., Lee, J.M., 2016. Optimization of microalgal photobioreactor system using model predictive control with experimental validation. Bioproc. Biosyst. Eng. 39 (8), 1235-1246. https://doi.org/10.1007/s00449-016-1602-0.

Yu, H., Kim, J., Rhee, C., Shin, J., Shin, S.G., Lee, C., 2022a. Effects of different pH control strategies on microalgae cultivation and nutrient removal from anaerobic digestion effluent. Microorganisms 10 (2), 357. https://doi.org/10.3390/microorganisms10020357.

Yu, L., Li, T., Ma, J., Zhao, Q., Wensel, P., Lian, J., Chen, S., 2022b. A kinetic model of heterotrophic and mixotrophic cultivation of the potential biofuel organism microalgae Chlorella sorokiniana. Algal Res. 64, 102701. https://doi.org/10.1016/j.algal.2022.102701.

Zhan, J., Rong, J., Wang, Q., 2017. Mixotrophic cultivation, a preferable microalgae cultivation mode for biomass/bioenergy production, and bioremediation, advances and prospect. Int. J. Hydrogen Energy 42 (12), 8505-8517. https://doi.org/10.1016/j.ijhydene.2016.12.021.

Zhang, J., Xia, A., Yao, D., Guo, X., Lam, S.S., Huang, Y., Zhu, X., Zhu, X., Liao, Q., 2022. Removal of oxytetracycline and ofloxacin in wastewater by microalgae-bacteria symbiosis for bioenergy production. Bioresour. Technol. 363, 127891. https://doi.org/10.1016/j.biortech.2022.127891.

Zhao, L., Li, Z., Qu, L., 2022. Forecasting of Beijing PM2.5 with a hybrid ARIMA model based on integrated AIC and improved GS fixed-order methods and seasonal decomposition. Heliyon 8 (12), e12239. https://doi.org/10.1016/j.heliyon.2022.e12239.