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Microwave-assisted pyrolysis of palm kernel shell: Optimization using response surface methodology (RSM)
Renewable Energy 55 (2013) 357e365
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Renewable Energy journal homepage: www.elsevier.com/locate/renene
Microwave-assisted pyrolysis of palm kernel shell: Optimization using response surface methodology (RSM) Muhammad ’Azim Jamaluddin a, b, *, Khudzir Ismail b, Mohd Azlan Mohd Ishak b, Zaidi Ab Ghani b, Mohd Fauzi Abdullah b, Muhammad Taqi-uddeen Safian b, Siti Shawalliah Idris c, Shawaluddin Tahiruddin d, Mohammed Faisal Mohammed Yunus d, Noor Irma Nazashida Mohd Hakimi d a
Faculty of Applied Sciences, Universiti Teknologi MARA Malaysia (UiTM), 40450 Shah Alam, Selangor, Malaysia Fuel and Biomass Energy Research Group, Universiti Teknologi MARA (UiTM), 02600 Arau, Perlis, Malaysia Faculty of Chemical Engineering, Universiti Teknologi MARA Malaysia (UiTM), 40450 Shah Alam, Selangor, Malaysia d Sime Darby Research Sdn Bhd, Carey Island, Selangor, Malaysia b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 18 March 2012 Accepted 17 December 2012 Available online 4 February 2013
In this study, response surface methodology (RSM) based on central composite rotatable design (CCRD) was applied to determine the optimum condition for pyrolysis of palm kernel shell (PKS) using microwave-assisted pyrolysis system. Three operating variables, namely reaction time (min), sample mass (g) and nitrogen gas flow rate (mL/min) with a total of 20 individual experiments were conducted to optimize the combination effects of the variables. RSM based upon CCRD can be applied to correlate the experimental microwave-assisted pyrolysis results, with regression coefficients of 96.6, 95.0, 96.4 and 99.2 for the calorific value, fixed carbon content, volatile matters content and yield percentage, respectively. This proved that the RSM based on CCRD is efficiently applicable for the pyrolysis study using microwave-assisted pyrolysis system. The predicted optimum conditions for the pyrolysis process was at 31.5 min for reaction time, 30 g for sample mass and 100 mL/min for nitrogen gas flow rate, resulting in calorific value, fixed carbon content, volatile matters content and yield percentage of 29.9 MJ/ kg, 59.8 wt%, 36.4 wt% and 40.0 wt%, respectively. Thus, maximum production of PKS char, with low volatile matters content and high calorific value and fixed carbon content via microwave-assisted pyrolysis system can be optimized using RSM. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Microwave-assisted pyrolysis Palm kernel shell Char Response surface methodology
1. Introduction Malaysia has become the largest producer and exporter of palm oil products, namely crude palm oil (CPO) and crude palm kernel oil (CPKO) thus it is not surprising that the industry contributes massive amount of the waste such as empty fruit bunches, palm mesocarp fibre, palm kernel shell and other waste products [1]. These wastes normally used as fuel sources to generate energy required for the operation of the mills, however, when burning biomass in boilers, several drawbacks such as fouling, low heating value, storage and handling of the biomass has become constraints to this conventional method [1,2]. Utilization of high heating value
* Corresponding author. Fuel and Biomass Energy Research Group, Universiti Teknologi Mara (UiTM), 02600 Arau, Perlis, Malaysia. Tel.: þ60 4 9882540; fax: þ60 4 9882186. E-mail addresses: [email protected], [email protected] (M.’Azim Jamaluddin). 0960-1481/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.renene.2012.12.042
materials such as coal on the other hands is more expensive and causes environmental damages. Thus, converting the biomass into up-graded fuel sources to reduce global coal consumptions and improve the quality of the biomass itself as energy sources is of paramount importance. Studies had been done to convert these wastes to more useful, value-added products through thermochemical conversion process such as bio oil and bio-char which have advantages in terms of storage and transportation in comparison to the raw wastes [3]. The bio-char, drawn major attention as it can be potentially used as energy source for combustion due to its higher fixed carbon contents and calorific value compared to the raw wastes [4]. Previously, conventional heating methods using tube furnace or fluidized bed reactor were widely used for pyrolysis of biomass; however these external heating methods consumed long heating duration that results in low quality products due to secondary reaction. Other disadvantages including more energy required for the heating process, heat transfer resistance and heat losses to the
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M.’Azim Jamaluddin et al. / Renewable Energy 55 (2013) 357e365 Table 1 Sample assays of palm kernel shell (PKS). Analyses Proximate analysis (db) (wt%) Volatile matter Fixed carbon Ash Ultimate analysis (daf) (wt%) Carbon Hydrogen Nitrogen Sulphur Oxygena Calorific value (MJ/kg) a
PKS 77.5 20.3 2.2 56.1 5.9 0.4 0.03 37.6 16.3
Calculated by difference.
surrounding [5]. Recently microwave pyrolysis was developed as an alternative heating source for pyrolysis process to overcome these problems. As a rapid pyrolysis process, microwave pyrolysis prevents formation of secondary reaction thus improved the quality of products produced. It also provides a uniform heating of the sample due to volumetric heating of the materials [6]. In this study, microwave irradiation heating has been used as an alternative to perform pyrolysis of biomass due to less time and energy consumption during the process compared to conventional pyrolysis devices [7]. Microwave-assisted pyrolysis of biomass including pine sawdust, coffee hulls, rice husks, and sewage sludge had been done, and the effects of different parameters on the pyrolysis process, including the sample size particles, reaction time and sample mass were studied [8e11]. Only few studies have been done for pyrolysis of palm oil wastes, and microwave-assisted pyrolysis of palm kernel shells using statistical approach is undoubtedly novel in this area. Recently, Selema et al. studied the effects of microwave absorbent in the pyrolysis of palm kernel shell and palm fibres [5]. Since biomass has low dielectric properties that almost resistant to microwave irradiation, absorber is needed to initiate the pyrolysis process [9,12]. Microwave radiation will heat the absorber first, then the biomass samples were then be heated indirectly by conduction, results in the decomposition of the sample [5,8]. Present paper focuses on the optimization of char production from palm kernel shells (PKS) via microwave-assisted pyrolysis process, based on response surface methodology (RSM). As a collection of statistical and mathematical method, RSM helps in the process of modelling and analysing engineering problem, in the same time useful for optimizing the response surface that is influenced by various parameters [13]. First described by Box and
Wilson, this multivariate statistic model simultaneously optimizes the effects of many factors and the interaction between the variables to achieve best system performance [14]. The main advantage of using of RSM is, it requires fewer tests and less time consuming compared to the full factorial design experimentation [15]. The goal of this work was to investigate microwave-assisted pyrolysis of palm kernel shell. RSM was used to model the multiple parameter process and to design the optimum operating conditions for the pyrolysis. The relationship between reaction variables (i.e. microwave power level, reaction time, sample mass and nitrogen flow rate) was also investigated in this study. Production of maximum yield percentage of char, with high fixed carbon and calorific values and low volatile matters content from the pyrolysis process was found to be necessity in this study. 2. Materials and methods 2.1. Materials Palm Kernel Shell (PKS) was kindly supplied by a local palm oil mill in Kedah. The sample was cleaned and dried overnight at oven temperature of 80 C. Then the sample was pulverized and sieved through progressively finer screen to obtain particle sizes of <212 mm. Proximate, ultimate analysis and calorific values of raw PKS sample are shown in Table 1. 2.2. Experimental set up Pyrolysis of PKS was carried out in a fabricated microwave, model Panasonic NNJ-993 with maximum power of 300 W at frequency of 2450 MHz. Schematic diagram of the microwave-assisted pyrolysis system was shown in Fig. 1. PKS is one of biomass samples that have very poor absorbance properties towards microwave irradiation. Thus, the system was improved with carbon bed surrounding the sample quartz reactor to overcome the problem. PKS sample was placed in the reactor and subjected to microwave irradiation with nitrogen gas used as inert carrier gas. Final temperature of the sample during the pyrolysis process was measured using type-K thermocouple. The condensable volatile matters were collected using condenser with cooling water under controlled temperature of 0e5 C. Solid and liquid fractions were determined from weight changes before and after the reaction completed, while gas fraction was taken by differences. All experiments were duplicated to determine the range and deviation between the results.
Fig. 1. Schematic diagram of microwave-assisted pyrolysis system.
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2.3. Sample analyses
2.5. Statistical analysis
Proximate analysis was done to determine the percentages of moisture, volatile matter, fixed carbon and ash content in the sample using Thermogravimetric Analyzer, DTA/DSC TA Model SDT Q600 according to the ASTM D2974 [16] under nitrogen gas atmosphere. Ultimate analysis was done using Elemental Analyzer CHNS-932 series model with helium gas as carrier to determine the percentage of C, H, N and S. The percentage of oxygen was determined by difference. Calorific value of the sample was determined using Bomb calorimeter Leco AC-350 model according to ASTM D5468 [17].
Design Expert 6.0.6 software from State-Ease Inc was used to fit the equations developed and also for determining and evaluating the statistical significance of the equations.
2.4. Experimental design Central composite rotatable design (CCRD) was used as design of experiment as it provides as much information with less number of experiments [15]. In order to obtain required data, series of 20 experiments, including eight factorial points, six axial points and five central points was done based on three variables mentioned in previous section, derived from Equation (1) [18].
N ¼ 2n þ 2n þ nc ¼ 23 þ 2 3 þ 6 ¼ 20
(1)
Before designing this experiment, suitable values for the three pyrolysis factors, i.e., reaction temperature, sample mass and nitrogen flow rate selected based on the preliminary study. CCRD with a full factorial was developed using the Design Expert software (Version 6.0.6, Stat-Ease Inc., MN, USA). Each factor is varied over five levels: the high level (þ1), the low level (1), the centre points (coded level 0) and two outer points corresponding to value of 1.68179 [15,18,19]. CCRD consisting of eight factorial points, six axial points and six central that rendered a total of 20 runs of experiment was used to analyse the data acquired from the experimental runs. These data are then used in the optimization analysis. In this study, the response variables measured were calorific value, fixed carbon content, volatile matter content and yield percentage of the char produced. The mathematical model selected from the CCRD has the highest polynomial order with significant terms and it was not aliased [19,20].
2.6. Optimization analysis The optimum condition for three variables, reaction time (A), sample mass (B) and nitrogen flow rate (C) were obtained using data from the statistical analysis. Design-Expert searches for a combination of factors that simultaneously satisfy the requirements placed on each of the response and factors [15]. 3. Results and discussion 3.1. Effect of different microwave power levels to product fractional yields and bio-char quality The weight percentages of the pyrolysis products under different microwave power levels are presented in Fig. 2. Solid fraction (bio-char) production decreased from 100 W to 300 W, and then there are no significant changes of the char yield from 300 W to 1000 W. This is because the pyrolysis of PKS is not adequately occurred at power below 300 W. Complete decomposition of the samples might only reached at power 300 W onwards with temperature ranging from 350 C to 450 C [21]. Lower microwave power level used results in lower heating rates of the pyrolysis process, thus results in higher production of the bio-char. Highest yield of bio-char occurred at power of 300 W, excluding the yield obtained at power 100 W due to incomplete bio-char formation at lower temperature. The trends agreed with studies done by other researchers, varying with different biomass sample used for biochar production [5,9,11]. On the other hand, the yields of liquid and gas fractions increased gradually over the range of microwave power level studied, respectively. Increasing the microwave power increases the heating rate of the pyrolysis process, thus results in secondary cracking of the liquid fraction into incondensable gases [22,23].
Fig. 2. Product fractional yields under different microwave power levels.
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Table 2 Final temperature achieved with different microwave power levels. Microwave power (W) Final temperature ( C)
100 342
300 372
600 404
Table 3 Central composite design of independent variables for process optimization. 1000 420
Therefore, as the power increased, the production of liquid and gas fraction increased. Table 2 showed final temperatures achieved during pyrolysis of PKS sample at different power levels of microwave irradiation. It was clearly shown that the final temperature is directly proportional to the microwave power level used for the pyrolysis process. The stronger the power used, the higher final temperature measured by the thermocouple. This is due to the increase of microwave density of capacity together with greater absorbance properties of the sample towards the microwave irradiation [22]. The microwave penetrates the sample, causing rapid movement of the sample at molecular level, thus heat the sample from inside out [6]. This leads to decomposition of the samples at higher power levels and affects the quantities of the different fractions produced. In order to select the best microwave power level to be used in statistical study using RSM, all bio-char produced at every power level were analysed. From Fig. 3, the volatile matter content of the bio-char reduced at power 100 W, and then remained almost constant at power 300 We1000 W. This might be due to inadequate release of volatile matter of the PKS sample during the pyrolysis process [23]. At temperature below 350 C, PKS, like almost all biomass that constitute polymer of cellulose, hemicelluloses and lignin, which are linked together with ether bonds (ReOeR). This relatively weak ether bonds are less resistant to the heat at low temperature of 350e500 C [24]. Thus, at microwave power of 100 W with low temperature of 342 C, insufficient energy to break the weak bonds caused incomplete release of volatile matter which results in higher volatile matter content of the char. However, at 300e1000 W, the maximum temperature achieved range from 372 C to 420 C, supplying enough energy to break the bond thus reduced the volatile matter during the pyrolysis process. The volatile matter content remains almost similar for each power used
Std
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Coded
Actual
A
B
C
A
B
C
1.00 0.00 1.00 1.00 1.00 0.00 1.00 1.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 1.68 0.00 0.00 1.68 0.00
1.00 0.00 1.00 1.00 1.00 0.00 1.00 1.00 0.00 0.00 1.00 1.00 0.00 1.68 1.68 0.00 0.00 0.00 0.00 0.00
1.00 0.00 1.00 1.00 1.00 0.00 1.00 1.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 1.68 0.00 1.68
45.00 30.00 15.00 15.00 45.00 30.00 15.00 15.00 30.00 30.00 45.00 45.00 30.00 30.00 30.00 55.23 30.00 30.00 4.77 30.00
10.00 20.00 30.00 30.00 30.00 20.00 10.00 10.00 20.00 20.00 30.00 10.00 20.00 3.18 36.82 20.00 20.00 20.00 20.00 20.00
300.00 200.00 300.00 100.00 100.00 200.00 100.00 300.00 200.00 200.00 300.00 100.00 200.00 200.00 200.00 200.00 200.00 31.82 200.00 368.18
may be due to the small maximum temperature difference between the three powers used. Comparing the trend of volatile matter content of the bio-char to the fixed carbon content, it was observed that increasing microwave power results in increasing fixed carbon content and calorific value of the char from 100 W to 300 W. The release of volatile matters over the range of microwave power used results in the concentration of fixed carbon content in the bio-char, thus increases the calorific value. There are small gradual decreases of the ash content of the bio-char as the microwave power increase. The more volatile matter released the less ash content of the sample. Based on the quantitative and qualitative analyses of the biochar produced, 300 W was selected as the optimum power to be used for statistical study of the pyrolysis process using RSM. Biochar produced at 300 W was the highest in term of yield
Fig. 3. Effect of different microwave power levels to volatile matter, fixed carbon content, calorific value and ash content of char.
M.’Azim Jamaluddin et al. / Renewable Energy 55 (2013) 357e365 Table 4 Experimental and predicted results for calorific value (P), fixed carbon content (Q), volatile matter content (R) and yield percentage (S). Std
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Experimental
Predicted
P
Q
R
S
P
Q
R
S
29.10 28.00 22.20 25.10 30.90 28.90 24.70 22.60 27.90 28.80 29.10 29.10 28.70 25.20 28.60 27.10 28.80 28.70 17.00 28.90
58.93 56.92 32.01 42.38 64.50 53.92 43.88 35.67 57.52 55.33 63.02 59.14 55.20 48.60 60.35 59.51 58.99 53.84 20.08 62.49
38.58 41.53 66.92 54.23 32.94 39.47 55.09 62.33 37.12 40.96 35.29 37.77 39.50 49.98 38.85 38.30 39.47 41.19 78.10 36.46
37.42 41.03 55.33 55.55 37.06 40.54 60.21 54.83 42.12 42.68 37.57 39.45 40.27 46.30 38.91 36.39 40.11 43.51 68.00 37.76
28.91 28.67 22.62 25.02 30.61 28.67 23.72 22.62 28.67 28.67 29.82 28.41 28.18 26.16 28.02 27.24 28.18 29.79 17.24 28.19
59.97 56.07 35.27 41.19 63.76 56.07 40.47 36.26 56.07 56.07 66.28 55.72 56.77 51.62 57.54 59.35 56.77 58.98 20.45 57.57
37.42 39.77 63.54 55.39 33.05 39.77 57.56 62.22 39.77 39.77 32.82 41.15 39.49 47.26 41.57 38.38 39.49 36.96 78.02 40.69
37.26 41.67 54.09 55.64 36.42 41.67 60.80 55.40 41.67 41.67 36.90 40.62 40.02 44.98 40.34 36.54 40.04 42.75 67.96 38.62
percentage that reached almost 40% from the initial sample mass, with almost comparable fixed carbon content, volatile matter content, calorific value and ash content in comparable to those at 600 W and 1000 W. Production of highest quantity of bio-char, with highest calorific value and fixed carbon content and lowest in volatile matters content ensures the efficiency of the pyrolysis process in term of energy and cost saving.
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Table 6 ANOVA for the regression model and respective model term for fixed carbon content. Source
Sum of squares
Degree of freedom
Mean of square
Fvalue
Prob > F
Remarks
Model A B C A2 B2 C2 AB AC BC Residual Lack of fit Pure error Cor total
2647.58 1827.09 42.36 2.40 512.31 8.64 4.05 26.68 35.66 1.47 131.19 116.10 15.09 2599.73
9 1 1 1 1 1 1 1 1 1 9 5 4 19
274.18 1827.09 42.36 2.40 512.31 8.64 4.05 26.68 35.66 1.47 14.58 23.22 3.77
18.81 125.35 2.91 0.16 35.15 0.59 0.28 1.83 2.45 0.10
<0.0001 <0.0001 0.1225 0.6945 0.0002 0.4612 0.6110 0.2091 0.1522 0.7580
Significant Significant
6.15
0.0514
Significant
Not significant
study, the response and variables were fitted to each other by multiple regressions. Regression analysis is the general approach to fit the empirical model with the collected response variable data [26]. The coefficients of the full regression model equation and their statistical significance were determined and evaluated using Design-Expert 6.0.6 software from State-Ease Inc. The final model in terms of coded value is given in Equations (2)e(5) for calorific value, fixed carbon content, volatile matter content and yield percentage, respectively.
YCV ¼ 28:42 þ 2:97A þ 0:55B 0:47C 2:10A2 0:39B2 þ 0:29C 2 þ 0:23AB þ 0:40AC 0:32BC
(2)
3.2. Statistical analysis The relationship between responses (calorific value, fixed carbon content, volatile matters content and yield percentage of biochar) and three independent factors (reaction time, sample mass and nitrogen flow rate) were investigated in this study. Designed variables suggested by the software are shown in Table 3, while the experimental and predicted results at each point obtained are shown in Table 4. The experimental sequence was randomized in order to minimize the effects of the uncontrolled factors [25]. For the model fitted, the software generated model coefficients, R2-values, Fvalues, and significant probabilities and from these values the significance of each experimental variable can be justified. In this
Table 5 ANOVA for the regression model and respective model term for calorific value.
Ywt%FC ¼ 56:42 þ 11:57A þ 1:76B 0:42C 5:96A2 0:77B2 þ 0:53C 2 þ 1:83AB þ 2:11AC 0:43BC (3)
Ywt%VM ¼ 39:63 11:78A 1:69B 1:11C þ 6:62A2 þ 1:74B2 0:23C2 1:48AB 2:10AC þ 0:87BC (4)
Table 7 ANOVA for the regression model and respective model term for volatile matter content.
Source
Sum of squares
Degree of freedom
Mean of square
Fvalue
Prob > F
Remarks
Source
Sum of squares
Degree of freedom
Mean of square
Fvalue
Prob > F
Remarks
Model A B C A2 B2 C2 AB AC BC Residual Lack of fit Pure error Cor total
198.20 120.62 4.14 3.06 63.56 2.15 1.17 0.41 1.28 0.84 7.04 6.21 0.83 206.82
9 1 1 1 1 1 1 1 1 1 9 5 4 19
22.02 120.62 4.14 3.06 63.56 2.15 1.17 0.41 1.28 0.84 0.78 1.24 0.21
28.16 154.25 5.29 3.91 81.29 2.75 1.50 0.52 1.64 1.08
<0.0001 <0.0001 0.0470 0.0793 <0.0001 0.1318 0.2516 0.4900 0.2327 0.3257
Significant Significant Significant
Model A B C A2 B2 C2 AB AC BC Residual Lack of fit Pure error Cor total
2672.77 1896.26 39.10 16.77 630.34 43.72 0.79 17.55 35.15 6.11 99.86 88.24 11.63 2772.64
9 1 1 1 1 1 1 1 1 1 9 5 4 19
296.97 1896.26 39.10 16.77 630.34 43.72 0.79 17.55 35.15 6.11 11.10 17.65 2.91
26.76 170.90 3.52 1.51 56.81 3.94 0.071 1.58 3.17 0.55
<0.0001 <0.0001 0.0932 0.2501 <0.0001 0.0784 0.7959 0.2401 0.1088 0.4771
Significant Significant
6.02
0.0532
Significant
Not significant
6.07
0.0526
Significant
Not significant
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Table 8 ANOVA for the regression model and respective model term for yield percentage. Source
Sum of squares
Degree of freedom
Mean of square
Fvalue
Prob > F
Remarks
Model A B C A2 B2 C2 AB AC BC Residual Lack of fit Pure error Cor total
1521.11 1191.65 25.96 20.62 268.48 12.37 0.76 0.46 2.08 7.40 12.58 9.68 2.90 1543.23
9 1 1 1 1 1 1 1 1 1 9 5 4 19
169.01 1191.65 25.96 20.62 268.48 12.37 0.76 0.46 2.08 7.40 1.40 1.94 0.73
120.93 852.61 18.57 14.75 192.09 8.85 0.54 0.33 1.49 5.29
<0.0001 <0.0001 0.0020 0.0040 <0.0001 0.0156 0.4799 0.5799 0.2534 0.0470
Significant Significant Significant Significant Significant Significant
2.67
0.1816
Significant Not significant
Ywt%Yield ¼ 40:85 9:34A 1:38B 1:23C þ 4:32A2 þ 0:93B2 þ 0:23C 2 þ 0:24AB þ 0:51AC þ 0:96BC (5) where Y is the response, and A, B and C are the coded terms for the three variables that has been selected, i.e. reaction time (A), sample mass (B) and nitrogen gas flow rate (C). Positive sign in front of each term represent synergistic effect, while antagonistic effect represented by negative sign. Analysis of Variance (ANOVA) was then used to assess the goodness of fit. The significant quadratic models and the corresponding significant model term for all responses are tabulated in Tables 5e8 for calorific value, fixed carbon content, volatile matters content and yield percentage, respectively. From Table 5, the model F-value of 28.16 implies that the model is significant. It was also observed that the linear term of reaction time (A) has large significant effect on the calorific value due to the high F-value of 154.25. The quadratic term of reaction time (A2) has also significant with F-value of 81.29. Sample mass (B) also give significant effect to the calorific value with F-value of 5.29. However, nitrogen gas flow rate (C) seems does not have significant effect on the calorific value of the bio-char. The interaction between reaction time (AB) and nitrogen gas flow rate (AC) also do not affect the calorific value of the bio-char significantly. The relationships between the variables are also shown in Fig. 4 a, b and c.
From Table 6, the model F-value of 18.81 implies that the model is significant. It was also observed that the linear term of reaction time (A) has large significant effect on the fixed carbon content due to the high F-value of 125.35. The quadratic term of reaction time (A2) has also significant with F-value of 35.15. Other factors seem does not have effect on the fixed carbon content of the bio-char significantly. Fig. 5 a, b and c showed the relationships between the variables to the fixed carbon content of the bio-char. F-value of 26.76 shown in Table 7 implies that the model is significant for the response of volatile matters content. It was also observed that the linear term of reaction time (A) has large significant effect on the volatile matters content of the bio-char due to the high F-value of 170.90. The quadratic term of reaction time (A2) has also significant effect with F-value of 56.81. However, sample mass (B) and nitrogen gas flow rate (C) does not affect the volatile matters content of the bio-char significantly. The interactions between the variables (AB, AC and BC) also do not affect the volatile matters content of the bio-char significantly. The relationships between the variables are shown in Fig. 6 a, b and c. From Table 8, the model F-value of 120.93 implies that the model is significant. Interestingly, all of the variables showed significant effects on the yield percentage of the bio-char produced due to the high F-values of 852.61, 18.57, and 14.75, respectively. The quadratic term of reaction time (A2) and sample mass (B2) have also significant effects with F-value of 192.09 and 8.85, respectively. The quadratic term of nitrogen flow rate (C2), on the other hand, does not have effect on the yield percentage of the bio-char significantly. Fig. 7 a, b and c showed the relationships between the variables to the yield percentage of the bio-char produced. Among the variables, reaction time (A) plays major effect to all responses investigated in this study. As the reaction time (A) increased, the calorific value also increased due to decrease of hydrogen and oxygen in the char produced. The removal of volatiles matter becomes more efficient, caused the saturation of aromatic rings consist mostly carbon thus increase the fixed carbon content of the sample [27]. The yield percentage is also reduced as the reaction time increased due to the removal of volatile matters and saturation of the fixed carbon in the bio-char produced. Sample mass does not really affect the responses probably due to the volumetric heating principle of the microwave itself. The heating process occurs at molecular level, penetrating the sample from inside out thus resulting in uniform heating of the sample regardless of the mass effect [6]. Nitrogen flow rate, on the other hand, has almost no significant effect to all responses. The nitrogen
Fig. 4. Countour plot of calorific value: Effect of reaction time and sample mass (a), effect of reaction time and N2 flow rate (b) and effect of sample mass and N2 flow rate (c).
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Fig. 5. Countour plot of fixed carbon content: Effect of reaction time and sample mass (a), effect of reaction time and N2 flow rate (b) and effect of sample mass and N2 flow rate (c).
Fig. 6. Countour plot of volatile matter content: Effect of reaction time and sample mass (a), effect of reaction time and N2 flow rate (b) and effect of sample mass and N2 flow rate (c).
gas used in this study might only helped to provide an inert condition for the pyrolysis process regardless of the flow rate of the gas during the process. In order to test the fit of the model, the regression equation and the determination coefficient (R2) were evaluated. For the
response of calorific values, the value of determination coefficient (R2 ¼ 0.9657) indicates that the sample variation of 96.57% for calorific value is attributed to the independent variables and only 3.43% of the total variation could not be explained by the model. The value of adjusted determination coefficient (Adj
Fig. 7. Countour plot of yield percentage: Effect of reaction time and sample mass (a), effect of reaction time and N2 flow rate (b) and effect of sample mass and N2 flow rate (c).
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R2 ¼ 0.9314) is also very high to advocate for a high significance of the model. Meanwhile for the response of fixed carbon content, the value of determination coefficient (R2 ¼ 0.9495) indicates that the sample variation of 94.95% for fixed carbon is attributed to the independent variables and only 5.05% of the total variation could not be explained by the model. The value of adjusted determination coefficient (Adj R2 ¼ 0.8990) is also very high to advocate for a high significance of the model. The response of volatile matters content, the value of determination coefficient (R2 ¼ 0.9640) indicates that the sample variation of 96.4% for volatile matters is attributed to the independent variables and only 3.6% of the total variation could not be explained by the model. The value of adjusted determination coefficient (Adj R2 ¼ 0.9280) is also very high to advocate for a high significance of the model. On the other hand, the response of yield percentage of the biochar produced, the value of determination coefficient (R2 ¼ 0.9918)
indicates that the sample variation of 99.18% for yield percentage of the bio-char is attributed to the independent variables and only 0.82% of the total variation could not be explained by the model. The value of adjusted determination coefficient (Adj R2 ¼ 0.9836) is also very high to advocate for a high significance of the model. The correlation between experimental values and predicted values of calorific value, fixed carbon content, volatile matters content and yield percentage of the bio-char are shown in Fig. 8 a, b, c and d, respectively. A higher value of the correlation coefficient for all responses justifies an excellent correlation between the independent variables [15,19]. 3.3. Optimization analysis All the factors and responses with respectively high and low limit experimental region have to satisfy the creations defined for the optimum working conditions as stated in Table 9. The goal was
Fig. 8. Relationship between predicted and actual values of a) calorific value, b) fixed carbon content, c) volatile matter content and d) yield percentage.
M.’Azim Jamaluddin et al. / Renewable Energy 55 (2013) 357e365 Table 9 The preset goal with constraints for all the independent factors and response in numerical optimization. Variables
Reaction time (min) Sample mass (g) N2 flow rate (mL/min) Calorific value (MJ/kg) Fixed carbon (wt%) Volatile matters (wt%) Yield percentage (wt%)
Ultimate goal
Minimize Is in range Minimize Maximize Maximize Minimize Is target ¼ 40
Experimental region Lower limit
Upper limit
15.00 10.00 100.00 17.00 20.08 32.94 36.40
45.00 30.00 300.00 30.90 64.50 78.10 68.00
set to optimize the calorific values, fixed carbon content, volatile matters content and yield percentage of the bio-char produced from the experiment, with maximum calorific values, fixed carbon content and lowest volatile matters content. The yield percentage, on the other hand, was set to achieve as high as 40% based on preliminary study discussed in Section 3.1. The optimized condition obtained by DOE was at 31.58 min of reaction time, 30 g of sample mass and 100 mL/min of the nitrogen gas flow rate. 4. Conclusion The response surface methodology (RSM) based on central composite rotatable design (CCRD) was employed for the optimization of pyrolysis of palm kernel shell via microwave-assisted pyrolysis system. The predicted optimum conditions of reaction time (A), sample mass (B), and nitrogen gas flow rate (C) were at 31.58 min, 30 g and 100 mL/min, resulting in calorific value, fixed carbon content, volatile matters content and yield percentage of 29.9 MJ/kg, 59.8 wt%, 36.4 wt% and 40.0 wt%, respectively. The correlation coefficients obtained for all of the responses justify an excellent correlation between the independent variables. Acknowledgement The authors acknowledge the Sime Darby Sdn. Bhd. for their financial support, and Universiti Teknologi MARA for their support. References [1] Yusoff S. Renewable energy from palm oil e innovation on effective utilization of waste. Journal of Cleaner Production 2006;14:87e93. [2] Harimi M, Megat Ahmad MMH, Sapuan SM, Idris A. Numerical analysis of emission component from incineration of palm oil wastes. Biomass and Bioenergy 2005;28:339e45. [3] Van de Velden M, Baeyens J, Brems A, Janssens B, Dewil R. Fundamentals, kinetics and endothermicity of biomass pyrolysis reaction. Renewable Energy 2010;35:232e42. [4] Sahu SG, Sarkar P, Chakraborty N. Thermogravimetric assessment of combustion characteristics of blends of coal with different biomass chars. Fuel Processing Technology 2010;91:369e78. [5] Salema AA, Ani FN. Microwave induced pyrolysis of oil palm biomass. Bioresource Technology 2011;102:3388e95.
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[6] Thostenson ET, Chou T-W. Microwave processing: fundamentals and applications. Composites: Part A 1999;30:1055e71. [7] Marra F, Bonis MVD, Ruocco G. Combined microwaves and convection heating: a conjugate approach. Journal of Food Engineering 2010;97:31e9. [8] Wang X-H, Chen HP, Ding XJ, Yang HP, Zhang SH, Shen YQ. Properties of gas and char from microwave pyrolysis of pine sawdust. Bioresources 2009;4(3): 946e59. [9] Dominguez A, Menendez JA, Fernandez Y, Pis JJ, Velente NAbais JM, Carrot PJM, et al. Conventional and microwave induced pyrolysis of coffee hulls for the production of a hydrogen rich fuel gas. Journal of Analytical and Applied Pyrolysis 2007;79:128e35. [10] Hu S, Xiang J, Sun L, Xu M, Qiu J, Fu P. Characterization of char from rapid pyrolysis of rice husk. Fuel Processing Technology 2008;89:1096e105. [11] Menendez JA, Dominguez A, Pis JJ, Iguanzo M. Microwave-induced drying, pyrolysis and gasification (MWDPG) of sewage sludge: vitrification of the solid residue. Journal of Analytical and Applied Pyrolysis 2005;74: 406e12. [12] Menéndez JA, Arenillas A, Fidalgo B, Fernandez Y, Zubizarreta L, Calvo EG, et al. Microwave heating processes involving carbon materials. Fuel Processing Technology 2010;91:1e8. [13] Aslan N. Application of response surface methodology and central composite rotatable design for modelling and optimization of a multi-gravity separator for chromite concentration. Powder Technology 2008;185:80e6. [14] Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA. Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 2008;76:965e77. [15] Ghani ZA, Ishak MAM, Ismail K. Direct liquefaction of Mukah Balingian lowrank Malaysian coal: optimization using response surface methodology. Asia Pacific Journal of Chemical Engineering 2010;6(4):581e8. [16] ASTM D3172 standard practice for proximate analysis of coal and coke1. Pennsylvania: ASTM International; 2007. [17] Gani A, Naruse I. Effect of cellulose and lignin content on pyrolysis and combustion characteristics for several types of biomass. Renewable Energy 2007;32:649e61. [18] Tan IAW, Ahmad AL, Hameed BH. Optimization of preparation conditions for activated carbons from coconut husk using response surface methodology. Chemical Engineering Journal 2008;137:462e70. [19] Ghani ZA, Khamil INM, Isa MRM, Jamaluddin MA, Ishak MAM, Ismail K. Pyrolysis of Jatropha curcas l. Husk: optimization solid, liquid and gas yield by using response surface methodology (RSM). In: Proceedings of 3rd international symposium & exhibition in sustainable energy & environment (ISESEE). The Research and Innovations in Sustainable Energy (RISE) Center of Institute of Science (IOS) Universiti Teknologi MARA (UiTM); 2011. [20] Gratuito MKB, Panyathanmaporn T, Chumnanklang RA, Sirinutawittaya N, Dutta A. Production of activated carbon from coconut shell: optimization using response surface methodology. Bioresource Technology 2008;99: 4887e95. [21] Zakaria. Z, Mohd Ishak. MA, Abdullah. MF, Ismail K. Thermal decomposition of coals, rice husk, rice husk char and their blends during pyrolysis and combustion via thermogravimetric analysis. International Journal of Chemical Technology 2010;2(3):78e87. [22] Hu Z, Ma X, Chen C. A study on experimental characteristic of microwaveassisted pyrolysis of microalgae. Bioresource Technology 2012;107:487e93. [23] Du Z, Li Y, Wang X, Wan Y, Chen Q, Wang C, et al. Microwave-assisted pyrolysis of microalgae for biofuel production. Bioresource Technology 2011; 102:4890e6. [24] Vuthaluru HB. Investigations into the pyrolytic behavior of coal/biomass blends using thermogravimetric analysis. Bioresource Technology 2004;92: 187e95. [25] Halim SFA, Kamaruddin AH, Fernando WJN. Continuous biosynthesis of biodiesel from waste cooking palm oil in a packed bed reactor: optimization using response surface methodology (RSM) and mass transfer studies. Bioresource Technology 2009;100(2):710e6. [26] DC M. Design and analysis of experiment. 5 ed. New York: John Wiley and Sons; 2001. [27] Bilba K, Ouensanga A. Fourier transform infrared spectroscopic study of thermal degradation of sugarcane bagasse. Journal of Analytical and Applied Pyrolysis 1996;38:61e73.
Contents lists available at SciVerse ScienceDirect
Renewable Energy journal homepage: www.elsevier.com/locate/renene
Microwave-assisted pyrolysis of palm kernel shell: Optimization using response surface methodology (RSM) Muhammad ’Azim Jamaluddin a, b, *, Khudzir Ismail b, Mohd Azlan Mohd Ishak b, Zaidi Ab Ghani b, Mohd Fauzi Abdullah b, Muhammad Taqi-uddeen Safian b, Siti Shawalliah Idris c, Shawaluddin Tahiruddin d, Mohammed Faisal Mohammed Yunus d, Noor Irma Nazashida Mohd Hakimi d a
Faculty of Applied Sciences, Universiti Teknologi MARA Malaysia (UiTM), 40450 Shah Alam, Selangor, Malaysia Fuel and Biomass Energy Research Group, Universiti Teknologi MARA (UiTM), 02600 Arau, Perlis, Malaysia Faculty of Chemical Engineering, Universiti Teknologi MARA Malaysia (UiTM), 40450 Shah Alam, Selangor, Malaysia d Sime Darby Research Sdn Bhd, Carey Island, Selangor, Malaysia b c
a r t i c l e i n f o
a b s t r a c t
Article history: Received 18 March 2012 Accepted 17 December 2012 Available online 4 February 2013
In this study, response surface methodology (RSM) based on central composite rotatable design (CCRD) was applied to determine the optimum condition for pyrolysis of palm kernel shell (PKS) using microwave-assisted pyrolysis system. Three operating variables, namely reaction time (min), sample mass (g) and nitrogen gas flow rate (mL/min) with a total of 20 individual experiments were conducted to optimize the combination effects of the variables. RSM based upon CCRD can be applied to correlate the experimental microwave-assisted pyrolysis results, with regression coefficients of 96.6, 95.0, 96.4 and 99.2 for the calorific value, fixed carbon content, volatile matters content and yield percentage, respectively. This proved that the RSM based on CCRD is efficiently applicable for the pyrolysis study using microwave-assisted pyrolysis system. The predicted optimum conditions for the pyrolysis process was at 31.5 min for reaction time, 30 g for sample mass and 100 mL/min for nitrogen gas flow rate, resulting in calorific value, fixed carbon content, volatile matters content and yield percentage of 29.9 MJ/ kg, 59.8 wt%, 36.4 wt% and 40.0 wt%, respectively. Thus, maximum production of PKS char, with low volatile matters content and high calorific value and fixed carbon content via microwave-assisted pyrolysis system can be optimized using RSM. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Microwave-assisted pyrolysis Palm kernel shell Char Response surface methodology
1. Introduction Malaysia has become the largest producer and exporter of palm oil products, namely crude palm oil (CPO) and crude palm kernel oil (CPKO) thus it is not surprising that the industry contributes massive amount of the waste such as empty fruit bunches, palm mesocarp fibre, palm kernel shell and other waste products [1]. These wastes normally used as fuel sources to generate energy required for the operation of the mills, however, when burning biomass in boilers, several drawbacks such as fouling, low heating value, storage and handling of the biomass has become constraints to this conventional method [1,2]. Utilization of high heating value
* Corresponding author. Fuel and Biomass Energy Research Group, Universiti Teknologi Mara (UiTM), 02600 Arau, Perlis, Malaysia. Tel.: þ60 4 9882540; fax: þ60 4 9882186. E-mail addresses: [email protected], [email protected] (M.’Azim Jamaluddin). 0960-1481/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.renene.2012.12.042
materials such as coal on the other hands is more expensive and causes environmental damages. Thus, converting the biomass into up-graded fuel sources to reduce global coal consumptions and improve the quality of the biomass itself as energy sources is of paramount importance. Studies had been done to convert these wastes to more useful, value-added products through thermochemical conversion process such as bio oil and bio-char which have advantages in terms of storage and transportation in comparison to the raw wastes [3]. The bio-char, drawn major attention as it can be potentially used as energy source for combustion due to its higher fixed carbon contents and calorific value compared to the raw wastes [4]. Previously, conventional heating methods using tube furnace or fluidized bed reactor were widely used for pyrolysis of biomass; however these external heating methods consumed long heating duration that results in low quality products due to secondary reaction. Other disadvantages including more energy required for the heating process, heat transfer resistance and heat losses to the
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M.’Azim Jamaluddin et al. / Renewable Energy 55 (2013) 357e365 Table 1 Sample assays of palm kernel shell (PKS). Analyses Proximate analysis (db) (wt%) Volatile matter Fixed carbon Ash Ultimate analysis (daf) (wt%) Carbon Hydrogen Nitrogen Sulphur Oxygena Calorific value (MJ/kg) a
PKS 77.5 20.3 2.2 56.1 5.9 0.4 0.03 37.6 16.3
Calculated by difference.
surrounding [5]. Recently microwave pyrolysis was developed as an alternative heating source for pyrolysis process to overcome these problems. As a rapid pyrolysis process, microwave pyrolysis prevents formation of secondary reaction thus improved the quality of products produced. It also provides a uniform heating of the sample due to volumetric heating of the materials [6]. In this study, microwave irradiation heating has been used as an alternative to perform pyrolysis of biomass due to less time and energy consumption during the process compared to conventional pyrolysis devices [7]. Microwave-assisted pyrolysis of biomass including pine sawdust, coffee hulls, rice husks, and sewage sludge had been done, and the effects of different parameters on the pyrolysis process, including the sample size particles, reaction time and sample mass were studied [8e11]. Only few studies have been done for pyrolysis of palm oil wastes, and microwave-assisted pyrolysis of palm kernel shells using statistical approach is undoubtedly novel in this area. Recently, Selema et al. studied the effects of microwave absorbent in the pyrolysis of palm kernel shell and palm fibres [5]. Since biomass has low dielectric properties that almost resistant to microwave irradiation, absorber is needed to initiate the pyrolysis process [9,12]. Microwave radiation will heat the absorber first, then the biomass samples were then be heated indirectly by conduction, results in the decomposition of the sample [5,8]. Present paper focuses on the optimization of char production from palm kernel shells (PKS) via microwave-assisted pyrolysis process, based on response surface methodology (RSM). As a collection of statistical and mathematical method, RSM helps in the process of modelling and analysing engineering problem, in the same time useful for optimizing the response surface that is influenced by various parameters [13]. First described by Box and
Wilson, this multivariate statistic model simultaneously optimizes the effects of many factors and the interaction between the variables to achieve best system performance [14]. The main advantage of using of RSM is, it requires fewer tests and less time consuming compared to the full factorial design experimentation [15]. The goal of this work was to investigate microwave-assisted pyrolysis of palm kernel shell. RSM was used to model the multiple parameter process and to design the optimum operating conditions for the pyrolysis. The relationship between reaction variables (i.e. microwave power level, reaction time, sample mass and nitrogen flow rate) was also investigated in this study. Production of maximum yield percentage of char, with high fixed carbon and calorific values and low volatile matters content from the pyrolysis process was found to be necessity in this study. 2. Materials and methods 2.1. Materials Palm Kernel Shell (PKS) was kindly supplied by a local palm oil mill in Kedah. The sample was cleaned and dried overnight at oven temperature of 80 C. Then the sample was pulverized and sieved through progressively finer screen to obtain particle sizes of <212 mm. Proximate, ultimate analysis and calorific values of raw PKS sample are shown in Table 1. 2.2. Experimental set up Pyrolysis of PKS was carried out in a fabricated microwave, model Panasonic NNJ-993 with maximum power of 300 W at frequency of 2450 MHz. Schematic diagram of the microwave-assisted pyrolysis system was shown in Fig. 1. PKS is one of biomass samples that have very poor absorbance properties towards microwave irradiation. Thus, the system was improved with carbon bed surrounding the sample quartz reactor to overcome the problem. PKS sample was placed in the reactor and subjected to microwave irradiation with nitrogen gas used as inert carrier gas. Final temperature of the sample during the pyrolysis process was measured using type-K thermocouple. The condensable volatile matters were collected using condenser with cooling water under controlled temperature of 0e5 C. Solid and liquid fractions were determined from weight changes before and after the reaction completed, while gas fraction was taken by differences. All experiments were duplicated to determine the range and deviation between the results.
Fig. 1. Schematic diagram of microwave-assisted pyrolysis system.
M.’Azim Jamaluddin et al. / Renewable Energy 55 (2013) 357e365
359
2.3. Sample analyses
2.5. Statistical analysis
Proximate analysis was done to determine the percentages of moisture, volatile matter, fixed carbon and ash content in the sample using Thermogravimetric Analyzer, DTA/DSC TA Model SDT Q600 according to the ASTM D2974 [16] under nitrogen gas atmosphere. Ultimate analysis was done using Elemental Analyzer CHNS-932 series model with helium gas as carrier to determine the percentage of C, H, N and S. The percentage of oxygen was determined by difference. Calorific value of the sample was determined using Bomb calorimeter Leco AC-350 model according to ASTM D5468 [17].
Design Expert 6.0.6 software from State-Ease Inc was used to fit the equations developed and also for determining and evaluating the statistical significance of the equations.
2.4. Experimental design Central composite rotatable design (CCRD) was used as design of experiment as it provides as much information with less number of experiments [15]. In order to obtain required data, series of 20 experiments, including eight factorial points, six axial points and five central points was done based on three variables mentioned in previous section, derived from Equation (1) [18].
N ¼ 2n þ 2n þ nc ¼ 23 þ 2 3 þ 6 ¼ 20
(1)
Before designing this experiment, suitable values for the three pyrolysis factors, i.e., reaction temperature, sample mass and nitrogen flow rate selected based on the preliminary study. CCRD with a full factorial was developed using the Design Expert software (Version 6.0.6, Stat-Ease Inc., MN, USA). Each factor is varied over five levels: the high level (þ1), the low level (1), the centre points (coded level 0) and two outer points corresponding to value of 1.68179 [15,18,19]. CCRD consisting of eight factorial points, six axial points and six central that rendered a total of 20 runs of experiment was used to analyse the data acquired from the experimental runs. These data are then used in the optimization analysis. In this study, the response variables measured were calorific value, fixed carbon content, volatile matter content and yield percentage of the char produced. The mathematical model selected from the CCRD has the highest polynomial order with significant terms and it was not aliased [19,20].
2.6. Optimization analysis The optimum condition for three variables, reaction time (A), sample mass (B) and nitrogen flow rate (C) were obtained using data from the statistical analysis. Design-Expert searches for a combination of factors that simultaneously satisfy the requirements placed on each of the response and factors [15]. 3. Results and discussion 3.1. Effect of different microwave power levels to product fractional yields and bio-char quality The weight percentages of the pyrolysis products under different microwave power levels are presented in Fig. 2. Solid fraction (bio-char) production decreased from 100 W to 300 W, and then there are no significant changes of the char yield from 300 W to 1000 W. This is because the pyrolysis of PKS is not adequately occurred at power below 300 W. Complete decomposition of the samples might only reached at power 300 W onwards with temperature ranging from 350 C to 450 C [21]. Lower microwave power level used results in lower heating rates of the pyrolysis process, thus results in higher production of the bio-char. Highest yield of bio-char occurred at power of 300 W, excluding the yield obtained at power 100 W due to incomplete bio-char formation at lower temperature. The trends agreed with studies done by other researchers, varying with different biomass sample used for biochar production [5,9,11]. On the other hand, the yields of liquid and gas fractions increased gradually over the range of microwave power level studied, respectively. Increasing the microwave power increases the heating rate of the pyrolysis process, thus results in secondary cracking of the liquid fraction into incondensable gases [22,23].
Fig. 2. Product fractional yields under different microwave power levels.
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Table 2 Final temperature achieved with different microwave power levels. Microwave power (W) Final temperature ( C)
100 342
300 372
600 404
Table 3 Central composite design of independent variables for process optimization. 1000 420
Therefore, as the power increased, the production of liquid and gas fraction increased. Table 2 showed final temperatures achieved during pyrolysis of PKS sample at different power levels of microwave irradiation. It was clearly shown that the final temperature is directly proportional to the microwave power level used for the pyrolysis process. The stronger the power used, the higher final temperature measured by the thermocouple. This is due to the increase of microwave density of capacity together with greater absorbance properties of the sample towards the microwave irradiation [22]. The microwave penetrates the sample, causing rapid movement of the sample at molecular level, thus heat the sample from inside out [6]. This leads to decomposition of the samples at higher power levels and affects the quantities of the different fractions produced. In order to select the best microwave power level to be used in statistical study using RSM, all bio-char produced at every power level were analysed. From Fig. 3, the volatile matter content of the bio-char reduced at power 100 W, and then remained almost constant at power 300 We1000 W. This might be due to inadequate release of volatile matter of the PKS sample during the pyrolysis process [23]. At temperature below 350 C, PKS, like almost all biomass that constitute polymer of cellulose, hemicelluloses and lignin, which are linked together with ether bonds (ReOeR). This relatively weak ether bonds are less resistant to the heat at low temperature of 350e500 C [24]. Thus, at microwave power of 100 W with low temperature of 342 C, insufficient energy to break the weak bonds caused incomplete release of volatile matter which results in higher volatile matter content of the char. However, at 300e1000 W, the maximum temperature achieved range from 372 C to 420 C, supplying enough energy to break the bond thus reduced the volatile matter during the pyrolysis process. The volatile matter content remains almost similar for each power used
Std
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Coded
Actual
A
B
C
A
B
C
1.00 0.00 1.00 1.00 1.00 0.00 1.00 1.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 1.68 0.00 0.00 1.68 0.00
1.00 0.00 1.00 1.00 1.00 0.00 1.00 1.00 0.00 0.00 1.00 1.00 0.00 1.68 1.68 0.00 0.00 0.00 0.00 0.00
1.00 0.00 1.00 1.00 1.00 0.00 1.00 1.00 0.00 0.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 1.68 0.00 1.68
45.00 30.00 15.00 15.00 45.00 30.00 15.00 15.00 30.00 30.00 45.00 45.00 30.00 30.00 30.00 55.23 30.00 30.00 4.77 30.00
10.00 20.00 30.00 30.00 30.00 20.00 10.00 10.00 20.00 20.00 30.00 10.00 20.00 3.18 36.82 20.00 20.00 20.00 20.00 20.00
300.00 200.00 300.00 100.00 100.00 200.00 100.00 300.00 200.00 200.00 300.00 100.00 200.00 200.00 200.00 200.00 200.00 31.82 200.00 368.18
may be due to the small maximum temperature difference between the three powers used. Comparing the trend of volatile matter content of the bio-char to the fixed carbon content, it was observed that increasing microwave power results in increasing fixed carbon content and calorific value of the char from 100 W to 300 W. The release of volatile matters over the range of microwave power used results in the concentration of fixed carbon content in the bio-char, thus increases the calorific value. There are small gradual decreases of the ash content of the bio-char as the microwave power increase. The more volatile matter released the less ash content of the sample. Based on the quantitative and qualitative analyses of the biochar produced, 300 W was selected as the optimum power to be used for statistical study of the pyrolysis process using RSM. Biochar produced at 300 W was the highest in term of yield
Fig. 3. Effect of different microwave power levels to volatile matter, fixed carbon content, calorific value and ash content of char.
M.’Azim Jamaluddin et al. / Renewable Energy 55 (2013) 357e365 Table 4 Experimental and predicted results for calorific value (P), fixed carbon content (Q), volatile matter content (R) and yield percentage (S). Std
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Experimental
Predicted
P
Q
R
S
P
Q
R
S
29.10 28.00 22.20 25.10 30.90 28.90 24.70 22.60 27.90 28.80 29.10 29.10 28.70 25.20 28.60 27.10 28.80 28.70 17.00 28.90
58.93 56.92 32.01 42.38 64.50 53.92 43.88 35.67 57.52 55.33 63.02 59.14 55.20 48.60 60.35 59.51 58.99 53.84 20.08 62.49
38.58 41.53 66.92 54.23 32.94 39.47 55.09 62.33 37.12 40.96 35.29 37.77 39.50 49.98 38.85 38.30 39.47 41.19 78.10 36.46
37.42 41.03 55.33 55.55 37.06 40.54 60.21 54.83 42.12 42.68 37.57 39.45 40.27 46.30 38.91 36.39 40.11 43.51 68.00 37.76
28.91 28.67 22.62 25.02 30.61 28.67 23.72 22.62 28.67 28.67 29.82 28.41 28.18 26.16 28.02 27.24 28.18 29.79 17.24 28.19
59.97 56.07 35.27 41.19 63.76 56.07 40.47 36.26 56.07 56.07 66.28 55.72 56.77 51.62 57.54 59.35 56.77 58.98 20.45 57.57
37.42 39.77 63.54 55.39 33.05 39.77 57.56 62.22 39.77 39.77 32.82 41.15 39.49 47.26 41.57 38.38 39.49 36.96 78.02 40.69
37.26 41.67 54.09 55.64 36.42 41.67 60.80 55.40 41.67 41.67 36.90 40.62 40.02 44.98 40.34 36.54 40.04 42.75 67.96 38.62
percentage that reached almost 40% from the initial sample mass, with almost comparable fixed carbon content, volatile matter content, calorific value and ash content in comparable to those at 600 W and 1000 W. Production of highest quantity of bio-char, with highest calorific value and fixed carbon content and lowest in volatile matters content ensures the efficiency of the pyrolysis process in term of energy and cost saving.
361
Table 6 ANOVA for the regression model and respective model term for fixed carbon content. Source
Sum of squares
Degree of freedom
Mean of square
Fvalue
Prob > F
Remarks
Model A B C A2 B2 C2 AB AC BC Residual Lack of fit Pure error Cor total
2647.58 1827.09 42.36 2.40 512.31 8.64 4.05 26.68 35.66 1.47 131.19 116.10 15.09 2599.73
9 1 1 1 1 1 1 1 1 1 9 5 4 19
274.18 1827.09 42.36 2.40 512.31 8.64 4.05 26.68 35.66 1.47 14.58 23.22 3.77
18.81 125.35 2.91 0.16 35.15 0.59 0.28 1.83 2.45 0.10
<0.0001 <0.0001 0.1225 0.6945 0.0002 0.4612 0.6110 0.2091 0.1522 0.7580
Significant Significant
6.15
0.0514
Significant
Not significant
study, the response and variables were fitted to each other by multiple regressions. Regression analysis is the general approach to fit the empirical model with the collected response variable data [26]. The coefficients of the full regression model equation and their statistical significance were determined and evaluated using Design-Expert 6.0.6 software from State-Ease Inc. The final model in terms of coded value is given in Equations (2)e(5) for calorific value, fixed carbon content, volatile matter content and yield percentage, respectively.
YCV ¼ 28:42 þ 2:97A þ 0:55B 0:47C 2:10A2 0:39B2 þ 0:29C 2 þ 0:23AB þ 0:40AC 0:32BC
(2)
3.2. Statistical analysis The relationship between responses (calorific value, fixed carbon content, volatile matters content and yield percentage of biochar) and three independent factors (reaction time, sample mass and nitrogen flow rate) were investigated in this study. Designed variables suggested by the software are shown in Table 3, while the experimental and predicted results at each point obtained are shown in Table 4. The experimental sequence was randomized in order to minimize the effects of the uncontrolled factors [25]. For the model fitted, the software generated model coefficients, R2-values, Fvalues, and significant probabilities and from these values the significance of each experimental variable can be justified. In this
Table 5 ANOVA for the regression model and respective model term for calorific value.
Ywt%FC ¼ 56:42 þ 11:57A þ 1:76B 0:42C 5:96A2 0:77B2 þ 0:53C 2 þ 1:83AB þ 2:11AC 0:43BC (3)
Ywt%VM ¼ 39:63 11:78A 1:69B 1:11C þ 6:62A2 þ 1:74B2 0:23C2 1:48AB 2:10AC þ 0:87BC (4)
Table 7 ANOVA for the regression model and respective model term for volatile matter content.
Source
Sum of squares
Degree of freedom
Mean of square
Fvalue
Prob > F
Remarks
Source
Sum of squares
Degree of freedom
Mean of square
Fvalue
Prob > F
Remarks
Model A B C A2 B2 C2 AB AC BC Residual Lack of fit Pure error Cor total
198.20 120.62 4.14 3.06 63.56 2.15 1.17 0.41 1.28 0.84 7.04 6.21 0.83 206.82
9 1 1 1 1 1 1 1 1 1 9 5 4 19
22.02 120.62 4.14 3.06 63.56 2.15 1.17 0.41 1.28 0.84 0.78 1.24 0.21
28.16 154.25 5.29 3.91 81.29 2.75 1.50 0.52 1.64 1.08
<0.0001 <0.0001 0.0470 0.0793 <0.0001 0.1318 0.2516 0.4900 0.2327 0.3257
Significant Significant Significant
Model A B C A2 B2 C2 AB AC BC Residual Lack of fit Pure error Cor total
2672.77 1896.26 39.10 16.77 630.34 43.72 0.79 17.55 35.15 6.11 99.86 88.24 11.63 2772.64
9 1 1 1 1 1 1 1 1 1 9 5 4 19
296.97 1896.26 39.10 16.77 630.34 43.72 0.79 17.55 35.15 6.11 11.10 17.65 2.91
26.76 170.90 3.52 1.51 56.81 3.94 0.071 1.58 3.17 0.55
<0.0001 <0.0001 0.0932 0.2501 <0.0001 0.0784 0.7959 0.2401 0.1088 0.4771
Significant Significant
6.02
0.0532
Significant
Not significant
6.07
0.0526
Significant
Not significant
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Table 8 ANOVA for the regression model and respective model term for yield percentage. Source
Sum of squares
Degree of freedom
Mean of square
Fvalue
Prob > F
Remarks
Model A B C A2 B2 C2 AB AC BC Residual Lack of fit Pure error Cor total
1521.11 1191.65 25.96 20.62 268.48 12.37 0.76 0.46 2.08 7.40 12.58 9.68 2.90 1543.23
9 1 1 1 1 1 1 1 1 1 9 5 4 19
169.01 1191.65 25.96 20.62 268.48 12.37 0.76 0.46 2.08 7.40 1.40 1.94 0.73
120.93 852.61 18.57 14.75 192.09 8.85 0.54 0.33 1.49 5.29
<0.0001 <0.0001 0.0020 0.0040 <0.0001 0.0156 0.4799 0.5799 0.2534 0.0470
Significant Significant Significant Significant Significant Significant
2.67
0.1816
Significant Not significant
Ywt%Yield ¼ 40:85 9:34A 1:38B 1:23C þ 4:32A2 þ 0:93B2 þ 0:23C 2 þ 0:24AB þ 0:51AC þ 0:96BC (5) where Y is the response, and A, B and C are the coded terms for the three variables that has been selected, i.e. reaction time (A), sample mass (B) and nitrogen gas flow rate (C). Positive sign in front of each term represent synergistic effect, while antagonistic effect represented by negative sign. Analysis of Variance (ANOVA) was then used to assess the goodness of fit. The significant quadratic models and the corresponding significant model term for all responses are tabulated in Tables 5e8 for calorific value, fixed carbon content, volatile matters content and yield percentage, respectively. From Table 5, the model F-value of 28.16 implies that the model is significant. It was also observed that the linear term of reaction time (A) has large significant effect on the calorific value due to the high F-value of 154.25. The quadratic term of reaction time (A2) has also significant with F-value of 81.29. Sample mass (B) also give significant effect to the calorific value with F-value of 5.29. However, nitrogen gas flow rate (C) seems does not have significant effect on the calorific value of the bio-char. The interaction between reaction time (AB) and nitrogen gas flow rate (AC) also do not affect the calorific value of the bio-char significantly. The relationships between the variables are also shown in Fig. 4 a, b and c.
From Table 6, the model F-value of 18.81 implies that the model is significant. It was also observed that the linear term of reaction time (A) has large significant effect on the fixed carbon content due to the high F-value of 125.35. The quadratic term of reaction time (A2) has also significant with F-value of 35.15. Other factors seem does not have effect on the fixed carbon content of the bio-char significantly. Fig. 5 a, b and c showed the relationships between the variables to the fixed carbon content of the bio-char. F-value of 26.76 shown in Table 7 implies that the model is significant for the response of volatile matters content. It was also observed that the linear term of reaction time (A) has large significant effect on the volatile matters content of the bio-char due to the high F-value of 170.90. The quadratic term of reaction time (A2) has also significant effect with F-value of 56.81. However, sample mass (B) and nitrogen gas flow rate (C) does not affect the volatile matters content of the bio-char significantly. The interactions between the variables (AB, AC and BC) also do not affect the volatile matters content of the bio-char significantly. The relationships between the variables are shown in Fig. 6 a, b and c. From Table 8, the model F-value of 120.93 implies that the model is significant. Interestingly, all of the variables showed significant effects on the yield percentage of the bio-char produced due to the high F-values of 852.61, 18.57, and 14.75, respectively. The quadratic term of reaction time (A2) and sample mass (B2) have also significant effects with F-value of 192.09 and 8.85, respectively. The quadratic term of nitrogen flow rate (C2), on the other hand, does not have effect on the yield percentage of the bio-char significantly. Fig. 7 a, b and c showed the relationships between the variables to the yield percentage of the bio-char produced. Among the variables, reaction time (A) plays major effect to all responses investigated in this study. As the reaction time (A) increased, the calorific value also increased due to decrease of hydrogen and oxygen in the char produced. The removal of volatiles matter becomes more efficient, caused the saturation of aromatic rings consist mostly carbon thus increase the fixed carbon content of the sample [27]. The yield percentage is also reduced as the reaction time increased due to the removal of volatile matters and saturation of the fixed carbon in the bio-char produced. Sample mass does not really affect the responses probably due to the volumetric heating principle of the microwave itself. The heating process occurs at molecular level, penetrating the sample from inside out thus resulting in uniform heating of the sample regardless of the mass effect [6]. Nitrogen flow rate, on the other hand, has almost no significant effect to all responses. The nitrogen
Fig. 4. Countour plot of calorific value: Effect of reaction time and sample mass (a), effect of reaction time and N2 flow rate (b) and effect of sample mass and N2 flow rate (c).
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Fig. 5. Countour plot of fixed carbon content: Effect of reaction time and sample mass (a), effect of reaction time and N2 flow rate (b) and effect of sample mass and N2 flow rate (c).
Fig. 6. Countour plot of volatile matter content: Effect of reaction time and sample mass (a), effect of reaction time and N2 flow rate (b) and effect of sample mass and N2 flow rate (c).
gas used in this study might only helped to provide an inert condition for the pyrolysis process regardless of the flow rate of the gas during the process. In order to test the fit of the model, the regression equation and the determination coefficient (R2) were evaluated. For the
response of calorific values, the value of determination coefficient (R2 ¼ 0.9657) indicates that the sample variation of 96.57% for calorific value is attributed to the independent variables and only 3.43% of the total variation could not be explained by the model. The value of adjusted determination coefficient (Adj
Fig. 7. Countour plot of yield percentage: Effect of reaction time and sample mass (a), effect of reaction time and N2 flow rate (b) and effect of sample mass and N2 flow rate (c).
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R2 ¼ 0.9314) is also very high to advocate for a high significance of the model. Meanwhile for the response of fixed carbon content, the value of determination coefficient (R2 ¼ 0.9495) indicates that the sample variation of 94.95% for fixed carbon is attributed to the independent variables and only 5.05% of the total variation could not be explained by the model. The value of adjusted determination coefficient (Adj R2 ¼ 0.8990) is also very high to advocate for a high significance of the model. The response of volatile matters content, the value of determination coefficient (R2 ¼ 0.9640) indicates that the sample variation of 96.4% for volatile matters is attributed to the independent variables and only 3.6% of the total variation could not be explained by the model. The value of adjusted determination coefficient (Adj R2 ¼ 0.9280) is also very high to advocate for a high significance of the model. On the other hand, the response of yield percentage of the biochar produced, the value of determination coefficient (R2 ¼ 0.9918)
indicates that the sample variation of 99.18% for yield percentage of the bio-char is attributed to the independent variables and only 0.82% of the total variation could not be explained by the model. The value of adjusted determination coefficient (Adj R2 ¼ 0.9836) is also very high to advocate for a high significance of the model. The correlation between experimental values and predicted values of calorific value, fixed carbon content, volatile matters content and yield percentage of the bio-char are shown in Fig. 8 a, b, c and d, respectively. A higher value of the correlation coefficient for all responses justifies an excellent correlation between the independent variables [15,19]. 3.3. Optimization analysis All the factors and responses with respectively high and low limit experimental region have to satisfy the creations defined for the optimum working conditions as stated in Table 9. The goal was
Fig. 8. Relationship between predicted and actual values of a) calorific value, b) fixed carbon content, c) volatile matter content and d) yield percentage.
M.’Azim Jamaluddin et al. / Renewable Energy 55 (2013) 357e365 Table 9 The preset goal with constraints for all the independent factors and response in numerical optimization. Variables
Reaction time (min) Sample mass (g) N2 flow rate (mL/min) Calorific value (MJ/kg) Fixed carbon (wt%) Volatile matters (wt%) Yield percentage (wt%)
Ultimate goal
Minimize Is in range Minimize Maximize Maximize Minimize Is target ¼ 40
Experimental region Lower limit
Upper limit
15.00 10.00 100.00 17.00 20.08 32.94 36.40
45.00 30.00 300.00 30.90 64.50 78.10 68.00
set to optimize the calorific values, fixed carbon content, volatile matters content and yield percentage of the bio-char produced from the experiment, with maximum calorific values, fixed carbon content and lowest volatile matters content. The yield percentage, on the other hand, was set to achieve as high as 40% based on preliminary study discussed in Section 3.1. The optimized condition obtained by DOE was at 31.58 min of reaction time, 30 g of sample mass and 100 mL/min of the nitrogen gas flow rate. 4. Conclusion The response surface methodology (RSM) based on central composite rotatable design (CCRD) was employed for the optimization of pyrolysis of palm kernel shell via microwave-assisted pyrolysis system. The predicted optimum conditions of reaction time (A), sample mass (B), and nitrogen gas flow rate (C) were at 31.58 min, 30 g and 100 mL/min, resulting in calorific value, fixed carbon content, volatile matters content and yield percentage of 29.9 MJ/kg, 59.8 wt%, 36.4 wt% and 40.0 wt%, respectively. The correlation coefficients obtained for all of the responses justify an excellent correlation between the independent variables. Acknowledgement The authors acknowledge the Sime Darby Sdn. Bhd. for their financial support, and Universiti Teknologi MARA for their support. References [1] Yusoff S. Renewable energy from palm oil e innovation on effective utilization of waste. Journal of Cleaner Production 2006;14:87e93. [2] Harimi M, Megat Ahmad MMH, Sapuan SM, Idris A. Numerical analysis of emission component from incineration of palm oil wastes. Biomass and Bioenergy 2005;28:339e45. [3] Van de Velden M, Baeyens J, Brems A, Janssens B, Dewil R. Fundamentals, kinetics and endothermicity of biomass pyrolysis reaction. Renewable Energy 2010;35:232e42. [4] Sahu SG, Sarkar P, Chakraborty N. Thermogravimetric assessment of combustion characteristics of blends of coal with different biomass chars. Fuel Processing Technology 2010;91:369e78. [5] Salema AA, Ani FN. Microwave induced pyrolysis of oil palm biomass. Bioresource Technology 2011;102:3388e95.
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