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# Optimization of *chlorella sorokiniana* growth via response surface methodology

Mahdokht Safaee *¹, Saba Abdolalian², Sepand Yamoot Moghaddam³

1. Department of chemical Engineering, Babol Noshirvani University of Technology (BNUT), Babol, Iran. Email: Mahdokhtsafaee73@gmail.com

2. Department of Civil and Environmental Engineering, Babol Noshirvani University of Technology (BNUT), Babol, Iran. Email: Saba.abdolalian71@gmail.com

3. Department of chemical Engineering, Babol Noshirvani University of Technology (BNUT), Babol, Iran. Email: Sepandym@gmail.com

## Abstract

Microalgae as typical organisms present in all freshwater systems are promising materials for various applications such as water, wastewater, and air treatment. The present study aimed to optimize the *chlorella sorokiniana* biomass growth. To do this, firstly, the *chlorella sorokiniana* was cultivated, then a number of 20 experiments were designed via Response surface methodology (RSM) to investigate the effect of different parameters of light intensity (1500-7000 lux), temperature (20-36 °C), and inoculation (4-12%). In addition, the single and interaction effects of operational parameters were evaluated. Finally, logical model to predict the biomass concentration with a 95% confidence interval was presented. Based on the results, an increase in light intensity, temperature and inoculation, caused to an increase in biomass concentration. Also, the operational parameters have a synergy effect on each other. Under the optimum condition, light intensity of 4250, temperature of 28 °C, and inoculation of 8%, the maximum biomass concentration of 2.43 g/L was obtained. The presented model can be used for biomass concentration prediction during *chlorella sorokiniana* biomass.

**Key words:** Microalgae, biomass concentration, algal growth, RSM.
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# 1. Introduction

Microalgae are a group of plants widely existed in nature, which are among the most important organisms in ecological evolution and history of the Earth. They are capable of shaping our future with a wide range of promising applications that can overcome worldwide issues. Microalgae can remediate various types of pollutants including nutrients, organic compounds, heavy metals and micro pollutants which are of different concentration, characteristic to toxicity level [1]. Microalgae are fast-growing and arable land or portable water is not the prerequisite for their growth [2]. Due to their high resistance to environmental stress, they can be cultivated in a wide range of environmental conditions, either in saltwater, freshwater or wastewater [3] [26]. It was revealed from Baharuddin et al. studies [4] that microalgae are likely to be used in aquaculture for fish and shrimp feed as they are appropriate in size, and are easily consumed. Microalgae feeds also enhance rapid growth rates and high nutritional content in terms of lipids, proteins, carbohydrates, amino acids and fatty acids in the aquaculture-cultivated fish and shrimps. Microalgae can be used to produce biofuel as an alternative energy source due to their high lipid content in the cells with an additional advantage of minimal land usage compared to terrestrial plants [5].

Microalgae production includes two major steps, namely culturing and harvesting. As progress has been achieved to optimize growth conditions and nutrient requirements for effective microalgae culturing, the second step has emerged as a major bottleneck for cost-effective micro algal biomass production [6]. The main factors which may influence microalgae cultivation are nutrient content such as nitrogen and phosphorus, followed by mode of cultivation, temperature, light intensity, light-dark period, salinity, pH, mixing, turbulence and also carbon dioxide concentration [7]. Microalgae can regulate their growth and cellular metabolism in response to exposure to different environmental factors (light, nutrients and other biotic and abiotic stressors), resulting in the modification of their pigment composition and shifts in carbon allocation patterns [8, 9] [23, 24]. Nutrient availability is one of the primary drivers for microalgae productivity in aquatic environments. Therefore, the modification of macronutrient concentrations in culture media has constituted the primary means by which cellular productivity and biochemical profiles have been previously enhanced [10] [25].

In the conventional optimization methods, the optimal parameters were obtained by changing one variable and fixing other variables[11]. These methods are time-consuming and has limitations in experiments with several independent and dependent variables. These problems can be solved using the response surface methodology (RSM) in which a model is obtained and variables are optimized [12] [22]. Moreover, by RSM modeling, individual and simultaneous effects of parameters is evaluated, as well as the interactive effects between parameters[13].
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This study mainly aimed at (i) presenting a logical model to predict the biomass concentration and (ii) optimizing effective parameters condition using RSM. For these purposes, first, experiments were designed and done in a batch reactor. Then the simultaneous effects of operational parameters such as light intensity, temperature, and inoculation percentage, and the interactive effects between them were investigated to find optimal condition using response surface methodology. In addition, an experimental model was developed to predict biomass concentration as a factor to represent growth of microalgae.

# 2. Materials and methods

## 2-1- Microalgae strain

A recently isolated strain of *Chlorella sorokiniana* pa.91 was utilized in this research. This microalgae was separated from Gela dairy wastewater treatment plant, then the cultivation and purification process were performed, subsequently[14].

## 2-2- Microalgae cultivation medium

*Chlorella sorokiniana* pa.91 was maintained in Bold's basal medium (BBM). The medium contained 0.25 g/L of NaNO3, 0.075 g/L of K2HPO4, 0.175 g/L of KH2PO4, 0.075 g/L of MgSO4, 0.025 g/L of CaCl2·2H2O, 0.025 g/L of NaCl, 0.005 g/L of FeSO4·7H2O, 0.05 g/L of Na2EDTA, 0.031 g/L KOH, 0.0015 g/L of ZnSO4·7H2O, 0.0003 g/L of CuSO4·5H2O, 0.0003 g/L of MnCl2·4H2O, 0.0001 g/L of CoCl2·6H2O, 0.00025 g/L of MnO3, 0.008 g/L of H3BO3. The pH was set to 6.4-6.8 with 1M NaOH solution before autoclaving at 120°C for 15 min. The cultures were maintained in an Erlenmeyer flask (250 mL) containing 120 mL medium with the initial cell density of 0.15 g/L. After 5 days (exponential phase), the seed culture was transferred to 1 L of Erlenmeyer flasks filled with 500 mL BBM medium containing 0.15 g/L inoculum and cultured for 10 days under the same condition. Cultures were aerated with sterile air at a gas flow rate of 0.3 vvm. After 10 days, cells were harvested for subsequent experiments.

## 2-3- Experimental procedure

In order to augment the process and recognize the combined effects of light intensity, temperature and inoculation percentage on microalgae growth, RSM was applied. In this regard, the process was modeled and optimized by considering three parameters and each measured at five levels using a central composite design (CCD). The
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variables in this experiment were light intensity (125, 1500, 4250, 7000, 8325 lux), the temperature (16, 20, 28, 36, 40 °C) and the inoculation percentage (2, 4, 8, 12, 14 %). This mathematical model is generally applied to optimize the process and build a quadratic model. A 500 mL baker flask containing 300 mL culture medium of BBM was used as the reactor. The batch bioreactors were placed onto an enclosure and based on each experiment, operational factors (light intensity, temperature and inoculation percentage), were set and controlled in this culture medium. The culture conditions of these bioreactors including light and temperature were being controlled using the lux meter device (Mastech, MS 6610) and a temperature controller (R₁ & BNT, STC-100), respectively.

## 2-4- Biomass concentration

As well as controlling the light intensity and temperature of the sample utilizing the aforementioned devices, Design expert version 11 was used for designing of the experiments and biomass concentration modeling by RSM. However, it is required to achieve the biomass concentrations of the microalgae specie during the following process. Firstly, cellular suspensions of the microalgae must be washed and dried. In this procedure, a concentrated suspension has been obtained using a centrifuge (Hermle Z 233 M-2, Germany) and oven (Binder 230V/10A, Germany) through centrifuging the microalgae suspension and removing the supernatant and washing it with distilled water. It was dried in the oven at 70 °C for 24 h. In the next stage, the standard curve of cell dry weight-light absorbance was plotted through sampling 100 cc of the cells and dilution process in 10 falcon tubes. Then the optical density in different concentration range was measured at the wavelength of 680 (OD₆₈₀) using a UV spectrophotometer (Jenway 6320D, United Kingdom). Finally, the correlation between OD₆₈₀ and biomass concentration (C) (g.L⁻¹) was acquired according to the Eq (1) via drawing standard curve:

$$C (g.L^{-1}) = 2.27 OD_{680} - 0.0845 \qquad \text{Eq (1)}$$

Where, C and OD₆₈₀ is biomass concentration and optical density, respectively[15].

## 2-5- Experimental design with response surface methodology

considering independent variables, this methodology provides models to predict the dependent variables. furthermore, individual effects, interaction and simultaneous effects of parameters on dependent variables, as well as analysis of variance of results are predictable in this method. The central composite design method was used to design the experiments. Independent variables and the dependent variables including
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light intensity, temperature, inoculation percentage and biomass concentration are shown in Table.

<table><caption>**Table 1: Independent variables and their levels used for central composite design.**</caption><thead><tr><th>Design point</th><th>Level 1 (min)</th><th>Level 2</th><th>Level 3</th><th>Level 4</th><th>Level 5 (max)</th></tr></thead><tbody><tr><td>Light intensity (h)</td><td>125</td><td>1500</td><td>4250</td><td>7000</td><td>8325</td></tr><tr><td>Temperature (°C)</td><td>16</td><td>20</td><td>28</td><td>36</td><td>40</td></tr><tr><td>Inoculation percentage (%)</td><td>2</td><td>4</td><td>8</td><td>12</td><td>14</td></tr></tbody></table>

## 3. Results and discussion

### 3-1- Single effect of operational parameters on the Biomass concentration

Figure 1 depict the single effect of operational parameters of light intensity, temperature, and inoculation on biomass concentration amount. When light intensity increased from 1500 to 4250 lux, in 28 degree and inoculation of 8%, the biomass concentration changed from a minimum of 1.71 g/L to a peak of 2.43 g/L. This event occurred due to the illumination of more photons resulting from increased intensity of light, leading to an enhanced light reactions and consequently better photosynthetic efficiency and biomass productivity. Excess amount of light intensity also increases the temperature and causes the cell damage resulting in a decrease in biomass concentration. Moreover, the accumulation of photons in high light intensity results in light inhibition, which is a decreasing factor for cell growth [17]. Furthermore, based on Figure1b, an increase in temperature caused in a growth in biomass concentration. It can be seen that, in the 28 °C and inoculation of 8%, the maximum biomass concentration of 2.43 was obtained. In contrast, the minimum biomass concentration of 2.06 g/L obtained in 20 °C. In addition, in high temperature (36 °C) the biomass concentration reduce to about 2% due to cell damage. Similarly, in the Figure1c, the maximum amount of biomass produced in inoculation of 8%, and the excess percentage of inoculation (10% and 12%) resulted in a decrease in biomass concentration. Similar results were observed in previous studies showed that the result of this study is correct[16].
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<figure><img src="image_13.png" /><figcaption>Figure1: Single effect of a) light intensity b) temperature c) inoculation percentage on biomass concentration</figcaption></figure>

## 3-2- Simultaneous effect of parameters on the Biomass concentration

### 3-2-1- Simultaneous effect of light intensity and temperature

As shown in Figure 2, the dependent variable enhanced with increasing light intensity and temperature in consistent amount of inoculation (8%). According to this figure, the minimum biomass production was 1.24 g/L with light intensity of 1500 lux and temperature of 36 °C. On the other hand, the maximum biomass concentration was 2.43 g/L, with light intensity of 4250 and temperature of 28 °C. Moreover, in the light
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intensity of 7000 lux and temperature of 36 °C the biomass concentration was 1.28 g/L.

<figure><img src="image_16.png" /><figcaption>Figure2: Simultaneous effect of light intensity and temperature on biomass concentration</figcaption></figure>

3-2-2- Simultaneous effect of light intensity and inoculation

According to Figure 3, the biomass concentration improves with increasing initial cell
density and light intensity in 28 °C. The maximum biomass concentration was 2.43
g/L, with light intensity of 4250 and inoculation of 8%. Furthermore, the biomass
concentration was 1.14 when the inoculation was 12% and the light intensity was 7000
lux. The minimum biomass production (1.07 g/L) was obtained at inoculation
percentage of 4% and light intensity of 1500 lux. It is clear that these two parameters
have a synergy effect and have interaction on each other.
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<figure><img src="image_19.png" /><figcaption>Figure 3: Simultaneous effect of light intensity and inoculation on biomass concentration</figcaption></figure>

### 3-2-3- Simultaneous effect of temperature and inoculation percentage

Figure 4 shows that the biomass concentration improved with increasing temperature and inoculation percentage. This is due to the fact that temperature improves the enzyme activities and photosynthetic efficiency through controlling the chemical reactions, and initial cell density enhances the photosynthetic activities, respectively. Based on RSM graphs, the minimum biomass concentration (1.32 g/L) was observed at temperature 36 °C and inoculation percentage of 4% in constant light intensity of 4250 lux. The maximum biomass concentration (2.43 g/L) was observed at temperature 28 °C and inoculation percentage of 8 %. As shown in Figure 4, biomass concentration of 1.49 g/L was achieved at temperature of 20 °C and inoculation percentage of 12 %. furthermore, biomass concentration was 1.38 g/L, at temperature 20 °C and inoculation percentage of 4 %. Accordingly, these two parameters have a synergy effect and have interaction on each other. The results of this study are in line with previous studies[18-21].
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<figure><img src="image_23.png" /><figcaption>Figure4: Simultaneous effect of temperature and inoculation on biomass concentration</figcaption></figure>

### 3-3- Modeling of maximum biomass concentration

This section provides a model to predict maximum biomass concentration on the independent variables of light intensity, temperature and inoculation percentage using the response surface methodology. Based on the results of the response surface methodology, the most appropriate model for predicting the microalgae growth was the quadratic model, with the coefficient of determination 0.99, which is presented in following equation:

Biomass concentration

$$ 
\begin{aligned}
&= 2.44 - 0.001 \times \text{Light intensity} - 0.0196 \times \text{Temprature} \\
&+ 0.0344 \times \text{Inoculation} + 0.0438 \times \text{Light intensity} \times \text{Tempratur} \\
&- 0.0038 \times \text{Light intensity} \times \text{Inoculation} \\
&+ 0.0063 \times \text{Temprature} \times \text{Inoculation} - 0.7312 \times \text{light intensity}^2 \\
&- 0.4010 \times \text{Temprature}^2 - 0.5987 \times \text{Inoculation}^2
\end{aligned}
$$

Closely high value of the adjusted R² indicates the capability of the developed model to satisfactorily describe the system behavior within the range of operating factors. Regarding this model, the inoculation and temperature have the highest effect on the biomass concentration and led to an increase in biomass concentration.
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### 3-4-Analysis of variance

ANOVA test for the effect of independent parameters on the biomass concentration is shown in Table 1. Accordingly, p-value demonstrates the significance of the central composite design model and R² determined the efficiency of the quadratic model developed. The closer the R² to 1, the superior the model calculates the response [11]. The results of this test showed that the model is significant within a 95% safety factor which means that only 5 % of results are not valid. The biomass concentration model is significant at P value <0.0001. The quadratic effects of inoculation had the P value <0.0001, indicating that these parameters had a significant effect on the biomass concentration and proof a high correlation between the response's actual and predicted values of the experiments. The regression coefficients of interaction of each pair of the independent variables are significant too. Furthermore, the p-value for parameters of AB, A², B², and C² are significant too, which mean that these parameters had a significant effect on the biomass growth based on presented model by RSM.

<table><caption>Table 2: Analysis of variance ANOVA associated with RSM model</caption><thead><tr><th>Source</th><th>df</th><th>F-value</th><th>p-value<br>Probe > F</th><th></th></tr></thead><tbody><tr><td>Model</td><td>2</td><td>126.35</td><td>0.0002</td><td>significant</td></tr><tr><td>A-<br>Light intensity</td><td>1</td><td>0.0005</td><td>0.9835</td><td></td></tr><tr><td>B-Temperature</td><td>1</td><td>2.09</td><td>0.1762</td><td></td></tr><tr><td>C-Inoculation</td><td>7</td><td>6.44</td><td>0.0276</td><td>significant</td></tr><tr><td>AB</td><td>1</td><td>6.66</td><td>0.0255</td><td>significant</td></tr><tr><td>AC</td><td>1</td><td>0.0490</td><td>0.8289</td><td></td></tr><tr><td>BC</td><td>1</td><td>0.1360</td><td>0.7193</td><td></td></tr><tr><td>A<sup>2</sup></td><td>1</td><td>252.70</td><td>&lt; 0.0001</td><td>significant</td></tr><tr><td>B<sup>2</sup></td><td>1</td><td>86.92</td><td>0.0004</td><td>significant</td></tr><tr><td>C<sup>2</sup></td><td>1</td><td>193.82</td><td>&lt; 0.0001</td><td>significant</td></tr></tbody></table>

### 3-5- Validation of the suggested models

Based on the optimum conditions reported in the response surface methodology, the optimized biomass concentration (2.43 g/L) was obtained at light intensity of 4250 lux,
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temperature of 28 °C, and inoculation percentage of 8%. Under the optimum conditions, the experimental biomass concentration was very close to the predicted values, in which case the error obtained was in the significant range. In order to validate the predictive model of the biomass concentration, Figure 5 is presented. Based on the Figure 5, the proximity of the actual and the predicted values indicate the high validity of the models.

<figure><img src="image_28.png" /><figcaption>Figure 5: The prediction values based on the real values of phosphorus removal efficiency</figcaption></figure>

4.Conclusion

The results showed that operational factors of light intensity, temperature and inoculation percentage have dramatic effect on algal growth. In addition, a logic model was developed using RSM methods in this study to predict the amount of biomass concentration during cultivation process. Optimum condition of light intensity of 4250 lux, temperature of 28 °C, and the inoculation of 8% was observed in which 2.43 g/L biomass concentration was achieved. Based on the RSM results, and the optimization of effective parameters of light intensity, temperature and inoculation percentage, the biomass concentration ranged from 0.48 to 2.43 g/L. The results of this research revealed achieving an improved amount of biomass concentration which is a satisfactory level of growth in comparison with reviewed literatures. The evaluation of simultaneous and interactive effects of parameters on the microalgae growth indicates that operational parameters of light intensity and inoculation have a synergy effect on
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the biomass concentration. As a future research, the authors recommend that impact of other growth parameters including different carbon sources can be evaluated through response surface methodology.

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