Producing Chlorella vulgaris in Ricotta Cheese Whey Substrate

Abstract

Ricotta cheese whey (RCW) is a by-product with nutritional potential, but its use in the human diet is limited due to its high salinity. Chlorella vulgaris can use RCW as a substrate to enhance biomass productivity. The aim of this work was to evaluate different conditions for C. vulgaris growth in RCW, during scaling-up analysis. After preliminary assays to select growth conditions, two systems were prepared as follows: 500 mL Erlenmeyer flasks (control-system) and a 3 L Bioreactor. Microfiltrated RCW was used as a substrate for C. vulgaris LPMA39 production. Biomass was measured and productivity at 96 h, cell growth kinetics behaviour, biomass biochemical characterisation, and the efficiency of nutrient removal were determined. Both systems presented the same biomass concentration at 96 h (2.2–2.8 g·L⁻¹) and productivity (0.021–0.027 g·L⁻¹·h⁻¹). Nevertheless, 11 h lag-period for cell adaptation to the 3 L Bioreactor was required; thereafter, cells grew faster (µ_max: 0.32 ± 0.08 h⁻¹) than control-system. Finally, slight but significantly lower C_max: 2.14 ± 0.08 was obtained when comparing it to control-system. Lipids, proteins, and pigment contents decreased by the scaling-up; meanwhile, higher reduction in chemical oxygen demand (COD), total phosphorus, and total nitrogen were recorded in the 3 L Bioreactor. Identifying the operating conditions that improve C. vulgaris performance in non-diluted RCW remains a challenge from a sustainability standpoint.

1. Introduction

The dairy sector, a pivotal component of the global agri-food system, is projected to experience mounting pressure to enhance its production capacity in response to rising demand. By 2031, global milk production is estimated to reach 1060 million metric tons, with approximately 30% of this volume expected to be converted into value-added dairy products, predominantly cheese [1]. For Argentine economy, dairy industry is one of the most important sectors, presenting in 2024 a generated gross value of US$ 13,216 million (considering industrial and primary production) and exports of US$ 1412.7 million [2]; during 2022, cheese production reached 460,000 tons [3]. Cheese production presents high water demand as well as effluent generation. Ricotta cheese whey (RCW) is the by-product obtained after cheese whey protein precipitation by heat or acidic treatment. It contains high chemical oxygen demand (COD) (50,000–80,000 mgO₂·L⁻¹), elevated total solids, salinity, and an acidic pH range of 4.5–6 [4,5]. Despite the nutritional potential of this dairy by-product, it has limited use in human diet due to its high salinity. Its disposal also represents a significant environmental challenge due to the high organic load, as well as nitrogen compounds, unpleasant odours, and intense turbidity [6]. In addition, high COD values could cause eutrophication if RCW was discharged untreated into water bodies. Recovering nutrients remaining in by-products or wastewater reduce waste generation and thus contribute to some of the actions required to achieve Sustainable Development Goals [7]. Bioprocesses can use RCW as microbial carbon source, mainly due to the lactose content [8].

Biological methods, particularly those employing microalgae, are gaining prominence as sustainable and cost-effective strategies for treating complex wastewater. Micro-algae are unicellular photosynthetic organisms inhabiting freshwater and marine ecosystems. They encompass eukaryotic genera (e.g., Chlorella, Scenedesmus, Dunaliella) and cyanobacteria (e.g., Arthrospira, Anabaena, Synechococcus). The microalgae’s adaptability and photosynthetic capacity enable oxygen generation, which supports the degradation of organic substrates and enhances overall treatment efficiency [9,10]. Moreover, their rapid growth, structural simplicity, and capacity to use wastewater as a substrate for enhancing biomass productivity and corresponding metabolites position microalgae as promising candidates for sustainable circular bioeconomy applications [10,11]. Microalgae, such as Chlorella, exhibit metabolic versatility by assimilating a broad spectrum of organic and inorganic substrates for growth [9,12,13], enabling the use of diverse wastewater streams as cost-effective nutrient sources. Nevertheless, some wastewater, like RCW, presents physicochemical features, such as hindering light penetration and altering osmotic balance, thereby reducing microalgal photosynthetic activity and biomass yield.

Previously, a non-axenic strain (Chlorella vulgaris LPMA39) isolated from a Patagonian river could be cultivated in a high COD brewery wastewater, by heterotrophic way [11]. Nevertheless, when the same microalga strain was cultivated in RCW, mixotrophic way was required, observing that turbidity affected Chlorella growth. RCW pre-treatments, by means of tangential flow microfiltration (TFMF) with a 12 h photoperiod with cool-white-fluorescent light, enabled microalgae to grow in 24–400 mL orbital shacked flask [4].

Efficient use of nutrient input is also a significant issue in large-scale microalgal cultivation [14]. Scaling up to stirred-tank photobioreactors introduces new challenges, including shear stress, light distribution, mixing efficiency, and mass transfer limitations, which can markedly influence cell growth and metabolite production. Metsoviti et al. [15] studied light sources for C. vulgaris growing in 50 L photobioreactor; basal medium was used in that opportunity. Grubišić et al. [16] studied the cultivation of C vulgaris S2 strain in a growth medium formulated mainly with mono- and disaccharides as a carbon source, by means of a 2 L stirred tank. Recently, Sciuto et al. [9] reported a review of the latest advances related to the valorization of RCW applying microalgae-based treatments. Specifically for this dairy wastewater, four studies were identified, each evaluating different microalgal species; only one of them employed C. vulgaris. In general, pretreatment of RCW, dilution, autoclaving, or filtration were needed, except for G. sulphuraria that could grow at low pH and without RCW pretreatment, in a 1 L Erlenmeyer bioreactor. To our knowledge, very little data were reported about C. vulgaris cultivation in microfiltrated RCW as the only source for growth medium preparation.

The aim of this study was to evaluate different conditions during the cultivation step—such as agitation, illumination, pH, inoculum concentration, and scale-up—in order to determine whether new challenges need to be addressed to improve the biomass production of this C. vulgaris strain in this substrate without dilution, saving drinking water and new inputs to the system.

2. Materials and Methods

2.1. Materials

The microalga Chlorella vulgaris LPMA39 was generously provided by the Patagonian University San Juan Bosco (Trelew, Chubut, Argentina), from the culture collection of the Microalgae Laboratory. The cultures were maintained using BG11 medium prepared as explained by Stanier et al. [17]. The analytical grade chemical reagents were used for the analysis (Research AG. S.A., CABA, Argentina) For the experiments, RCW was supplied by Javifer (Marcos Paz, BA, Argentina). RCW was pre-treated by tangential flow microfiltration (0.1 μm pore size hollow fibre (1 mm fibre id) autoclavable polysulfone membrane of 420 cm² (GE Healthcare Life Sciences, Model CFP-1-E-4A, Mississauga, ON, Canada)) [4]. The microfiltrated ricotta cheese whey (RCW-TFMF) used for this test presents a COD of (78 ± 1) × 10³ mgO₂·L⁻¹ and contains total phosphorus (TP), 320 ± 2 mgP·L⁻¹, and total nitrogen (TN), 320 ± 5 mgN·L⁻¹.

2.2. Selecting Light Source and Optimal Mixing Speed Determination

The trial consisted of exposing mixotrophic cultures of Chlorella vulgaris to two different light sources, both emitting white light: fluorescent light and LED light, with a 12/12 h light/dark photoperiod, at photosynthetically active radiation (PAR) of 50 µmol·m⁻²·s⁻¹ (LI-COR, LI-250–LI-190 Quantum sensor, Lincoln, NE, USA). The systems were prepared by adding 5 mL of inoculum to 45 mL of RCW-TFMF medium in 250 mL Erlenmeyer flasks, based on previous results [4]. Continuous orbital shaking was applied at a speed of 100 rpm. This experiment was performed in duplicate. Thereafter, four experimental conditions were evaluated as follows: static (0 rpm) and with continuous orbital agitation at 50, 100, and 150 rpm (Cole Parmer, model OS-200, Vernon Hills, IL, USA). Each condition was tested in triplicate, incorporating 5 mL of inoculum into 20 mL of RCW-TFMF in 125 mL Erlenmeyer flasks. The cultures were incubated as explained for 96 h at 26 ± 2 °C, with a 12 h light/12 h dark photoperiod, starting with the white light period under LED illumination (50 μE·m⁻²·s⁻¹). The performance of each system was evaluated by its productivity, calculated based on the dry biomass quantified at the beginning and at the end (96 h) of the experiment.

2.3. Selecting Initial pH and Inoculum

The effect of the initial pH (pHi) and inoculum concentration (Ci) on the microalga productivity and the pH achieved at 96 h (pHf) were studied. The systems were prepared by adding 5 mL of inoculum to 45 mL of RCW-TFMF to achieve different Ci, according to the experimental design, using 250 mL Erlenmeyer flasks. The pH of each system was adjusted after inoculation by adding sterile 1 M NaOH and 1 M HCl solutions. The inoculated samples were cultured for 96 h at 26 ± 2 °C, with a 12/12 h light/dark photoperiod PAR of 50 µmol·m⁻²·s⁻¹, starting the cycle with 12 h of light. In addition, continuous orbital shaking was applied at a speed of 100 rpm.

2.4. Scaling Up

The scaling-up process consisted of comparing the following two systems: cultures in 500 mL Erlenmeyer flasks with 200 mL of medium (control system) and a 3 L stirred-tank bioreactor (Applikon Biotechnology, Mod MYCONTROL, Delft, The Netherlands), containing 2 L of culture. Temperature was measured and controlled at 26 ± 2 °C. The pHi was fitted at 8.00 and recorded but not controlled during fermentation. No air, CO₂, nor other pH regulators were added during fermentation, in order to replicate the criteria used in lower-scale systems. Accordingly, the same external LED illumination was applied, with a 12/12 h light/dark photoperiod and a PAR of 50 µmol·m⁻²·s⁻¹. Supplementary figures are provided to show the device configured to deliver the external LED illumination (Figures S3 and S4, Supplementary Material). The bioreactor was configured with a marine impeller, chosen for its ability to generate a more efficient axial flow than other impellers to prevent sedimentation of C. vulgaris LPMA39 cells [18]. To determine the stirring speed, the energy dissipation rate per unit volume (P_V) of the culture performed in the stirred Erlenmeyer flasks was used as the scaling criterion [19,20]. The process began by calculating the Reynolds numbers (Re) for fluids under agitations according to Equation (1).

$$\mathrm{Re}={\displaystyle \frac{\mathrm{N}·{\mathrm{ds}}^2·\mathsf{\rho }}{\mathsf{\mu }}}$$

(1)

where N is the rotational speed and ds is the impeller diameter for the stirred tank bioreactor. In the case of an Erlenmeyer flask, the maximum diameter inside was selected as the characteristic length [19,21]. The density (ρ) of the RCW-TFMF taken as a reference was 1.023 ± 0.004 g·mL⁻¹ [22]. On the other hand, regarding viscosity (μ), the value for water at the working temperature was used [23].

From the Re values, the Power Number (N_p) was estimated for dinoflagellate cultures in the Erlenmeyer flask, according to Equation (2) [18,24], where VL is the culture volume, which allowed for the calculation of the power consumed, Equation (3) and, subsequently, the energy dissipation rate, or power, per unit volume, P_V, Equation (4).

$${\mathrm{N}}_{\mathrm{p}}={\displaystyle \frac{1.94·{\mathrm{VL}}^{1/3}}{{\mathrm{Re}}^{0.2}·\mathrm{ds}}}$$

(2)

$$\mathrm{P}={\mathrm{N}}_{\mathrm{p}}·\mathsf{\rho }·{\mathrm{N}}^3·{\mathrm{ds}}^5$$

(3)

$${\mathrm{P}}_{\mathrm{V}}={\displaystyle \frac{\mathrm{P}}{\mathrm{VL}}}$$

(4)

A constant N_p value of 0.5, characteristic of the turbulent regime for marine propellers [25], was used for the bioreactor, and it was verified that Re ≥ 10,000. From the P_V obtained in the Erlenmeyer flask cultures, the power was determined and by inverting the calculations, the agitation speed required in the bioreactor (N) was also obtained.

2.5. Calculation of the Critical Eddy Length and the Cell Diameter of C. vulgaris

The critical eddy length (λ_C) was estimated based on Garcia-Camacho et al. [24], with minor modifications. Briefly, the energy dissipation rate per unit mass (P_m) was calculated using Equation (5), and subsequently, λ_C was determined using Equation (6). Cell size was analysed by performing a dimensional analysis of 5157 Chlorella vulgaris cells during 24 days of an autotrophic culture. Images of cells contained in the Neubauer chamber were analysed and size was measured considering the cells as spheric shape. Thereafter, cell size distribution was recorded and provided as the Supplementary Material, Figure S1.

$${\mathrm{P}}_{\mathrm{m} }={\displaystyle \frac{\mathrm{P}}{\mathrm{VL}·\mathsf{\rho }}}$$

(5)

$${\mathsf{\lambda }}_{\mathrm{C}}={\left[{\displaystyle \frac{{\left(\raisebox{1ex}{$\mathsf{\mu }$}\!\left/ \!\raisebox{-1ex}{$\mathsf{\rho }$}\right.\right)}^3}{{\mathrm{P}}_{\mathrm{m}}}}\right]}^{{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$4$}\right.}}$$

(6)

2.6. Biomass Quantification

Biomass concentration was measured through optical density (O.D.), cell count, and dry weight. O.D. was measured at 680 nm wavelength after appropriate dilution with deionized water in a UV-visible spectrophotometer (Shimadzu, model UV-1700, Tokyo, Japan) [4]. Cell counting was performed using a Neubauer chamber following the protocol of Moheimani et al. [26]. Cell dry weight (CDW) was quantified gravimetrically after centrifugation of the samples (Dragon Lab, model D2012, Beijing, China) at 6700× g for 5 min. The resulting pellet was washed three times with deionized water to remove residual salts [27] and centrifuged again after each wash. Finally, the biomass was dried at 60 °C and weighed until a constant weight was reached [28].

2.7. Kinetic Growth Parameters Determination

Growth kinetic parameters were determined by fitting experimental cell count to a modified Gompertz model [29]

$$\ln \left({\displaystyle \frac{\mathrm{N}}{{\mathrm{N}}_0}}\right)={\mathrm{C}}_{\max }·{\mathrm{e}}^{\left\{{-\mathrm{e}}^{\left[\left({\mathsf{\mu }}_{\max }/{\mathrm{C}}_{\max }\right)·\left(\mathsf{\lambda }-\mathrm{t}\right)+1\right]}\right\}}$$

(7)

where N and N₀ are C. vulgaris cell count at time t and at initial time, respectively. The λ parameter represents the latency or lag period for adapting cells to the new culture conditions; μ_max represents the maximum specific growth rate; and C_max represents the maximum cell ratio [ln (N/N₀)].

Total biomass productivity (P_x) was calculated from the initial to final time,

$${\mathrm{P}}_{\mathrm{X}} \left(\mathrm{g}·{\mathrm{L}}^{-1}·{\mathrm{h}}^{-1}\right)={\displaystyle \frac{{\mathrm{X}}_{\mathrm{f}}-{\mathrm{X}}_{\mathrm{i}}}{{\mathrm{t}}_{\mathrm{f}}-{\mathrm{t}}_{\mathrm{i}}}}$$

(8)

where X_f: cell concentration (g·L⁻¹) at final time, t_f (h); X_i: cell concentration (g·L⁻¹) at initial time, t_i (h).

Both systems, 500 mL Erlenmeyer and 3 L Bioreactor, were cultured for 96 h under the same temperature, light intensity, and photoperiod conditions described previously. For the P_x calculation, cell concentration (g·L⁻¹) at 96 h incubation was used. At least triplicate independent measurements were performed. Mean values ± SD were reported.

2.8. Biochemical Composition of the Biomass

For biochemical composition analysis, 2 mL aliquots of the culture were centrifuged for 15 min at 15,000 rpm. The resulting biomass was used to determine macromolecules and pigments. Lipid content was determined using the sulfo-phospho-vanillin (SPV) method described by Mishra et al. [30], using canola oil as the standard. Protein content was quantified following the method of Lowry et al. [31], using bovine serum albumin (BSA) as the standard. Carbohydrates were measured using the phenol–sulfuric acid method [32], using glucose as the standard. Chlorophyll and carotenoids were determined by biomass extraction with 90% acetone and sonication (Qsonica, model Q55, Newtown, CT, USA). Subsequently, a spectrophotometric scan was performed between 400 and 700 nm, applying the corresponding equations for the calculation of chlorophyll a (Chl a), chlorophyll b (Chl b), and total carotenoids (CT) [33,34,35]. Chemical composition was expressed as a percentage of the biomass in dry base (db). The biochemical composition of the biomass was evaluated after 96 h of cultivation. Mean values ± SD of triplicate measurements were reported.

$$\mathrm{Chl} \mathrm{a} \left(\mathsf{\mu }\mathrm{g}/\mathrm{mL}\right)=10.3·{\mathrm{abs}}_{663 \mathrm{nm}}- 0.918·{\mathrm{abs}}_{644 \mathrm{nm}}$$

(9)

$$\mathrm{Chl} \mathrm{b} \left(\mathsf{\mu }\mathrm{g}/\mathrm{mL}\right)=19.7·{\mathrm{abs}}_{644 \mathrm{nm}}- 3.87·{\mathrm{abs}}_{633 \mathrm{nm}}$$

(10)

$$\mathrm{CT} \left(\mathsf{\mu }\mathrm{g}/\mathrm{mL}\right)=4.20·{\mathrm{abs}}_{653 \mathrm{nm}}- 0.0264·\mathrm{Chl} \mathrm{a} \left(\mathsf{\mu }\mathrm{g}/\mathrm{mL}\right)- 0.496·\mathrm{Chl} \mathrm{b} (\mathsf{\mu }\mathrm{g}/\mathrm{mL})$$

(11)

2.9. Nutrient Removal

The efficiency of nutrient removal was determined by quantifying total phosphorus (TP), total nitrogen (TN), and COD in the RCW-TFMF medium before inoculation and at the end of the 96 h cultivation period. TN, TP, and COD were measured using the HACH 10072 method (persulfate digestion method) [36], AOAC 995.11 method [37], and HACH 8000 method (dichromate digestion method) [38], respectively. Removal efficiency was expressed as the percentage decrease relative to the initial values. Mean values ± SD of triplicate measurements were reported.

2.10. Statistical Analysis of Results

A central composite factorial design (3²) was applied for studying the effect of the pHi, in the range 7.00–11.00 and Ci into the O.D.₆₈₀ range of 0.1–0.5 on the microalga productivity and pH at 96 h. The central point was performed in triplicate, resulting in a total of 11 trials (n = 11). Systems and central points are summarised in

Table 1. Central composite factorial design (3²). Independent variables pHi and Ci [O.D.₆₈₀]. Response variable pHf measured at 96 h incubation.

Systems	Ci (O.D.680)	pHi	pHf
A c	0.3	9.00	8.19
B c	0.3	9.00	8.18
C c	0.3	9.00	8.07
D	0.2	8.00	7.90
E	0.4	8.00	8.05
F	0.2	10.00	8.45
G	0.4	10.00	8.49
H	0.1	9.00	8.24
I	0.3	11.00	9.11
J	0.3	7.00	7.97
K	0.5	9.00	8.39

^c Central Points.

. The results were analysed using response surface methodology, fitting experimental data to a second-degree polynomial function

$$\psi =B_0+B_1{x}_1+B_2{x}_2+B_{11}{x}_1^2+B_{22}{x}_2^2+B_{12}{x}_1{x}_2$$

(12)

where ψ is the dependent variable and x₁ and x₂ are the independent variables. B₀ is the value of the adjusted response at the central point of the design, B₁ and B₂ are the linear regression coefficients, B₁₁ and B₂₂ are the quadratic regression coefficients, and B₁₂ is the interaction coefficient. The coefficients of the terms of the polynomial equations were analysed using an analysis of variance (ANOVA) for each response with a significance level of 95%. The adequacy of the model was evaluated through the coefficient of determination (R²) and lack of fit test (p ≥ 0.05).

Statistical analysis of results was performed through ANOVA, or test-t, when corresponding, for a level of significance of 95% followed by a Tukey’s multiple comparison post-test to identify significant differences between samples. The non-linear growth fitted curves were validated with (R²adj) higher than 0.95 and a Durbin–Watson (DW) value higher than 1. The experimental design, corresponding analysis, as well as non-linear regression were performed using the Statgraphics Centurion XV program (V 2.15.06, 2007, The Plains, VA, USA).

3. Results

Pre-treating the RCW with TFMF technology improved the growing kinetic parameters of C. vulgaris by reducing the medium turbidity [4]. Therefore, light incidence seems to be a key factor for the growth of this C. vulgaris strain in this medium. Under these conditions, the P_x value determined under a 12 h light photoperiod with fluorescent illumination was 0.020 ± 0.001 g·L⁻¹·h⁻¹. Meanwhile, when LED light photoperiod was applied, P_x was 0.023 ± 0.004 g·L⁻¹·h⁻¹. No significant differences (p > 0.05) were observed; therefore, LED light was used for the continuing assays due to its advantages in terms of energy efficiency.

3.1. Optimal Mixing Speed

The biomass productivity P_x of C. vulgaris under different mixing conditions is shown in Figure 1. Cultures subjected to orbital mixing at 100 and 150 rpm showed significantly higher values (p < 0.05) compared to the non-agitated and 50 rpm agitated systems. No significant differences were observed between the cultures at 100 and 150 rpm (p > 0.05). Considering energy efficiency, a mixing speed of 100 rpm was selected as the optimal condition.

3.2. Selecting Initial pH and Inoculum

From the experimental design performed, experimental data fitted adequately to a second-degree polynomial function that described the response surface shown in Figure 2a. The Ci did not present significant effects on P_x, while pHi showed a significant (p < 0.05) quadratic effect on the P_x response. The negative quadratic effect means that there is an optimal pHi value that maximizes P_x in the range analysed. In this optimal condition, the pH was 8.01. Moreover, no significant effects of Ci or pHi were observed on pHf. As can be observed on Table 1, the pHf of systems measured at 96 h ranged 7.90–9.11.

3.3. Scaling Up

The hydrodynamic and dimensionless parameters used for scaling up the system are detailed in

Table 2. Hydrodynamic and dimensionless parameters for scaling up the system.

System-Capacity	VL (mL)	ds * (m)	rpm	Re	Np	P (w)	PV (w.m−3)
Erlenmeyer 125 mL	25	0.065	100	8067	0.14	0.00079	31.74
Erlenmeyer 500 mL	200	0.098	100	18,337	0.16	0.00696	34.79
Bioreactor 3 L	2000	0.047	500	21,088	0.50	0.06789	33.94

* ds: diameter of Erlenmeyer base or diameter of the bioreactor impeller.

. As can be observed, from the 500 mL Erlenmeyer, turbulent regime was obtained with the condition herein selected (Table 2). For reaching the similarity criterion for hydrodynamic viewpoint, the same regime and the same energy dissipation rate per unit volume (P_v) should be achieved, when scaling up to stirred tank. Therefore, for comparable conditions, the 500 mL Erlenmeyer, 100 rpm, was selected as the control system for comparison with the 3 L Bioreactor operating at 500 rpm. As can be observed, both systems operated in turbulent flow (Re > 10,000) and with similar P_V values (Table 2).

For cultivation in the bioreactor, a critical eddy length (λ_C) of approximately 70 μm was calculated with Equation (6). From the overall cell size distribution provided in the Figure S1, Supplementary Material, it can be seen that cell diameters ranged between 6.4 ± 1.5 μm and 12.9 μm. Thus, it was confirmed that the value of λ_C resulting from the application of an impeller speed of 500 rpm is significantly greater than the diameters of the C. vulgaris cells found in this study, therefore does not represent a risk for their integrity [24].

3.4. Kinetic Growth Parameters

The growth of C. vulgaris in both systems was adequately fitted to the modified Gompertz equation [29], with coefficients of determination (R²) of 0.9777 and 0.9757 for the control-system and for the bioreactor, respectively (Figure 3). The kinetic parameters derived from the fitting are presented in the table inserted in Figure 3. Meanwhile, biomass concentration (g·L⁻¹), as well as productivity, is shown in

Table 3. Biomass and productivity, obtained for control systems (Erlenmeyer 500 mL) and for scaled up (Bioreactor 3 L).

Parameter	Erlenmeyer 500 mL	Bioreactor 3 L
Xi (g·L−1)	0.243 ± 0.009 a	0.243 ± 0.009 a
Xf (g·L−1)	2.2 ± 0.5 a	2.8 ± 0.3 a
Px (g·L−1·h−1)	0.021 ± 0.005 a	0.027 ± 0.005 a
Px (g·L−1.d−1)	0.5 ± 0.2 a	0.7 ± 0.1 a
pHf	8.07 ± 0.03 a	8.17 ± 0.05 a

X_i: initial biomass concentration; X_f: final biomass concentration; P_x: biomass productivity; pH_f; pH at 96 h. Results are expressed as mean value ± SD, n = 3. Biomass concentration and productivity are calculated after 96 h of culture. The same superscript letters in the same row, indicate non-significant differences (p > 0.05).

, not presenting significant differences (p > 0.05) between the scales studied. In addition, both systems presented an average pHf of 8.12, near to the pHi adjusted at 8.00. An increase (p < 0.05) in the maximum specific growth rate (μ_max) was observed in the scaled-up system (0.32 ± 0.08 h⁻¹) compared to the control-system (0.24 ± 0.03 h⁻¹), although with a longer lag phase (λ = 11 ± 2 h) that could not be detected in the control-system (Figure 3).

3.5. Biochemical Biomass Composition

The biochemical composition of the biomass cultivated in both systems, 500 mL Erlenmeyer and 3 L Bioreactor, is summarized in

Table 4. Biochemical composition of C. vulgaris in the control system and in the scaled-up system.

	Erlenmeyer 500 mL	Bioreactor 3 L
Carbohydrates (g·100 g−1)	13 ± 3 a	10 ± 3 a
Lipid (g·100 g−1)	9.4 ± 0.2 a	4.9 ± 0.4 b
Protein (g·100 g−1)	22 ± 1 a	13.5 ± 0.7 b
Chlorophyll a (mg·g−1)	0.8 ± 0.1 a	0.34 ± 0.06 b
Chlorophyll b (mg·g−1)	0.7 ± 0.1 a	0.36 ± 0.07 b
Carotenoid (mg·g−1)	0.54 ± 0.07 a	0.26 ± 0.04 b

Results are expressed as: mean value ± standard deviation. Different superscript letters in the same row indicate significant differences (p < 0.05).

. Significant differences (α < 0.05) were detected in all macronutrients, except for carbohydrates. In particular, the lipid and protein content was higher in the control-system (9.4 ± 0.2 and 22 ± 1 g·100 g⁻¹, respectively) compared to the bioreactor (4.9 ± 0.4 and 13.5 ± 0.7 g·100 g⁻¹). Likewise, a significant reduction in photosynthetic pigments (chlorophylls and carotenoids) was observed in the scaled-up system.

3.6. Nutrient Removal

The efficiency of pollutant removal from RCW was superior in the scaled-up system for all parameters evaluated (

Table 5. Removal of environmental load from RCW by C. vulgaris.

Reduction (%)	Erlenmeyer 500 mL	Bioreactor 3 L
COD	14.7 ± 0.6 a	17.6 ± 0.5 b
TP	30 ± 1 a	96 ± 4 b
TN	39 ± 1 a	70 ± 2 b

Results are expressed as the average value ± standard deviation, n = 3; different superscript letters in the same row indicate significant differences (p < 0.05); COD: chemical oxygen demand; TP: total phosphorus content; TN: total nitrogen content.

). The reduction in COD reached 17.6 ± 0.5% in the bioreactor, significantly higher than in the control-system (14.7 ± 0.6%, p < 0.05). The removal of TP and TN was also significantly higher in the scaled-up system, reaching 96 ± 4% and 70 ± 2%, respectively, compared to values of 30 ± 1% and 39 ± 1% in the control- system.

4. Discussion

The identification of an optimal mixing speed of 100 rpm is consistent with previous research demonstrating that moderate agitation intensities favour algal growth by improving mass transfer without inducing hydrodynamic stress [39]. The selection of these conditions using LED lamp to provide light for the photoperiod is associated with cost-effective design. Under the conditions assayed herein, the pHi of 8.01 maximized biomass productivity independently from the initial inoculum. It is important to note that the systems showed a tendency to self-regulate pH, in the 96 h period herein studied, ending within the 7.90–9.11 pH-range. C. vulgaris acts as a biological driver that shifts pH upward in dairy wastewater. Luzzi et al. [40] studied C vulgaris sp. productivity in diluted dairy wastewater controlling the pH in the range 7.4–8.6 by adding CO₂. They reported that during the first four cultivation days CO₂ injection did not improve biomass productivity. As can be observed in Table 1, the systems with this microalga strain tended to the pH 8.3 ± 0.3 at 96 h naturally, without external controlling. This behaviour was also maintained in the scaled systems (Table 3), keeping average pHf of 8.12 at 96 h, near to the optimal statistically determined, 8.01.

Bioprocesses integrate substrate transport and biochemical reactions, with the overall conversion rate governed either by reaction kinetics or mass transfer limitations. When transport exceeds reaction rate, kinetics dominate; conversely, slower transport can become the rate-limiting step and may itself be influenced by reaction dynamics. Increasing agitation speed improved mass transfer rate. On the other hand, agitator tip speed determines shear stress in the tank, producing possible cell damage [41]. With the hydrodynamic criteria of keeping the energy dissipation rate per unit volume, P_v ≈ 34 w·m⁻³, it could be corroborated that a scaled-up 3 L Bioreactor stirred at 500 rpm produced the same biomass concentration and productivity as the 500 mL Erlenmeyer stirred at 100 rpm. In these conditions, cell damages were not detected since cell size < λc. Shear stress generated during mixing by microeddies of similar or smaller size than microalgal cells can produce damage and cell death [39]. The evaluation of Chlorella vulgaris performance in the systems herein assayed, with RCW as a growth media, showed results consistent with previous studies under mixotrophic conditions. The final biomass concentrations obtained for the 500 mL Erlenmeyer control-system were comparable to those reported by the authors previously, which achieved similar biomass concentration, 2.28 g·L⁻¹ [4]. By comparing it to other studies, it must be highlighted that, despite variations in media, culture systems, and operating conditions, the biomass productivity obtained herein (≈0.5–0.7 g·L⁻¹·d⁻¹) remains within the range reported in the literature. For example, Barghbani et al. [42] achieved similar productivity (≈0.51 g·L⁻¹·d⁻¹) under autotrophic conditions with constant aeration, while Soto et al. [43] reported significantly lower productivities (0.28 ± 0.15 g·L⁻¹·d⁻¹) using vinasse as a substrate. Lois-Milevicich [18], on the other hand, obtained a productivity of 0.28 ± 0.07 g·L⁻¹·d⁻¹ and a biomass concentration of 1.7 ± 0.2 g·L⁻¹, when the same C. vulgaris strain was cultured in diluted brewery effluent. Although the same productivity was obtained in both systems, the 3 L Bioreactor system needed 11 ± 2 h lag phase to be adapted before starting to grow. Thereafter, microalga cells grew faster compared to control-system (Figure 3), and as previously discussed, the stirring conditions calculated and used did not produce cell damages. This point could represent an advantage if pre-adapted cells were inoculated or if fed-batch or continuous culture mode were assayed, in this way trying to reduce the lag period.

The pigments’ (carotenoids, chlorophyll a, and chlorophyll b) concentrations were reduced in the scaled-up system 3 L Bioreactor compared to the 500 mL Erlenmeyer. In the cultures carried out in Erlenmeyer flasks, light can penetrate more efficiently due to its smaller volume and larger exposed surface area. The exposure to adequate light intensity stimulates the production of these compounds, which are essential for photosynthesis and cellular protection. Although the bioreactor allows a constant concentration of nutrients and aeration, the incidence of light may be less effective due to the geometry of the bioreactor and the possible shadowing generated by the biomass of the microalgae present in the medium. Pictures are provided in the Figures S3 and S4, Supplementary Material to help understand the external illumination system. This effect can limit photosynthesis and, therefore, pigment production [44,45,46]. Chlorella vulgaris exhibits a more efficient utilization of light energy under mixotrophic conditions. In this mode, chloroplasts play a key coordinating role in energy and carbon metabolism. The mixotrophic strategy enables C. vulgaris cells to exploit both light energy and organic carbon more effectively than in photoautotrophic or heterotrophic modes, thereby enhancing the accumulation of biomolecules such as chlorophyll, lipids, and proteins [47] Chlorella vulgaris growth increases with light intensity up to an optimum, but excessive light causes photo-inhibition. Khalili et al. [48] reported maximum growth at 80 µmol·m⁻²·s⁻¹, while 50 was insufficient and 110 was excessive. Growth was also higher under warm white light compared to red or blue wavelengths. Moreover, microalga metabolic can change in response to stress due to low light intensity and also to osmotic imbalance. Among the nutrients provided by RCW mineral salts have usually presented high content, ranged from 0.5 to 2.5% [5,49]. In the case of RCW-TFMF, herein assayed, it was supplemented with NaNO₃ by the authors to improve this C. vulgaris strain growing in a previous work. Contrary to expectations, the effect observed was significantly negative, by inhibition of C. vulgaris submerged in osmotic stress RCW medium [4]. According to Balasubramaniyan et al. [50], protein and lipid synthesis were affected by microalga environmental stress, diverting cellular metabolism towards osmotic regulation mechanisms. Under stress, microalgae exhibit increased respiratory activity, an energetically demanding process. This up-regulation may be supported by the catabolism of intracellular lipid reserves, which could account for the reduced lipid content. Despite controlling hydrodynamic conditions to avoid cell damage, stress responses mainly to low light intensity could explain the need for the acclimatization step of 11 h lag phase and the lowest pigments, lipid, and protein contents in the 3 L Bioreactor. It should be recommended to increase light intensity in future assays with this strain, and with this substrate.

Regarding lipids, despite the low concentrations recorded, these values are within the wide range reported in the literature. Safi et al. [51] indicated that the lipid content in C. vulgaris can vary from 5 to 58% db, depending on the culture conditions. Gao et al. [52] highlighted the key role of the carbon/nitrogen (C/N) ratio in this variability, observing a direct correlation between an increase in this ratio and lipid accumulation. Likewise, Ratomski and Hawrot-Paw [53] showed that nutritional stress conditions can substantially modify lipid profiles. Therefore, future research could explore optimizing the C/N ratio in the medium to increase lipid accumulation, especially if the cultivation is aimed at bioenergy or cosmetic applications. Regarding carbohydrates, although no significant differences were observed between the systems, the absolute levels are below those reported in studies that applied conditions designed to maximize this component. Choix et al. [54] and Ho et al. [55] achieved values higher than 50% db by imposing nitrogen deficiencies and using cell immobilization or process engineering strategies. This finding reinforces the idea that the final biomass composition can be significantly modulated according to the production objective by adjusting the culture conditions. Regarding protein content, the values obtained were lower than those reported by Peng et al. [56] and Lois Milevicich [18], who reported concentrations between 33% and 50% in cultures carried out in different effluents.

In terms of nutrient removal, the scaled-up system results showed a substantial improvement in TP and TN removal, achieving efficiencies of 96% and 70%, respectively. These values exceed those reported by Casá et al. [4], 75 ± 1% for TP and 55 ± 1% for TN. However, the efficiency in COD reduction was relatively low (17.6%), below the value reported by Soto et al. [43], which suggests the need to explore complementary strategies, such as the use of microalgae–bacteria consortia [57], capable of significantly improving this parameter stress to microalgal cells [58]. It was previously reported that the mesophilic aerobic microorganism belonging to this non axenic C. vulgaris strain was negatively affected by pH increasing, recording non-detectable count at pH 8 when brewery wastewater was used as substrate [11]. Therefore, considering the condition herein assayed the nutrient removal could be mainly attributed to C. vulgaris metabolism. The stress responses of microalgal cells previously discussed may explain the removal of TP by the cells from the bioreactor. Phosphate salts could be accumulated by C. vulgaris cells to maintain osmotic balance [50]. This new insight may clarify why neither biomass concentration nor productivity (g·L⁻¹·h⁻¹) changed, while C_max, protein, and lipid contents decreased.

5. Conclusions

C. vulgaris could be produced using RCW-TFMF as substrate, without dilution or adding of new inputs. When scaling up to the 3 L Bioreactor similar, biomass productivities to those of the reference system in laboratory scale could be obtained; nevertheless, adaptation phase of 11 h was required. The 3 L Bioreactor stirred at 500 rpm did not produce cell damage, improving µ_max. However, C_max value, lipid, protein, and pigments contents in the biomass decreased by the scaling up to the 3 L tank. In addition, TP and TN removal achieved efficiencies of 96% and 70%, respectively. Pre-adapted microalga cells, different light intensities or diverse culture modes, such as the as fed-batch or continuous mode, are recommended to be analysed in future studies for improving biomass productivity and composition as well as COD removing with this C. vulgaris strain. Searching the operation conditions for improving C. vulgaris performance in RCW, avoiding RCW dilution, continues to be a challenge from a sustainable viewpoint. Techno-economic analysis should also be performed to complete the quantification of this contribution to the dairy industry.