Growth Conditions and Growth Kinetics of Chlorella Vulgaris Cultured in Domestic Sewage

Abstract

To assess the feasibility of achieving the dual objectives of domestic wastewater treatment and biomass accumulation, growth kinetic models were used to analyze the growth pattern of Chlorella in domestic wastewater. The logistic model simulated the growth trend of Chlorella in domestic wastewater better than the other two models. However, the currently developed model still cannot fully predict the growth of Chlorella. Factors such as nutrient removal and aging and the death of algae need to be taken into account to develop a more accurate model.

1. Introduction

The content of nitrogen and phosphorus in domestic sewage is rich, and a large number of inorganic elements such as nitrogen and phosphorus enter the relatively closed and slow water bodies, which makes it easy to cause the eutrophication of the water body. At present, the secondary biochemical sewage treatment method can only remove 30~50% of nitrogen and phosphorus.

The main role of the tertiary treatment of sewage is to further remove other pollution components such as nitrogen, phosphorus, fine suspended solids, trace organic matter, and inorganic salts in the sewage secondary treatment. Due to the high cost and secondary pollution of the traditional three-level treatment method (physical and chemical method), more and more scholars have begun to pay attention to the microalgae biological treatment method.

Due to similar nutrient composition, domestic wastewater can be used as an alternative nutrient source for microalgae [1,2]. To prevent the eutrophication of the stowed water, biological treatment is required to reduce the nitrogen and phosphorus content prior to discharge [3,4]. Therefore, using domestic wastewater for microalgae cultivation can achieve both the enrichment of biomass and the effective purification of nitrogen, phosphorus, and organic matter in the water body. Chlorella is a good option to achieve a simultaneous microalgae culture and wastewater treatment; it can absorb elements such as nitrogen, phosphorus, and carbon from the environment for the synthesis of cellular components [5,6].

There are still many uncertainties in microalgae culture technology based on domestic wastewater [7,8]. Microalgae themselves have specific growth rates, which are mainly related to the activity of relevant enzymes inside the cells and nutrient reserves in the environment. Therefore, three types of growth conditions, initial pH, light intensity, and algae addition, were selected for the study. Subsequently, the growth kinetic analysis and evaluation were performed to illustrate the growth pattern of Chlorella in domestic sewage medium and the effectiveness of the growth kinetic model applied in this scenario.

The purpose of this study is to analyze the causes of model errors by evaluating the application effects of the three most widely used kinetic growth models in specific situations (domestic sewage culture of Chlorella). It provides a reference for the future development of the kinetic growth model, which is more suitable for microalgae cultivation in wastewater, and achieves the dual goals of wastewater treatment and microalgae cultivation.

2. Materials and Methods

2.1. Culture Medium Culture

The algal species used for the study were purchased from the Freshwater Algal Species Bank of the Chinese Academy of Sciences: Chlorella vulgaris (No. FACHB31). Before the experiment, Chlorella vulgaris was preserved and cultivated in BG11. The culture process was carried out in a constant temperature light incubator with a light intensity of 50 μmol·m⁻²·s⁻¹, a light time of 24 h, and a temperature of 25 °C. The composition and content of BG11 are shown in

Table 1. BG-11 composition and content.

Chemical Composition	Dosage (mg·L−1)
NaNO3	1500
K2HPO4	40
MgSO4·7H2O	75
CaCl2·2H2O	36
C6H8O7	6
C6H8FeNO7	6
EDTANa2	1
Na2CO3	20
H3BO3	2.86
MnCl2·4H2O	1.86
ZnSO4·7H2O	0.22
Na2MoO4·2H2O	0.39
CuSO4·5H2O	0.08
Co(NO3)2·6H2O	0.05

.

2.2. Domestic Wastewater Culture

Domestic sewage samples were collected from the Guodian North Sewage Treatment Plant (Shenyang) secondary treatment entrance point. The water quality is shown in

Table 2. Water quality index of the domestic wastewater.

Water Quality Indicators	CODcr	NH3-N	TP	TN	pH
Concentration (mg·L−1)	195–200	42–46	4.1–4.3	81–86	8.1–8.2

. The collected wastewater samples were allowed to stand for 24 h, and then the supernatant of the wastewater samples was filtered using a GF/C filter with a pore size of 1.2 mm to avoid any effect during the growth of Chlorella due to the presence of suspended solids. The TN (total nitrogen), TP (total phosphorus), NH₃-N, and COD (chemical oxygen demand) in the filtrate were analyzed by the national standard water sample detection method, as shown in

Table 3. Determination method of the water quality index.

Testing Index	Method
NH3-N	HJ 535-2009 Nessler ‘s reagent spectrophotometry
TN	HJ 636-2012 Alkaline potassium persulfate digestion UV spectrophotometry
TP	GB 11893-1989 Ammonium molybdate spectrophotometric method
COD	GB/T 11914-1989 Dichromate method

. The algae species cultured in the medium were inoculated into the filtered domestic sewage for experiments. The experimental process was carried out in a self-built photobioreactor, shown in Figure 1, which can stably control the ambient temperature, light intensity, and illumination time.

2.3. Collection of Chlorella

After Chlorella reached a stable growth period, aeration to the culture medium was stopped. Chlorella settled naturally for three days. Then, two different layers formed, and the upper layer containing water was suspended. Chlorella cells and the bottom layer contained concentrated Chlorella. The algal cells in the reactor at the end of the culture were enriched using a high-speed centrifuge (5000 r/min, 10 min, RCF:1100 g) and then transferred to a culture dish and dried using a blast drying oven with heating for 24 h (65 °C). The dried algal slurry was ground into powder and stored airtight in a dry environment at about 10 °C for backup.

2.4. Measurement of Chlorella Biomass

Stem cell weight is commonly used to determine the biological yield of a biological process, and it is obtained by measuring the total suspended solid concentration in the medium. The absorbance of the algal solution at 680 nm was measured using a spectrophotometer, followed by drying the samples in an oven at 65 °C for 24 h and then weighing them. In this way, the relationship between the concentration of algal cells and the absorbance of algal solution was established, and the standard curve was plotted. The correlation between microalgal biomass concentration and the optical density was determined by Equation (1).

$$N_x=0.3638O{D}_{680}-0.0294\left(R^2=0.9983\right)$$

(1)

$$\mathrm{Specific} \mathrm{growth} \mathrm{rate}: \mu =\frac{ln\frac{N_2}{N_2}}{t_2-t_1}$$

(2)

$$\mathrm{Biomass} \mathrm{production}: P=\frac{N_2-N_1}{t_2-t_1}$$

(3)

The specific growth rate (μ) and biomass production (P) were determined by Equations (2) and (3), respectively, where N₁ and N₂ are the biomass (g/L) at t₁ (days) and t₂ (days), respectively.

2.5. Kinetic Growth Models

Three nonlinear mathematical models were used to predict the growth of Chlorella in different concentrations of domestic wastewater. The experimental data were referenced to the biomass production of Chlorella when different volumes of wastewater were added to the culture setup.

The logistic model was originally developed by Pearl and Reed to describe the growth process of organisms based on the initial amount, growth rate, time, and final amount [9,10].

$$y=\frac{A+C}{1+ex{p}^{-B\left(t-M\right)}}$$

(4)

The Gompertz model has been widely used in the literature, and most of the kinetic data are described based on the model shown below [11]:

$$y=A+Cex{p}^{-\exp \left\{-B\left(t-M\right)\right\}}$$

(5)

The Richards model is a four-parameter model and is shown below [12]:

$$y=A{[1+vexp\left\{k\left(\tau -t\right)\right\}]}^{-\frac{1}{v}}$$

(6)

A: $ln\frac{x_t}{x_0}$(asymptotic value at t→0); C: $ln\frac{x_t}{x_0}$ (asymptotic value at t→∞); B: relative growth rate at time m (day 1); t: residence time (s); M: time to reach maximum growth rate (s); X_t: biomass concentration at t (g·L⁻¹); X₀: initial biomass concentration (g·L⁻¹); τ: lag time of biological decay point; v, k: parameters

2.6. Methods for Studying the Growth Kinetics of Chlorella Vulgaris

This study used a nonlinear regression technique to solve the growth model. The algorithm for the analysis of microalgal growth kinetics was as follows: (1) values were calculated from the experimental data; (2) data were loaded into the IBM SPSS Statistics (hereafter SPSS) program; (3) the corresponding growth models were then inserted into the SPSS program under the nonlinear column; (4) after the respective models were inserted and checked, the program showed the independent and model variables; (5) initial estimation of the parameter values; (6) the accuracy of the growth kinetic model predictions was assessed using S², R², RMSD, and prediction plots with residual plots.

$$R^2=1-\frac{{{\displaystyle \sum }}_{i=1}^n{(y_{iobs}-y_{calc})}^2}{{{\displaystyle \sum }}_{i=1}^n{(y_{iobs}-\overline{y})}^2}$$

(7)

$$\overline{y}=\frac{1}{n}\left({{\displaystyle \sum }}_{i=1}^n{y}_{iobs}\right)$$

(8)

$$S^2=\frac{{{\displaystyle \sum }}_{i=1}^n{(y_i-\overline{y})}^2}{n-1}$$

(9)

$$RMSD=\frac{1}{n}{\left\{{{\displaystyle \sum }}_{i=1}^n{(y_{iobs}-y_{calc})}^2\right\}}^2$$

(10)

In Equations (7)–(10), the subscript n refers to the number of detections, obs refers to the actual detection data, and calc refers to the model calculation data.

3. Results and Discussion

The results of the significance test (

Table 4. The significance test results of influencing factors.

Factor	Coefficient	Standard Error	p Value	95% Confidence Interval
				Low	High
Temperature	0.051	0.011	0.0009	0.027	0.075
pH	−0.076	0.011	<0.0001	−<0.1	−0.052
Microalgae inoculum concentration	−0.04	0.011	0.0043	−0.064	-0.016

) showed that temperature, pH, and algae inoculation concentration had significant effects on the specific growth rate of Chlorella (p < 0.001).

3.1. Effect of Light Intensity

Microalgal growth is influenced by different factors, among which light intensity is one of the most important ones. Light is the necessary source for the autotrophic growth of microalgae and the most important element for photosynthetic activity. It contributes to cell proliferation, respiration, and photosynthesis. Microalgae need light to produce ATP and NADPH and to synthesize molecules necessary for growth. The optimal light intensity for growth and biomass production varies mainly from one microalgal species to another. In culture, the biomass of microalgal species usually increases with increasing light intensity due to the higher absorption and utilization of light by the photosynthetic machinery. However, at high light intensities, beyond the saturation point, photoinhibition is observed due to photooxidation reactions occurring within the cells. This saturation point depends on the particular algal species and culture conditions. In the present study (Figure 2 and Figure 3), light intensity was found to have a large effect on the growth and biological yield of Chlorella. In the constant temperature experiment, the specific growth rate and biomass production of Chlorella increased with increasing light intensity. However, when the light intensity reached a certain range (>150 μmol·m⁻²·s⁻¹), both data of Chlorella showed a decreasing trend, and moreover, a precipitous drop occurred when the light intensity exceeded 175 μmol·m⁻²·s⁻¹.

3.2. Effect of Initial pH

Environmental acidity and alkalinity have important effects on the growth and metabolism of microalgae cells. Neutral or weakly alkaline environments are suitable for the growth of most microalgae [13]. However, there are some specific microalgal species (e.g., Duchenne) that are able to grow in extremely acidic conditions with pH values as low as 1 [14]. According to Goldman et al. (1982), pH in biomass media significantly affects the production of green microalgae (e.g., Chlorella) in continuous culture [15]. The extent to which cell metabolism is affected by pH determines the pH tolerance limit of microalgae. (Goldman, 1981) claimed that the maximum tolerance pH is not affected by the availability of inorganic carbon [15]. However, pH is a determinant of the growth and development of carbon species in water and regulates the availability of different carbon sources used for photosynthesis in microalgae. The inconsistency of CO₂ concentration in the ambient gas can lead to pH changes [16]. Figure 4 shows the effect of the pH of the medium on the growth of Chlorella. As can be seen from the figure, the growth of Chlorella vulgaris cultured at pH 3 and 5 showed an almost linear growth trend, and algal cell concentrations reached a maximum after 12 h, at about 0.15–0.25 g·L⁻¹. In environments with high pH, chlorella did not show a satisfactory growth curve. At pH 7, 9, and 12, the growth of Chlorella stagnated, and there was no significant increase in the biomass of microalgae after 12 days of incubation. As can be seen in Figure 5, the specific growth and biological yield of the medium with pH 3 were 0.1366 day⁻¹ and 0.0149 g·L⁻¹·day⁻¹, respectively, on day 1. The results indicated that the Chlorella used in this study was well adapted to the low pH medium, which facilitated the suppression of other biological contaminants (e.g., fungi) that may be present in the unsterilized wastewater medium. At a pH below 4.5, when CO₂ was dissolved in water, carbon in the medium was dominated in the form of free CO₂ molecules or CO₃²⁻. Therefore, this study speculated that Chlorella prefers to absorb carbonic acid (H₂CO₃) as a carbon source for growth [17].

3.3. Effect of Microalgae Inoculum Concentration

During the complete culture process, as the density of microalgae increases with time, the growth rate is slowed down by nutrient depletion, and this is accompanied by an increase in the density of microalgae, resulting in a gradual decrease in the light transmission of the culture system. Therefore, it is important to allow a uniform distribution of light in the microalgae culture system [18]. To increase the initial growth rate of microalgae and enhance their survival rate, a sufficient number of algal species should be added to the culture. Figure 6 shows the growth of microalgae at different inoculation concentrations. All five experimental groups of Chlorella showed similar growth trends throughout the incubation process. As can be seen in Figure 7, the highest biological yield was in the 0.02 v/v experimental group (0.0229 g·L⁻¹·day⁻¹), followed by 0.15 v/v, 0.10 v/v, 0.05 v/v, and 0.03 v/v. The highest specific growth rate of 0.1675 d⁻¹ was observed in the experimental group with the lowest inoculum concentration for Chlorella vulgaris.

Therefore, a certain reduction in algal concentration can increase the specific growth rate and thus facilitate the removal of nutrients from the wastewater.

3.4. Nutrient Removal

If domestic wastewater is discharged directly into a water body, then the high content of N, P, and organic matter in domestic wastewater can cause eutrophication of water bodies by direct discharge [19,20]. Elemental N in domestic wastewater exists mainly as NH₄⁺, NO₂⁻, NO₃⁻, and organically bound nitrogen, while phosphorus is usually present in the form of phosphate (PO₃⁴⁻) ions. Therefore, nutrients in domestic wastewater can be used for microalgal growth [21]. Microalgal systems can be used as tertiary treatment units in typical wastewater treatment plants to further improve the efficiency of removing the remaining NO₃⁻ after the post-denitrification process [22]. Figure 7 depicts the effect of the conventional incubation process in a laboratory setting (light intensity of 150 μmol·m⁻²·s⁻¹, light time of 24 h, temperature of 25 °C, pH of 3, and inoculation ratio of 0.03 v/v). Figure 8 shows the removal efficiency of TN, TP, NH₃-N, and COD in wastewater samples, which are 94.01%, 90.08%, 97.33%, and 85.37%, respectively, of the removal effect [23,24].

3.5. Growth Kinetic Studies of Chlorella Vulgaris

In recent years, mathematical models have been widely used to predict the growth trends of microorganisms, which are necessary in microbial growth studies and industrial microbiology [25,26,27,28,29,30]. In this study, three growth kinetic models were selected, namely Logistic, Gomperz, and Richards models.

From

Table 5. R², RMSD, and variance values of different growth models of Chlorella under different inoculation concentrations.

Model	0.03 v/v	0.05 v/v	0.1 v/v	0.15 v/v	0.2 v/v
R2
Logistic	0.99639	0.9907	0.99482	0.99242	0.99396
Gompertz	0.99461	0.98672	0.99106	0.97707	0.97785
Richards	0.99677	0.99096	0.99454	0.98988	0.99192
RMSD
Logistic	0.04946	0.6967	0.0365	0.03752	0.03119
Gompertz	0.05699	0.07864	0.0452	0.06152	0.0563
Richards	0.04681	0.06883	0.03748	0.04335	0.03606
Variance
Logistic	0.01957	0.03897	0.01066	0.01126	0.00778
Gompertz	0.02923	0.05566	0.01839	0.03406	0.02853
Richards	0.01753	0.0379	0.01124	0.01504	0.0104

, the three models fit better (R² > 0.99) at an algal inoculum concentration of 0.03 v/v (hereafter 0.3 v/v). However, successive additions of algal species to the microalgal culture resulted in a decrease in R² values (R² < 0.99) for some of the models. This may be due to the inaccuracy of the kinetic model for inferring the decaying growth of microalgae. As seen in Figure 9, the growth of Chlorella predicted by the kinetic model coordinated well with the experiments performed in the 0.03 v/v group. However, in the experiment at 0.2 v/v, the deviation of predicted values increased in the 0.2 v/v group experiments. Since all three models did not take into account the intra-species competition of Chlorella during growth and the aggregation of algal cells due to insufficient aeration disturbance, it led to an increase in the prediction error when the inoculum concentration increased. The Gompertz growth kinetic model also showed higher R² (R² = 0.99461) and lower RMSD values (0.5699) in the 0.02 v/v group, but they were more discrete from the measured values than the Logistic and Richards models predicted values.

Figure 10 reflects the difference between the predicted values and the actual growth of Chlorella. Good residual plots must have y-values close to the x-axis and should not have a random distribution. From Figure 10c,d, the Gompertz model shows a higher order S-shaped curve distribution along the x-axis, which indicates that the model was inaccurate in fitting the experimental data. In particular, Figure 10a shows a greater convergence of the data to the x-axis compared to the other groups. This observation suggests that the logistic model predicts less error than the other growth models and, therefore, can represent the growth of Chlorella vulgaris, but only in the case of cultures with low inoculum concentrations. At higher inoculum concentrations (e.g., 0.2 v/v), it can be seen that the calculated values of all three kinetic models shown in Figure 10b,d,f deviate more from the X-axis than at low inoculum concentrations (0.03v).

4. Conclusions

Using domestic wastewater as a nutrient source to culture microalgae can further improve the sustainability of this renewable feedstock.

(1) This study demonstrated the considerable degree of feasibility of culturing Chlorella in domestic wastewater. The fastest growth rate was achieved at an algal inoculum of 0.03 v/v, an initial pH of 3, a temperature of 25 °C, and a light intensity of 150 μmol·m⁻²·s⁻¹, with a biological yield of 0.0229 g·L⁻¹.

(2) The nutrient removal effect of Chlorella in domestic wastewater was better during the culture process, and the removal rate reached more than 85% in all cases.

(3) After comparing the three types of kinetic growth models, the logistic model simulated the growth trend of Chlorella in domestic wastewater better compared to the other two models. However, when the algal inoculum concentration increased, the model did not include the simulation calculation of the microalgal death process, which made the error between the calculated data and the experimental data increase.

(4) Existing growth kinetic models cannot fully and accurately describe the growth process of microalgae, and more stable models need to be developed by considering various factors such as changes in nutrient transport, senescence and death of microalgae, and intra-species competition.