Statistical Design Optimization of Hydrogen Production ... · Index Terms—ethanol reforming,...

مجلة الجامعة األسمرية للعلوم األساسية والتطبيقية

7302،الجزء الثاني ، يونيو (03)العدد

112

Statistical Design Optimization of Hydrogen Production through

Ethanol Steam Reforming using a Ni/Al2O3 Catalyst

Ahmed Bshish

1,*, Ali Ebshish

2, Zahira Yaakob

3

1,2Department of Chemical and Petroleum Engineering, Faculty of Engineering, elmergib

University, Al khoms, Libya. 3

Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment

Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia.

Abstract—Factorial experimental design and response surface methodology, together with

central composite design, were employed to investigate the effect of the process variables in

hydrogen production via ethanol steam reforming. The influence of temperature (T), water–

ethanol molar ratio (MR), and liquid hourly space velocity (SV) on hydrogen yield ( ),

ethanol gasification ( ), and CO yield ( ) were determined. In coded units, X1, X2,

and X3 represent T, MR, and SV, respectively. The multiple regression analysis results

showed that temperature and water–ethanol molar ratio significantly affects the responses. By

contrast, all the responses were not significantly affected by the liquid hourly space velocity.

ANOVA indicated that the linear terms X1 and X2, the quadratic term X2, and the interaction

term X1X2 exerted the highest influence on the , and

. was most affected by

the linear terms X1 and X2 and the interaction term X1X2. The optimal conditions for maximal

and

, and minimal were obtained at a temperature of 500 °C, a water–

ethanol molar ratio of 20, and a liquid hourly space velocity of 0.72 h-1

. These optimal

conditions resulted in a hydrogen yield of 4.7 mol/mol of ethanol, an ethanol gasification rate

of 85%, and a CO yield of 0.25 mol/mol of ethanol. Therefore, special consideration must be

given to the temperature and the water–ethanol molar ratio for hydrogen production via

ethanol steam reforming using Ni/Al2O3 catalysts.

Index Terms—ethanol reforming, response surface, experimental design, optimization.

I. INTRODUCTION

Hydrogen production through ethanol steam reforming has been performed using different catalytic

systems. Developing an active, selective, and stable catalytic system for producing hydrogen is a

major challenge as far as ethanol steam reforming is concerned. Bshish et al [1] extensively

reviewed various catalytic systems for hydrogen production through ethanol reforming. Nickel-

based catalysts have been broadly investigated in ethanol steam reforming reaction on different

supports because they are inexpensive and are extensively used in hydrocarbon hydrogenation and

steam reforming [2-10] . Aside from catalytic properties, operating parameters such as temperature,

water–ethanol molar ratio, and space velocity are also control catalyst activity. Therefore,

determining the most important operating parameters and their values are important in enhancing

ethanol utilization to maximize hydrogen production and minimize the percentage of carbon

monoxide (CO) in the effluent gas. Several studies have investigated the effect of operating

parameters such as steam to carbon (S/C) molar ratio, reaction temperature, feed flow rate, and

space time on hydrogen production [11,12]. However, these studies assumed the operating

parameters do not interact [13], which is inefficient for optimizing the reaction conditions [14].



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Therefore, studying the interactions between the operating parameters is essential to optimizing

hydrogen production.

Statistically, the experimental design is established and used to control the experiments [15].

Experimental design is used to analyze the influence of separate and interacting factors, and to

develop models that simulate the responses with a minimum number of experiments. Response

surface methodology (RSM) is used to examine the relationship between one or more responses and

a set of quantitative experimental variables. Its simplicity and high efficiency make RSM an

optimization technique that can be commonly applied to optimize process variables in different

applications [16-20]. Moreover, RSM reduces the experimental trails needed to estimate the process

variables [21].

In this paper, we used factorial design to optimize the variables that control hydrogen production

via ethanol steam reforming, namely, temperature (X1), water–ethanol molar ratio (X2), and liquid

hourly space velocity (X3). Hydrogen production via ethanol steam reforming was performed using

a Ni/Al2O3 catalyst, where the alumina support, denoted as AlS.G, was prepared via a sol–gel

process. The optimization process was performed via RSM using central composite design (CCD)

to evaluate the maximum hydrogen yield ( ) and ethanol gasification (

), and the

minimum CO yield ( ). Ni loading was maintained at 6 wt% for all experiments.

II. MATERIALS AND METHODS

A. Catalyst preparation

The sol–gel alumina support (AlS.G.) was prepared according to (Seo et al 2007 [22], where 96 g

of precursor (aluminum sec-butoxide, Sigma–Aldrich) was dissolved in 828 ml of ethyl alcohol

under constant stirring at 80 °C (solution 1). Up to 1.37 ml of HNO3 and 4.12 ml of distilled water

diluted with 549 ml of ethyl alcohol (solution 2) were then mixed with solution 1. This mixture was

fixed at 80 °C to form the sol. After cooling the sol, a transparent gel was obtained by adding drop

wise 8.23 ml of distilled water and 68.66 ml of ethyl alcohol into the sol. Subsequently, the alumina

gel was covered and kept for one day before it was dried overnight. The solid formed was calcined

at 800 °C to obtain the alumina sol–gel. The support was denoted as AlS.G.

B. Instrumentation

To evaluate the optimum operating parameters for hydrogen production via ethanol steam

reforming, all the runs were conducted in a Pyrex glass tube reactor (internal diameter: 8 mm;

length: 50 cm) at 400 °C and atmospheric pressure. The schematic of the experimental set-up for

the reforming reaction is shown in Fig. 1. Before the reaction, the catalyst was heated to 150 °C for

1 h, and subsequently reduced in situ under flowing H2 for another 1 h. A mixture of liquid water

and ethanol was introduced into the reactor containing 0.5 g of catalyst together with the carrier

nitrogen (40 ml/min) gas. The output gas stream was analyzed via gas chromatography (GC)

(Model SRI 8610 C) using a molecular sieve and a silica gel column with a thermal conductivity

detector with helium as the carrier gas. The condensed liquids were collected and analyzed using a

gas chromatograph (SUPELCO) equipped with a flame ionization detector and an Equity-1

capillary column (30 m × 0.32 mm × 0.1 μm film thickness).

The criteria used to determine catalyst performance included H2 yield, ethanol gasification, and CO

yield. Equations (1), (2), and (3) were used to calculate ,

, and , respectively.

(1)

(2)



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(3)

where and

represent the molar flow rate of the ethanol inlet and outlet of the

reactor, respectively, and and

represent the flow rate of the hydrogen and carbon

monoxide outlets of the reactor, respectively.

E-3

Gaseous

products

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Figure 1. Schematic diagram of the experiment for the production of hydrogen by the reforming of

ethanol.

C. Experimental design

The three independent variables temperature (X1), water–ethanol molar ratio (X2), and liquid

hourly space velocity (X3) were verified based on the experimental design as follows. The CCD

method was implemented to investigate the influences of these operating factors on the ,

, and of hydrogen production via ethanol steam reforming. Table 1 shows the three

independent factors with their experimental ranges and the levels employed in this study. The center

of the design is coded as (0), and the upper and lower levels of the design are coded as (1) and (-1),

respectively. The factorial CCD of the independent factors is presented in Table 2. According to the

CCD, 20 sets of runs were selected and each experiment was replicated twice.

TABLE 1. Coded Values Of Variables Chosen For Trails

-1 0 1

Temperature

(oC)

200 350 500

Molar Ratio 3 13.5 24

LHSV (h-1

) 0.125 0.5625 1



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TABLE 2. Central Composite Experimental Design

Coded variables

Run Pt Type T (oC) Molar Ratio LHSV (h

-1)

1 -1 200 13.5 0.5625

2 1 200 24 1

3 1 500 3 1

4 1 500 24 0.125

5 -1 350 13.5 1

6 (C) 0 350 13.5 0.5625

7 -1 350 3 0.5625

8 1 200 3 0.125

9 (C) 0 350 13.5 0.5625

10 (C) 0 350 13.5 0.5625

11 1 500 24 1

12 1 500 3 0.125

13 (C) 0 350 13.5 0.5625

14 -1 350 13.5 0.125

15 (C) 0 350 13.5 0.5625

16 (C) 0 350 13.5 0.5625

17 1 200 3 1

18 1 200 24 0.125

19 -1 350 24 0.5625

20 -1 500 13.5 0.5625

(C): Centre point

The experimental design and statistical analysis were conducted using the Minitab software

package (version 14.12.0). The experimental data were modeled via an ANOVA, and a second-

order polynomial model was determined (Equation (4)) for each output response ( ,

,

and ). The observed experimental data were then compared with the data from the obtained

models.

(4)

where represents the response variables ( ,

, and ); is a constant; , ,

and are the linear, quadratic, and interaction effect coefficients, respectively; and and are

the input variables. For the three independent variables, the model represented is shown in Equation

(5).



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(5)

The significance of the regression model was determined using an ANOVA with p-value less than

0.05. In addition, the coefficient of determination (R2) was used to evaluate the performance of the

model. In general, the higher the R2, the better the model fits the data. A reduced model was

obtained by eliminating the insignificant coefficients from the completed model. The influence of

the three experimental variables on each response was explored using RSM plots.

III. RESULT AND DISCUSION

A. Empirical models

Two replicates of 20 sets of experiments were performed to investigate the influence of reaction

temperature, ethanol to water molar ratio, and liquid hourly space velocity on hydrogen production

during ethanol steam reforming. Prior to the reaction, all runs were conducted under the same

preheating and reduction procedure. Table 3 illustrates the experimental (observed) and predicted

data obtained from the model for the three responses ( ,

, and ). Standard

deviations and mean values of the three observed responses are also shown in Table 3.

The estimated regression coefficients of reduced and nonreduced models with their corresponding

p-values are shown in Tables 4 and 5, respectively. These tables also provide the R2 and adjusted R

2

values, which were used to evaluate the goodness of fit of the regression model. ANOVA were

conducted to identify the terms that significantly influenced the responses at a p-value less than

0.05. Tables 4 and 5 indicate that the most important terms are X1, X2, X22, and X1X2, which

represent the linear terms of temperature, molar ratio, quadratic term of molar ratio, and the

interaction term of temperature and molar ratio, respectively. These terms significantly influenced

all responses ( ,

, and ). Notably, the liquid hourly space velocity (LHSV),

representing X3, had negligible influence on the responses when all the p-values were greater than

0.05.



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TABLE 3. Factorial Central Composite Design With The Observed And Predicted Responses

Values

(YH2.Y) (YCO.Y) (YETOH.Con.)

Run Observed

H2 yield

Predicted

H2 yield

Observed

CO yield

Predicted

CO yield

Observed

ETOH. Con.

Predicted

ETOH. Con.

1 0.54±0.028 0.94 0.18±0.03 0.169 26±0.70 24.75

2 0.89±0.018 0.71 0.15±0.003 0.1 25±0.70 26.088

3 2.1±0.99 2.7 0.42±0.029 0.396 80±2.12 73.087

4 4.8±0.07 4.69 0.21±0.009 0.205 81±0.70 78.113

5 2.7±0.21 2.63 0.17±0.015 0.202 61±2.1 56.8

6 (C) 2.8±0.14 2.61 0.23±0.0015 0.226 58±4.24 55.75

7 1.5±0.42 1.35 0.28±0.004 0.172 45±3.53 37.1

8 0.0±0.00 0.008 0.00±0.00 0.032 0.0±0.00 1.112

9 (C) 2.6±0.07 2.61 0.22±0.003 0.226 59±17.67 55.75

10(C) 2.7±0.14 2.61 0.23±0.001 0.226 61±2.1 55.75

11 4.8±0.49 4.72 0.21±0.0033 0.19 79±1.41 80.213

12 2.4±0.21 2.67 0.38±0.077 0.388 65±3.5 70.988

13(C) 2.7±0.28 2.61 0.26±0.071 0.226 45±5.65 55.75

14 2.9±0.14 2.60 0.33±0.0088 0.214 65±3.5 54.7

15(C) 2.6±0.00 2.61 0.22±0.076 0.226 54±1.4 55.75

16(C) 2.5±0.07 2.61 0.22±0.078 0.226 58±1.41 55.75

17 0.0±0.00 0.023 0.00±0.00 0.00 0.0±0.00 3.212

18 0.89±0.017 0.681 0.10±0.015 0.132 24±0.71 23.988

19 2.2±0.07 2.70 0.06±0.022 0.119 52±1.4 52.1

20 4.3±0.07 4.29 0.30±0.0054 0.392 85±0.092 86.75

TABLE 4. Non reduced Model And Coefficient Parameters For The Response

Regression

coefficient Term

Non reduced model

Estimated Regress. Coefficients P- value

H2 Y.

(Y1)

CO Y.

(Y2)

ETOH

Con. (Y3)

H2 yield

(Y1)

CO Y.

(Y2)

ETOH

conversion (Y3)

Linear

X1 0.011 -0.0003 0.328 0.005 0.65 0.000

X2 0.159 0.0335 4.499 0.000 0.000 0.000

X3 -1.25 0.0836 -13.29 0.133 0.66 0.451

Quadratic

X2

1

-0.00 0.000002 -0.0001 0.381 0.052 0.206

X2

2 -0.005 -0.0007 -0.097 0.000 0.006 0.000

X2

3 1.131 -0.0926 13.1 0.072 0.51 0.32

interaction

X1X2 0.0002 -4.5E-05 -0.002 0.000 0.000 0.011

X1X3 0.0005 0.000067 0.021 0.593 0.78 0.333

X2X3 -0.013 -0.0012 -0.503 0.371 0.72 0.124

R2

96.9% 76.5% 95.6%

R2

adjusted 96.0% 69.5% 94.2%



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TABLE 5. Reduced Model And Coefficient Parameters For The Responses

Regression

coefficient Term

Reduce model

Estimated Regress. Coefficients P- value

H2 Y.

(Y1)

CO Y.

(Y2)

ETOH

Con. (Y3)

H2 yield

(Y1)

CO Y.

(Y2)

ETOH

conversion (Y3)

Linear

X1 0.0083 0.0013 0.2404 0.000 0.000 0.000

X2 0.1354 0.031 4.319 0.000 0.003 0.000

X3 0.0354 -0.013 2.4 0.803 0.70 0.437

Quadratic

X2

1

- - - - - -

X2

2 -0.0053 - -0.101 0.000 - 0.000

X2

3 - - - - - -

interaction

X1X2 0.00021 -4.5E-05 -0.0025 0.000 0.00 0.013

X1X3 - - - - - -

X2X3 - - - - - -

R2

96.5% 66.6% 94.7%

R2

adjusted 96.0% 62.8% 94.0%

As shown in Tables 4 and 5, R2 and adjusted R

2 for

and indicated that the

proposed model adequately fitted the experimental data. For , the R2 and adjusted R

2 values of

the regression model were quite low, which suggests the presence of other important parameters.

The high R2 (0.965) for

in Table 5 indicates that the proposed model can account for most

of the observed variations. The high R2 value of 0.947 and the small p-value (p < 0.05) for the

indicated that the regression model had a good correlation between the independent

variables and the response. However, Table 5 shows that the linear terms of X1 and X2, the

quadratic term of X22, and the interaction term of X1X2 significantly influenced both and

with p-value less than 0.05. By contrast, was mostly affected by the linear terms of

X1 and X2 and the interaction term of X1X2.

The following second-order polynomial equations (Equations (6), (7), and (8)) describing the

relationship between the process variables and their predicted responses for all the three dependent

variables ( ,

, and ,) were generated.

(6)

(7)

(8)



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Equations 6 and 8 show that X2 had the greatest influence on and . The terms X3 and

X1 also affected , but to a lesser extent than X2. The interaction between X1X2 only slightly

affected . As shown in Equation (7),

was mostly affected by the linear term of X2,

followed by X3 and X1. However, the quadratic term X22 and the interaction term X1X2 had lower

effect on than the linear terms.

B. Effect of process variables on the responses

Figure 2 presents the 3D response surface plots of the relationships between the independent

variables and . The advantage of using response surface plots with two independent variables

at a time is to enable the examination of the relation of the main and the interaction factors with the

response. Figure 2a illustrates the effect of X1 and X2 on . The surface plot shows that the

highest hydrogen yield was obtained when both independent variables (X1 and X2) were high

(interaction effect). This finding was in agreement with other data [22], which indicate that high

temperatures and high water–ethanol ratios enhance H2 production. On the other hand, Fig. 2b

shows that varied only with X1, whereas X3 had no effect on

. Figure 2c also shows that

was significantly affected by X2 up to 18, but was not significantly changed beyond this ratio.

Generally, increased as X1 and X2 increased, which was expected because the steam reforming

reaction is endothermic and positively affected by high temperature.

The response surface in Fig. 3 (a, b, and c) illustrates the influence of the interaction between

varying temperature and water–ethanol molar ratio, between temperature and liquid hourly space

velocity, and between water–ethanol molar ratio and liquid hourly space velocity on ethanol

gasification, respectively. Figures 3a and 3b indicate that significantly increased as X1

increased. From Fig. 3a, the orientation of the principal axes of the surface plot indicate that the

interaction between X1 and X2 significantly affected , and the optimum values of both

variables occurred in the experimentally explored area. The effect of the interaction between X1 and

X2 on shown in Fig. 3a was higher than that between X1 and X3 shown in Fig. 3b,

wherein X3 had no effect on . Figure 3c shows that

was significantly influenced

by X2, especially at low and medium levels. Meanwhile, X3 had a lower effect on .

Therefore, at a constant X2, slightly increased as X3 increased. Generally, Figs. 2 and 3

indicate that the ethanol gasification and hydrogen yield expectedly increased as the temperature

and molar ratio increased because the steam reforming reaction is endothermic and favors at high

temperature. The ethanol gasification and hydrogen yield varied non-significantly with the LHSV

range in this study.



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Figure 2. Response surface plot of the interaction effects of process variables on hydrogen yield:

(a) temperature and molar ratio, (b) temperature and LHSV, (c) molar ratio and LHSV.

Response surface plots describing the effects of the main and the interaction variables on

are shown in Fig. 4. The effect of the interaction between X1 and X2 on is illustrated in Fig.

4a. decreased as X2 increased at high X1 values, but it did not exceed 0.18 mol/mol for all

values of X2 at low X1 values. The surface plot also shows that the lowest was obtained at the

lowest X1 value. Figure 4b shows that the values significantly varied with high X1 values,

whereas the variation in X3 values had no effect on . At a constant X1, significantly

varied with X2. Figure 4c shows that the lowest was obtained at high X2 levels.

Figure 3. Response surface plot of the interaction effects of process variables on CO yield: (a)

temperature and molar ratio, (b) temperature and LHSV, (c) molar ratio and LHSV.



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However, the low at high X2 can be attributed to the acceleration of the water gas shift

reaction caused by the catalyst (Equation (9)).

(9)

Figure 4. Response surface plot of the interaction effects of process variables on conversion into

gaseous product: (a) temperature and molar ratio, (b) temperature and LHSV, (c) molar ratio and

LHSV.

C. Response optimization

The optimum levels of the three independent variables, namely, temperature, water–ethanol molar

ratio, and liquid hourly space velocity were determined using the response optimization method.

The optimum conditions that maximized ethanol gasification and hydrogen yield and minimized

carbon monoxide yield were a temperature of 500 °C, a water–ethanol molar ratio of 20, and a

liquid hourly space velocity of 0.72 h-1

. Applying these optimum conditions resulted in hydrogen

yield of 4.7 mol/mol of ethanol, an ethanol gasification rate of 85%, and a carbon monoxide yield of

0.25 mol/mol of ethanol.

IV. CONCLUSION

This study focused on hydrogen production via ethanol steam reforming using a Ni/Al2O3

catalyst. The hydrogen yield, ethanol gasification and CO yield were optimized and the effects of

temperature, water–ethanol molar ratio, and liquid hourly space velocity variations as independent

variables were analyzed through statistical analysis. A second-order polynomial model was



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developed using an ANOVA. According to the reduced models, temperature (X1) and water–

ethanol molar ratio (X2) significantly affect the responses. All of the responses were not

significantly affected by the liquid hourly space velocity (X3), possibly because of the small LHSV

range applied. The response optimization method was used to identify the combination of input

variable settings that jointly optimized the three responses. The optimum values for the independent

variables were a temperature of 500 °C, a water–ethanol molar ratio of 20, and a liquid hourly space

velocity of 0.72 h-1

. These conditions result in an ethanol gasification rate of 85%, a hydrogen yield

of 4.7 mol/mol of ethanol, and CO yield of 0.25 mol/mol of ethanol. In conclusion, special

consideration must be given to temperature and the water–ethanol molar ratio for hydrogen

production via ethanol steam reforming using Ni/Al2O3 catalysts.

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