La2O3MgAl2O4 using multiple response surface methodology

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Process optimization of DBD plasma dry reforming of methane over Ni/La2O3eMgAl2O4 using multiple response surface methodology Asif Hussain Khoja a,b, Muhammad Tahir a, Nor Aishah Saidina Amin a,* a

Chemical Reaction Engineering Group (CREG), School of Chemical & Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor Bahru, Malaysia b Department of Thermal Energy Engineering, U.S.-Pakistan Centre for Advanced Studies in Energy (USPCASE), National University of Sciences & Technology (NUST), H-12 Sector (44000) Islamabad, Pakistan

article info

abstract

Article history:

In this study, 10% Ni/La2O3eMgAl2O4 nano-flake catalyst was synthesized, characterized

Received 21 August 2018

and tested in a catalytic dielectric barrier discharge (DBD) plasma for dry reforming of

Received in revised form

methane (DRM). With design of experiment (DoE), the influence of process parameters

5 February 2019

namely (1) total feed flow rate (ml min
1), (2) feed ratio (CO2/CH4), (3) input power (W) and

Accepted 7 March 2019

(4) catalyst loading (g) were examined using multiple response surface methodology (RSM)

Available online 30 March 2019

through a four-factor, five-level central composite design (CCD). Second-order regression models were applied for evaluating the interaction between the process parameters and

Keywords:

responses. Input power (X3) and total feed flow rate (X1) were the two most influential

Dry reforming of methane

process parameters followed by catalyst loading (X4) and feed ratio (X2). The experimental

Plasma-catalysis

and predicted results from the optimum conditions fitted-well with less than ±5% margin

Response surface methodology

of error. The possible dynamic interactions between the process variables were elucidated.

Process optimization

The optimum values are feed flow rate ¼ 18.8 ml min
1, feed ratio ¼ 1.05, input

MgAl2O4

power ¼ 125.6 W and catalyst loading ¼ 0.6 g. At these conditions, the predicted CH4 and CO2 conversions are 79.86% and 84.03%, respectively. The H2 and CO yields are predicted as 41.37% and 40.47%, respectively while H2/CO ratio is above unity. The calculated EE from the RSM model is predicted as 0.135 mmol kJ
1. Low carbon deposition observed on the spent catalyst is attributed to the highly basic and oxidative nature of the La2O3 cosupported catalyst. © 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Introduction Cold plasma is one of the emerging technologies for mitigating greenhouse gases (GHGs) via dry reforming of methane (DRM) (Eq. (1)). Unlike the conventional thermo-catalytic DRM

process, plasma-catalysis expresses a great potential in producing syngas and other valuable fuels [1e4]. The coupling of plasma and catalyst offers multiple advantages over conventional thermo-catalytic DRM. Cold plasma prevails over energy input, easy handling, low installation and operating cost [5,6]. Dielectric barrier discharge (DBD) cold plasma is widely

* Corresponding author. E-mail address: [email protected] (N.A. Saidina Amin). https://doi.org/10.1016/j.ijhydene.2019.03.059 0360-3199/© 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

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Nomenclature ANOVA BET CCD DBD DF DoE DRM EDX FESEM FID GHGs HRTEM MSSSR MSSSE RSM RWGS SSR SSE TCD TGA TPD TPR XRD Vcat VD

Analysis of variance Brunauer, Emmett and Teller Central composite design Dielectric barrier discharge Degree of freedom Design of experiment Dry reforming of methane Energy-dispersive X-ray spectroscopy Field emission electron microscopy Flame ionization detector Greenhouse gases High resolution transmission electron microscopy Mean of square regression Mean of square residual Response surface methodology Reverse water gas shift reaction Sum of square regression Sum of square residual Thermal conductivity detector Thermogravimetric analysis Temperature programmed desorption Temperature programmed reduction X-ray diffraction Volume of catalyst Volume of discharge

considered for DRM process due to its simple operational design at ambient temperature and pressure [7e9]. Moreover, DBD plasma works efficiently for the excitation, activation and dissociation of the reagent gases (CH4, CO2). For instance, the reactant gases can be activated by plasma at mild conditions that produce reactive species to initiate and accelerate the rate of surface reactions. Meanwhile, the presence of catalyst enhances the electric field due to dielectric properties and changes the discharge behaviour of the DBD plasma [10,11]. The catalyst morphology is affected in the plasmacatalysis and facilitates the adsorption and activation of reactant gases by overcoming the activation barrier [1,12]. Due to the influence of plasma on catalyst properties and catalyst on plasma behaviour, the mechanism of the reactions and surface chemistry is complex [13]. Consequently, the coupling of catalytic plasma enhances the CH4 and CO2 conversion and H2/CO ratio. However, unwanted by-products including carbon and water formation pose a persistent challenge to DBD plasma reaction [14]. The commercialization of DBD plasma DRM has been hampered by its low energy efficiency (EE, mmol kJ
1), caused by power dissipation to the dielectric and carbon formation via methane cracking and Boudouard reaction (Eqs. (2) and (3)). Carbon is instantaneously deposited on the catalyst surface, blocking the active sites and lowering the plasma-catalytic activity. Moreover, the reverse water gas shift reaction (RWGS) (Eq. (4)) affects the composition of the syngas making it inappropriate for further utilization in

liquid fuel processing via Fischer Tropsch (FT) synthesis. The coupling of DBD plasma with a stable catalyst bring about a synergistic effect and improve the EE of the DBD reactor [15,16].
1

CH4 þCO2 ⇔2CO þ 2H2 DH+ 25+ C ¼ 247kJmol ðDRMÞ
1

CH4 ⇔C þ H2 DH+ 25+ C ¼ 75 kJ mol

ðMethane crackingÞ

(1) (2)

2 CO ⇔C þ CO2 DH+ 25+ C ¼
172 kJ mol
1 ðBoudouard reactionÞ (3) CO2 þ H2 ⇔H2 O þ CODH+ 25+ C ¼ 41 kJ mol
1 ðRWGSÞ

(4)

The EE of the DBD systems has been significantly improved using Ni-based catalysts i.e., Ni/Al2O3, Ni/MgO, Ni/ SiO2 [17e21]. In this study, MgAl2O4 has been chosen as the main support due to its strong stability at mild conditions and its highly basic nature. MgAl2O4 mixed with transition metal oxide such as La2O3 has the ability to inhibit carbon deposition and high stability [22]. The influence of plasma coupling with catalyst could be determined by evaluating the process parameters such as total feed flow rate, feed ratio, input power, catalyst loading and the discharge volume of the DBD reactor [23,24]. The evaluation of the plasmacatalytic performance has mostly been done experimentally. Nevertheless, few studies have been involved in screening significant process parameters and its effect on DBD reactor EE [23,25]. Response surface methodology (RSM) is widely used in DRM to optimize the process parameters. Based on the design of experiments (DoE), RSM develops the optimization models and also determines the effect of process variables on the multiple responses. RSM also demonstrates the process variable interaction and determines the optimum conditions [24]. The DoE approach has several advantages over single factor study including a reduced number of experiments, dynamic interaction between process variables and determination of significant variables. The investigation on the plasmacatalytic activity using RSM is aimed at optimizing the process variables and investigating the dynamic interaction using newly synthesized catalyst. In this study, Ni/La2O3eMgAl2O4 catalyst was prepared using a modified co-precipitation method followed by a hydrothermal process for the DBD plasma DRM process. The catalysts were characterized by X-ray Diffraction (XRD), N2 adsorption-desorption (BET), H2 temperature-programmed reduction (H2-TPR), CO2 temperature-programmed desorption (CO2-TPD), field emission electron microscopy (FESEM), thermogravimetric analysis (TGA), and high-resolution transmission electron microscopy (HRTEM). The performance of the 10 wt% Ni/La2O3eMgAl2O4 catalyst was investigated under various process parameters such as feed flow rate (X1), feed ratio (X2), input power (X3) and catalyst loading (X4) using RSM. The four factorial, five-level central composite design (CCD) was used to investigate the effect of input (independent) variables on multiple responses (dependent variables) and possible interaction between them. The experimental and predicted results at optimum conditions were validated.

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Experimental Catalyst preparation MgAl2O4 support was prepared using modified coprecipitation followed by hydrothermal method as reported elsewhere [26]. Briefly, nitrate Mg(NO3)2$6H2O (99%, SigmaAldrich) and Al(NO3)3$9H2O (98.5%, Sigma-Aldrich) (molar ratio Mg/Al ¼ 1/2) were dissolved in NH4OH (ACS reagent, 28.0e30.0% NH3 basis) solution and then transferred dropwise into 0.01 M citric acid (Sigma Aldrich, 99%) solution while stirring and heating. During continuous stirring and heating at 110  C, the pH of the solution was adjusted to 10.0. The homogeneous nitrate solution was brought to the autoclave for hydrothermal process and the temperature maintained at 160  C for 24 h in an electric muffle furnace. The precipitates were centrifuged and washed several times with distilled water and absolute ethanol. The samples were dried, crushed and calcined at 700  C for 3 h. In a typical La2O3 nanoparticles preparation process, 2 ml of dimethylformamide (DMF) (ACS reagents) was dissolved in 80 ml deionized (DI) water under vigorous stirring. 2.5 g of lanthanum nitrate hexahydrate La(NO3)2$6H2O was added to the DMF based solution. Next, 3.2 ml ammonia solution (28%) was added to the solution dropwise under continuous stirring at 110  C until the slurry turned into a milky white gel. The gelatinous mixture, with pH adjusted to 10, was stirred for 5 h for complete gel formation. The sample was then transferred to a 200 ml stainless steel Teflon-lined autoclave. The autoclave was kept at 160  C for 24 h in an electric furnace and was then cooled down naturally to room temperature. The slurry was separated by centrifuge, washed with DI water and absolute ethanol several times to remove the impurities. Finally, the sample was dried overnight at 120  C in the oven and calcined at 700  C for 3 h in an electric muffle furnace. The Ni-dispersed catalyst was prepared by incipient wetness impregnation method. The La2O3 and MgAl2O4 support nano-powders with ratio of 1:4 (wr. %) were prepared via emulsion mixing using ethylene glycol (ACS reagents) and 100 ml of DI water. Nickel nitrate hexahydrate Ni(NO3)2$6H2O) (99%) (Sigma Aldrich) was dissolved in required quantity of deionized water to achieve 0.01 M nitrate solution. The Ni nitrate solution was stirred for 10 min at 60  C and La2O3e MgAl2O4 was then added along with the 2 ml of citric acid as a surfactant and for improving metal dispersion. The solution was vigorously stirred for 3 h at 110  C. Finally, the sample was dried overnight and calcined at 700  C for 3 h.

more than 5 nA. The particle size, lattice and diffraction pattern were examined by HRTEM using HITACHI 120 kV. N2 adsorption-desorption isotherms were performed at
196  C using Thermo Scientific, SURFER analyser. Prior to each analysis, the samples were degasified at 200  C for 4 h to ensure complete removal of moisture. The BJH method was used to calculate the average pore diameter. H2-TPR and CO2TPD were carried -out to analyse the reduction profile of Ni and catalyst basicity (CO2-uptake) using Micromeritics AutoChem II 2920. The details of the experimental procedure of H2-TPR and CO2-TPD were reported previously [27]. The spent catalyst was also characterized by XRD, TGA and EDX. TGA analysis was carried out using TGA Q500 (TA Instruments) while using air as the reference gas. The TGA was carried out to determine the percent (%) weight loss of the spent catalyst in a temperature range of 30 1000  C with a ramp rate of 10  C min
1 with air flow of 30 ml min
1. The weight loss (%) in the air is based on the reactive carbon and accumulative weight loss is considered for carbon-based. EDX was carried out using Hitachi 200 kV.

Experimental setup and catalytic activity The experimental setup for catalytic-DBD plasma reactor was similar with the one reported previously [27]. Briefly, the catalytic-DBD reactor consisted of a gas feeding system, AC power supply, plasma reactor and gas analysis system. CH4 and CO2 flow rates and feed ratio were varied using mass flow controllers (MFCs) to investigate the effect of various flow rates on the DRM activity. The input power was controlled using a voltage regulator (1e30 kV) from 60 to 140 W. Meanwhile, various catalyst loadings were used in experiments as a packed bed. The flow rates of gaseous products were measured by a digital flow meter and analysed online by a gas chromatograph (GC) (Agilent 6890N) equipped with thermal conductivity detector (TCD) and flame ionization detector (FID). The details of the GC columns can be found elsewhere [27]. The responses to the plasma-catalytic activity test include reactant conversions, yields and EE (Eqs. (5)e(9)). CH4 conversion ðXCH4 Þ% ¼

# "   ðnCO2 Þconverted  100 CO2 conversion XCO2 % ¼ ðnCO2 Þfeed   H2 yield YH2 % ¼

"

ðnH2 Þproduced ð2  nCH4 Þfeed

"

Material characterization CO yield ðYCO Þ% ¼ The structure and crystallinity analysis of the prepared catalysts were carried-out using XRD technique. The XRD patterns were obtained by Regaku Smart lab X-diffractometer reported elsewhere [27]. The average crystallite size was calculated using the Scherrer equation [28]. The morphology of the catalyst was examined by FESEM and EDX using Hitachi SU8020 integrated with the beam of X-MaxN by OXFORD instrument optics and full control of probe current from 1 pA to

" # ðnCH4 Þconverted  100 ðnCH4 Þfeed

 EE

(5)

(6)

#  100

# ðnCOÞ produced  100 ðnCH4 þ nCO2 Þfeed

 
1  mmol ðnCH4 þ nCO2 Þconverted ðmmol min Þ ¼
1 kJ Input power ðkJ min Þ

(7)

(8)

(9)

The flow rate was measured in ml min
1 and converted to mmol min
1 using conditions T ¼ 25  C, P ¼ 1 atm and conversion factor, 1 mmol ¼ 24.04 ml. The input power (P) was calculated by measuring the input peak voltage (V) and total

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current (I) at a constant frequency (f ¼ 7.5 kHz) as explained in Eq. (10) [6,27,29]. Zt1 P¼f

VðtÞIðtÞdt

(10)

t0

In this study, the statistical analysis was carried out using STATISTICA (Stat. Soft Inc. USA). The design of experiments (DoE) was set to study the important process parameters in catalytic-DBD plasma DRM. The four factorial, 5-level central composite design (CCD) based RSM was designed to investigate the interaction of process parameters in DBD plasma DRM and to predict the optimum process conditions using 10% Ni/La2O3eMgAl2O4 nano-catalyst. The catalytic-DBD plasma reactor was operated at room temperature and atmospheric pressure. The process independent variables i.e. total feed flow rate (X1), feed ratio (CO2/CH4) (X2), input power (X3) and catalyst loading (X4) were selected. The major products were CO and H2 with traces of higher hydrocarbons (HCs) such as ethane, propane and butane. However, only H2 and CO were taken into consideration to simplify the analysis. The responses were: (1) CH4 conversion (Y1) (2) CO2 conversion (Y2) (3) H2 yield (Y3) (4) CO yield (Y4) and (5) EE (Y5). Each independent variable has 5-level factorial design and each factor was evaluated at star low (-a), low (
1), centre (0), high (þ1) and star high (þa) according to Eq. (11) [24] whereas Xi referred to the actual value of ith parameter, Xo is the centre point of ith parameter and DXi is the step size. xi ¼

Xi
Xo DXi

(11)

The DoE is listed in Table 1. The ranges of the independent variables (X1, X2, X3, X4) are set with five-level ranges. In reactor configuration, the 0.5 g loading catalyst corresponds to Vcat ¼ 2.75 cm3 and VD ¼ 10.45 cm3. Similar for 0.75 g, 1.0 g, 1.25 g and 1.5 g loading of catalyst the Vcat is 4.12 cm3, 5.5 cm3, 6.87 cm3 and 8.25 cm3, respectively. Consequently, the VD is 9.07 cm3, 7.7 cm3, 6.33 cm3 and 4.95 cm3 for the corresponding Vcat. The discharge volume (VD) to catalyst loading (Vcat) ratio is calculated using method reported in previous work [15,27]. The bulk density of catalyst loading is used to calculate Vcat. The VD/Vcat ratio is 3.80, 2.19, 1.40, 0.92 and 0.60 for catalyst loading 0.50 g, 0.75 g, 1.00 g, 1.25 g and 1.50 g respectively. For multiple responses, the relationship between the independent variables and responses can be expressed by

Table 1 e Design of experiments (DoE) and independent variable ranges. Independent variables X1 (Total flow rate, ml min
1) X2 (Feed ratio, CO2/CH4) X3 (Input power, W) X4 (Catalyst loading, g)

second-order regression model. The second order regression model (Eq. (12)) is employed for conversions of CH4 (Y1), CO2 (Y2), and H2 (Y3) yield, CO yield (Y4) and EE (Y5) respectively. Yx ¼ bo þ

4 X i¼1

Design of experiments (DoE)

Ranges -a


1

0

1

þa

10 1 60 0.5

20 2 80 0.75

30 3 100 1

40 4 120 1.25

50 5 140 1.5

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bi Xi þ

4 X

bii Xii þ

i¼1

3 4 X X

bij Xi Xj

(12)

i
1 j¼iþ1

Where Yx is the predicting response whilst X1, X2, X3 and X4 are input (independent) variables. The bo is an offset term, bi, bii and bij are linear terms, quadratic and interaction coefficients respectively [24]. Analysis of variance (ANOVA) is used to analyse the adequacy and model fitting. The significance of the second order model equations are determined by F-value, P-value and coefficient of determination (R2). The F-value and its relation to the degree of freedom (DF) are calculated [30]. The calculated F-value defined as the ratio between the mean of square regression (MSSSR) and mean of square residual (MSSSE), where MSSSR and MSSSE are obtained by dividing the sum of squares (SSR) and the sum of residual (SSE) over the degree of freedom (DF), respectively. The tabulated F-values are obtained from F-distribution based on DF for regression and residual respectively, at a specified level of significance, defined as a-value. The interaction of the process variables is analysed using multiple response surfaces and contour plots from the regression model. A set of 27 experiments are performed including 3 replicates (9, 18, 27) according to the CCD design as shown in Table 2.

Results and discussion Material characterization The prepared 10% Ni/La2O3eMgAl2O4 was characterized by XRD to investigate the crystallography and phase identification as demonstrated in Fig. 1. The diffraction peaks were identified with hkl indices for MgAl2O4 spinal (JCPDS# 01072-6947) at 18.9 (111), 37 (220), 38.75 (311), 44.93 (400), 55.92 , 59.57 and 65.5 (440) respectively [31]. The average crystallite size of 10.35 nm were determined using Scherrer Equation [32]. La2O3 (JCPDS#01-071-5408) was detected at 2q ¼ 26.33 (100), 29.18 (002), 30.03 (011) 39.57 (012), 46.14 (110), 55.5 and 60.4 . The average crystallite size was calculated as 10.96 nm. NiO (JCPDS# 00-044-1159) peaks were identified in the XRD pattern at 2q ¼ 37.5 , 43.92 and 63.01 with hkl indices (101), (012) and (110) respectively [33]. The average crystallite size for NiO was 9.7 nm. The smaller crystallite size can play a significant role to resist Ni sintering in reforming reactions. The morphology of the 10% Ni/La2O3eMgAl2O4 is shown in Fig. 2a and b displaying a flake structure for MgAl2O4 while La2O3 and Ni are dispersed over the support. The HRTEM micrographs also confirm the formation of flake-structured MgAl2O4 in Fig. 2c. Furthermore, fringes with d-spacing illustrate the formation of spinal MgAl2O4 of 0.24 nm corresponding to (311) hkl planes [34]. The fringe spacing of 0.201 nm for NiO corresponds to the (101) plane [35]. In Fig. 2d, La2O3 displays d-spacing of 0.34 nm with the hkl indices (100) in good agreement with the literature [36].

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Table 2 e Full factorial CCD design matric of independent variables along with experimental responses.

01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Independent variables

Responses

X1 Feed flow rate (ml min
1)

X2 Feed ratio (CO2/CH4)

X3 Input power (W)

X4 Cat. loading (g)

Y1 CH4 conversion (%)

Y2 CO2 conversion (%)

Y3 H2 Yield (%)

Y4 CO Yield (%)

Y5 EE (mmol kJ
1)

20 20 20 20 40 40 40 40 30 20 20 20 20 40 40 40 40 30 10 50 30 30 30 30 30 30 30

2 2 4 4 2 2 4 4 3 2 2 4 4 2 2 4 4 3 3 3 1 5 3 3 3 3 3

80 120 80 120 80 120 80 120 100 80 120 80 120 80 120 80 120 100 100 100 100 100 60 140 100 100 100

1.25 0.75 0.75 1.25 0.75 1.25 1.25 0.75 1.00 0.75 1.25 1.25 0.75 1.25 0.75 0.75 1.25 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.5 1.5 1.00

63.50 75.12 66.65 75.04 51.99 68.00 63.60 68.50 70.50 71.00 77.49 74.70 78.00 54.00 66.00 59.00 72.45 67.49 82.00 52.00 72.00 75.90 40.10 81.00 75.10 78.80 73.00

59.89 72.36 62.12 69.10 48.43 64.60 54.80 62.99 65.50 67.50 74.94 63.70 69.25 48.60 62.80 54.00 57.90 65.10 77.50 44.00 71.50 68.10 38.60 73.80 67.40 68.10 66.02

31.80 34.68 29.30 31.04 22.80 26.00 26.25 29.00 30.50 33.50 35.57 29.00 34.70 25.00 28.80 25.25 29.55 30.60 35.93 25.80 33.50 29.00 23.22 33.00 29.40 30.40 30.20

33.10 36.10 33.99 36.19 24.70 31.10 28.52 34.20 33.10 35.30 38.10 34.00 38.40 26.00 31.50 30.30 33.90 32.98 38.90 27.80 34.40 36.00 25.40 37.50 34.40 37.00 34.30

0.122 0.129 0.123 0.132 0.129 0.120 0.101 0.125 0.123 0.127 0.119 0.125 0.124 0.128 0.116 0.127 0.123 0.124 0.119 0.128 0.130 0.125 0.124 0.122 0.125 0.125 0.127

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No. of Experiments

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reduces the pore volume and diameter [37]. The N2 adsorption-desorption isotherms depicts type IV isotherm with H3-type hysteresis loop shown in Fig. S1b and categorized for mesoporous material [38]. H2-TPR demonstrates two distinct peaks ascribed to the reduction of NiO as depicted in Fig. S1c. The lower temperature (552  C) peak displays the lesser interaction of metal and support, while the higher temperature (695  C) peak identifies strong metal-support interaction and reduction of Niþ2 to Nio [39]. The basicity of prepared samples are demonstrated in Fig. S1d. The addition of La2O3 to the Ni/MgAl2O4 enhances the basicity of nanocomposite. The higher CO2 uptake after adding La2O3 as cosupport confirms the existence of stronger basic sites in 10% Ni/La2O3eMgAl2O4 [22].

Statistical analysis using RSM Fig. 1 e XRD pattern of prepared samples fresh (a) MgAl2O4 (b) La2O3 (c) 10% Ni/La2O3eMgAl2O4.

The physico-chemical properties of the freshly-prepared catalyst are available in Supplementary information (S) Fig. S1. BET surface area and pore size distribution are shown in Fig. S1a. The BET surface area (SA) of MgAl2O4 (101.88 m2 g
1) is larger than the nano-composite 10% Ni/La2O3eMgAl2O4 (90.02 m2 g
1) since the addition of metal and co-support La2O3

Analysis of regression models The DoE is used to generate optimum experiments for catalytic-DBD plasma DRM. Following the DoE, 27 sets of experiments are carried out to analyse the process parameters effects and interaction using CCD design. The experimental data is presented in Table 2. The interaction between independent and dependent variables are established in regression models (second-order (quadratic) model) for each response Y1 to Y5 presented in supplementary information Table S1. The statistical significance test is carried out using ANOVA, and the important variables and their interaction using

Fig. 2 e FESEM and HRTEM micrograph of fresh 10% Ni/La2O3eMgAl2O4 (a) FESEM (1.0 mm) (b) FESEM (500 nm) (c) HRTEM (d) high magnification HRTEM for d-spacing of NiO, La2O3, MgAl2O4.

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Table 3 e Summary of Analysis of Variance (ANOVA) for CCD catalytic DBD plasma DRM. Responses

(Y1) (Y2) (Y3) (Y4) (Y5)

Sum of squares (SS)

Degree of freedom (DF)

Mean squares (MS)

F-Test

SS(T*)

SS(E*)

SS(R*)

DF(T*)

DF(E*)

DF(R*)

MS(R*)

MS (E*)

F-value

F0.05

2608.7 2428.9 375.1 451.9 184

282.3 231.6 24.5 25.5 13

2326.4 2197.3 350.6 426.3 171

26 26 26 26 26

12 12 12 12 12

14 14 14 14 14

166.26 156.95 25.04 30.45 12.21

23.51 19.29 2.04 2.12 1.01

7.06 8.13 12.27 14.36 12.09

>2.15 >2.15 >2.15 >2.15 >2.15

(T*) ¼ For total; (E*) ¼ For residual; (R*) ¼ For regression.

F-value, P-value and R2 are revealed in Table 3. The greater Fvalue (tabulated one with 95% confidence level) and the lower P-value (<0.05) indicate that the variable is significant in the presented model. The regression coefficient (R2) that is close to unity indicates that the model is statistically valid and significant. The ANOVA table (Table 3) and P-values (Fig. 3) suggest that the process parameters that are significant on CH4 conversion are X1, X3 and X24 since the P-values are below 0.05. CO2 conversion is significantly affected by X3, X1 and X24 . The quadratic relation of catalyst loading (X24 ) to the conversions of CH4 and CO2 reflect the physico-chemical properties of catalyst such as high metal support interaction and the highly basic nature of the flake-structure catalyst. The adequacy of the fitted model can be checked by R2 and F-test using ANOVA. The P-plot (parity-plot) and

corresponding P-values are shown in Fig. 3 to compare the predicted values using the regression model with observed experimental values. For (Y1eY4) responses (Table S1) R2 are commensurate with the variation in the predicted and observed values in catalytic-DBD plasma DRM. The P-values for CH4 conversion (Y1) are depicted in Fig. 3a with R2 ¼ 0.892 which determines variability of the data in an applied model. The P-plot and P-values indicate the significance of each coefficient and interaction, respectively. The P-plot is presented for CH4 conversion (Y1), where the input power (X3) and feed flow rate (X1) predicts the linear interaction. The quadratic X21 and X23 terms suggest these terms are significant. The catalyst loading (X4) infers linear (X3X4) relation with input power (X3) as well as a quadratic relation (X24 ) in the model due to the better dispersion of metal over support. Higher catalyst

Fig. 3 e Pareto-chart and parity-plot for developed regression models for dependent variables: (a) CH4 conversion (Y1) (b) CO2 conversion (Y2) (c) H2 yield (Y3) (d) CO yield (Y4).

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Fig. 4 e Pareto-chart and parity-plot for developed regression models for energy efficiency (EE, mmol kJ¡1) Y1.

loading promotes CH4 activation and it is imperative to get the maximum conversion of CH4 (Y1). Feed ratio (X2) identifies a linear relation with input power (X3) and it is insignificant in the model for CH4 conversion (Y1). The lowest P-values infers the significance of the variable in the developed model [30].

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The P-values for CO2 conversion (Y2) with R2 ¼ 0.904 indicates the variation in the applied quadratic model Fig. 3b. The significant variables influencing the CO2 conversion are analysed in P-plot with corresponding P-values. The most significant process variables are total flow rate (X1) and input power (X3). Both linear as well as quadratic terms are significant in the model. Feed flow rate is proportional to the residence time and input power related to the electrical field is generated in discharge zone to initiate excitation and ionization of dissociation processes [27,40]. The observed and predicted H2 yield (Y3) are exhibited in Fig. 3c with R2 ¼ 0.935 The significant variables in the model are input power (X3) and flow rate (X1) with linear relation in the model for H2 yield (Y3). However, the significance of feed ratio on H2 yield (Y3) is represented by a quadratic relation (X22 ). DRM chemistry suggests that H2 is produced via dissociation of CH4. Since CH4 concentration is lower in feed composition with increasing feed ratio, it is more likely that H2 yield (Y3) decreases with changes in feed ratio (CO2/CH4 ¼ 1 to 5). The observed and predicted CO yield (Y4) values are presented in Fig. 3d with the value of R2 ¼ 0.943. The most significant variables are feed flow rate (X1), feed ratio (X2) and input power (X3). While catalyst loading (X4) shows a quadratic relation,

Fig. 5 e Three dimensional responses and contour plot; effect of the process parameters of the conversion of (YCH4) CH4: (a) flow rate and input power; (feed ratio ¼ 1, catalyst loading ¼ 0.5 g) (b) flow rate and catalyst loading; (P ¼ 100 W, feed ratio ¼ 1) (c) flow rate and feed ratio (P ¼ 100 W, catalyst loading ¼ 0.5 g) (d) input power and catalyst loading (total flow rate ¼ 20 ml min¡1, feed ratio ¼ 1).

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which is significant in the model. Catalyst loading (X4) is also significant due to the highly basic nature of Ni/La2O3e MgAl2O4. The basicity of the catalyst favors CO2 adsorption and activation to produce CO [11,41]. The variability in the EE (Y5) is shown in Fig. 4 with R2 ¼ 0.931 and displays input variables contribution in the Pplot. The significant variables are total feed flow rate and input power (X1X3) with a linear relation to the model. The feed ratio and catalyst loading (X2X4) also display linear relation and plays the second most important contribution in EE (Y5). Each process parameter significantly contributes to EE (Y5) validated by P-values since coupling with catalyst enhances the discharge behaviour and contribute to gas activation and conversion. For all the responses (Y1eY5), values of the coefficient of determination are above 0.89. The values indicate least variation in the data since minimum variability in the assay should not be less than 0.75 reported in the literature [24,42]. The feed flow rate (X1), input power (X3) and catalyst loading (X4) play a significant role in the conversion of CH4 (Y1), CO2 (Y2) and EE (Y5). While, the feed ratio (X2) is significant on the H2 (Y3) and CO (Y4) yields. The F-test is carried out for all responses using ANOVA to check the adequacy of models shown in Table 3. The F-values

achieved in the study for Y1, Y2, Y3, Y4 and Y5 are higher than the critical value at F0.05 ¼ 2.15, suggesting that the employed regression models are statistically significant [24,43].

Effect of processing parameters on the conversion of CH4 and CO2 The interaction between process parameters are investigated by analysing the assessment of the P-values. The effect of process parameters on the conversion of CH4 is presented in the three-dimensional surface plot in Fig. 5. Input power and total flow rate are the most influential parameters, followed by catalyst loading. Fig. 5a demonstrates the linear effect of input power and feed flow rate on the conversion of CH4 in a semi-spherical response. Increasing input power and total flow rate simultaneously significantly improves the CH4 conversion. Increasing total flow rate decreases CH4 conversion as evident in the contour plot. Increasing input power enhances the CH4 conversion; nonetheless contrary to the feed flow rate. Increasing the input power intensifies the plasma-gas interaction, and the possibility of collision between plasma species and gas molecules are enhanced, resulting in higher CH4 conversion. Increasing total feed flow rate reduces the residence time lowering the conversion of reactant gas [44]. Feed flow rate and catalyst loading (Fig. 5b) have a dynamic

Fig. 6 e Three dimensional responses and contour plot; effect of the process parameters on conversion of (YCO2) CO2: (a) input power and flow rate; (feed ratio ¼ 1, catalyst loading ¼ 0.5 g) (b) flow rate and catalyst loading; (P ¼ 100 W, feed ratio ¼ 1) (c) flow rate and feed ratio; (P ¼ 100 W, catalyst loading ¼ 0.5 g) (d) input power and catalyst loading; (flow rate ¼ 20 ml min¡1, feed ratio ¼ 1).

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 4 4 ( 2 0 1 9 ) 1 1 7 7 4 e1 1 7 8 7

relation. The effect of high catalyst loading suppresses the feed flow rate negative effect on CH4 conversion due to elongated residence time. The catalyst assists in the activation of CH4 due to the presence of active metal and with La2O3 as a cosupport [18]. The combined effect of flow rate and feed ratio can be seen in Fig. 5c and increasing the feed flow rate and feed ratio (CO2/CH4) increases the H2/CO ratio [45]. The interaction between catalyst loading and input power (Fig. 5d) is pertinent due to the plasma effect on the catalyst properties which enhances electrical field and changes the discharge behaviour of DBD reactor. Furthermore, the rate of surface reaction increases with strong collision between the reactive species. Thus, electron impact dissociation and dissociative ionization are greatly improved leading to an increase in the reactant conversion. The effect of different process parameters on the conversion of CO2 is depicted in Fig. 6. The interaction of the processing variables is considered according to the P-value ¼ 0.05, a critical indicator for a process variable to be significant. The combined effect of input power and feed flow rate on the CO2 conversion is presented in Fig. 6a. Increasing the flow rate, decreases the CO2 conversion due to lower residence time, while increasing the input power increases the CO2 conversion. The interaction of reactant gas molecules and energetic species is lower in higher flow rates decreasing the CO2 conversion.

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The effect of catalyst loading and flow rate is presented in Fig. 6b. The effect of catalyst loading is more pronounced on the CO2 conversion; increasing the catalyst loading increases the CO2 conversion until 10e35 ml min
1 of feed flow rate. The higher catalyst loading increases the residence time and promotes the CO2 chemisorption due to the high basicity and porosity of the catalyst [46]. While higher feed flow rate also assists to inhibit mass transfer limitations, it also affects the conversion of CO2 due to the lower interaction of gas molecules with plasma energetic species [24]. The interaction between feed ratio (CO2/CH4) and flow rate significantly affects the CO2 conversion. The gas composition in the feed influences the conversion and the relation can be seen in the presented contour plot (Fig. 6c). Increasing the catalyst loading correspond to VD/Vcat ratio and input power ultimately enhances the conversions of CH4 and CO2. It increases the contact time of reactive species and generates a large electric field, significantly improving the CO2 conversion (Fig. 6d) [47]. However, the conversion of CO2 is lower compared to CH4 owing to the larger CO2 dissociation energy [48].

Effect of processing parameters on H2 and CO yield The process parameters significantly affecting the H2 yield (YH2) are analysed by three-dimensional response and contour

Fig. 7 e Three dimensional responses and contour plot; effect of process parameters on H2 yield (YH2): (a) feed ratio and feed flow rate; (P ¼ 100 W, feed ratio ¼ 1) (b) feed flow rate and input power (feed ratio ¼ 1, catalyst loading ¼ 0.5 g) (c) feed flow rate and catalyst loading (P ¼ 100 W, feed ratio ¼ 1) (d) feed ratio and catalyst loading (feed flow rate ¼ 20 ml min¡1, P ¼ 100 W).

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plots as demonstrated in Fig. 7aed. The most significant parameters for H2 yield are feed ratio, input power, and feed flow rate with the F-values higher than the critical value, F(0.05) ¼ 2.15. The increase in feed ratio (CO2/CH4) decreases the H2 yield with an increase in feed flow rate due to lower composition of CH4 in feed and lower residence time [43,49]. Input power is directly related to the H2 yield, as it is associated with processing the reactants gases [50]. Increasing catalyst loading surges the H2 yield. The surface response between feed ratio and catalyst loading confers a semispherical surface depicting the strong interaction between these parameters. Furthermore, the reverse water gas shift reaction (RWGS) is suppressed due to the intermediate carbonates formation (La2O2CO3) on the surface of 10% Ni/La2O3e MgAl2O4. The La2O2CO3 species are responsible for better performance by resisting carbon deposition on the catalyst surface [51]. The combined effect of the leading processing parameters on the CO yield (YCO) using three-dimensional surface and contour plots is presented in Fig. 8. The most influential factors are X1, X3, X2 and X24 with a P-value lower than 0.05. The interaction between feed flow rate and feed ratio is presented in Fig. 8a. The increase in feed ratio plays a significant role in the CO yield while increasing the feed flow rate, decreases the

CO yield. The input power and feed flow rate are significant on the CO yield as suggested by P-values (<0.05) owing to the influence from energetic species generation and residence time (Fig. 8b). The interaction between feed flow rate and catalyst loading is presented in Fig. 8c. Catalyst loading enhances CO yield whereas flow rate reduces it. The CO yield depends on higher chemisorption of CO2 [52]. Influence of input power and catalyst significantly affects the DBD plasma DRM and syngas composition (Fig. 8d). The combined effect of input power and catalyst loading is significant and enhances the CO yield. Both parameters assist the processing of feed gases and inhibit the unwanted reaction. This observation is supported by P-values (0.76) for X3X4.

Effect of process parameters on energy efficiency (EE) The combined effect of feed flow rate and feed ratio for EE (YEE) is presented in Fig. 9a. The spherical surface and contour plot suggest strong interaction between feed ratio and feed flow rate. Lower catalyst loading and higher feed flow rate enhance the EE of the catalytic-DBD plasma system (Fig. 9b). The input power is inversely related to EE (Eq. (9)); however, when coupled with feed flow rate it gives a balancing effect on the EE as demonstrated in Fig. 9c. The highest EE can be achieved at

Fig. 8 e Three dimensional responses and contour plot; effect of process parameters on CO yield (YCO): (a) feed flow rate and feed ratio; (P ¼ 100 W, catalyst loading ¼ 0.5 g) (b) feed flow rate and input power; (feed ratio ¼ 1, catalyst loading ¼ 0.5 g) (c) feed flow rate and catalyst loading; (P ¼ 100 W, feed ratio ¼ 1) (d) input power and catalyst loading; (feed flow rate ¼ 20 ml min¡1, feed ratio ¼ 1).

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the lowest input power and highest feed flow rate, while feed ratio is insignificant [24] The interaction between feed ratio and catalyst loading is significant as indicated by the Pvalue ¼ 0.00. Increasing input power and catalyst loading simultaneously increases the EE of DBD plasma reactor as depicted (Fig. 9d). Both parameters contribute to various reaction mechanisms such as dissociation, ionization and ion attachment and radical recombination [53]. Thus, YEE is the most important response to assess the performance of catalytic-DBD plasma reactor [54].

Process optimization and model validation The optimum conditions for catalytic-DBD plasma DRM over 10% Ni/La2O3eMgAl2O4 are determined by CCD via RSM basis on EE. The optimum values are feed flow rate ¼ 18.8 ml min
1, feed ratio ¼ 1.05, input power ¼ 125.6 W and catalyst loading ¼ 0.6 g. At these conditions, the predicted CH4 and CO2 conversions are 79.86% and 84.03%, respectively. The high conversions affirm the physico-chemical properties of the reported catalyst such as higher metal dispersion, high basicity and porous structure. While H2 and CO yields are predicted as 41.37% and 40.47%, respectively while the H2/CO ratio is above unity. This is attributed to suppressed RWGS and lower carbon deposition due to the oxidative capabilities of La2O3. The calculated EE from the RSM model is predicted as

Table 4 e Model validation for catalytic DBD plasma DRM using 10% Ni/La2O3eMgAl2O4 using optimized conditions. Factors YCH4 (%) YCO2 (%) YH2 (%) YCO (%) YEE (mmol kJ
1)

Predicted

Observed

% Error

79.8 84.0 41.4 40.5 0.135

83.2 81.5 38.7 39.5 0.131

4.01
3.10 6.89
2.45 2.96

0.135 mmol kJ
1. The desirability of the models is more than 95% for these predicted values. The experiments are carriedout with the predicted process variables to validate the model (Table 4). The observed values for all responses are below ±5% error margin except for the value of H2 yield. The YH2 positive error of 6.89% is not a fatal mark for H2 yield in the process optimization study as the P-value is lower than 0.05 [55].

Characterization of spent catalyst The spent catalyst for DRM in the DBD plasma reactor was characterized by XRD, EDX and TGA for investigating the carbon deposition on the catalyst surface. The EDX chromatogram can be found in Fig. S2. EDX demonstrates the lowintensity peak for C suggesting low carbon formation (Fig. S2a). XRD (Fig. S2b) demonstrates a clear peak at 2q ¼ 26

Fig. 9 e Effect of the process parameters on EE (kJ mol¡1) (YEE) demonstrated in three dimensional surface responses and contour plots: (a) feed ratio and feed flow rate; (P ¼ 100 W, catalyst loading ¼ 0.5 g) (b) flow rate and catalyst loading; (P ¼ 100 W, feed ratio ¼ 1) (c) feed flow rate and input power; (feed ratio ¼ 1, catalyst loading ¼ 0.5 g) (d) feed ratio and catalyst loading; (P ¼ 100 W, feed flow rate ¼ 20 ml min¡1).

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for carbon (JCPDS 00-041-1487) [56]. The formation of intermediate carbonate (La2O2CO3) is detected at 2q ¼ 25.17 , 34.0 , 44.7 , 47.7 and 57.5 respectively (JCPDS# 00-037-0804) [51]. The TGA analysis (Fig. S2c) presents weight loss at lower temperature i.e. 100e200  C mostly ascribed to moisture, volatile compounds and reactive carbon. Meanwhile the weight loss 300e500  C is mostly ascribed to a stable form of carbon. There is no weight loss after 600  C temperature suggesting lower formation of filamentous carbon [51,57].

Conclusion The study investigates the effect of process parameters on conversions, product yield and EE in catalytic-DBD plasma reactor using RSM. The significance and adequacy of the established regression models are evaluated using ANOVA. The interaction among the process variables and effect on the responses are assessed using three-dimensional surface and contour plots based on the P-chart. By applying the regression model, the most influential process parameters in descending order are: input power, total feed flow rate, catalyst loading and feed ratio. The R2 > 0.89 for all five responses indicates the desirability of the regression model. In addition, the optimum conditions, determined using RSM, are validated with experimental results with ±5% margin of error. The conversion of CH4 and CO2 is 79.86% and 84.30% respectively. The H2 and CO yields are 41.37% and 40.47% respectively while the EE is 0.135 mmol kJ
1. The better plasma-catalytic performance at the optimum condition is due to the superior physico-chemical properties of Ni/La2O3eMgAl2O4 such as high Ni dispersion, large oxidative capability and high basicity. The findings from this study can be used for future investigations to determine scaling-up parameters for the hybrid-DBD plasma reactor in DRM.

Acknowledgement The authors would like to extend their deepest gratitude to the Universiti Teknologi Malaysia for the financial support of this research under RUG (Research University Grant, Vot13H35) and the Ministry of Education, Malaysia for FRGS-MRSA grant (Vot 4F988).

Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.ijhydene.2019.03.059.

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