Optimization of hydrogen enriched biogas by dry oxidative reforming

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Optimization of hydrogen enriched biogas by dry oxidative reforming with nickel (Ni) nanopowder using response surface methodology Pali Rosha, Rattanvir Singh, Saroj Kumar Mohapatra, Sunil Kumar Mahla, and Amit Dhir Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b00819 • Publication Date (Web): 18 May 2018 Downloaded from http://pubs.acs.org on May 18, 2018

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Optimization of hydrogen enriched biogas by dry oxidative reforming with nickel (Ni) nanopowder using response surface methodology Pali Roshaa, Rattanvir Singhb, Saroj Kumar Mohapatrab, Sunil Kumar Mahlac, Amit Dhira* a

School of Energy and Environment, Thapar Institute of Engineering and Technology Patiala, 147004, India

b

Department of Mechanical Engineering, Thapar Institute of Engineering and Technology Patiala, 147004, India

c

Department of Mechanical Engineering, I.K. Gujral Punjab Technical University Campus Hoshiarpur, 146001, India

ABSTRACT Today, the worldwide research is focussed on the development of alternative energy sources for power generation; thus, the present study aims to optimize the dry oxidative reforming (DOR) process parameters for H2 enriched biogas production by integrating response surface methodology with 3-level, 3-factor Box-Behnken design in the presence of commercial Ni nanopowder. First, the effect of the CH4/CO2 and O2/CH4 ratios on the catalytic performance of DOR was assessed in the temperature range of 800 to 900°C. The reactant (CH4 and CO2) conversions, product (H2 and CO) yields, selectivity of H2 as well as CO, H2/CO ratio and specific energy consumption (SEC) were chosen as responses. The empirical regression models were developed to identify the influential and most significant parameters. More than 95% value of determination coefficients by analysis of variance proved that the developed regression models were highly satisfactory. Experimentally, maximum H2 enrichment of 38.7 % with 82.9 and 90.8% of CH4 and CO2 conversions, respectively, were achieved at optimal reaction conditions of 900°C, 1.5 of CH4/CO2 ratio and 0.10 ratio of O2/CH4. The combination of regression model and dry oxidative technique for biogas reforming could provide an attractive proposition for enhancing the yield of H2 in product gases with subsequent increase in energy density and production of environment friendly gas.

Keywords: Dry oxidative reforming; biogas; hydrogen; response surface methodology; catalyst; ANOVA. Corresponding author: Mobile: +91-8968176900; E-mail: [email protected] 1 ACS Paragon Plus Environment

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1. INTRODUCTION Today, the conversion of biomass to valuable energy source has a significant potential in addressing the scarcity of conventional fuels. In this direction, researches have been linearly shifted towards the production of clean, renewable and sustainable alternative fuels1. Biogas as a gaseous fuel is considered as a very peculiar long-period renewable fuel for dual fuel CI engine. It is produced by anaerobic degradation or fermentation of various organic matters such as cow dung, agricultural waste, municipal waste, sewage sludge, etc, and its composition largely depends on the feedstock used2. Typically, biogas produced from cow dung normally contains methane (CH4), carbon dioxide (CO2) and trace gases in the range of 55-65%, 35-45% and 0-1%, respectively; and organic waste digesters produces biogas with 60-70% CH4, 30-40% CO2 and 0-1% trace gases3. Many studies have been reported in the past for exploring the impact of CH4 and CO2 percentage in biogas by utilizing in CI engine under dual fuel mode4. Likewise, Jiang et al. revealed that increased CH4 content in biogas significantly improves the engine performance with slight deterioration in the tailpipe emissions5. The higher CO2 content (up to 40%) in biogas consequently decreases the fuel quality in terms of combustion6. Moreover, it has been thoroughly discussed about the influence of gaseous fuel induction on various characteristics of a dual fuel CI engine7. Therefore, biogas exploitation to produce energy efficient renewable gaseous fuel, which is so called hydrogen (H2) enriched biogas would be an attractive proportion for dual fuel CI engine. For this, production of H2 enriched biogas via dry reforming (DR) technique has been chiefly suggested in the previous literature, because it utilizes the two major greenhouse gases (CH4 and CO2)8. Still, this reforming technique is not commercialized yet, because it offers significant challenges during operation. These include rapid catalyst deactivation and huge energy requirement which leads to increased operating cost9,10. Therefore, in order to address these issues, it is imperative to hunt out alternative reforming technique that could make overall energy efficient process. In this way, plenty of researchers have been focussing on the utilization of CH4 and CO2 along with oxygen (O2), and process is called dry oxidative reforming (DOR)11-13. Indeed, there is enormous research articles published on the DR and DOR techniques; with some articles on biogas DR over MgO supported Ni catalyst14. Zhan et al. reported that Mg added in Ni/Al2O3 enhanced the basicity of catalyst and increased the Ni metal sintering15. It has been reported that when the catalyst is reduced at higher temperature, a low Ni loading is enough to improve the performance of catalyst16. Furthermore, support plays a key role in catalyst activity owing to its chemical effect and metal sites interaction. Tanios et 2 ACS Paragon Plus Environment

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al. observed that a small amount of Co added to Ni-Mg-Al significantly enhances resistance towards carbon deposition17. The effect of Ni-Co bimetallic catalyst supported over La2O3/Al2O3 was discussed by Xu et al.18. They reported that the synergy between Ni-Co has improved catalyst activity along with anti-coking properties. The addition of oxygen (O2) along with CH4 and CO2 in DOR was recommended as an effective technique to enhance the reactant conversion, as well as to improve catalyst performance. Vernon et al. tailored different CH4:CO2:O2 ratio for obtaining a thermo neutral reaction19. Nematollahi et al. obtained a high catalytic stability without any effect in CH4 conversion during reaction of 50 h20. Asencios et al. also found the effect of Ni content on NiO–MgO–ZrO2 catalysts21. They reported that high Ni loading surpluses the formation of Ni particles during reforming, which results in decreasing the catalytic performance. Similarly, Tsyganok et al. revealed that metal content could be reduced to large extent without any variation in catalytic activity22. In this way, a deluge of the research was undertaken on biogas reforming with different processes. A considerable amount of researchers devoted their attention towards the equilibrium thermodynamic analysis for DOR process, and documented the optimal equilibrium conditions for the different CH4:CO2:O2 ratio and temperature ranges23, 24. Thus before delving further investigation on DOR process, it is imperative to discuss the implementation of statistical tool like response surface methodology (RSM) for the optimization of H2 enriched biogas production. RSM is most popular method based on nonlinear multivariate model, which significantly reduces the time requirement. The overall goal of present study is to maximize the conversion efficacies of CH4 and CO2 and enhance the yield of H2 in product gases for its better utilization in various applications. Indeed, lot of catalysts have been tested in biogas reforming, but, to the best of our knowledge, study over pure Ni nanopowder in DOR has not been reported previously. The present study emphasized the influence of reaction parameters like temperature, CH4/CO2 and O2/CH4 ratio on the activity of commercial grade nickel (Ni) nanopowder for production of H2 enriched biogas and its optimization using RSM technique.

2. MATERIAL AND METHODS 2.1.Material Commercial nickel (Ni) nanopowder (particle size: 20nm; surface area: 55m2/g) was supplied by Loba Chemie, Mumbai, India. Ultrapure reactant gases consist of CO2 (99.999%), CH4 (99.999%) and O2 (99.99%) were procured locally. 3 ACS Paragon Plus Environment

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2.2.Experimentation Schematic layout of the experimental set up for biogas reforming is shown in Fig. 1. The DOR of biogas was performed in a tubular type fixed-bed down-flow reactor (ID: 10mm and long: 70mm) under temperature range between 800-900°C at 1 bar. Firstly, 0.2 g Ni catalyst was loaded in between the quartz wool packed reactor, and heated continuously up to the desired temperature using 10°C/min ramp rate under pure nitrogen (N2) gas flow as an inert gas. The total mass flow rate (75 ml/min) of reactant gases was monitored and controlled by mass flow controllers (MFC). A thermocouple inside the thermo-well inserted directly into the reactor in order to monitor the bed temperature. Continuous gas flow analyzer was employed to analyze product gases with their measurement range given in parenthesis such as CO (0-100%), CO2 (0-50%), CH4 (0-100%) and H2 (0-100%). The performance data was recorded after 2 h of the reaction period. The repeatability and consistency of the results were ensured by taking all the reading at least thrice and their average value was taken into consideration for calculating performance parameters. The CHNS elemental analyzer (Flash 2000 HT by Thermo Scientific) was used to determine the carbon deposition (wt. %) on the catalyst surface after reaction. The different performance parameters for the DOR process were determined, as mentioned in Eqs. (1) to (6). CH 4 (In) − CH 4 (Out) × 100 CH 4 (In) CO 2 (In) − CO 2 (Out) CO 2 Conversion (%) = × 100 CO 2 (In) H 2 (Out) H 2 Yield (%) = × 100 2 CH 4 (In) CO (Out) CO Yield (%) = × 100 CH 4 (In) + CO 2 (In) H2 (Out) H2 Selectivity (%) = × 100 2 (CH4 (In) - CH4 (Out))

(1)

CH 4 Conversion (%) =

CO Selectivity (%) =

(2) (3) (4) (5)

CO (Out) × 100 (CH4 (In) - CH4 (Out))+(CO2 (In) - CO2 (Out))

(6)

Specific energy consumption (SEC) per ml of H2 produced under DOR process was calculated using following procedure: •

Maximum reactor power capacity = 4.5 kW

•

Maximum temperature limit = 1100°C

•

Minimum energy requires to attain 700°C = 2.86 kJ/sec or 171.8 kJ/min 4 ACS Paragon Plus Environment

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•

SEC (kJ/ml of H2) =

Minimum energy required (kJ/min) H2 produced (ml/min)

Figure 1. An experimental setup used for dry oxidative reforming of biogas

3. EXPERIMENTAL DESIGN AND STATISTICAL ANALYSIS The optimization of H2 enriched biogas was statistically performed by RSM with Box– Behnken factorial design technique using Minitab 17 software. The set of experiments was built using a 15 run, 3-level, 3-factor Box–Behnken factorial design and randomly performed to elucidate the effect of each independent variable on the dependent variables. The independent variables considered for this study is the reaction temperature: X1, the ratio of CH4 to CO2: X2 and O2/CH4 ratio: X3. These independent variables were studied at different levels (-1, 0 and 1). Moreover, reactant (CH4 and CO2) conversions, product (H2 and CO) yields, selectivity of H2 as well as CO, H2/CO ratio and SEC were selected as the responses. The maximum temperature level, 900°C, was chosen for being the highest CH4 conversion, as reported in previous works25, 26. The minimum temperature level of 800°C was chosen in order to neglect the influence of side reactions such as boudouard (Eq.7) and methane decomposition reaction (Eq.8)27,

28

. The various CH4/CO2 ratios were chosen considering

different CH4 and CO2 proportion in biogas composition. Whereas, different O2/CH4 ratio to evaluate the effect of DOR process on H2 enriched biogas production were used. List of ranges and levels of the independent variables with coded and actual values of each prominent parameter are tabulated in Table.1. 2CO → C + CO 2

(7)

C H 4 → C + 2H 2

(8)

Table 1. Selected reaction parameters and their levels for analysis

After experimentation, an empirical model was generated using analysis of variances (ANOVA) at 95% confidence level. This model was examined through second order quadratic equation, using Eq. 9. Y = β 0 + β1X1 + β 2 X 2 + β 2 X 3 + β12 X1X 2 + β13 X1X 3 + β 23 X 2 X 3 + β11X12 +β 22 X 2 2 + β 33 X 32 Where,

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(9)

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Y = predicted response; β0 = intercept constant; β1, β2 and β3 = linear coefficients; β12, β13 and β23 = intersection coefficient; β11, β22 and β33 = squared coefficients.

4. RESULTS AND DISCUSSIONS In the present study experiments were conducted to evaluate the influence DOR process parameters on the H2 enriched biogas production. The 15 experiments run were carried out for optimizing the three independent variables (reaction temperature: X1, CH4/CO2: X2 and O2/CH4 ratio: X3). The experimental design and results regards to exit gas concentration, reactants conversion, product selectivity, yield as well as H2/CO ratio and SEC, are summarized in Table 2. Table 2. Experimental data for 3-level-3-factor response surface analysis

4.1 Effect of Reaction Parameters on Biogas Dry Oxidative Reforming The results obtained from the DOR of biogas proved that H2 production significantly increased with increasing all three independent variables. The maximum H2 enrichment of biogas was found to be 38.7% at conditions of 900°C, as well as, 1.5 and 0.1 of CH4/CO2 and O2/CH4 ratio, respectively, along with 0.032 (wt. %) of carbon deposition. Notable, this enrichment was achieved at 0.1 O2/CH4, which is 39.1% less than requires for stoichiometric of the reaction. At 900°C, the increased CH4/CO2 and O2/CH4 ratio to 2.0 and 0.30, respectively, consequently decreased the reactant (CH4 and CO2) conversions and H2 production, while negligible effect on product yield and ratio was observed. It is further examined that under all experimental tests, the 100 % O2 conversion was observed, while CO2 conversion was found to be continuously decreasing on increasing O2/CH4 ratio due to the oxidation of CO to CO2 with added O229. Further, it can be seen that CH4 combustion occurred to a larger extent at lower temperature ranges, which shows the high CO2 product selectivity with higher H2O production30. Meanwhile, the H2/CO ratio steps up with rising temperature from 800 to 900°C, while under same temperature conditions, H2/CO ratio doesn’t shows a significant effect relative to CH4/CO2 ratios. Djinovic et al. used CH4/CO2 ratio (>1) and reaction temperature (≥800°C) to minimized the effect of reverse water gas shift (RWGS) reaction (Eq.10)31. On the contrary, reduction in the SEC per ml of H2 produced was observed corresponding to increasing the all independent variables. Likewise, at 900°C temperature, 1.0 CH4/CO2 and 0.20 O2/CH4 ratios, the SEC reduced to 8.45 kJ/ml of H2 when compared to


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800°C under same reactant conditions. Additionally, this decrement in SEC is able to increase the H2 production at same working conditions. (10)

CO+H 2 O ⇔ CO 2 +H 2

4.2 Statistical Analysis The regression correlation analysis was performed in order to correlate dependent and independent variables. Table 3 summarizes the experimental matrix that includes three independent factors and the responses based on 15 experimental runs, and finally, the fitted second order polynomial equations for responses such as products (CO and H2), reactants conversion (CH4 and CO2), H2/CO ratio and SEC were obtained.

Table 3. DOE and responses for dry oxidative reforming of biogas

The positive correlation coefficient represents an increment of a process variable is followed by an increase of another variable. On the contrary, if given increment of a process variable is followed by a decrement of another variable; it indicates a negative correlation coefficient. From ANOVA analysis (Table 4), the response surface quadratic models were formulated for each dependent variable individually. Strategically, if the model P-value is 0.05, and the empirical model could be used for the prediction, because of the insignificant lack of fit. Similarly, Table 4 shows significance of the model terms for selective dependent variables. Table 4. ANOVA for response surface quadratic model of CH4 conversion

Apart from that, the goodness of the empirical model was explained by adjusted R-square values. The adjusted R-square rises only with the addition of independent factors that are much significant to the dependent factor, and if the non-significant factors are added to the


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model, the value of adjusted R-square will significantly decrease. As shown in the Table 5, the adjusted R-square values of CO, H2, CH4 and CO2 conversion, were determined 97.0, 95.89, 98.61 and 94.75, respectively, indicating that only 3.0, 4.11, 1.39 and 5.25% of the total variation in the model was not demonstrated by the input factors. It has been examined that an excellent fit was achieved for all responses except H2/CO ratio which are further validated by comparing experimental and predicted values (Table 3). The predicted and experimental values of CH4 conversion using Eq. (11) are depicted in Fig.2. From the plot, more accurate description of the experimental data was examined, as all the distributed points are very close to the line of fit. In addition, R2 value of 0.955 indicating a very good agreement good between the predicted values and experimental data mash points. Table 5. Coefficients of significant terms for dependent variables

YCH4 = −2256 + 4.905X1 +136.3X2 − 673X3 − 0.0720 X1X2 + 0.565X1X3 −1.5 X2X3 − 0.002619 X12

Eq. (11) −20.22 X22 + 282 X32 The importance of independent variables on DOR process was determined by illustrating a 3D response surface plot. The 3D representation of response surface plot for CH4 conversion is given in Fig. 3. The positive value of process temperature (X1) and CH4/CO2 ratio (X2) in Eq. (10) reveals that the higher the process temperature and CH4/CO2 ratio, is the higher CH4 conversion. Whereas, negative value of the O2/CH4 ratio (X3) indicates that a decline in the O2/CH4 ratio leads to a rise in CH4 conversion. The results shows that CH4 conversion increased significantly as an increase of process temperature from 800 to 900°C at a constant CH4/CO2 ratio. However, at higher temperature (900°C), an increase in O2/CH4 ratio from 0.10 to 0.30 significantly decreased the CH4 conversion. From further analysis of surface response plots, it can be observed that temperature, CH4/CO2 and O2/CH4 ratio play a dominant role in CH4 conversion, while given ratios alters the conversion with respect to one another but temperature always found to be increasing the conversion. Figure 2. Predicted vs. actual value of CH4 conversion

Figure 3. Surface plot of CH4 conversion versus process temperature and CH4/CO2 ratio

5. CONCLUSIONS The response surface methodology was employed to evaluate the parametric influence of DOR process on H2 enriched biogas production over commercial Ni nanopowder. Over the range of process parameters studied, H2 enrichment and CH4 conversion increases with the 8 ACS Paragon Plus Environment

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process temperature, while variations with respect to CH4/CO2 and O2/CO2 ratios were observed. The results revealed that the CH4/CO2 with a coefficient of 40.0 has a greatest positive effect while the interaction between CH4/CO2 and O2/CO2 ratio with 48.5 of coefficient has a greatest negative effect on the H2 enriched biogas production. Moreover, it was shown that the CH4 conversion and H2 enrichment enhanced when the process temperature increased, as a result of endothermic reaction. On the other hand, the CO production decreased on increasing the O2/CO2 ratio. Increase in the O2/CO2 ratio beyond 0.20 leads to a decrease in CO and H2 production due to the reaction proceeding towards complete combustion. However, H2 and CO production volumes were close to each other at 900°C. The highest H2/CO product ratio (1.02) was obtained under optimized conditions (reaction temperature of 900°C, CH4/CO2 ratio of 1.5, and O2/CH4 of 0.10) with coke deposition of 0.032 (wt. %). It was also observed that SEC per ml of H2 produced was significantly reduced with the addition of O2.

ACKNOWLEDGEMENT The authors extend their appreciation to the Ministry of New & Renewable energy (MNRE) for providing financial support through the project bearing sanction number 103/223/2014NT.

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Nomenclature

CI

Compression Ignition

DR

Dry Reforming

DOR

Dry Oxidative Reforming

MFC

Mass Flow Controller

H2

Hydrogen

N2

Nitrogen

CH4

Methane

CO2

Carbon Dioxide

O2

Oxygen

CO

Carbon Monoxide

RWGS

Reverse Water Gas Shift

SEC

Specific Energy Consumption

RSM

Response Surface Methodology


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 1. An experimental setup used for dry oxidative reforming of biogas


Page 14 of 20

Page 15 of 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Figure 2. Predicted vs. actual value of CH4 conversion

Figure 3. Surface plot of CH4 conversion versus process temperature and CH4/CO2 ratio


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 20

Table 1. Selected reaction parameters and their levels for analysis

Independent Variables

Range and levels

Symbol

Unit

Reaction Temp.

X1

(°C)

-1 800

0 850

1 900

CH4/CO2

X2

--

1.0

1.5

2.0

X3

--

0.10

0.20

0.30

O2/CH4


Page 17 of 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Energy & Fuels

Table 2. Experimental data for 3-level-3-factor response surface analysis Temp. (°C)

DOE

CH4:CO2 O2:CH4

Product

Conversion

Selectivity

Yield

H2:CO

SEC

18

0.61

23.73

9.3

22.3

0.5

24.64

51.81

24.6

37.5

0.87

8.31

32.92

45.44

15.3

20.8

0.88

13.3

24.8

31.28

64.36

10.6

19.8

0.71

19.15

43

64.3

59.79

60.23

25.7

32.3

0.8

11.29

28.36

61.7

70.6

42.92

54.42

26.5

35.5

0.89

8.93

9.7

38.71

82.9

90.8

41.27

46.86

34.2

40.3

1.02

6.84

8.95

14.89

31.34

67.2

80.3

51.27

51.74

34.5

38.2

0.9

8.45

30.16

12.07

21.88

26.64

59.2

66.2

42.03

54.5

24.9

33.8

0.88

9.48

0.3

34.44

8.61

14.1

34.21

72.3

74.6

46.55

55.52

33.6

40.6

0.99

7.87

1.5

0.3

13.55

24.5

38.32

12.03

24.6

27.7

48.01

61.8

11.8

16

0.89

19.24

850

2

0.3

26.92

17.77

24.49

21.96

55.9

36

35.34

65.54

19.8

32.3

0.82

11.55

-1

850

1

0.1

32.74

16.02

19.3

21.39

59.5

66.4

37.77

54.64

22.5

34.4

0.65

11.63

0

900

2

0.2

36.04

7.77

12.44

34.59

78.9

73.6

37.29

52.98

29.4

40.8

0.96

7.74

A

B

C

X1

X2

X3

CO

CO2

CH4

H2

CH4

CO2

H2

CO

H2

CO

-1

-1

0

800

1

0.2

16.34

25.84

38.68

10.03

14.9

43.2

74.03

61.92

11

0

0

0

800

1.5

0.2

19.95

25.92

37.72

9.94

29.6

27.4

31.35

77.79

0

1

-1

850

2

0.1

35.15

8.82

17.08

30.7

72.7

71.8

33.8

-1

0

-1

800

1.5

0.1

19.64

20.85

30.27

17.34

46.5

44.7

-1

1

0

800

2

0.2

17.49

22.12

38.94

12.44

33.8

0

-1

1

850

1

0.3

28.1

15.52

24.78

22.36

0

0

0

850

1.5

0.2

31.7

10.5

20.53

1

0

-1

900

1.5

0.1

38.04

3.47

1

-1

0

900

1

0.2

34.7

0

0

0

850

1.5

0.2

1

0

1

900

1.5

-1

0

1

800

0

1

1

0

-1

1

1

17

ACS Paragon Plus Environment

Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Page 18 of 20

Table 3. DOE and responses for dry oxidative reforming of biogas DOE

CO

H2

CH4 conversion

CO2 conversion

H2:CO

SEC

A

B

C

Exp.

Predicted

Exp.

Predicted

Exp.

Predicted

Exp.

Predicted

Exp.

Predicted

-1

-1

0

16.34

16.74

10.03

8.79

14.90

16.83

43.20

39.94

0.61

0.58

23.73

23.48

0

0

0

19.95

17.80

9.94

13.22

29.60

30.77

27.40

35.81

0.50

0.72

24.64

20.10

0

1

-1

35.15

34.64

30.70

30.96

72.70

73.31

71.80

71.99

0.87

0.90

8.31

6.95

-1

0

-1

19.64

20.87

17.34

16.28

46.50

44.64

44.70

44.07

0.88

0.77

13.30

16.05

-1

1

0

17.49

17.57

12.44

12.01

33.80

34.61

24.80

22.09

0.71

0.70

19.15

19.46

0

-1

1

28.10

28.07

22.36

22.92

43.00

42.68

64.30

66.21

0.80

0.82

11.29

11.51

0

0

0

31.70

31.47

28.36

26.68

61.70

60.16

70.60

66.30

0.89

0.83

8.93

10.34

1

0

-1

38.04

38.14

38.71

38.44

82.90

84.66

90.80

90.50

1.02

1.04

6.84

6.24

1

-1

0

34.70

35.16

31.34

30.95

67.20

66.10

80.30

80.91

0.90

0.85

8.45

9.28

0

0

0

30.16

31.47

26.64

26.68

59.20

60.16

66.20

66.30

0.88

0.83

9.48

10.34

1

0

1

34.44

33.75

34.21

34.45

72.30

73.86

74.60

73.13

0.99

1.04

7.87

6.25

-1

0

1

13.55

13.99

12.03

11.48

24.60

22.54

27.70

25.90

0.89

0.81

19.24

20.97

0

1

1

26.92

27.21

21.96

21.71

55.90

56.71

36.00

37.36

0.82

0.82

11.55

11.21

0

-1

-1

32.74

31.92

21.39

22.46

59.50

58.98

66.40

67.14

0.65

0.70

11.63

10.83

1

1

0

36.04

36.18

34.59

35.01

78.90

76.68

73.60

74.76

0.96

0.93

7.74

9.13

18

ACS Paragon Plus Environment

Exp. Predicted

Page 19 of 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Table 4. ANOVA for response surface quadratic model of CH4 conversion Source

Sum of square

Degree of freedom

Mean square

F-Value

P-Value

Model

5974.67

9

663.852

111.28

0.000

X1

166.54

1

166.537

27.92

0.003

X2

55.49

1

55.487

9.30

0.028

X3

57.14

1

57.173

9.58

0.027

X1X2

12.96

1

12.960

2.17

0.201

X1X3

31.92

1

31.922

5.35

0.069

X2X3

0.02

1

0.022

0.00

0.953

X12

138.42

1

138.423

23.20

0.005

X22

88.71

1

88.709

14.87

0.012

X32

27.61

1

27.610

4.63

0.084

Residual

29.83

5

5.966

-

-

Lack of fit

26.70

4

6.676

2.14

0.469

Pure error

3.13

1

3.125

-

-

Significant at p < 0.05, Not significant at p > 0.05.


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Page 20 of 20

Table 5. Coefficients of significant terms for dependent variables

Conversion Coefficients

CO

H2

CH4

CO2

H2:CO

SEC

Constant

-1386

-

-2256

-2282

-

-

X1

3.159

-

4.905

5.10

-

-

X2

-

-

136.3

-

-

-

X3

-

-

-673

-

-

-

X1X2

-0.001766

-

-

-

-

-

X1X3

-

-

-

-

-

-

X2X3

-

-

-

-1.5

-

-

X12

-

-

-0.002619

-0.002619

-

-

X22

-

-11.28

-20.22

-

-

-

X32

-

-

-

-

-

-

R-Sq(Adj.)

97.00%

95.89%

98.61%

94.75%

20.01%

76.18%


Optimization of hydrogen enriched biogas by dry oxidative reforming

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