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SN Applied Sciences

, 1:156 | Cite as

Optimization for enhanced hydrogen production from Rhodobacter sphaeroides using response surface methodology

  • Swetha Garimella
  • Archana Vimal
  • Ramchander MeruguEmail author
  • Awanish KumarEmail author
Short communication
  • 170 Downloads
Part of the following topical collections:
  1. Chemistry: Chemical Engineering: Materials, Biochemistry, Fuels, Energy

Abstract

In the present study, Rhodobacter sphaeroides isolated from sewage water was investigated for photoproduction of hydrogen under different cultural conditions. The variation was done in carbon and nitrogen sources along with growth factors. It was found that lactose promoted more amounts of hydrogen production under anaerobic light conditions followed by mannitol, galactose, and sucrose respectively. Uracil was observed as a better nitrogen source as compared to histidine and thiourea. After screening most appropriate carbon source, nitrogen source and growth factor by the classical approach, statistical optimization technique opted for the enhancement of hydrogen production. The response surface methodology adopted in the present work is an efficient tool for strategic experimental design for optimizing process parameters of a multivariable process. Firstly, experiments were performed according to Box-Behnken design. The model predicted is quadratic with a high value of R2 i.e. 0.9681 and maximum production recorded was 6.8 ml/30 ml. Thereafter, Central composite design based experiments were performed and 7.6 ml/30 ml production was achieved with an R2 value of 0.8848 fitted inthe quadratic model. The work presented here suggests a green approach of enhanced hydrogen production to meet the future demand of clean fuel.

Keywords

Rhodobacter sphaeroides Hydrogen production Sewage water Box-Behnken design Central composite design 

1 Introduction

The global hydrogen production is 0.1 Gton, that contribute energy of 2500 TWh with a market value of over US$ 140 bn/year [1]. The Inter-governmental Panel on Climate Change (IPCC) in 2007 suggested that by 2100, an increase of 2.5–6 °C in the world’s global temperature will occur. The prime reason behind this would be increased atmospheric concentration of greenhouse gases, due to consumption of fossil fuels that may trigger drastic changes climate [2]. The constant use and burning of fossil fuels cause a permanent increase in CO2 concentration of the atmosphere. Along with this, the high price of oil warrants the search for the sustainable and economical source of energy [3]. It is assumed that the hydrogen would become the main energy carrier in the coming decades [4]. The hydrogen has great potential as an alternative to depleting fossil fuel resources [5]. It has higher energy content as compared to fossil fuels, it is renewable and produces water upon oxidation [6, 7, 8]. Biologically, hydrogen can be produced through photosynthetic or fermentative routes. However, the yield through the microbial system is comparatively lower with respect to chemical methods. Since, the microbial processes are not that much economical, metabolic engineering based techniques are applied to enhance the fermentative hydrogen production [9]. Multi-organism systems and combining strategies in two-stages components were also investigated to increase outputs so as to make the option of sustainable production of hydrogen realistic for future hydrogen energy production [10, 11].

It was reported that cultural condition has a great impact on the photoevolution of hydrogen by phototrophic bacteria. Photo evolution is the process of splitting of the water molecule into H2 and CO2 mediated by a catalytic agent here in the presence of Rhodobacter sphaeroides bacteria. The most probable mechanism of anaerobic photoevolution of hydrogen is through water–gas shift reaction. It proceeds in two steps where oxidation and reduction of ferrodoxintake place. It is mediated by two key enzymes that are carbon monoxide hydrogenase and carbon monoxide dehydrogenase [12]. It is reported that wide variety of organic substrates such as lactate, malate, benzoate, sucrose are utilized by different species of phototrophic bacteria as electron donors for hydrogen production [13, 14, 15, 16]. However, the substrate specificity for hydrogen production varied with the species [12]. In view of the above facts, the present study focused on the influence of different cultural conditions on hydrogen production. Then, optimization strategies using RSM were employed to enhance hydrogen production by oxygenic phototrophic bacteria.

2 Materials and methods

2.1 Chemicals

All the chemicals used in the present study were procured from Hi-Media (India).

2.2 Isolation of phototropic bacteria

The phototrophic bacteria were isolated from the sewage water samples by enrichment techniques as described by Merugu et al. [17]. In this method, the culture was inoculated in Bieble and Pfennig smedium and incubated an aerobically in the light (2000 lx) for 72 h.

2.3 Identification of bacterial isolates

To identify the isolated bacteria, identification keys provided in Bergey’s manual of systematic bacteriology (1994) were adopted [18].

2.4 Determination of bacterial growth

To determine the growth, optical density at 660 nm using UV–Vis spectrophotometer was measured.

2.5 Hydrogen production

The basic technique used for the hydrogen production was that described by Vincenzini et al. [19].

2.6 Optimization of hydrogen production by classical method (one factor at a time)

Logarithmic cultures of Rhodobacter sphaeroides were inoculated (1% v/v) into basal medium containing different carbon sources (0.5, 1, and 1.5 mg) along with Uracil (0.5 mg) as a nitrogen source. Four carbon sources lactose, mannitol, sucrose, and galactose were tested for H2 production. When different nitrogen sources were tested, lactose was used as a source of carbon. The nitrogen sources that were used in the present study for increasing the production were thiourea, uracil, and histidine. The various level of nitrogen source studied in classical methods were 0.5, 1.0 and 1.5 mg. Along with this, two growth factors vitamin biotin and nicotinic acid were also examined with varied concentration of 10, 20 and 30 µg.

2.7 Software

The software package Design Expert (Version 10, Stat-Ease Inc., Minneapolis, USA) was used in the present study for the designing experimental condition and statistical analysis of the results. All the experiments were performed in triplicate and the average value obtained was used for data analysis.

2.8 Three-factor Box-Behnken design (BBD) for optimizing hydrogen production

A three-factor and three-level BBD was employed for the optimization of hydrogen production. The three independent variables chosen were carbon source (X1 = lactose), nitrogen source (X2 = uracil) and growth factor (X3 = biotin). The hydrogen produced was recorded as the response variable. The software designed twelve experiments along the factorial point and five center point experimental sets comprising a total of seventeen experimental sets. The different range of carbon and nitrogen source have taken were 0.5, 1.0 and 1.5 mg (lactose and uracil). The range of growth factor was 10, 20 and 30 µg.

After analysis of experimental values of hydrogen production, an optimal point was predicted that describes the relationship between independent variables and response (hydrogen production). The relationship was fitted into the following second-order polynomial model equation:
$$Y = \beta_{0} + \beta_{1} X_{1 } + \beta_{2} X_{2} + \beta_{3} X_{3 } + \beta_{12} X_{1 } X_{2 } + \beta_{13} X_{1 } X_{3} + \beta_{23} X_{2 } X_{3 } + \beta_{11} X_{1 }^{2} + \beta_{22} X_{2}^{2} + \beta_{33} X_{3}^{2}$$
(1)
Where Y is the predicted response, X1, X2, and X3 are independent variables, β0 is the intercept term; β1, β2, and β3 are linear coefficients; β12, β13 andβ23 are cross product coefficients; β11, β22 and β33 are quadratic coefficient.

2.9 Central composite design (CCD) for optimization of hydrogen production

After BBD, the hydrogen production was further optimized via designing a three-factor and five-level CCD. There were eight factorial, six axial and six center points that comprise a total of twenty experimental runs. The value of α chosen was 1.68. The α-value indicates the distance from the center point to axial point. The equation representation of α is, α = (2n)1/4. Lactose (X1), uracil (X2) and biotin (X3) were considered as three independent variables while designing the experimental sets. The five different values of carbon and nitrogen source considered during experiment design were 0.1591, 0.5, 1.0, 1.5 and 1.8409 mg. The five levels of growth factor taken were 3.1820, 10, 20, 30 and 36.8179 µg.

After the analysis of experimental data, a second order polynomial equation was generated. Thereafter, the response model was fitted in the quadratic Eq. (1). The efficiency of the fitted model was assessed on the basis of the coefficients of determination and analysis of variance.

2.10 Determination of hydrogen production activity

To test the hydrogen production activity at the different cultural condition, hydrogen produced was measured by injecting 0.5 ml of the gas phase from the reaction vessels into a gas chromatograph (Thermo Scientific) which is attached to a thermal conductivity detector (TCD).

3 Results and discussion

The bacteria isolated were motile, oval-shaped and the color of cell suspension was greenish yellow which turned to pink gradually. The size of the bacterium isolated was approximately 2.0–3.3 µm. The bacteria produce spheroidene pigment. It was observed that slime production was present while gelatinase production was absent in the isolated bacteria. The UV spectrum (Thermo scientific) of the microorganism showed peaks at 370, 458, 479, 501, 516, 597, 802, and 859 nm. All these characteristic feature suggest that the isolated microorganism is Rhodobacter sphaeroides. It was found that the Rhodobacter sphaeroides showed maximum hydrogen production at pH 7.0 ± 0.2 after anincubation period of 96 h. There were variations in the initial pH and final pH but the variations were minute and not greater than ± 0.20.

After applying the classical approach of optimization i.e. one factor at a time, it was found that among the entire carbon source investigated lactose promoted the high yield of hydrogen i.e. 6.9 ml/30 ml followed by mannitol (6.6 ml/30 ml) and galactose (6.2 ml/30 ml). The lowest yield was seen when the electron donor was sucrose i.e. 5.8 ml/30 ml. When different nitrogen sources were explored for the enhancement of the hydrogen production, uracil was emergedas better among all with a production of 6.0 ml/30 ml. While in the case of histidine and thiourea, 5.8 ml/30 ml and 5.4 ml/30 ml of hydrogen was produced respectively. The effect of vitamins biotin and nicotinic acid were also analyzed for hydrogen production. Biotin was found to enhance the production of hydrogen as compared to nicotinic acid. Carbon, nitrogen and growth factors influence hydrogen production via nitrogenase enzyme which accounts for thevariation of hydrogen production in the presence of different substrates [20].

Once the suitable carbon source, nitrogen source and growth factor were screened using the classical approach (one factor at a time), the statistical approach was applied for the further enhancement of hydrogen production. It was accomplished in two attempts by employing two different design of response surface methodology that are Box-Behnken design and Center Composite design. The different levels of lactose, uracil, and biotin were examined through seventeen experimental runs as designed by the software. The result obtained for different runs were analyzed using Design Expert (Version 10, Stat-Ease Inc., Minneapolis, USA). The analysis of the design matrix in BBD predicts a quadratic model. The predicted model is significant that is estimated on the basis of higher F-value i.e. 23.53. At the same time, the small p value of 0.0002 indicates that there is only a 0.02% chance that the high F-value obtained is due to noise. The model terms are also significant as the values of “Prob > F” is 0.002. The value of the coefficient of determination (R2) is 0.9680 that indicates there is astrong correlation between experimental values and model predicted values. The close agreement between the experimental and predicted values can be seen in Fig. 1a. The complete design matrix and comparative values of experimental and predicted hydrogen production are presented in Table 1. The quadratic polynomial equation obtained from the multiple regression analysis is described in Eq. (2) that depicts the effects of each variable along with their interactions:
$$\begin{aligned} H_{2} production & = 6.90 + \left( {0.075*lactose} \right) + \left( {0.050*{\text{uracil}}} \right) + \left( {0.22*biotin} \right) \\ & \quad + \,\left( {0.025*{\text{lactose}} * {\text{uracil}}} \right) + \left( {0.025*lactose*biotin} \right) + \left( {0.12*{\text{uracil}}*{\text{biotin}}} \right) \\ & \quad + \,\left( {0.037*lactose^{2} } \right) + \left( {0.087{\text{uracil}}^{2} - 0.21biotin^{2} } \right) \\ \end{aligned}$$
(2)
In Fig. 1b 3-D response surface plot and 2-D contour plot is shown. The elliptical shape depicts that there is ahigher mutual interaction between the test variables that are lactose and uracil. The model was validated through the numerical optimization that predicts the optimized condition of hydrogen production. The optimal condition predicted by the model is X1(lactose) = 1.3485 mg, X2(uracil) = 1.4780 mg, X3(biotin) = 26.779 µg with H2 production = 7.2626 ml/30 ml. When the validation experiment was performed, there was close proximity in experimental i.e. 7.1542/30 ml and the predicted value recorded that justifies the significance of the model.
Fig. 1

a The graphical representation depicting correlation between the experimental and predicted values of H2 production in different experimental runs of BBD. b 3-D surface plot and contour plots showing interaction between lactose and uracil during H2 production using Rhodobacter sphaeroides in BBD. c The graphical representation depicting correlation between the experimental and predicted values of H2 production in different experimental runs of CCD. d 3-D surface plot and contour plots showing interaction between lactose and uracil during H2 production using Rhodobacter sphaeroides in CCD

Table 1

The experimental design matrix of BBD along with comparative hydrogen production (experimental and predicted) at different run condition

Run

Factor 1

Factor 2

Factor 3

Experimental values

Predicted

X1:Lactose

X2:Uracil

X3:Biotin

of H2 production

Values of H2 production

mg

mg

µg

ml/30 ml

ml/30 ml

1

1.5

0.5

20

7.00

7.03

2

0.5

1

10

6.40

6.45

3

1

1

20

6.90

6.90

4

1

0.5

30

6.80

6.83

5

1

1.5

30

7.10

7.17

6

1.5

1

30

7.10

7.05

7

1.5

1.5

20

7.20

7.18

8

1

1

20

6.90

6.90

9

1

0.5

10

6.70

6.63

10

1

1.5

10

6.50

6.48

11

1

1

20

6.90

6.90

12

0.5

1

30

6.90

6.85

13

1

1

20

6.90

6.90

14

1

1

20

6.90

6.90

15

0.5

0.5

20

6.90

6.93

16

0.5

1.5

20

7.00

6.97

17

1.5

1

10

6.50

6.55

In order to further enhance the hydrogen production, the center composite design opted. The seventeen sets of designed experiment were performed and maximum production of 7.66 ml/30 ml was achieved at the condition where lactose = 1 mg, uracil = 1.8409 mg, and biotin was 20 µg. The high F-value 8.54 and low “Prob > F” value 0.0012 suggests that predicted model and model parameters are significant. The p-value is 0.0012 suggesting there is only 0.12% chance that the high F-value is obtained due to noise. The predicted model is quadratic with the regression coefficient, R2 = 0.8848. The quadratic polynomial Eq. (3) obtained from the model analysis is mentioned below that sum up the effect of an individual variable along with interactions among them.

$$\begin{aligned} H_{2} production & = 6.901 + \left( {0.30*lactose} \right) + \left( {0.69*uracil} \right) + \left( {1.13*biotin} \right) \\ & \quad + \,\left( {0.32*lactose*uracil} \right) - \left( {0.37*lactose*biotin} \right) + \left( {0.33*uracil*biotin} \right) \\ & \quad - \,\left( {0.39*lactose^{2} } \right) - \left( {0.30*uracil^{2} } \right) - \left( {0.71*biotin^{2} } \right) \\ \end{aligned}$$
(3)
In Fig. 1c correlation between experimental and predicted values of hydrogen at different run condition is compared. In Fig. 1d 3-D response surface plot and 2-D contour plot are presented. The experimental and predicted hydrogen production values for each run along with complete design matrix are presented in Table 2. The obtained pattern is similar as obtained in BBD i.e. elliptical shape indicating that lactose and uracil are highly interactive and their interaction influence overall hydrogen production. The validation of model was done through numerical optimization at the condition X1(lactose) = 1.0226 mg, X2(uracil) = 1.2595 mg, X3(biotin) = 28.0977 µg, and H2 production = 7.7806 ml/30 ml. The product obtained in the validation set was 7.69 ml/30 ml, which is in close agreement of predicted value and satisfy the model predicted.
Table 2

The experimental design matrix of CCD along with comparative hydrogen production (experimental and predicted) at different run condition

Run

Factor 1

Factor 2

Factor 3

Experimental values

Predicted

X1:Lactose

X2:Uracil

X3:Biotin

H2 production

Values of H2 production

mg

mg

µg

ml/30 ml

ml/30 ml

1

1

1

20

6.90

6.91

2

0.5

0.5

10

3.82

3.67

3

1.5

0.5

10

4.12

4.36

4

1

1

20

6.90

6.91

5

1

1

20

6.90

6.91

6

1

1

20

6.90

6.91

7

1

1

20

6.90

6.91

8

0.5

1.5

10

4.18

3.75

9

1.5

0.5

30

4.52

5.24

10

1.5

1.5

30

7.47

7.90

11

1

1.8409

20

7.66

7.22

12

0.5

1.5

30

7.36

7.40

13

0.5

0.5

30

6.66

6.00

14

1

0.159104

20

4.86

4.91

15

1

1

36.8179

6.98

6.80

16

0.159104

1

20

4.44

5.29

17

1.5

 

10

4.78

5.72

18

1.8409

1

10

7.55

6.30

19

1

1

3.18207

3.22

3.00

20

1

1

20

6.90

6.91

Hydrogen is considered as “energy of future” because of its energy content and renewability. The research is in progress for bio-hydrogen production through various methods like (a) water splitting by photosynthetic algae, (b) dark fermentation of carbohydrate rich wastes and (c) photofermentation of organic acid rich wastewaters. A wide range of bacteria like Rhodopseudomonas gelatinosa, Rhodospirillum rubrum, Clostridium aceticum, Acetobacterium woodii, Clostridium ljungdahlii, and Clostridium thermoaceticum are the known producer of hydrogen [12]. However, each microorganism is unique and requires different fermentation condition and medium constituents for the enhanced hydrogen production. The example includes Rhodobacter capsulatusthat promoted large amounts of hydrogen production using ethanolamine as nitrogen source with sugars d-glucose, d-xylose and d-cellobiose [21]. Najafpour et al. [12] reported that R. rubrumproduces 2.35 mmol of H2 using 1.5 g/l acetate. It is reported that immobilized cells of Rp. palustris liberated 36, 37, 41, 42 and 48 µl mg d w−1 h−1 hydrogen using the substrates acetate, lactate, pyruvate, malate, and succinate respectively [19]. Glucose-stimulated hydrogen production is under nitrogen gas sparging is also reported [22]. It was found that l-cysteine as a nitrogen source enhanced dark fermentative hydrogen production [23]. Therefore, the optimization of the process parameter is required and the use of the statistical tool is a better approach as compared to the classical method.

4 Conclusions

The present studies showed the ability of phototrophic bacteria Rhodobacter sphaeroides to produce hydrogen in different cultural conditions that can be beneficial for future research in the field of bioenergy. The present study includes identification of suitable medium constituents through the classical approach. Thereafter, statistical optimization was done in two steps that are based on BBD and CCD respectively. It was done to save time and minimize the number of experiments along with predicting the interaction among different process variables. Although H2 production is higher after applying CCD (7.66 ml/30 ml) no significant rise in production was observed as compared to BBD (7.20 ml/30 ml). The study motivates the adoption of sustainable biological hydrogen production using this bacterium. This bacteria produce bioplastic (poly hydroxybutyrate), bioherbicide (amino levulinic acid), and other useful by-products in the same culture vessel apart from hydrogen that would be the additional benefit for future commercialization of the process. Apart from this, anaerobic dark conditions with cheaper substrates need to be explored as an alternative of sustainable hydrogen production.

Notes

Acknowledgements

Authors thank the Department of Biotechnology (DBT, MRP-BT/PR4819/PBD/26/289/2012), Ministry of Science and Technology, India for funding this work. Authors are also thankful to Mahatma Gandhi University, Nalgonda (TS), India and National Institute of Technology, Raipur (CG), India for providing the facility, space, and resources for this work.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University College of ScienceMahatma Gandhi UniversityNalgondaIndia
  2. 2.Department of BiotechnologyNational Institute of Technology (NIT)RaipurIndia

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