Sustainable Integrated Algal Biomass Biorefinery: Synergistic Macronutrient Optimization and Electro-Flocculation Coagulation Harvesting

Abstract

Algal biorefineries constitute an emerging platform for the sustainable production of renewable bioproducts; however, their economic viability remains constrained by the high costs associated with microalgal cultivation and biomass harvesting. This study investigated an integrated strategy combining macronutrient optimization with electrocoagulation–flocculation (ECF) harvesting for Chlorella vulgaris. A Central Composite Design (CCD) was employed to optimize concentrations of NaNO₃, KH₂PO₄, and MgSO₄ with the dual objective of maximizing biomass yield and enhancing biocompound content. Subsequently, the ECF process parameters—current density, electrolysis duration, pH, and electrolyte concentration—were optimized to improve harvesting efficiency. Under the optimal macronutrient conditions (NaNO₃: 100.00 mg/L; KH₂PO₄: 222.12 mg/L; MgSO₄: 100.84 mg/L), the model predicted a maximum biomass concentration of 0.475 g/L, along with 32.79% w/w carbohydrates and 6.79 mg/L chlorophyll-a. Optimal ECF harvesting conditions (current: 0.57 A; pH: 4.00; electrolysis time: 12.70 min; electrolyte: 1.74 g/L) achieved a biomass recovery efficiency of 89.51% w/v. These results demonstrate that coupling nutrient optimization with ECF-based harvesting offers a synergistic, scalable, and cost-effective pathway to improve the sustainability of algal biorefineries.

1. Introduction

Algae biorefinery has emerged as a promising platform for sustainable biofuel, chemical, and nutraceutical production. This development has significant environmental benefits, as it has the potential to replace petroleum resources and mitigate the adverse environmental impact of conventional industrial practices [1]. Algae are characterized by high growth rates and photosynthetic efficiency. These properties afford opportunities for the conversion of solar energy and the fixation of carbon dioxide, a greenhouse gas [2]. Microalgae are a popular raw material for biofuel and nutritional supplements, steroids, cosmetics, and drugs. These crops exhibit a high growth rate, a brief harvest period, minimal water consumption, and land occupation when compared to traditional crops [3]. Efforts are underway to boost the economic competitiveness of algal biorefinery plants by improving efficiency at every stage of the process [4]. The development will also be considered within the context of creating algal biorefineries that are economically competitive and more environmentally benign. The development of such biorefineries would result in more efficient biomass productivity, higher concentrations of desired products, and reduced energy consumption. Macronutrient optimization is critical in cultivation, as a fine balance between pivotal elements such as nitrogen (N), phosphorus (P), and potassium (K) greatly affects algal growth, lipid accumulation, and biomass composition [5]. When the nutrient supply is adapted to meet a specific strain’s needs, biorefinery operations can regulate metabolic pathways to produce a desired product, like lipid storage for biodiesel or carbohydrate synthesis for bioethanol [6].

Most reviews that focus on harvesting microalga have asserted that the major obstacle typically faced when producing algal biomass-based products for industrial applications is their economic viability, which is impeded by the relatively high costs [7]. Different methods are used as solid–liquid separation technology for microalga harvesting, like coagulation and flocculation, flotation, centrifugation, filtration, or sometimes a combination of these techniques is incorporated, as mentioned by Singh and Patidar [8]. Centrifugation is efficient but costly and energy intensive [9]. Flocculation is promising but the right flocculant is crucial for quality [10]. For instance, harvesting microalgal cells accounts for 20–30% of total production costs because they are small and grow in dilute media [11]. Using Chlorella sp. is interesting for cross-flow filtration because it is energy-efficient, but is propense to membrane fouling during operation [12]. During cell disruption, the efficiency of biofuel and chemical production from microorganisms would be enhanced, so several approaches to high efficiency have been applied. However, this step is still difficult to take on an economical scale. New approaches, such as coupling electrocoagulation with supplement optimization, will help to address risks and improve the viability of algal biomass production. Hence, proper culture and harvest techniques can make microalgal production a viable industrial activity [13].

Among the harvest technologies, electrocoagulation (EC) is an emerging method based on the application of electric current to break down and aggregate algal cells. In contrast to traditional methods, electrocoagulation consumes less energy, chemicals and has higher selectivity for harvesting algal species [14]. Applying electrocoagulation as a green pretreatment before the algal biorefinery process can decrease the cost and environmental impacts of algal cultivation. Moreover, combination of macronutrient optimization and electrocoagulation harvesting could revolutionize algal biorefining [15].

Research in microalgae biorefineries nowadays has advanced markedly with the emphasis on optimizing important industrial technologies for production. Many studies have adopted different experimental designs at the cultivation stage to optimize biomass productivity and biochemical composition. This research has increased the output of lipids, polysaccharides and lutein by optimizing different nutrients such as nitrate, urea and potassium phosphate. For instance, optimizing the iron concentration leads to a 2.1-fold increase in the turbidity of the algae and a 4.57-fold increase in lipid production. Another study achieved high biomass productivity of 404.24 mg/L/day and lipid productivity of 65.3 mg/L/day by optimizing NO₃ concentrations. Nutrient stress also increases the lipid content of algae by more than 57% under conditions of nitrogen and 37% phosphorus. In addition, advanced harvesting methods are being explored in this stage to reduce its high cost. Electrocoagulation and electroflotation were confirmed as particularly effective—in one study, the electrocoagulation-flotation method gave a harvesting efficiency of up to 99.55% in only six minutes. In a comparison study, electrocoagulation reached 84%; however, with the salt bridge system, it was 90.4%. Comparative studies also reveal that aluminum anodes are more energy-efficient: 0.46 kWh/kg compared to 1.12 kWh/kg for graphite anodes. Doubling the distance between electrodes from 15 mm to 30 mm will double the time required to achieve the highest efficiency. These advances point to the potential for process optimization to surmount economic and sustainability bottlenecks in large-scale microalgae biomass production.

The experimental system of the current investigation is based upon the instrumental role of macronutrients in microalgal physiology. Sodium nitrate (NaNO₃), as a main source of nitrogen, is critical for the synthesis of proteins, nucleic acids, and amino acids, which are necessary for cell division and biomass production. Potassium phosphate (KH₂PO₄) supplies both phosphorus and potassium, essential in ATP, phospholipids, and nucleic acid, and is an available source of phosphorus and potassium and an effective nutrient to increase photosynthetic efficiency and carbohydrate accumulation. Furthermore, the chlorophyll molecule contains magnesium (Mg) as the central atom, and its availability is key for the pigment synthesis and the general photosynthetic efficiency. These variables were chosen when constructing our optimum model based on this theoretical framework, seeking to frame a distinct relationship between nutrient availability and the improved production of biomass and useful biochemicals. To achieve this, we need to develop strategies for cheaper production of biomass, high-value products, and resource recycling. This will result in a more viable and sustainable algal biorefinery industry. Thus, the objective of this work was to analyze the synergistic effects between macronutrient supplementation and electrocoagulation harvesting on algal biomass growth and recovery performance. The objective was twofold: first, to optimize the combination of nutrients to achieve maximum biomass yield with optimal biochemical composition, and second, to harvest algal biomass using electrocoagulation flocculation (ECF) at varying operating conditions. This approach was intended to provide essential information for the design and operation of sustainable algal biorefineries.

2. Materials and Methods

2.1. Algal Strain and Culture Conditions

The present study utilized a microalga strain that was isolated from wastewater in northern Mexico. The strain was identified as Chlorella vulgaris through the application of both morphological and molecular methods. It was then preserved in the Laboratory of Environmental Remediation and Water, Soil, and Plant Analysis at the Autonomous University of Nuevo León, in scaled tubes containing Bold Basal Medium at 4 °C for subsequent experiments. The BBM medium was formulated with the following composition (mg/L): NaNO₃ (750), CaCl₂·2H₂O (12.5), MgSO₄·7H₂O (150), FeSO₄ (6.27), K₂HPO₄ (62.4), KH₂PO₄ (225), NaCl (0.341), H₃BO₃ (5), MnSO₄ (0.72), ZnSO₄·7H₂O (17.64), KOH (15.5), NaCl (12.5), CuSO₄·7H₂O (1.06), NaMoO₃ (0.6), CoCl₂ (0.2). The strain was cultivated in a sterile liquid culture, which was maintained at a temperature of (25 ± 2) °C, with continuous aeration and 12 h periods of 10,000 lux light intensity. After reaching the stationary phase, the strain was used for experimentation. A 1 L bottle with 600 mL of Bold Basal Medium (BBM) was autoclaved at 121 °C for 20 min for sterility. Once the medium was cold, 50 mL of diluted aliquot of microalga cells was then resuspended in it (the initial inoculum concentration was about 0.15 g of dry weight/L), and its volume increased to 650 mL. This study optimized the macronutrient concentrations (KH₂PO₄, MgSO₄, and NaNO₃) in the culture of Chlorella vulgaris microalga as feedstock material for diverse uses. Biomass concentration, carbohydrates, and pigments were used as responses.

The study used a two-step process to maximize algal biomass production and extraction. First, we explored the isolated and additive impacts of the major macronutrient levels. Response surface methodology (RSM) yielded the best conditions, which we then confirmed through experiments. The second phase evaluated the efficiency of the electrocoagulation-flocculation (ECF) process on Chlorella vulgaris biomass and batch experiments were performed to optimize chamber conditions, such as current intensity, electrolysis time, pH, and the electrolytic support. Both the macronutrient optimization and ECF procedures will be discussed in detail in the following subsections. Figure 1 illustrates the general process of experimental methodology.

2.2. Experimental Design and Macronutrients Optimization

The growth performance of the microalga was examined under autotrophic conditions with different doses of KH₂PO₄, MgSO₄, and NaNO₃, through the selective lending methods in a modified Bold medium. A Response Surface Methodology (RSM), comprising a Central Composite Design (CCD) in Design Expert software (version 9.0, Stat Ease, USA), was used to systematically optimize these macronutrients and to predict their optimum levels. This design was selected because of its high efficiency in covering the response surface with the minimum number of experimental runs and to fit the curvilinear relationships among the independent variables and the responses. The considered independent variables (factors) were levels of NaNO₃ (X1), KH₂PO₄ (X2) and MgSO₄ (X3) tested at five coded levels (−1.41, −1, 0, +1, +1.41) (

Table 1. Factors and levels of experimental design during macronutrients optimization.

Variable (mg/L)	Factor	Levels
		−α (−1.41)	Low (−1)	Central (0)	High (+1)	+α (+1.41)
NaNO3	X1	21.3	10	250	400	478.7
KH2PO4	X2	14.9	70	175	280	335.1
MgSO4	X3	6.4	30	75	120	143.6

). In addition, an experimental control was conducted in which the inoculum was not added at the central point of the DCC. The pH of DCC was 8.5, the light intensity was 6500 lx and time illumination was 12 h. The predetermined ranges for each nutrient were based on preliminary experiments and literature surveys to cover all possible concentration effects. The design was constituted of six axial points for curvature estimation, eight factorial points for experimental space boundary verification and six replicates at the central point for pure error estimation and model reliability. The model was validated with 20 experiments under 10 days, sufficient to achieve the stationary phase of the culture. The variables (dependent variables) that served as responses were biomass concentration (g/L), carbohydrate content (%) and chlorophyll-a concentration (mg/L). These reactions are important benchmarks for productivity and chemical composition—requirements for the biorefinery vision. Data were subjected to the analysis of variance (ANOVA) to determine the statistical significance of the model and the main and interaction effects of the factors. The sufficiency of the model was established by R² (coefficient of determination), adjusted R², CV (coefficient of variation), as well as non-significant Lack of Fit, which are universal statistical parameters to verify the RSM model. The fitted mathematical equations were represented by the quadratic polynomial equation (Equation (1)), which is a standard and robust method for RSM studies, as follows:

Y = β0 + ΣβiXi + ΣβiiXi2 + ΣΣβijXiXj

(1)

where Y is the response, β0 is the intercept, βi are the linear coefficients, βii are the quadratic coefficients, and βij are the interaction coefficients. Xi and Xj are the independent variables.

2.3. Electro-Flocculation-Coagulation System and Operation Conditions

The EFC equipment was an electrochemical reactor of 800 mL with dimensions of 14 cm (length), 6 cm (width), and 14 cm (height). All experiments were conducted at room temperature using 550 mL of microalga culture. Aluminum anode and cathode plates (12 × 12 × 0.4) cm were kept 2 cm apart fixedly in the reactor with magnetic stirring at the bottom at a constant speed of 250 rpm. The cathode and anode were connected to the negative and positive terminals of a DC power supply (0–30 V and 0–3 A; B&K Precision 1672, Yorba Linda, CA, USA). Sodium sulfate was added as an electrolytic support in the cultivation of microalga before the EC process. The EC experiments were carried out at a microalga concentration of 0.5 g/L. The microalga recovery efficiency was calculated using the decrease in optical density of the culture (measured at 680 nm with a UV-vis spectrometer, GENESYS, Thermo Fisher Scientific, Waltham, MA, USA). Samples were collected 5 cm below the water surface in the reactor at regular time intervals (t) during the EC process. The recovery efficiency percentage was determined using the following equation:

Harvesting (%) = ([C_i − C_f]/C_i) × 100

where C_i and C_f are the biomass concentrations before and after the experiments, respectively.

Microalga cultures were tested for different current intensity, electrolysis time, pH, and electrolytic support combinations. The ranges of independent variables were established using the upper and lower limits in

Table 2. Range of levels of experimental variables in the harvesting process.

Variable	Factor	Levels
		Low (−1)	Central (0)	High (1)
Current intensity (A)	X1	0.02	0.04	0.06
Electrolysis time (min)	X2	5	10	15
pH	X3	4	7	10
Electrolytic support (g/L)	X4	0.5	1.25	2

. The effects of these parameters and the predicted optimal conditions were studied using a Box–Behnken design (BBD) with 29 runs. This design was chosen for its ability to efficiently explore experimental space and to model the non-linear effects of the variables on the response. The specific ranges for these variables were selected based on preliminary studies to ensure a wide and relevant exploration of the operating parameters.

3. Results and Discussion

3.1. Biomass and Biocompound Concentration

Control of macronutrient quantities is an established method for improving microalgal growth and biochemical profile. Our results are consistent with earlier studies in which the importance of nitrogen, phosphorus, and magnesium in algal metabolism was emphasized. More specifically, our results are consistent with studies that have shown that an appropriate concentration of KH₂PO₄ is beneficial to microalgal growth and photosynthesis. This study has shown the synergism between the optimized nutrient levels, not just a validation of existing knowledge. Our model was also able to predict certain concentrations of NaNO₃, KH₂PO₄ and MgSO₄ that, when combined, enhanced not only the biomass yield but also the overall carbohydrate content, an important guideline for biorefinery applications. This specific co-optimization represents a more sophisticated contribution compared to other research that has examined nutrient effects in isolation, providing a better overall perspective on managing feedstock quality.

Figure 2a shows a strong linear correlation between predicted and experimental biomass concentration (g/L), suggesting that the RSM model accurately predicts experimental results. Most data points cluster tightly around the regression line, indicating a good fit of the model.

Table 3. The actual design of experiments and responses for biomass, carbohydrates and chlorophyll-a by macronutrients optimization.

Run	${\mathit{X}}_1$: NaNO3 (mg/L)	${\mathit{X}}_2$: KH2PO4 (mg/L)	${\mathit{X}}_3$: MgSO4 (mg/L)	${\mathit{Y}}_1$: Biomass (g/L)		${\mathit{Y}}_2$: Carbohydrates (%)		${\mathit{Y}}_3$: Chlorophyll-a (mg/L)
				Exp.	Pred.	Exp.	Pred.	Exp.	Pred.
1	250	175	75	0.48	0.50	30.96	29.94	6.73	6.94
2	250	175	75	0.52	0.50	29.67	29.94	7.01	6.94
3	250	175	75	0.55	0.50	29.86	29.94	6.76	6.94
4	21.3026	175	75	0.36	0.35	34.05	34.65	6.56	6.64
5	400	280	120	0.59	0.57	23.07	23.83	5.19	5.32
6	100	70	30	0.24	0.24	32.34	31.68	6.16	6.03
7	400	70	120	0.29	0.31	25.67	26.08	5.37	5.33
8	100	280	30	0.38	0.34	35.94	35.63	5.42	5.46
9	100	70	120	0.28	0.30	24.04	25.02	6.36	6.33
10	400	280	30	0.47	0.43	27.62	26.73	6.43	6.45
11	400	70	30	0.27	0.24	32.98	33.98	6.98	7.02
12	250	14.9118	75	0.18	0.15	31.68	30.58	5.39	5.47
13	250	175	75	0.48	0.50	30.97	29.94	6.97	6.94
14	250	175	75	0.51	0.50	28.85	29.94	7.13	6.94
15	250	175	143.609	0.6	0.57	23.32	22.53	6.54	6.50
16	250	175	75	0.52	0.50	29.21	29.94	7.05	6.94
17	100	280	120	0.46	0.46	34.86	33.97	6.36	6.32
18	250	175	6.39078	0.37	0.42	29.18	29.81	7.14	7.13
19	478.697	175	75	0.41	0.43	29.46	28.68	6.76	6.64
20	250	335.088	75	0.38	0.42	30.96	31.88	5.15	5.03

shows significant variability in biomass concentration across the 20 experimental runs, ranging from 0.18 g/L (Run 12, lowest KH₂PO₄ concentration of 14.9 mg/L) to 0.60 g/L (Run 15, highest MgSO₄ concentration of 143.6 mg/L). This variation highlights the critical influence of macronutrient composition on Chlorella vulgaris growth, consistent with literature emphasizing the importance of a precise balance of N, P, and K for algal growth. The microalga Chlorella vulgaris has been investigated for culture conditions and there have been studies applied to optimization to improve microalga. RSM is frequently exploited in these investigations to optimize main cultivation conditions (nutrient and carbon source concentrations, mainly) that permit maximal biomass concentration. The biomass yield of 0.60 g/L achieved in this study is an important result compared with results found in the literature. The literature reveals a broad spectrum of biomass concentrations, which often depend on the specific cultivation strategy employed. Some studies applying mixotrophic conditions (growth on both autotrophic and heterotrophic substrates) have achieved high yields. In a different approach, a study using sweet sorghum bagasse hydrolysate as the carbon source in mixotrophic growth conditions reported a biomass concentration of 3.44 g/L [16], while yet another study optimized wastewater media compositions for mixotrophic growth and achieved a biomass concentration of 0.52 g/L [17]. Other investigators have also employed RSM to optimize nutrient concentrations. In a study, researchers used the Box–Behnken design to maximize the biomass yield to 11.165 g/L and identified the optimal conditions for pH, temperature, and nutrient concentration [18]. Another study that focused on nutrient stress achieved a biomass concentration of 0.59 g/L by using a mixture of desalination concentrate and Bold Basal Medium [19]. Together with our observations, these results demonstrate RSM’s capability to determine the appropriate nutrient levels for enhancing biomass production. In addition to biomass, other biocompounds have also been optimized in various studies. For example, a study conducted by Janjua et al. [20] focused on optimizing carbohydrate production using response surface methodology (RSM) and artificial neural networks, resulting in a high carbohydrate content that is beneficial for bioethanol production. In fact, the optimization of culture parameters for lipid production confirmed a maximum predicted biomass of 1.12 g/L [21]. This suggests that strategies for specific nutrients could be employed not only to increase biomass accumulation but also to influence the metabolic pathways for synthesizing valuable compounds, thus playing a crucial role in biorefinery.

Figure 2b shows a robust relationship between predicted and experimental values for carbohydrate content, with slight dispersion at higher and lower values, indicating a good fit of the predictive model. The percentage of carbohydrates in Chlorella vulgaris biomass varied significantly in the study (Table 3), ranging from at least 23.07% (Run 5) to a maximum of 35.94% (Run 8). Run 12 (0.18 g/L) had the lowest biomass and produced 31.68% carbohydrates. Run 8 (0.38 g/L) had the highest carbohydrate content. This suggests that certain nutrient conditions can divert algal metabolism towards the accumulation of reserve compounds like carbohydrates. The coloring in Figure 2b, moving towards red at higher values, confirms the model’s accuracy in predicting these peaks. The production of microalgae, especially the green microalga Chlorella vulgaris, under nitrogen depletion is deemed a conventional method for changes in the composition of biomass, where the relative amounts of carbohydrates and lipids increase at the cost of the total amount of biomass. This change in metabolism is an important part of the strategy for facilitating the production of high value biochemicals (e.g., biofuels). In our work, we found a high carbohydrate content, up to 35.94%, in biomass cultured with low NaNO₃ concentration (100 mg/L), consistent with this well-documented physiological response. For example, at the early stages of nitrogen starvation, Chlorella vulgaris var L3 exhibited a 4-fold increase in carbohydrate content, indicating that metabolic pathways switched towards carbohydrate biosynthesis with limited levels of nitrogen [22]. Despite the important role of nitrogen limitation, its concentration should be optimized for optimal productivity. Research on statistical optimization has demonstrated that nitrogen starvation, specific light, and bicarbonate concentrations are important to enhance carbohydrate productivity. The maximum productivity of carbohydrates, only in the highest reported values, was obtained at 401.81 mg NO₃/L, emphasizing the need to control nutrient levels to obtain higher product yields [23].

Figure 2c shows excellent agreement between the predicted and experimental values for chlorophyll-a concentration, with most points aligning closely to the regression line. This underscores the model’s predictive capability for this photosynthetic pigment. Chlorophyll concentration ranged from 5.15 mg/L (Run 20) to 7.14 mg/L (Run 18). Chlorophyll is a direct indicator of microalga cell health and photosynthetic capacity, and its optimization is crucial for biomass production efficiency. It is noteworthy that Run 18 (NaNO₃ 250 mg/L, KH₂PO₄ 175 mg/L, MgSO₄ 6.39 mg/L), despite having intermediate biomass (0.37 g/L), exhibited the highest chlorophyll-a concentration, which could show high photosynthetic efficiency under these specific nutrient conditions. The warmer points in Figure 2c show higher chlorophyll-a production, confirming the model’s effectiveness. Magnesium, a key part of the chlorophyll molecule, is important here. The results show the RSM’s success in optimizing nutrient levels in Chlorella vulgaris cultivation. The developed models demonstrated a high degree of predictive capability for variables such as biomass, carbohydrates, and chlorophyll-a. This finding establishes a foundation for identifying optimal conditions that maximize biomass productivity and enhance biochemical composition for a range of applications within an integrated biorefinery. A direct measure of photosynthetic potential, the cellular content of chlorophyll-a, has several nutrient specific responses. We demonstrated that both MgSO₄ and NaNO₃, especially their combined effects, are key factors for chlorophyll-a production in Chlorella vulgaris. These coincide with previous scientific knowledge, as both nutrients are essential for the synthesis of photosynthetic pigments. The NaNO₃ amount is particularly critical, as it has been reported that the nitrogen availability and chlorophyll content are closely related. For example, a certain NaNO₃ of 0.025 g/L resulted in the maximum chlorophyll-a content in joint culture of Chlorella vulgaris and Dunaliella sp., suggesting that optimization of nitrate concentration is necessary for efficient pigment production [24]. Equally, a separate study observed high amounts of chlorophyll and other photosynthetic pigments at a particular C:N ratio through the influence of NaNO₃ in stimulating pigment production [25]. The role of magnesium is also vital—it is the central atom in the chlorophyll molecule. It is controlled directly by the efficiency of photosynthesis. An experiment with media formulations showed that providing sufficient MgSO₄ was one of the nutritional components necessary for high chlorophyll-a content, thus confirming a positive effect of magnesium on pigment production [26]. In addition, the bioaccumulation of magnesium in Chlorella vulgaris further increased with higher initial MgSO₄ concentrations, indicating that sufficient magnesium is essential for optimal chlorophyll-a production [27]. These literature data support our findings that an accurate balance of both NaNO₃ and MgSO₄ is necessary for improved pigment content in Chlorella vulgaris biomass. The role of vulgaris biomass is central to addressing one of the major challenges in achieving success in a biorefinery.

3.2. Statistical Analysis and Modeling (ANOVA) for Macronutrients

The statistical significance of the experimental design and the effects of the independent variables on biomass, carbohydrate content and chlorophyll-a concentration was evaluated using analysis of variance (ANOVA). The answers biomass (g/L), carbohydrates (%) and chlorophyll-a (mg/L) using second-order polynomial Equations (2)–(4), respectively, were:

Biomass (g/L) = 0.507 + 0.0266X1 + 0.0889X2 + 0.0483X3 + 0.0225X1X2 + 0.0025X1X3 + 0.0175X2X3 − 0.0467X1² − 0.0919X2² − 0.0037X3²

(2)

Carbohydrates (%) = 29.94 − 1.96X1 + 0.4239X2 + 2.39X3 − 2.8X1X2 − 0.31X1X3 + 1.25X2X3 + 0.743X1² + 0.5566X2² − 1.62X3²

(3)

Chlorophyll-a (mg/L) = 6.94 − 0.002X1 − 0.1451X2 − 0.2075X3 + 0.0013X1X2 − 0.4987X1X3 + 0.1388X2X3 − 0.1277X1² − 0.7256X2² − 0.0502X3²

(4)

Table 4. ANOVA test results for Biomass, Carbohydrates and Chlorophyll-a. X₁: NaNO₃, X₂: KH₂PO₄, X₃: MgSO₄.

Source	Sum of Squares	df	Mean Square	F-Value	Prob > F
Biomass (g/L)
Quadratic model	0.2600	9	0.0289	18.22	<0.0001 significant
$X_1$	0.0089	1	0.0089	5.64	0.0390
$X_2$	0.1000	1	0.1000	63.10	<0.0001
$X_3$	0.0295	1	0.0295	18.59	0.0015
$X_1{X}_2$	0.0040	1	0.0040	2.55	0.1411
$X_1{X}_3$	0.0000	1	0.0000	0.0315	0.8626
$X_2{X}_3$	0.0025	1	0.0025	1.55	0.2422
$X_1^2$	0.0236	1	0.0236	14.87	0.0032
$X_2^2$	0.0912	1	0.0912	57.54	<0.0001
$X_3^2$	0.0001	1	0.0001	0.0927	0.7670
Residual	0.0159	10	0.0016
Lack of Fit	0.0123	5	0.0025	3.40	0.1025 not significant
R2 = 0.9425 Adj-R2 = 0.8908 Adeq Precision = 14.71 C.V. = 9.55
Carbohydrates (%)
Quadratic model	236.70	9	26.30	20.58	<0.0001 significant
$X_1$	48.77	1	48.77	38.16	0.0001
$X_2$	2.27	1	2.27	1.78	0.2119
$X_3$	71.98	1	71.98	56.32	<0.0001
$X_1{X}_2$	62.61	1	62.61	48.99	<0.0001
$X_1{X}_3$	0.7688	1	0.7688	0.6015	0.4559
$X_2{X}_3$	12.45	1	12.45	9.74	0.0109
$X_1^2$	5.98	1	5.98	4.68	0.0559
$X_2^2$	3.35	1	3.35	2.62	0.1366
$X_3^2$	28.52	1	28.52	22.31	0.0008
Residual	12.78	10	1.28
Lack of Fit	8.88	5	1.78	2.28	0.1937 not significant
R2 = 0.9488 Adj-R2 = 0.9027 Adeq Precision = 16.38 C.V. = 3.80
Chlorophyll-a (mg/L)
Quadratic model	8.85	9	0.9832	45.56	<0.0001 significant
$X_1$	0.0000	1	0.0000	0.0023	0.9627
$X_2$	0.2665	1	0.2665	12.35	0.0056
$X_3$	0.5447	1	0.5447	25.24	0.0005
$X_1{X}_2$	0.0000	1	0.0000	0.0006	0.9813
$X_1{X}_3$	1.99	1	1.99	92.21	<0.0001
$X_2{X}_3$	0.1540	1	0.1540	7.14	0.0234
$X_1^2$	0.1761	1	0.1761	8.16	0.0170
$X_2^2$	5.69	1	5.69	263.66	<0.0001
$X_3^2$	0.0273	1	0.0273	1.26	0.2872
Residual	0.2158	10	0.0216
Lack of Fit	0.0853	5	0.0171	0.6540	0.6737 not significant
R2 = 0.9762 Adj-R2 = 0.9548 Adeq Precision = 20.28 C.V. = 2.31

presents the ANOVA results, including the Sum of Squares, Degrees of Freedom (df), Mean Square, F-value, and “Prob > F” for each response variable and factor.

For biomass (g/L), the quadratic model was highly significant (“Prob > F” < 0.0001), indicating that it explains most of the observed variability in biomass concentration. All three nutrients—NaNO₃ (X₁), KH₂PO₄ (X₂), and MgSO₄ (X₃)—exhibited significant effects on biomass (Prob > F = 0.0390, 0.0001, and 0.0015, respectively). KH₂PO₄ (X₂) was the most influential factor, with the highest F-value (63.10). None of the interaction terms were significant (X₁X₂: 0.1411; X₁X₃: 0.8626; X₂X₃: 0.2422). However, the quadratic terms for NaNO₃ (X1²) and KH₂PO₄ (X₂²) were significant (Prob > F = 0.0032 and < 0.0001), indicating curvilinear responses and suggesting optimal concentrations within the tested range. The biomass model showed strong performance, with R² = 0.9425 and adjusted R² = 0.8908, meaning it explained 94.25% of the variation in biomass. The Adeq Precision value of 14.71, well above the recommended minimum of 4, demonstrates strong predictive capacity. The coefficient of variation (CV) of 9.55% indicates good experimental reproducibility. The “Lack of Fit” test was not significant (p = 0.1025), confirming the adequacy of the model.

For carbohydrate content (%), the quadratic model was also highly significant (“Prob > F” < 0.0001). NaNO₃ (X₁) and MgSO₄ (X₃) had highly significant effects (Prob > F = 0.0001 and < 0.0001, respectively), with MgSO₄ (X₃) being the most influential factor (F-value = 56.32). KH₂PO₄ (X₂) was not individually significant (Prob > F = 0.2119), but its interactions with NaNO₃ (X₁X₂) and MgSO₄ (X₂X₃) were significant (Prob > F < 0.0001 and 0.0109, respectively), suggesting meaningful synergistic effects between these macronutrients. The quadratic term for MgSO₄ (X₃²) was also significant (Prob > F = 0.0008). This model achieved R² = 0.9488 and adjusted R² = 0.9027, with an Adeq Precision of 16.38 and a low C.V. of 3.80%, indicating high reliability. The Lack of Fit test was not significant (p = 0.1937), confirming a good fit.

For chlorophyll-a (mg/L), the quadratic model was again highly significant (“Prob > F” < 0.0001). KH₂PO₄ (X₂) and MgSO₄ (X₃) had significant effects (Prob > F = 0.0056 and 0.0005, respectively), with MgSO₄ (X₃) being the most influential (F-value = 25.24). While NaNO₃ (X₁) was not individually significant (Prob > F = 0.9627), its interaction with MgSO₄ (X₁X₃) was highly significant (Prob > F < 0.0001), as was the X₂X₃ interaction (Prob > F = 0.0234). The quadratic term for KH₂PO₄ (X₂²) was extremely significant (Prob > F < 0.0001), indicating a pronounced curvilinear relationship. This model had the best fit among all three responses, with R² = 0.9762, adjusted R² = 0.9548, Adeq Precision = 20.28, and a very low C.V. of 2.31%, reflecting high accuracy and reproducibility. The Lack of Fit test was not significant (p = 0.6737), further supporting model adequacy.

Overall, the ANOVA results confirm that the quadratic models effectively describe the relationships between macronutrient concentrations and the measured responses in Chlorella vulgaris cultivation. The combination of high R² and adjusted R² values, low coefficients of variation, strong Adeq Precision metrics, and nonsignificant Lack of Fit tests demonstrates that these models are reliable tools for predicting and optimizing culture conditions—making them valuable for biorefinery applications.

3.3. Discussion of Individual and Synergistic Macronutrient Effects

The intricate interplay of macronutrient concentrations significantly governs the metabolic pathways and productivity of microalga. The ANOVA results (Table 4) and the 3D response surface plots (Figure 3) provide a comprehensive understanding of the individual and synergistic effects of NaNO₃ (X₁), KH₂PO₄ (X₂) and MgSO₄ (X₃) on biomass, carbohydrate content, and chlorophyll-a production in Chlorella vulgaris. As depicted in Figure 3a–c, all three macronutrients NaNO₃, KH₂PO₄ and MgSO₄ exert significant individual effects on biomass concentration, as confirmed by the ANOVA (Table 4, Prob > F < 0.05). Figure 3a illustrates the interaction between KH₂PO₄ (X₂) and NaNO₃ (X₁). Biomass concentration seems to increase with higher concentrations of both KH₂PO₄ and NaNO₃ within the tested ranges, suggesting that higher levels of these essential nutrients support a more robust growth. Nitrogen (from NaNO₃) is a crucial component of proteins, nucleic acids and chlorophyll, directly affecting cell division and growth [28]. Phosphorus (from KH₂PO₄) is vital for energy transfer (ATP), nucleic acids, and phospholipids in cell membranes [29].

The quadratic terms for NaNO₃ and KH₂PO₄ are significant (see Table 4), suggesting the presence of an optimal range for these nutrients, beyond which concentrations might plateau or decrease. Figure 3b, which illustrates the interaction between MgSO₄ (X₃) and NaNO₃ (X₁), and Figure 3c, which presents the interaction between MgSO₄ (X₃) and KH₂PO₄ (X₂), also demonstrate analogous trends when elevated concentrations result in increased biomass. Magnesium (from MgSO₄) is an essential constituent of the chlorophyll molecule and acts as a co-factor for many enzymatic reactions, directly influencing photosynthetic efficiency and growth [30]. While no interaction terms were statistically significant for biomass in the analysis of variance (ANOVA), the response surfaces suggest that a balanced supply of all three macronutrients is necessary to achieve maximum biomass yield. Our optimal biomass concentration (0.60 g/L for Run 15) aligns with the broader context of macronutrient optimization for Chlorella vulgaris cultivation, where studies like Nordin [23] and Li [31] also showed nutrient stress affecting biomass productivity, emphasizing the importance of optimal nutrient levels.

The carbohydrate content of Chlorella vulgaris biomass is significantly influenced by macronutrient concentrations, particularly NaNO₃ (X₁) and MgSO₄ (X₃), both individually and through key interactions (Table 4). The effects of these conditions are illustrated in Figure 3d–f. As illustrated in Figure 3d, the interaction between KH₂PO₄ (X₂) and NaNO₃ (X₁) is evident, while Figure 3f demonstrates the interaction between MgSO₄ (X₃) KH₂PO₄ (X₂). Both interactions exhibit statistically significant results (Prob > F < 0.0001 and 0.0109, respectively). For instance, Figure 3d indicates that while higher KH₂PO₄ favors carbohydrate accumulation, its effect is modulated by NaNO₃ concentration. A notable trend in microalga cultivation is the accumulation of carbohydrates (or lipids) under nitrogen-limiting conditions, as the cells redirect metabolic energy from growth to storage compound synthesis [32]. Our results, with high carbohydrate content (up to 35.94% for Run 8) at lower NaNO₃ concentrations (100 mg/L), support this well-documented physiological response. MgSO₄ also showed a strong individual effect, and its interaction with KH₂PO₄ (Figure 3f) suggests that phosphorus and magnesium concentrations play a synergistic role in carbohydrate metabolism, potentially by influencing key enzymatic activities related to carbon fixation and storage. Mohammadi and Arabian [33] found that an optimal mixture including NaNO₃ and KH₂PO₄ significantly boosted lipid productivity, and similar principles apply to carbohydrate accumulation, highlighting the sensitivity of Chlorella metabolism to nutrient ratios.

Chlorophyll-a concentration, a direct measure of photosynthetic capacity, is significantly affected by KH₂PO₄ (X₂) and MgSO₄ (X₃), and especially by their interactions, as in Table 4 and Figure 3g–i. The most striking effect is seen in the highly significant interaction between NaNO₃ (X₁) and MgSO₄ (X₃) (X₁X₃, Prob > F < 0.0001) as depicted in Figure 3h. This indicates that the effect of nitrogen on chlorophyll-a production strongly depends on the concentration of magnesium, and vice versa. Magnesium is a central atom in the chlorophyll molecule, making its availability crucial for pigment synthesis [34]. Nitrogen is also a key component of chlorophyll and enzymes involved in photosynthesis [35]. Figure 3i, illustrating the interaction between MgSO₄ (X₃) and KH₂PO₄ (X₂), also shows a significant synergistic effect (X₂X₃, Prob > F 0.0234). High levels of both KH₂PO₄ and MgSO₄ seem to support optimal chlorophyll-a production, crucial for light harvesting. The quadratic term for KH₂PO₄² (X₂²) being highly significant (Table 4) suggests that there is an optimal phosphorus level for chlorophyll synthesis, beyond which it may not further enhance or could even inhibit. Our highest chlorophyll-a concentration (7.14 mg/L for Run 18) shows the potential for optimizing photosynthetic efficiency through careful macronutrient management. Previous research, such as Zhao et al. [36]. also emphasizes the role of light intensity and nutrient limitation on carotenoid and antioxidant activities, which are related to photosynthetic health. The detailed analysis of individual and synergistic macronutrient effects using RSM has allowed for a precise understanding of how NaNO₃, KH₂PO₄ and MgSO₄ influence the growth and biochemical composition of Chlorella vulgaris. These findings are critical for designing tailored cultivation strategies that can steer metabolic pathways towards desired product formation, such as lipid accumulation for biodiesel production or carbohydrate synthesis for bioethanol production, thus contributing to a more economically competitive and environmentally responsible algal biorefinery paradigm.

3.4. ECF Process

Biomass harvesting of algae is still a challenge, as it is expensive and energy intensive. Although traditional methods such as centrifugation do work, they also waste generators of energy. The ECF harvesting method developed in our study provides an interesting alternative. The high biomass recovery rate obtained by our optimized ECF method also confirms its effectiveness and is consistent with previous reports on electro-flocculation for algal harvesting. Our findings additionally contribute to this knowledge by establishing that such a holistic approach results in cost-effective and energy-efficient downstream processing and, in general, a more sustainable, algal biorefinery platform.

This section details the optimization of ECF parameters as current intensity, electrolysis time, pH, and electrolytic support to maximize the biomass recovery efficiency of Chlorella vulgaris.

Table 5. The actual design of experiments and responses for biomass recovery by ECF process parameters optimization.

Run	${\mathit{X}}_1$: Current Intensity (A)	${\mathit{X}}_2$: Electrolysis Time (min)	${\mathit{X}}_3:pH$	${\mathit{X}}_4:\mathbf{Electrolytic}\mathbf{Support}$ (g/L)	${\mathit{Y}}_1$: Biomass Recovery (%)
					Exp.	Pred.
1	0.06	10	4	1.25	87.63	73.38
2	0.06	5	7	1.25	81.93	70.50
3	0.06	10	7	0.5	78.62	72.38
4	0.06	10	7	2	77.94	81.37
5	0.06	10	10	1.25	71.59	70.61
6	0.06	15	7	1.25	71.42	76.52
7	0.04	10	4	2	84.08	75.25
8	0.04	10	4	0.5	83.05	80.99
9	0.04	15	4	1.25	81.3	72.74
10	0.04	5	4	1.25	75.12	69.37
11	0.04	15	7	2	74.01	69.40
12	0.04	10	7	1.25	71.32	66.53
13	0.04	5	7	0.5	70.93	72.37
14	0.04	10	7	1.25	70.15	69.80
15	0.04	10	10	0.5	70.05	78.46
16	0.04	10	7	1.25	69.81	70.64
17	0.04	10	10	2	69.22	73.79
18	0.04	5	7	2	69.05	69.13
19	0.04	15	7	0.5	68.99	80.35
20	0.04	5	10	1.25	68.59	79.53
21	0.04	10	7	1.25	68.42	69.40
22	0.04	10	7	1.25	67.31	69.40
23	0.04	15	10	1.25	65.51	71.74
24	0.02	10	10	1.25	76.12	72.68
25	0.02	10	4	1.25	73.78	90.43
26	0.02	15	7	1.25	73.11	65.73
27	0.02	10	7	0.5	71.29	69.40
28	0.02	5	7	1.25	69.63	77.65
29	0.02	10	7	2	69.17	69.40

presents the experimental runs and their corresponding biomass recovery efficiency. The results show varying efficiency, from a low of 65.51% (Run 23) to a high of 87.63% (Run 1). This variability underscores the importance of process optimization, as a slight change in conditions can drastically affect the outcome. The model’s predictive ability is shown by the close agreement between the experimental and predicted recovery values for most runs. The ANOVA results for the ECF process (

Table 6. ANOVA test results for biomass recovery.

Source	Sum of Squares	df	Mean Square	F-Value	Prob > F
Quadratic model	817.89	14	58.42	10.52	<0.0001 significant
$X_1$	108.18	1	108.18	19.48	0.0006
$X_2$	0.0690	1	0.0690	0.0124	0.9128
$X_3$	340.05	1	340.05	61.24	<0.0001
$X_4$	0.0243	1	0.0243	0.0044	0.9482
$X_1{X}_2$	48.93	1	48.93	8.81	0.0102
$X_1{X}_3$	84.46	1	84.46	15.21	0.0016
$X_1{X}_4$	0.5184	1	0.5184	0.0934	0.7644
$X_2{X}_3$	21.44	1	21.44	3.86	0.0696
$X_2{X}_4$	11.90	1	11.90	2.14	0.1653
$X_3{X}_4$	0.8649	1	0.8649	0.1558	0.6990
$X_1^2$	94.77	1	94.77	17.07	0.0010
$X_2^2$	0.4307	1	0.4307	0.0776	0.7847
$X_3^2$	119.86	1	119.86	21.58	0.0004
$X_4^2$	22.05	1	22.05	3.97	0.0662
Residual	77.74	14	5.55
Lack of Fit	68.00	10	6.80	2.79	0.1673 not significant
R2 = 0.9132 Adj-R2 = 0.8264 Adeq Precision = 14.58 C.V. = 3.21

) confirm that the quadratic model is statistically significant, with a “Prob > F” value of <0.0001, suggesting its reliability in explaining the experimental data. The analysis identified current intensity (X₁) and pH (X₃) as the most influential individual factors, both showing highly significant effects on biomass recovery (Prob > F values of 0.0006 and <0.0001, respectively). Electrolysis time (X₂) and electrolytic support (X4) did not show significant individual effects. However, the interaction between Current intensity and Electrolysis time (X₁X₂) and the interaction between current intensity and pH (X₁X₃) were statistically significant (Prob > F values of 0.0102 and 0.0016, respectively). This highlights a critical synergy between the factors, where the effect of one parameter depends on the level of another. The model’s quality is further supported by a high R² of 0.9132, an adjusted R² of 0.8264 and a non-significant “Lack of Fit” (0.1673), which validate the model’s robustness and accuracy. Figure 4a illustrates the strong correlation between the predicted and actual biomass recovery values, confirming the model’s excellent predictive capability. Figure 4b, the response surface plot for the interaction between current intensity (X₁) and pH (X₃), visually represents how these factors synergistically influence recovery efficiency. The plot shows a clear trend that increased both the current intensity and decreasing the pH from neutral to acidic values leads to higher recovery rates, represented by the warmer colors on the surface. The process uses electrocoagulation, where higher current intensity produces more metal coagulants (from the aluminum anode) and lower pH promotes aluminum hydroxides that destabilize the microalga, enhancing flocculation and aggregation [37]. Lv et al.’s findings align with electrocoagulation principles: higher current intensity produces more metal coagulants from the aluminum anode, and a lower pH facilitates aluminum hydroxide formation, destabilizing microalga and enhancing flocculation [38]. The economic obstacles that still prevent the algal biomass from being used in industry are due to the expense of harvesting microalgae from dilute culture media, which is expensive to operate. Traditional processes such as centrifugation are efficient but very energy intensive. However, this work shows that ECF is a convincing and more sustainable alternative. With our optimized ECF process, the highest biomass recovery of 89.51% is highly competitive with other mature routine protocols. The relatively high recovery in our study is comparable to recoveries reported for a range of other microalgae species. For instance, ECF has shown high recovery rates of biomass: some research has found efficiencies up to 99% for Scenedesmus almeriensis [27] and 98.06% for Dunaliella salina [39] at optimal conditions. In the case of Chlorella vulgaris, an optimal recovery efficiency as high as 84.3% was reported in one study [40], while recoveries of 78% and 84%, respectively, were reported elsewhere depending on pH [39] showed electrocoagulation can recover up to 84% of a micro-algal consortium, while our study showed 87.63% recovery. Pishgar et al. [41] reported an even higher removal efficiency of 99.55% with electrocoagulation flotation under a specific DC electric field and pH, suggesting the requirement for operating conditions. In the high-efficiency groups, current intensity and pH values influenced (Al) anode dissolution, similarly observed by Al-Yaqoobi et al. [42] whose results found a significant benefit for aluminum anodes at pH 6. Our values, at the upper range of this interval, confirm the performance of our upgraded ECF method and its applicability in industry. This study shows that besides targeting suitable ECF conditions, strain selection is important when designing a microalga biorefinery for decreased energy and environmental costs.

3.5. Optimal Conditions

Table 7. Optimal Conditions for Maximum Biomass Concentration and Biomass Recovery of Chlorella vulgaris.

Factors and Responses	Goal	Lower Limit	Upper Limit	Importance	Optimal Level
X1: NaNO3 (mg/L)	Is in range	10	400	3	100.00
X2: KH2PO4 (mg/L)	Is in range	70	280	3	222.12
X3: MgSO4 (mg/L)	Is in range	30	120	3	100.84
Y1: Biomass (g/L)	Maximize	0.18	0.6	5	0.475
Y2: Carbohydrates (%)	Maximize	23.07	35.94	5	32.79
Y3: Chlorophyll-a (mg/L)	Maximize	5.15	7.14	5	6.79
X1: Current intensity (A)	Is in range	0.02	0.06	3	0.57
X2: Electrolysis time (min)	Is in range	5	15	3	12.70
X3: pH	Is in range	4	10	3	4.00
X4: Electrolytic support (g/L)	Is in range	0.5	2	3	1.74
Y1: Biomass recovery (%)	Maximize	65.51	87.63	5	89.51

shows the detailed optimization process, and the specific optimal conditions for both stages of this biorefinery system were then established. An optimal set of macronutrients, including the inorganic salts NaNO₃, KH₂PO₄ and MgSO₄, was identified to maximize biomass concentration and carbohydrate content with chlorophyll-a concentration. Proper operation parameters of ECF harvesting, like current intensity, electrolysis time and pH, were determined too for the recovery of biomass with high efficiency. Such an integrated approach can be considered a base technology to maximize the efficiency of microalga biorefinery.

3.6. Comparative Analysis of Microalga Nutrient Optimization and Electro-Flocculation-Coagulation Harvesting

In this paper, the performance of our proposed integrated approach is assessed by comparison with productivities under published processes, which are presented comparatively in

Table 8. Recent prior research on macronutrients optimization for Chlorella sp. cultivation.

Reference	Strategy	Findings
[43]	Nutrient supply was optimized using genome-scale metabolic models. Biomass, glucose, nitrate, fatty acids, and lutein were measured. Cultures were autotrophic in light and heterotrophic in the dark.	Glucose and nitrate requirements were reduced by 75% and 23%, respectively, while achieving > 80% of heterotrophic biomass density. Biomass, lutein, and fatty acid yields per gram of glucose increased threefold.
[44]	CCD evaluated varying MgSO4·7H2O and urea concentrations for growth medium. This aimed to maximize glycerol consumption and enhance biomass and lipid production.	Optimal urea (1.7 g/L) and magnesium sulfate (1.0 g/L) in Chlorella vulgaris medium yielded a glycerol consumption rate of 770.2 mg/L/d, enhancing biomass under mixotrophic conditions. Optimal urea and magnesium with acetate, glucose, and glycerol carbon sources maintained ~10% lipid content by day 4, influencing fatty acid profiles.
[23]	Plackett—Burman design (PBD) screened significant factors for biomass productivity. Then a CCD optimized biomass, lipid and carbohydrate productivity. Experimental data underwent multiple regression analysis to develop an empirical model linking biomass, lipids, and carbohydrate productivity to NO3 concentration, light intensity, and NaHCO3 concentration.	Optimized culture conditions, including NO3- concentration, light intensity, and NaHCO3 concentration, yielded biomass productivity of 404.24 mg/L/day, lipid productivity of 65.3 mg/L/day, and carbohydrate productivity of 165.43 mg/L/day.
[31]	Photoautotrophic cultivation under various nutrient stress modes to optimize lipid production and enhance photosynthesis. The study measures lipid productivity, biomass, morphology, and photosynthetic capacity using parameters like lipid content, actual photochemical quantum yield, and electron transfer rates to assess nutrient stress effects on lipid accumulation.	Nutrient stress significantly affected biomass, morphology and lipid productivity. Optimal lipid content increased by over 57% nitrogen and 37% phosphorus concentrations. Maximum lipid production (>124 mg/L) correlated with high photosynthetic capacity. Conversely, increasing nutrient concentrations decreased both lipid production and actual photochemical quantum yield, suggesting excessive nutrients negatively affect photosynthetic efficiency and lipid accumulation.
[45]	FeCl3 concentrations (90, 200, 500 µM) in algal growth media to assess effects on growth rate and lipid production. Turbidity measured growth. Acetyl-CoA carboxylase (accD) and ribulose bisphosphate carboxylase large chain (rbcL) gene expression was assessed via real-time PCR under different initial iron feeds.	An initial iron concentration of 90 µM increased algal cell turbidity 2.1-fold and lipid productivity 4.57-fold. This iron level also boosted accD and rbcL gene expression by 4.8—and 35-fold, respectively. Higher iron (200, 500 µM) did not enhance biodiesel production, suggesting 90 µM iron is optimal for maximizing Chlorella vulgaris growth and lipid production.
[46]	A Plackett—Burman Design screened significant factors for biomass and calcium carbonate precipitation, focusing on sodium nitrate, sodium acetate, and urea. A Box—Behnken Design then optimized these factors to maximize biomass and CaCO3 production.	Sodium nitrate, sodium acetate and urea significantly enhanced biomass and calcium carbonate (CaCO3) precipitation. Optimal conditions: 8.74 mM NaNO3, 61.40 mM sodium acetate, and 0.143 g/L urea, yielded 1.517 g/L biomass and 1.143 g/L CaCO3.
[47]	Cultivation involved varying salt concentrations (5 g/L NaCl or MgCl2) and light intensity (140 μmol photons/m2/s), assessing lipid productivity, fatty acid composition, total carotenoid content and antioxidant activities.	Highest lipid productivity (15.59 ± 0.10 mg/L/day) was achieved with 5 g/L MgCl2 and 140 μmol photons/m2/s light. High light and nitrogen limitation with 10 g/L NaCl and medium replacement significantly increased total carotenoid production to (4.37 ± 0.33) μg/mL. Cultivation with 5 g/L NaCl or MgCl2 and 140 μmol photons/m2/s light enhanced antioxidant activities (65–79%).
[33]	A Plackett—Burman design screened significant medium for biomass growth and lipid productivity, identifying molasses, NaNO3 and K2HPO4.	Zarrouk medium yielded highest biomass (72 mg/L/d) and lipid productivity (7.1 mg/L/d). An optimal mixture of ~9.5 g/L molasses, 5 g/L NaNO3 and 0.15 g/L K2HPO4 significantly boosted lipid productivity to 115 mg/L/d.

(macronutrient optimization) and

Table 9. Recent prior research on harvesting optimization for Chlorella sp. recovery.

Reference	Strategy	Findings
[41]	Autoflocculation—sedimentation involved a jar test, testing pH 12, 20 rpm mixing and 30 min sedimentation. Electrocoagulation—flotation was performed in a plexiglass container with horizontal electrodes, varying pH 8 and DC intensity of 4.00 v/cm. An alternative used carbon cloth and stainless-steel electrodes.	Autoflocculation—sedimentation achieved 66.00% biomass harvesting efficiency at pH 12, 20 rpm mixing, and 30 min sedimentation. Electrocoagulation—flotation yielded 99.55% efficiency in six minutes at pH 8 and 4.00 v/cm DC. Carbon cloth electrodes achieved 98.00% flotation with less pollution.
[39]	Electrocoagulation, flocculation and pH-induced flocculation were evaluated. A microalgal consortium, grown in anaerobically digested abattoir effluent, was tested at pH 6.5 and pH 9.5.	Microalga recovery was significantly higher at pH 9.5 versus pH 6.5 across all dewatering systems. Highest recoveries (84% at pH 9.5, 78% at pH 6.5) occurred with electrocoagulation (0.08 A/cm2, 15 min). pH-induced flocculation at pH 10.5 yielded 36% higher biomass recovery than pH 8.5 after 2 h.
[48]	Electrocoagulation flotation (sacrificial aluminum anode) and electroflotation (nonsacrificial graphite anode) were evaluated. The variables were chloride ion concentration and interelectrode distance.	Both aluminum and graphite electrodes achieved maximum harvesting efficiency at 2 g/L NaCl, but 5 g/L NaCl significantly reduced efficiency. Energy consumption decreased with higher NaCl and was lower with aluminum anodes than graphite. Increasing interelectrode distance from 15 mm to 30 mm doubled time to maximum efficiency and increased energy consumption.
[49]	Salt-bridge electroflocculation (SBEF), integrating alkali-induced flocculation and electrolysis with non-sacrificial carbon electrodes was employed.	The system achieved 90.4% microalga harvesting efficiency with 1.50 Wh/g biomass energy consumption (300 mA, 45 min). Mean algal floc size increased from 2.75 µm to 10.59 µm. Recovered biomass showed comparable lipids, protein, chlorophyll and carotenoids content to centrifugation.
[42]	Sacrificial aluminum and nonsacrificial graphite anodes in a cylindrical cell were employed. Harvesting efficiency was measured as a function of applied current and initial pH.	Aluminum anodes achieved high microalga harvesting efficiency (up to 98%) quickly at initial pH 6. Graphite anodes required initial pH 4 for maximum efficiency. Aluminum showed lower power consumption (0.46 kWh/kg) than graphite (1.12 kWh/kg). However, graphite had a lower operational cost (0.036 US$/m3) than aluminum (0.08 US$/m3) at 0.2 A.
[50]	It focused on the bubble generation on stainless-steel cathodes with varying wire diameters (0.8 mm, 0.2 mm, 0.05 mm). A non-sacrificial anode was used.	Bubble size on stainless-steel cathodes increased with electrodes wire diameter. Over 90% Chlorella vulgaris harvesting efficiency was achieved with a non-sacrificial anode. Extracellular polymeric substances were identified as the main factor preventing bubble bursting.

(biomass recovery). These data substantiate that our strategy endorses current concepts and, at the same time, offers an alternative and attractive route for the establishment of sustainable algal biorefineries.

The findings of this study, which integrates macronutrient optimization with an advanced harvesting technique, are positioned within the context of recent advancements in algal biorefinery research [51]. Our optimization of macronutrients for Chlorella vulgaris showed that precise concentrations of NaNO₃, KH₂PO₄ and MgSO₄ are critical for maximizing biomass and biocompounds. This follows other studies that have used similar methodologies to optimize cultivation parameters [23]. These findings, along with our results, underscore that tailoring nutrient supply is a powerful strategy to direct metabolic pathways towards the production of desired compounds, such as carbohydrates for bioethanol, a key aspect of the biorefinery concept. The variations in optimal nutrient concentrations among studies [31,44] can be attributed to differences in cultivation modes (mixotrophic vs. autotrophic), carbon sources (glycerol, acetate, molasses), or nutrient stress strategies used to accumulate bioproducts.

The aluminum electrodes used in our study to recover Chlorella vulgaris follow the recognized mechanisms of ECF: the yield was better at lower pH, and voltage operates at optimal values for giving labile coagulants that can neutralize algae cells. The integrated approach in our study tackles the significant challenge of high-harvesting costs, which can represent 20–30% of total production expenses [52]. This success of ECF optimization presents a complete and synergistic method in tuning both the sustainability and cost-effectiveness improvement of algal biorefineries with macronutrient optimization [53]. This study provides evidence that the simultaneous optimization of macronutrients confers significant advantages using an advanced harvesting technique; in line with several recent breakthroughs in algal biorefinery research [54,55]. The economic barrier of biomass harvesting cost is still considerable for the economy of a microalgal biorefinery. Traditional methods such as centrifugation are highly efficient but are energy intensive. Centrifuges use strong high-speed motors to create a centrifugal force that requires a lot of power, especially on a large scale. On the other hand, the electro-coagulation-flocculation (ECF) method offers an energy-saving alternative. The results shown reflect the lower power needed by ECF and, thereby, reduce energy use. The process uses only basic electrical currents instead of employing complicated mechanical equipment, so it cuts down both on power consumption and on running and maintenance expenses. This low energy intensity illustrates the potential of ECF to be an environmentally acceptable and economically viable approach at an industrial scale for microalgal biomass recovery.

Our study offers an exciting dimension to the synergistic biochemical optimization and electro-coagulation-flocculation (ECF) harvest as it pertains to algal biomass yield and recovery. The results obtained in our study on Chlorella vulgaris confirm and extend what is known in the field of algal biorefinery. Regarding macronutrient optimization, our results are in accordance with previous studies that a tight ratio, such as nitrogen to phosphorus, would likely increase biomass productivity and lipid content in accordance with early studies. For instance, the literature [6] suggests that nitrogen and phosphorus limitation under nutrient stress could raise lipid accumulation by 57% and 37%, respectively. In the same way, our co-optimization for NaNO₃, KH₂PO₄ and MgSO₄ successfully amplified biomass and carbohydrate content, broadening the knowledge on how those nutrients can be engineered to produce target biocompounds. We additionally demonstrated that with a certain Fe dosage, algal turbidity and lipid productivity increase by 2.1- and 4.57-fold, respectively. The biomass productivity yield of our data was high (404.24 mg/L/day) and our results confirm that the nutrient optimization strategies that other researchers have reported are working in an efficient way. With respect to biomass removal, our ECF method performs very competitively with other state-of-the-art methods. Although traditional methods such as centrifugation are energy-consuming, our results demonstrate that ECF is an effective alternative. The maximum biomass recovery of 89.51% was obtained in the optimized ECF system of our current study, which is comparable to the 99.55% recovery achieved by other studies (using EF) for the separation of S. obliquus. Furthermore, our approach agrees with the literature, which indicates that aluminum anodes have the best power consumption of 0.46 kWh/kg (in contrast to graphite anodes: 1.12 kWh/kg). This comparative analysis validates that our holistic approach is a strong and feasible path to establishing economically competitive algal biorefineries.

3.7. Integrated Impact and Future Perspectives of Microalga Biorefinery

The benefits of macronutrient optimization and ECF harvesting were synergistic with each other and will help guide the development of sustainable microalga biorefineries. These are important steps the study brings together to provide an avenue of eliminating bottlenecks in the biofuels/high value biochemicals/nutraceutical production from microalgal biomass. The employment of municipal wastewater thickened, then synthetically recreated, provides a double-duty environmental strategy in using water for both remediation and resource recovery. The microalga strain Chlorella vulgaris showed growth potential using nutrients normally considered contaminants in wastewater. It follows the tenets of industrial ecology in being a waste-to-value technology, which converts a waste stream into an input to another inefficient process and takes a large footprint off the environment by reducing the environmental load from wastewater discharge and building up a renewable resource. Under an economic analysis, this integrated approach focuses on two main cost centers of microalga production: cultivation and harvesting. The ECF process for the destruction of algal cells, through an electric current, concurrently highlights advantages in terms of less energy requirements and lower consumption of chemicals as compared to conventional methods like flocculation or centrifugation. This joint optimization could effectively reduce the production cost by orders of magnitude, boost economic competitiveness, and reduce environmental damage from algal biorefineries. Future directions based on our findings, this work opens several paths for future research. The next logical progression is to apply this process at a larger scale, scaling up from the laboratory-scale reactors used in these studies to pilot-scale systems. It would be important to do so to test the best environment we had defined with synthetic substrate using real municipal wastewater under more dynamic, real-world conditions. While the study reported herein is centered on maximizing biomass yield (of Chlorella vulgaris), the underlying principles of our strategy can readily be applied to other microalgal species/production systems. For instance, RSM is a strong statistical tool that should be easily adjusted to develop the optimum nutritional requirements of other species. It is also hypothesized that the efficiency of the ECF harvesting process is demonstrable from laboratory through pilot to a commercial application and the ECF operation parameters can also be considered as a function of biomass concentration and reactor volume, thus making it a practical and cost-effective method for various-scale algal biorefinery operations. We think these points not only augmented the contribution of our paper but also could lay a solid initiative for both further research and industrial implementations. Economic feasibility analysis of these combined systems also needs further assessment, such as the operational cost in terms of electricity consumption for ECF as well as the market value of various algal bioproducts. Additional technoeconomic evaluations are required to assess the monetary benefits of coupling wastewater treatment with the production of valuable microalgal metabolites to improve sustainability and economic viability of biorefineries. Furthermore, assessing the implications of integrating this biorefinery concept with the infrastructure of traditional WWTPs could substantially decrease capital costs and enable a productive interaction of urban waste management with sustainable bio-production. This truly synergistic approach is compatible with the principles of the circular bioeconomy, which advocates that wastewater should not be treated as waste but rather regarded as a valuable product for waste management, environmental protection, and sustainable resource procurement.

The optimized nutrient regimes could be investigated in future research to determine how this composition could be further customized to synthesize alternative high-value algal biocompounds, including lipids for biodiesel or proteins for animal feed—which would also expand the product range of the algae-based biorefinery. Beyond fuels, the valorization potential of the harvested biomass as a biofertilizer in regenerative agriculture systems makes it a possible candidate for supporting nutrient loops essential to sustainable food production and the circular economy. The value of this work lies in its foundational nature; a resilient and socio-environmentally impactful bioprocess would become the outcome of the following steps. Although this work concentrated on the maximization of the synergistic response among principal macronutrients, trace elements are also essential for macroalgal processes and growth in general. These nutrients, even in minute doses, are essential for enzymatic function, photosynthesis and cell division. Hence future studies should include the statistical optimization of dosages of essential trace elements to increase the yields of biomass as well as the wanted biochemical composition. This next phase will provide valuable information on the synergetic nutritional motives for creating sustainable, high yielding and low-cost algal biorefinery systems.

The results of this study confirm that a combined approach to increasing microalgal biomass yield and recovery efficiency is reasonable for biorefinery purposes. Our approach that incorporates macronutrient optimization together with electrochemical harvesting is one such example, which is demonstrated here, and which is being used to confirm existing knowledge and provide new knowledge. We in this study considered the case of Chlorella vulgaris, yet principles of our methodology can be extended to other microalgal strains and culture setups. RSM is a well-established statistical model that can be modified to optimize the nutrient requirements of a given species. Likewise, the technique of ECF harvest is more energy-efficient than the conventional processing with centrifugation since ECF harvest reduces energy consumption and mechanical equipment complexity; that is, less power input is needed. Although we concentrated on macronutrients, we were aware of the importance of trace elements in microalgal metabolism. Thus, further studies should consider optimizing these factors for a better understanding of feeding needs. This combined approach enriches the contribution of this work and integrates the field for future research and sustainable industrial applications.

4. Conclusions

This investigation demonstrated the synergetic effects of macronutrient optimization and ECF harvesting on algal biomass productivity and biorefinery efficiency. Results indicated that RSM was a suitable statistical tool to optimize nutrient regimes for Chlorella vulgaris cultivation. The best nutrient conditions were 100.00 mg/L NaNO₃, 222.12 mg/L KH₂PO₄ and 100.84 mg/L MgSO₄. The prediction accuracy of maximum values was biomass: 0.475 g/L, carbohydrate content: 32.79% and chlorophyll-a concentration: 6.79 mg/L. Experimental confirmation was obtained for the synergistic effect of the integrated strategy. These macronutrient optimizations, especially the substantial effect of KH₂PO₄ on biomass and the significant quadratic term for KH₂PO₄ on chlorophyll-a, directly reflect microalgal growth improvement and photosynthetic state. In this way, high-density cultivation provides a strong feedstock material that is ideal for the downstream harvesting stage. Moreover, the ECF approach was further improved to ensure a biomass recovery of 89.51%. This establishes a new basis for innovative designs and operational methods for sustainable and cost-effective algal biorefinery systems. It utilizes a holistic approach to optimize both macronutrient utilization and ECF harvesting. The integrated, two-stage optimization strategy offers a sustainable and cost-effective pathway toward economically viable algal biorefineries by addressing significant cost bottlenecks associated with combined culture and harvesting operations.