Application of response surface methodology in catalytic co-gasification of palm wastes for bioenergy conversion using mineral catalysts

Biomass and Bioenergy 132 (2020) 105418

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Research paper

Application of response surface methodology in catalytic co-gasification of palm wastes for bioenergy conversion using mineral catalysts Muddasser Inayat a, *, Shaharin A. Sulaiman a, Muhammad Shahbaz b, Bilawal A. Bhayo a, c a

Department of Mechanical Engineering, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalif University, Qatar Foundation, P.O. Box 5825, Doha, Qatar c Mehran University of Engineering and Technology, Shaheed Zulfiqar Ali Bhutto Campus, 66020 Khairpur Mir’s, Sindh, Pakistan b

A R T I C L E I N F O

A B S T R A C T

Keywords: Catalytic co-gasification Syngas Methane Palm wastes Portland cement

Biomass gasification is a promising approach for bioenergy conversion. Usually, biomass gasification is facing interruption in feedstock supply due to seasonal availability of biomass. In biomass gasification, formation of tar also affects the gasification efficiency. Therefore, in this study, catalytic air co-gasification of two palm wastes (coconut shells; CS, oil palm fronds; OPF) was investigated for syngas (H2þCO) and methane production in downdraft gasifier using three mineral catalysts such as Portland cement, dolomite, and limestone to address the issues. The three main process variables were investigated within the specific range, the temperature of 700–900 � C, catalyst loading of 0–30 wt%, and the biomass blending ratio of 20–80 wt%. Response Surface Methodology, Box-Behnken Design was used for process optimization. The results showed that temperature was the most influencing parameter for syngas production, followed by catalyst loading and blending ratio. The maximum methane produced from Portland cement catalyst followed by limestone and dolomite. The syngas and methane yield was obtained 38.81 vol% and 19.96 vol% respectively at optimized conditions of catalyst loading of 20 wt %, temperature of 900 � C, and blending ratio of CS20:OPF80 using Portland cement as a catalyst. The higher syngas and methane yields from catalytic co-gasification as compared to non-catalyst co-gasification was due to the catalytic effect of Ca, Fe, Mg, K, P, and Al oxides present in catalysts and biomass materials.

1. Introduction Energy is the prime mover for ever-increasing human activities of modern life. The progress and prosperity of a country are highly dependent on a reliable, easily accessible, affordable, and sustainable energy sources [1]. Currently, 81.7% of the world’s primary energy needs covered by fossil fuels, among it, 22.1% comes from natural gas [2]. Natural gas (NG) is recognized as a clean fuel among fossil fuel, and its reserves are limited as compared to oil and coal [3,4]. International Energy Agency predicted that existing reserves of oil and NG would be available only for the next 55–60 years at current consumption rate [2,3, 5]. In this context, syngas appears as a renewable energy source that can be used as an alternative to NG. Syngas is obtained from the conversion of biomass materials through thermochemical conversion technologies, especially from gasification [6]. Biofuels have the complete capacity to fulfill energy demand in terms of heating, fuel, and electricity generation in many developed countries [2,7]. The biofuels such as syngas and methane have advantages like these fuels can be directly utilized in the

existing gas facility without any major modification in the system [4]. Biomass gasification is an attractive approach to convert the locally available biomass waste into useful fuel such as syngas. However, biomass gasification of any discrete feedstock is interrupted the continuous gasification process due to the shortage of feedstock supply. To overcome this issue, many researchers have tried to co-gasification of biomass with other different material and biomass for uninterrupted feedstock supply [8–26]. In addition, biomass gasification process, along with syngas, also produces unwanted tar that affects the downstream equipment which eventually decreased the process efficiency by choking the piping and filters. Catalyst application in biomass co-gasification appears as a tar reformer that transforms tar into useful gas at a high temperature, which enhanced the syngas composition by decomposed the tar [27]. Numerous, metallic, synthetic, and mineral catalysts have been investigated for syngas production that not only enhanced tar reforming as well as acceleration of some reaction to enrichment of targeted gas composition [28]. The different catalysts used in gasification have varied catalytic abilities to catalyze the

* Corresponding author. E-mail address: [email protected] (M. Inayat). https://doi.org/10.1016/j.biombioe.2019.105418 Received 10 July 2019; Received in revised form 30 September 2019; Accepted 4 November 2019 Available online 20 November 2019 0961-9534/© 2019 Elsevier Ltd. All rights reserved.

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Table 1 Summary of previous catalytic co-gasification studies. Ref

Feedstock

Catalyst

Conditions

Findings

Ma et al., 2019 [27]

Pine sawdust/coal, BR 50:50 (w/w)

Dolomite

H2 ¼ 50.60 vol%, CO ¼ 29.80 vol%, CH4 ¼ 9.10 vol%

Chen et al., 2018 [35]

Acid hydrolysis residues/sewage sludge, BR 50:50 (w/w)

CaO as primary catalyst

T 900 C, PS 4.0 mm, S/F 0.5, ER 0.2 T 800 � C, ER 0.15–0.28

Esfahani et al., 2017 [36]

HDPE/coconut shells, BR 50:50 (w/w)

Ni primary and dolomite as secondary catalyst

T 870 � C, S/F 2.0

Peng et al., 2017 [41]

Wood residue/coal, BR 50:50 (w/w)

K2CO3 as bed material

Hu et al., 2016 [37]

Pine saw/wet sewage sludge, BR 40:60 (w/w)

NiO/Modified dolomite as a secondary catalyst

T 850 � C, FR 2.5 mL/min, ER 0.4 T 800 � C

Robinson et al., 2016 [38]

Wood–PET pellets, BR 50:50 (w/w)

Synthetic olivine as bed material

T 800 � C, ER 0.22

Tursun et al., 2016 [39]

Pine sawdust/bituminous, BR 50:50 (w/w)

Calcined olivine as bed material

T 700 � C S/C 1.3 g/g

Brachi et al., 2014 [40]

Olive husk/PET pellets, Olive husk/tyre pellets, BR 75:25 (w/w) Wood pellets/lignite, BR 33:66 (w/w)

γ-Al2O3 (alumina), Ni/γ-Al2O3 as bed material

T 839–852 � C, ER 0.1, S/F 0.73–1.07 T 850 � C

wood/coal pellets, BR70:30 (w/w)

Ni-γ-alumina as bed material

Kern et al., 2013 [42] Ruoppolo et al., 2010 [43]

�

Olivine as bed material

gasification for different biomass materials. Furthermore, metallic and synthetic catalysts are expensive, which increase the overall cost of the gasification process [29,30]. Additionally, these catalysts deactivate rapidly due to carbon coking and fouling [31,32]. Moreover, the regeneration of catalyst, add another additional cost in the process. The presence of tar in syngas, the effective catalyst application, and efficient tar removal methods are still challenges for advance syngas applications [33,34]. Mineral catalysts have been used in conventional gasification process due to low cost and effectiveness. Some studies have been reported to use of conventional mineral dolomite catalyst as presented in Table 1. Ma et al. [27] co-gasified coal and sawdust with 50:50 blending ratio in the presence of dolomite and olivine as a catalyst in air-steam co-gasi­ fication for H2 production. The H2 yield was increased from 52.9 to 55.5 (g/kg-fuel) for dolomite and from 47.5 to 52.1 (g/kg-fuel) for olivine with the increase of catalyst (dolomite and olivine) loading from 3.0 to 12.0 (wt.%). Chen et al. [35] performed co-gasification of acid hydro­ lysis residues/sewage sludge in a downdraft fixed-bed reactor using CaO as a catalyst. LHV of the gas and CGE were reported highest values of 6.42 MJ/Nm3 and 67.33%, respectively at 800 � C and 50:50 blending ratio. In another co-gasification study, for clean gas production, coconut shells and HDPE was investigated in gasifier during catalytic steam gasification. The results show that H2 yield varied from 22.84 to 45.94 vol% and syngas from 29.35 to 48.19 vol% in the presence of dolomite [36]. Hu et al. [37] investigated the performance of catalytic in-situ steam co-gasification of wet sewage sludge and sawdust mixture in a bench-scale reactor in the presence of NiO supported on modified dolomite (MD) as a catalyst. Results showed that at optimum conditions, 40% pine saw and 900 � C temperature, H2 yield of 14.44 mol/kg, tar yield of 2.19 wt%, gas yield of 1.23 Nm3/kg, LHVsyngas of 10.65 MJ/Nm3, and carbon conversion efficiency of 84.56 wt% were obtained. Robinson et al. [38] determined the feasibility of incorporating poly­ meric waste (PET) into biomass-based power generation systems based on dual fuel operation of diesel engines. With composite of wood-PET

H2 ¼ 9.77–11.55 vol%, CO ¼ 13.37–16.72 vol%, CH4 ¼ 4.78–5.94 vol% H2 ¼ 80.75 vol%, CO ¼ 4.91 vol%, CH4 ¼ 10.22 vol% H2 ¼ 52.07 vol%, CO ¼ 35.14 vol%, CH4 ¼ 5.75 vol% H2 ¼ 45.00 vol%, CO ¼ 27.00 vol%, CH4 ¼ 5.00 vol% H2 ¼ 5.40 vol%, CO ¼ 11.30 vol%, CH4 ¼ 3.00 vol% H2 ¼ 40.60 vol%, CO ¼ 8.98 vol%, CH4 ¼ 9.80 vol% H2 ¼ 37–39 vol%, CO ¼ 18.60–20.20 vol%, CH4 ¼ 5.70–6.70 vol% H2 ¼ 45.00 vol%, CO ¼ 27.50 vol%, CH4 ¼ 5.00 vol% H2 ¼ 32.00 vol%, CO ¼ 9.50 vol%, CH4 ¼ 3.00 vol%

T 850 � C, ER 0.17, S/F 0.64

pellets, no formation of coking was observed. However, performance of wood pellets was better than wood-PET pellets in term of tar forma­ tion. Co-gasification of sawdust and bituminous coal was investigated for the effect of biomass ratio, pyrolyzer temperature, gasifier temper­ ature, and steam to carbon mass ratio (S/C), on the performance of the co-gasification in a decoupled gasification system. The results showed that concentration of H2, CO, and CH4 increased while CO2 yield decreased with the increasing biomass ratio [39]. Brachi et al. [40] carried out the co-gasification of olive husk with PET and tyre pellets in fluidized bed gasifier using γ-Al2O3 (alumina) and Ni/γ-Al2O3 as bed material in steam and oxygen-enriched air atmosphere. Results showed no significant differences in gas composition in PET to tyre based pellets, although olive husk/PET pellets found the best performance in terms of produced tar and particle elutriation under all operating conditions [40]. Palm oil wastes and coconut shells are the most important biomass feedstock due to abundant availability for energy production in Table 2 Thermochemical characteristics of coconut shells/oil palm fronds blends. Ultimate analysis (wt.%) Elements

CS20:OPF80

CS50:OPF50

CS80:OPF20

C H N S Oa Proximate analysis (wt.%) Moisture content Fixed carbon Volatile matter Ash content Higher heating value (MJ/kg)

43.47 5.36 0.48 0.27 50.44

44.76 4.83 0.57 0.24 49.60

46.06 4.31 0.65 0.21 48.77

5.38 16.64 80.78 2.58 17.52

4.22 16.96 81.11 1.93 18.24

3.06 17.28 81.45 1.27 18.95

By difference; Oa ¼ 100% fronds. 2

(H þ N þ S þ C) CS; coconut shells, OPF; oil palm

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2. Materials and methods

Table 3 X-ray fluorescence (XRF) analysis of catalysts (Portland cement, dolomite, and limestone) and biomass feedstock (coconut shells and oil palm fronds). Chemical composition (wt.%)

Portland Cement

Dolomite

Limestone

CS

OPF

CaO SiO2 Fe2O3 SO3 Al2O3 MgO K2O P2O5 MnO SrO CuO ZrO2 NiO TiO2 MoO3 ZnO Rb2O Cl Br

85.70 6.17 3.32 1.38 1.20 0.79 0.46 0.40 0.21 0.04 0.03 0.02 0.0095 0.19 – – – – –

88.20 0.34 0.37 – 0.22 10.30 0.05 0.44 0.03 0.05 0.0085 – – – – – – – –

98.30 0.30 0.18 0.02 0.21 0.58 0.02 0.30 0.06 0.04 – 0.0003 0.0073 – – – – – –

9.28 2.00 11.80 3.62 – 1.50 49.30 10.00 – – 1.20 – – – – 0.38 0.12 10.50 0.29

53.30 3.07 1.12 3.34 – 0.68 20.30 2.94 0.31 – 0.11 – 0.12 0.25 0.08 0.08 – 14.20 0.09

2.1. Biomass feedstock and catalyst characterization The coconut shells and (CS) oil palm fronds (OPF) were used as potential feedstock for catalytic co-gasification. The OPF was collected from palm oil plantation and CS was received from a local wet market. The feedstock preparation was carried out in two steps. First one is for thermochemical characterization of feedstock, and the second one is drying and chopping for co-gasification experiments. Both biomass materials (CS and OPF) were chopped roughly about 2-3-inches in size by using lab-scale granulator (flake cutter series, power 4 kW). The feedstock was dried under the sun for four fully sunny days and later these biomasses dried in an electric oven at 105.4 � C for 24.0 h. The biomass was ground to 250 μm for thermochemical characterization, and it was stored in airtight bottle to avoid gain in moisture content of ground feedstock due to the humid weather of Malaysia. The moisture content of feedstock was determined using ASTM E871-82 standard method [46]. Proximate analysis was performed as per ASTM E1755-01 procedure in a TGA analyzer [47]. The ultimate analysis was determined using CHNS analyzer as per ASTM D3176-09 standard procedure [48]. Bomb calorimeter was used to measure the gross calorific value, which is also referred to as higher heating value (HHV) as per ASTM D4809-00 [49]. The results of thermochemical characteristics of CS/OPF blends are given in Table 2. The feedstock (CS and OPF) was prepared for co-gasification experiments using granulator and sieved to 2 � 6 mm particle sizes. CS and OPF were mechanically mixed into CS20:OPF80, OPF50:CS50, and CS80:OPF20 blends and catalysts also mixed at 0, 15, and 30 wt% with blends. In this present study, three natural mineral materials, namely; Portland cement, dolomite, and limestone, were used as catalysts. A local company, Universal Lime Sdn Bhd, Batu Gajah, Perak, Malaysia, provided free dolomite and limestone. Portland cement was purchased from a local hardware shop. The catalysts were calcinated at 900 � C for 4.0 h. The chemical composition and oxides presence of catalysts and biomass materials were determined by X-ray Fluorescence (XRF) anal­ ysis. The results of the XRF are given in Table 3. CaO, MgO, and Fe2O3 are the main oxides present in catalysts. The XRF of OPF shows that CaO, K2O, and P2O5 are main oxides in it, whiles, CaO, Fe2O3, K2O, P2O5, and CuO are detected in coconut shells sample. These metallic oxides have been used as a catalyst in gasification. Therefore, presence of these ox­ ides will be act as catalyst during co-gasification CS/OPF blends. The physicochemical properties of catalysts determined by BrunauerEmmett-Teller (BET) and the results are given in Table 4. The cata­ lysts were mechanically mixed with blended feedstock as per design of experiments on the weight basis given in Table 6.

CS; coconut shells, OPF; oil palm fronds.

Malaysia. Palm kernel shells (PKS), empty fruit bunches (EFB), and co­ conut shells (CS) are utilized for syngas production in conventional gasification using dolomite, Ni, and coal bottom ash as catalysts [44,45]. Response surface methodology is a method to design the experiment and investigate the effect relation of experimental results with predicted ones by using ANOVA analysis and optimization using tools inside. Very few studies are made using Malaysian palm oil waste for desecrate biomass gasification process using some catalysts [6,44]. Based on the previous literature on co-gasification, it is concluded that the performance of catalytic co-gasification has some issues to address due to limited studies reported on the co-gasification of two different biomass materials which need to be addressed. In addition, the effect of different mineral catalysts should also be investigated in cogasification of two different biomass materials. Whereas, no studies have been reported so far, using minerals catalysts, especially Portland cement in co-gasification of different biomass materials. Furthermore, there is no optimization study has been reported for the co-gasification of coconut shells (CS) and oil palm fronds (OPFs). Very less study re­ ported for tar elimination for CS and OPFs even in conventional dese­ crates gasification. In order to address these issue, the catalytic cogasification of different biomass was performed to achieve some ad­ vantages and enhancement in syngas quality by decomposition of tar. The main objective of this catalytic co-gasification of CS and OPF using mineral catalyst of Portland cement, dolomite, and limestone to produce clean syngas and methane for energy applications. For this purposes, the effect of three operating parameters ranges, temperature of (700–900 � C), catalyst loading (0–30 wt%), and blending ratio (20–80 wt%) were investigated on syngas and methane production in the presence of the mineral catalyst of Portland cement, dolomite, and limestone. In addition, the effect of different catalysts is compared to­ ward syngas and methane production. Design expert was utilized for the design of experiment and response surface methodology; Box Behnken design was used to optimize the process parameters for the desired gas production from catalytic co-gasification.

2.2. Design of experiments and response variable Response surface methodology (RSM) with a standard technique of Box-Behnken Design (BBD) was chosen to develop the matrix of exper­ iments for catalytic co-gasification runs using Design Expert 11® soft­ ware. RSM is an important optimization technique, which develops 3-D response surface between output response and operating parameters to analyze the effect of mutual interactions of operating parameters on the response. It also develops the empirical relationship between the Table 5 Levels of operating parameters for the Box Behnken Design (BBD) used in this study.

Table 4 Physicochemical properties of Portland cement, dolomite, and limestone. Physicochemical properties

Portland Cement

Dolomite

Limestone

Specific surface area (BET) (m2/g) Pore volume (cm3/g) Pore size (nm)

1.6652 0.0143 34.225

4.1215 0.0590 57.302

0.6734 0.0028 18.035

Operating parameters

Symbol Coded

Temperature ( C) Catalyst loading (wt.%) Blending ratio (wt.%) �

3

A B C

Levels 1 (Low)

0 (Medium)

þ1 (High)

700 0 CS20:OPF80

800 15 CS50:OPF50

900 30 CS80:OPF20

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Table 6 Experimental design matrix devolved by BBD and response results. Std. Run

6 11 17 5 9 14 7 16 10 8 2 3 13 15 1 4 12

Run

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Temperature, A (� C)

900 800 800 700 800 800 700 800 800 900 900 700 800 800 700 900 800

Catalyst loading, B (wt. %)

15 0 15 15 0 15 15 15 30 15 0 30 15 15 0 30 30

Blending ratio, C (wt. %)

CS20:OPF80 CS80:OPF20 CS50:OPF50 CS20:OPF80 CS20:OPF80 CS50:OPF50 CS80:OPF20 CS50:OPF50 CS20:OPF80 CS80:OPF20 CS50:OPF50 CS50:OPF50 CS50:OPF50 CS50:OPF50 CS50:OPF50 CS50:OPF50 CS80:OPF20

operating parameters and response. The optimum variable’s value ob­ tained from response surface based on the desired output. The three influencing variables, temperature (A), catalyst loading (B), and blending ratio of CS/OPF (C) were used to observe the response on syngas (H2þCO) and CH4 (vol%) production using three three-level BBD. Table 5 illustrates the ranges of operating parameters which include 700-900 � C, 0–30 wt%, 20–80 wt% were selected for tempera­ ture, catalyst loading, and blending ratio of CS/OPF respectively. By default, the high levels of the factors are coded as þ1, and the low levels are coded as 1. The higher and lower boundaries of the input variables were determined based on the preliminary results, literature, and considering experimental setup facility [50]. Experimental design matrix devolved by BBD and response results of syngas (H2þCO) and CH4 vol% production in the presence of Portland cement, dolomite, and limestone are presented in Table 6. The promi­ nence feature of BBD, it avoids to perform experiments under extreme conditions and does not involve all combinations of factors which are all at once their highest or lowest levels [51].

Gas responses (vol%) Portland cement

Dolomite

Limestone

Syngas (H2þCO)

CH4

Syngas (H2þCO)

CH4

Syngas (H2þCO)

CH4

37.94 31.95 32.51 18.83 31.18 31.50 24.72 32.15 29.20 33.40 36.61 20.69 31.82 32.33 26.19 38.37 26.41

19.56 17.00 15.98 16.08 18.38 16.59 19.28 16.25 19.39 19.45 15.58 16.34 16.47 16.12 14.48 18.23 21.70

31.32 29.95 22.76 19.29 31.18 22.23 13.92 23.45 26.34 29.40 36.21 13.58 22.84 24.79 23.19 30.69 22.80

12.73 17.00 12.56 10.52 18.38 12.08 9.79 12.77 14.08 10.95 15.58 8.94 12.42 12.71 14.48 12.10 13.74

38.49 31.05 27.23 26.49 31.19 27.21 22.49 26.29 27.13 36.37 36.21 23.58 26.76 26.76 26.38 38.85 26.75

13.09 17.00 18.16 21.48 18.38 18.58 17.60 19.42 16.26 18.38 15.58 18.81 18.75 18.04 14.48 12.15 20.71

2.3. Catalytic co-gasification and experimental facility Catalytic co-gasification of CS/OPF blends was performed on benchscale downdraft gasifier as experimental facility shown in Fig. 1. The gasifier was encircled with an electric heater. The temperature of elec­ tric heater was monitor and controlled by a PID microcontroller. Electric heater and online gas analyzer and its associated electric instruments were switched on before the start of experiment. A systematic increment was applied to reach the targeted temperature. After achieving the desired temperature, a measured and controlled air was supplied to the gasifier at a constant rate of 3.0 L/min by using air compressor. After that, a blend of CS/OPF and catalyst was fed from top of the gasifier. Produced gas was tapped from the bottom of gasifier and was passed through the tar removal facility to remove the tar. Clean and cool gas was analyzed in online gas analyzer that was connected to the computer by local LAN internet connection. The gas composition readings were logged in the computer automatically for every second. Once biomass was consumed completely, the electric heater and air supply were switched off and allowed to cool down the gasifier. The char and ash were collected and measured at the end of the co-gasification process for further analysis.

Fig. 1. Experimental facility used for catalytic co-gasification of CS/OPF blends. 4

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Table 7 The empirical mathematical relationship of syngas and methane production with process variables in term of coded factors developed based on ANOVA. Catalysts

Correlation equations in terms of coded values

Portland cement

Syngas, H2 þ CO (vol%) ¼ þ32.06 þ 6.99A - 1.41B - 0.0837C þ 1.81AB - 2.61AC - 0.8900BC - 1.28A2 - 0.3173B2 - 2.06C2 CH4 (vol%) ¼ þ 16.28 þ 0.8302A þ 1.28B þ 0.5029C þ 0.1965AB - 0.8275AC þ 0.9228BC - 0.3268A2 þ 0.1972B2 þ 2.64C2 Syngas, H2 þ CO (vol%) ¼ þ23.22 þ 7.20A - 3.39B - 1.51C þ 1.02 AB þ 0.8609AC - 0.5746BC - 0.6914A2 þ 3.39B2 þ 0.9598C2 CH4 ¼ þ 12.51 þ 0.9555A - 2.07B - 0.5288C þ 0.5139AB - 0.2639AC þ 0.2590BC - 2.27A2 þ 2.53B2 þ 0.7579C2 Syngas, H2 þ CO (vol%) ¼ þ26.11 þ 6.37A - 1.06B - 0.8300C þ 1.36AB þ 0.4700AC - 0.0600BC þ 3.54A2 þ 1.61B2 þ 1.31C2 CH4 ¼ þ 18.59–1.65A þ 0.3104B þ 0.5608C - 1.94AB þ 2.29AC þ 1.46BC - 1.89A2 - 1.44B2 þ 0.9399C2

Dolomite Limestone

2.4. Statistical analysis

the real experimental results. F-value compares the variable within and across the model. The smaller P-value and larger F-value quantify the influence of variables on response. The lack of fit is the difference be­ tween measured and predicted value can be used to determine the effect of operating variables on the response variables, for good model fitness lack of fit is used to non-significant [52]. The regression model (R2) measures the accuracy of experimental data from the model; its range lies between 0 and 1. A value closer to unity indicates that the data close to the ideal value that has a significant effect on the response. The R2Adj

The statistical analyses were performed using a statistical tool analysis of variance (ANOVA) to determine the significance of the overall model and its related terms. There are three types of statistical tests performed namely, significant of terms, lack of fit test, and coef­ ficient of the regression model [6]. The significance of model and its terms were determined by higher F-value and lower value of P-value. A P-value � 0.05 indicates that model terms are significant and closer to

Fig. 2. 3-D response surface graphs of syngas production the combined effects of operating variables (temperature, 700–900 � C, catalyst loading, 0–30 wt%, blending ratio, 20–80 wt%) for (a–c) Portland cement, (d–f) dolomite, and (g–i) limestone as a catalyst. 5

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value measures the variation of modified data from the model. The difference between R2Adj and R2Pred values is the referee of the model quality that should be � 0.20 [53]. The greater difference in R2Adj and R2Pred is due to the non-significate model terms. The main feature of ANOVA analysis is the development of 3-D plots that represent the interaction of operating variables and their effect on the response. Furthermore, 3-D graphs help to obtain intermediate points that have been not conducted experimentally [44].

order polynomial equations were developed in terms of the coded fac­ tors with the inclusion of both significant and insignificant terms for syngas and methane under the use of three catalysts as shown in Table A. In which linear effect of process variables coded A, B, and C, two-way interaction as AB, AC and BC, and square interaction as A2, B2, and C2 on response variables as shown in Table 7. Numerous researchers found the second-order polynomial equation best fitted for the data [6,44, 52–57]. The empirical mathematical equation can be used to predict the response for given levels of each factor. The coded equation is also useful for identifying the relative impact of the factors by comparing the factor coefficients. Statistical analysis of variance (ANOVA) has been conducted to investigate the significance of model and variables for syngas and methane production (ANOVA Table A is provided in annex). The lower P-value < 0.0001 obtained for all models and higher F-values that varied from 18.87 to 95.80 and 35.35 to 97.48 for syngas and methane models

3. Results and discussion 3.1. Regression equation development and ANOVA analysis RSM developed a mathematical relationship between variables and experimental responses of syngas and methane in catalytic cogasification of CS/OPF. The regression analysis established second-

Fig. 3. (a, c, e) Pareto graphic analysis and (b, d, f) perturbation plot of model factors (A; temperature, B; catalyst loading, C; blending ratio for syngas production (a–b) Portland cement, (c–d) dolomite, and (e–f) for limestone. 6

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respectively, which confirms the fitness and significance of the models. There is only a 0.01% chance that an F-value this large could occur due to noise. P-values less than 0.05 indicate model terms are significant. In most cases, model terms were found significant for syngas and methane. All models have the R2 value closer to unity shows the model predicted data to approach the response data. The R2Adj values were also very close to the R2 values. The maximum difference between R2Adj and R2Pred is varied from 0.06 to 0.37 and 0.06–0.19 for syngas and methane respectively, that indicates a good agreement among experimental data and model predicted data. A good correlation between predicted values and actual experimental values of syngas and methane production for Portland cement, dolomite, and limestone are shown in Fig. 6. The most interactive terms were significant toward the response values. The lack of fit values are also presented in ANOVA Table (ANOVA Table A is provided in annex) non-significant lack of fit indicates that lower sys­ tematic and random in experimental and model data. Non-significant lack of fit is good for a model to fitness.

3.2. Syngas production response of catalytic co-gasification of CS/OPF at different variables interaction The combined effect of operating parameters of temperature, cata­ lyst loading, and blending ratio on syngas was investigated by 3D response surface obtained from RSM as shown in Fig. 2. Among two variables, the third variable was fixed at the middle value. The com­ bined effect of temperature and catalyst loading on syngas production in the presence of Portland cement is shown in Fig. 2a. Results show that as the temperature is increased from 700 � C to 900 � C the syngas yield increased from 20.69 to 38.37 vol% at a catalyst loading of 30 wt% and CS50:OPF50 blending ratio. A similar temperature trend was observed for lower catalyst loading. Temperature is the most influencing factor that affects the process yield at every stage of co-gasification. The increased in syngas yield is due to the activeness of endothermic re­ actions [44]. The increased in syngas yields is due to enhancement in water gas shift reaction that is due to the shifting reaction equilibrium in forward direction that enriches the H2 and CO content. In addition, the reforming of methane reaction is due to cracking of methane and higher hydrocarbon that occurs at higher temperature which also contributed

Fig. 4. 3-D response surface graphs of methane production the combined effects of operating variables (temperature, 700–900 � C, catalyst loading, 0–30 wt%, blending ratio, 20–80 wt%) for (a–c) Portland cement, (d–f) dolomite, and (g–i) limestone as a. 7

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Biomass and Bioenergy 132 (2020) 105418

H2 and CO [58]. The combined effect of temperature with blending show that at low blending ratio and temperature syngas yield was observed low. However, trend was changed as temperature and pro­ portion of CS in blending ratio increased as shown in Fig. 2b. Fig. 2c shows that blending ratio and catalyst loading has a marginal combined effect on the syngas production at 800 � C temperature. The combined effect of temperature and catalyst loading on syngas production using dolomite catalyst is shown in Fig. 2d. The results show that syngas yield exponentially increases with the lower catalyst loading and increasing process temperature. A similar trend was seen for the combined effect of blending ratio and temperature. In the case of blending ratio and catalyst loading maximum syngas is obtained at lower catalyst loading and lower proportion of OPF in the blend. A similar trend of syngas production of observed in the presence of lime­ stone for the combined effect of temperature, catalyst loading, and

blending ratio as shown in Fig. 2g–i. In term of catalyst performance, maximum syngas was obtained from limestone followed by Portland cement and dolomite. The higher production of syngas using limestone is due to presence of higher amount of CaO about 98%. CaO is important catalyst that not only enhanced the endothermic reaction and carbon­ ation reaction by capturing CO2 which ultimate the increase H2 and CO content in product gas [44,59]. Among operating parameters of tem­ perature, catalyst loading, and blending ratio, Pareto graphic analysis, and perturbation plot show that temperature was a most influencing factor for syngas production followed catalyst loading and blending ratio with a marginal difference as shown in Fig. 3. The relationship between actual and predicted syngas values presented in Fig. 6 with acceptable regression model accuracy R2 � 0.96 as presented in ANOVA Table (ANOVA Table A is provided in annex).

Fig. 5. (a, c, e) Pareto graphic analysis and (b, d, f) perturbation plot of model factors (A; temperature, B; catalyst loading, C; blending ratio for methane production (a–b) Portland cement, (c–d) dolomite, and (e–f) for limestone. 8

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Fig. 6. Actual vs. predicted values of syngas and methane responses (a,b) Portland cement, (c,d) dolomite, and (e,f) limestone.

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Table 8 Optimum process variables, model-predicted, and confirmation values of syngas and methane yield. Catalysts

Temperature (� C)

Catalyst loading (wt.%)

Blending ratio (wt.%)

Model-predicted values (vol%) Syngas

CH4

Syngas

CH4

Portland cement Dolomite Limestone

900 875 900

20 30 15

CS20:OPF80 CS80:OPF20 CS80:OPF20

38.81 31.38 20.64

19.96 14.02 16.36

38.07 � 0.74 32.36 � 0.98 22.22 � 1.58

20.35 � 0.39 13.69 � 0.33 16.91 � 0.55

3.3. Methane production response of catalytic co-gasification of CS/OPF at different variables interaction

Confirmation Run values (vol%)

Desirability 0.87 0.65 0.80

dolomite (13.74 vol%) at 30 wt% catalyst loading and 800 � C with CS80: OPF20 blending ratio. The most influencing factors are C2, B, A for Portland cement, B, B2, A2 for dolomite, and A, AC, A2. AB for limestone catalyst. These model factors are contributed 80% cumulated effect on the CH4 production as Pareto graph presented in Fig. 5. The relationship between actual and predicted CH4 presented in Fig. 6 with acceptable regression model accuracy R2 � 0.98 as presented in Table A.

The methane (CH4) produced from catalytic co-gasification of CS/ OPF blends is presented in Table 6. Fig. 4 illustrations the 3-D graphs of response surface for CH4 vol% with the combined effect of temperature, catalyst loading, and blending ratio. Fig. 4a–c show the CH4 yield response with respect to the temperature, catalyst loading, and blending ratio in the presence of Portland cement. The 3-D graph shows higher temperature favors the CH4 production while in the case of dolomite, it is reverse. The high-temperature combination of the high cement (catalyst) loading increased significant CH4 yield from 14.48 to 21.70 vol%. The high CH4 yield was contributed from the tar decom­ position in the presence of catalyst where catalytic reactions are more dominated at high temperature [60]. The combined effect of tempera­ ture and blending ratio shows that the maximum CH4 (21.70) was ob­ tained at CS80:OPF20 blending ratio, at 800 � C temperature with 30 wt % catalyst loading. Fig. 4d–f represented responses of CH4, from CS/OPF co-gasification using dolomite as a catalyst. The CH4 yield has a decreasing trend with the temperature as shown in Fig. 4d. Fig. 4d and 4e response surface show that the maximum points of CH4 are lying outside the variable range given in Table 5. The reduction in CH4 yield is with the increase in temperature is due to activeness of methane reforming reaction that take place at a higher temperature more than 600 � C [4,61]. It decreased from 15.58 vol% to 14.48 vol% as the tem­ perature decreased from 900 � C to 700 � C temperature in the absence of a catalyst [62]. The CH4 yield was decreased from 15.58 vol% to 12.10 vol% as dolomite loading increased from 0 to 30 wt%. The CH4 yield with respect to catalyst loading, maximum 14.08 vol% yield was obtained with a catalyst loading of 30 wt% and temperature of 800 � C at CS20:OPF80 blending ratio. The decreasing of methane content at higher catalyst loading is due the favors of theses catalyst towards the acceleration of methane reforming, tar reduction reaction and water gas shift reaction [44,61]. The combined effect of blending ratio and cata­ lyst loading is shown in Fig. 4f. The maximum CH4 was predicted at lower proportion of CS in the blend and lower catalyst loading. Similar to the dolomite, limestone also has a decreasing trend of CH4 yield with the increased temperature and catalyst loading (limestone) during the co-gasification of CS/OPF blends as shown in Fig. 4g. The CH4 increased from 17.00 vol% to 20.71 vol% as limestone loading increased from 0 to 30 wt% at 800 � C temperature. However, at a high temperature, this trend turns to reverse. The maximum 21.48 vol% CH4 was achieved at 700 � C and 15 wt% catalyst loading at CS20:OPF80 blending ratio. Among the three catalysts, Portland cement performed well in terms of CH4 production (21.70 vol%) followed by limestone (20.71 vol%) and

3.4. Process optimization and model confirmation The optimum values of temperature, catalyst loading, and blending ratio for syngas and methane production under three catalysts Portland cement, dolomite, and limestone were determined from the design expert 11® software. The confirmation runs ware conducted at optimum conditions and repeated three times. The average values of confirmation runs with standard deviation were compared with model-predicted values as shown in Table 8. A good agreement was observed between predicted, and confirmation runs values. The close predicted values and confirmation runs values confirmed the validity of the model with adequate accuracy. 4. Conclusions The air catalytic co-gasification of CS/OPF blends has investigated the effect of temperature, catalyst loading, and blending ratio on syngas (H2þCO) and methane production in a downdraft gasifier. The process optimization was performed using response surface methodology, BoxBehnken Design. Maximum syngas yield was obtained at a higher tem­ perature, followed by catalyst loading and blending ratio. In term of catalyst performance, the maximum syngas was obtained when lime­ stone used as a catalyst. Whereas, dolomite appeared to be less effective for syngas production as compared to Portland cement as well. The maximum methane produced from Portland cement catalyst followed by limestone and dolomite. At optimum conditions, predicted values have good agreement with confirmation runs. Results concluded that cata­ lytic co-gasification of CS/OPF is a promising thermochemical process for syngas and methane production, which can be used for different application in the energy sector. Acknowledgment The authors would like to acknowledge the Universiti Teknologi PETRONAS, Malaysia for providing facilities and financial support for current work.

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Model A:Temperature B:Catalyst loading C:Blending ratio AB AC BC A2 B2 C2 Residual Lack of Fit Pure Error Cor Total R-squared, (R2) Adjusted R2 (R2Adj) Predicted R2 (R2Pred)

Source

477 390.5 15.85 0.056 13.18 27.2 3.17 6.9 0.424 17.86 3.87 3.22 0.655 480.9 0.9919 0.9816 0.8908

<0.0001 <0.0001 0.0011 0.7594 0.0018 0.0002 0.0479 0.0096 0.4105 0.0007

0.0505

95.8 705.83 28.65 0.1014 23.82 49.16 5.73 12.47 0.7661 32.29

6.55

56.75 5.51 13.06 2.02 0.155 2.74 3.41 0.45 0.164 29.26 1.07 0.816 0.253 57.82 0.9815 0.9578 0.7675 4.3

41.32 36.13 85.57 13.26 1.01 17.95 22.32 2.95 1.07 191.75 0.0962

<0.0001 0.0005 <0.0001 0.0083 0.3479 0.0039 0.0021 0.1298 0.3347 <0.0001

588.47 415.12 91.93 18.18 4.17 2.96 1.32 2.01 48.47 3.88 6.73 2.88 3.85 595.21 0.9887 0.9741 0.9124

SS

1.00

67.97 431.53 95.57 18.90 4.33 3.08 1.37 2.09 50.39 4.03

F-value

H2þCO vol% P-value

SS

F-value

CH4 vol%

SS

P-value

H2þCO vol%

F-value

Dolomite

Portland Cement

0.4792

<0.0001 <0.0001 <0.0001 0.0034 0.08 0.12 0.2797 0.1913 0.00 0.08

P-value 94.30 7.30 34.38 2.24 1.06 0.28 0.27 21.63 27.03 2.42 0.75 0.45 0.31 95.05 0.9921 0.9819 0.9200

SS

CH4 vol%

1.93

97.48 67.95 319.83 20.81 9.83 2.59 2.50 201.25 251.50 22.50

F-value

0.2663

<0.0001 <0.0001 <0.0001 0.0026 0.0165 0.1514 0.1582 <0.0001 <0.0001 0.0021

P-value 424.85 324.87 9.07 5.51 7.4 0.8836 0.0144 52.69 10.88 7.25 17.51 12.4 5.11 442.36 0.9604 0.9095 0.5334

SS

3.24

18.87 129.87 3.63 2.2 2.96 0.3532 0.0058 21.06 4.35 2.9

F-value

H2þCO vol%

Limestone

Table A ANOVA analysis for syngas and methane production from co-gasification of CS/OPF using Portland cement, dolomite, and limestone as catalysts.

Appdendix.

0.1432

0.0004 <0.0001 0.0985 0.1813 0.1292 0.571 0.9416 0.0025 0.0755 0.1324

P-value

97.15 21.67 0.7708 2.52 15.1 21.02 8.5 15.11 8.76 3.72 2.14 0.9402 1.2 99.29 0.9785 0.9508 0.8296

SS

CH4 vol%

1.05

35.35 70.98 2.52 8.24 49.45 68.83 27.85 49.49 28.69 12.18

F-value

0.4632

<0.0001 <0.0001 0.1561 0.0240 0.0002 <0.0001 0.0012 0.0002 0.0011 0.0101

P-value

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