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Optimum manufacturing parameters for compressed lumber from oil palm (Elaeis guineensis) trunks: Respond surface approach
Composites: Part B 43 (2012) 988–996
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Composites: Part B journal homepage: www.elsevier.com/locate/compositesb
Optimum manufacturing parameters for compressed lumber from oil palm (Elaeis guineensis) trunks: Respond surface approach Nurjannah Salim a, Rokiah Hashim a,⇑, Othman Sulaiman a, Mazlan Ibrahim a, Masatoshi Sato b, Salim Hiziroglu c a b c
Division of Bio-resource, Paper and Coatings Technology, School of Industrial Technology, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia Graduate School of Agricultural and Life Science, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, OK 74078-6013, USA
a r t i c l e
i n f o
Article history: Received 11 July 2011 Received in revised form 13 October 2011 Accepted 1 November 2011 Available online 10 November 2011 Keywords: A. Oil palm trunk C. Response Surface Methodology D. Compression ratio D. Recovery ratio
a b s t r a c t The objective of this investigation was to evaluate optimum manufacturing parameters to produce compressed lumber from oil palm (Elaeis guineensis) trunks. Experimental samples were made using steaming time, pressure, pressure time and temperature ranging from 2 to 4 h, from 5 to 12 MPa, from 20 min to 60 min and from 100 °C to 200 °C, respectively. Compression and recovery ratios of the specimens were determined employing Response Surface Methodology (RSM) approach within the scope of Central Composite Design (CCD) computer program. Experimental and calculated values were compared to each other. Based on the results of the work, the specimens steamed for 2 h before they were compressed using a pressure of 11.16 MPa at a temperature of 200 °C for 60 min resulted in optimum conditions. Measured and calculated compression and recovery ratios of the samples showed 0.70 and 0.83 correlation coefficient values, respectively. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction Oil palm is one of the largest agricultural plantations in Malaysia. The life span of oil palm tree is about 25 years before regeneration is done. During the replanting, abundant of oil palm biomass was produced which currently is left to rot on the ground. According to Shuit et al. [1] 1 hec. of oil palm plantation can produce approximately 50–70 tonnes of biomass residues making oil palm the largest biomass resource in Malaysia. It is expected area of oil palm resource in Malaysia will be growing, and the rate of biomass will significantly increase in future. Researches on oil palm biomass have been carried out to assist in reducing waste of the oil palm and in the meantime can benefit economic return for the country [2]. Crisis of wood shortage also makes this resource as a promising alternative material as a substitute of wood due to its abundant availability. One of the potential uses of oil palm trunk would be considered to convert it into compressed material similar to wood in order to enhance its mechanical properties, as well as better dimensional stability. Compressed oil palm trunk could be used for various applications especially for constructional materials due to its high strength and easy machinability [3]. The process of compressing the wood has been found to be an effective way to increase the ⇑ Corresponding author. Tel.: +60 4 6535217; fax: +60 4 6573678. E-mail address: [email protected] (R. Hashim). 1359-8368/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.compositesb.2011.11.002
density of wood [4]. The products from compressed wood may become a substitute for commercial wood products with high density and strength [5]. Saito [6] has found that application of the heat treatment to the wood after pressing resulted in significance reduction in its springback. By combining high temperature steam treatment and compressing of wood is believed to improve the dimensional stability of wood because of changes in its chemistry [7]. The process of compressing a wood has been known for decades. Since 1930, the compression wood or with the trade name Lignostone was produced in Germany [8]. In the United States, there are two methods to make compressed wood that have been investigated, namely Compreg [9] and Staypack [8]. Compreg is the type of compressed wood normally impregnated with watersoluble phenol formaldehyde resin before it is compressed to a desired specific gravity and thickness while Staypack is not impregnated with resin. During the past decades, research has been conducted to investigate on manufacturing conditions of compressed wood but yet, no study has been reported for manufacturing conditions of compressed products from oil palm trunk. Recovery ratio is the main factor that should be considered in the manufacture of compressed wood. Recovery ratio is the percentage of the compressed material to springback to its original size after being compressed. Springback is caused by built up internal stress in the panel and results in excessive thickness swelling [10]. Oil palm is non-wood species therefore, its anatomical
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structure is different from the typical wood species. Oil palm consists of vascular bundles and parenchyma cells while wood consists of fibers, tracheids, vessels, parenchyma and ray parenchyma cells [2]. Hence, special attention has to be applied in using oil palm due to its inherent density variation and different anatomical features which affect overall properties of compressed material. Based on the findings of a previous research, to prevent springback of wood, various chemical treatments have been applied such as using maleic acid glycerol and impregnation of resin [3]. However, these methods are not really practical due to their certain disadvantages such as changing the nature of wood, creating hazardous material and high cost [3]. Steaming and compressing of wood would result in reduction of springback phenomena. By steaming process, the hemicellulose hydrolyzes, and the lignin will become softer. By compressing wood under condition that cause sufficient flow of the lignin will also subsequently relief the internal stresses resulting from the compression. Both processes have been found possibly to eliminate the springback [11–14]. The thickness swelling of compressed wood after water absorption decreased with increasing press temperature and increased with an increasing amount of compression [15]. Inoue et al. [16] also found that the recovery of the compression set after boiling in water decreased with increasing heating time and temperature. A study carried out by [17] showed that recovery of the compression set of wood decreased with increasing press temperature and time. Response Surface Methodology (RSM) is a statistical method for exploring optimum condition in experiment. This program analyzes the effects of several independent variables on the desirable responses [18]. The objectives of using RSM are to improve quality, including reduction of variability, having an improved process and product performance. Therefore, Response Surface Methodology (RSM) is proposed to determine the influence of individual and interactive parameters as far as properties of compressed samples are concerned. The main advantage of this method is the reduced number of experimental trials needed to evaluate multiple parameters and their interactions [19,20]. Numerous studies on compressed wood have been investigated [3,5,6,8] but no such research has been reported by using oil palm trunk as a raw material. In addition to this, no study has been carried out in optimizing the manufacturing parameters using RSM for either compressed wood or oil palm trunk lumber. Therefore, the objective of the study was to determine the optimum manufacturing conditions to manufacture compressed lumber from oil palm trunk using RSM. In order to find the optimum manufacturing conditions for making compressed oil palm trunk, a set of experiments were developed by employing central composite design. It is expected that data from the findings of this work should lead to produce value-added compressed oil palm trunk with low level of recovery ratio and in the future, this material can be used more effectively and efficiently.
2. Materials and methods 2.1. Sample preparation compressed lumber manufacture Oil palm trunks with an approximate age of 28 years old were harvested from local plantation in Northern Malaysia. After felling, the trunks were immediately cut into small pieces with a dimension of 20 cm 20 cm 4 cm in tangential direction. The samples were wrapped in a plastic bag and kept in a freezer before any further tests were carried out. Thirty experimental trials were designed using Design Expert software. For each experimental trial, the oil palm samples were first steamed using an autoclave at a temperature of 130 °C before they were dried in an oven. In the
next step, the samples were pressed using computer controlled hot press. It is known that the density of the oil palm varies across in the radial direction that might affect the quality of the oil palm lumber. The density of oil palm trunk samples used in this research ranged from 0.43 g/cm3 to 0.50 g/cm3 before being compressed and ranged from 0.76 g/cm3 to 0.80 g/cm3. Later, samples were cut and conditioned in a climate chamber with a temperature of 20 °C and relative humidity of 65% before their recovery and compression ratios were determined. 2.2. Determination of the responses of the samples Compression and recovery ratios are the responses of the specimens. Both tests were performed after the compressed lumber was produced. The compression ratio for each sample was calculated according to the following equation:
Compression ratio ¼ ðT r1 T 0 100Þ=T 1
ð1Þ
where Tr1 = thickness after recovery, T1 = thickness before compression, T0 = Thickness after compression. For recovery ratio, the test samples were cut into 20 mm 20 mm dimensions. Each sample was boiled in water for 2 h, and the thickness of the samples was measured at four points from the corners before and after they were boiled in water [16,21]. Recovery ratio was evaluated according to the following equation:
Recovery ratio ¼ ðT r1 T 1 100Þ=T 0
ð2Þ
where Tr1 = thickness after recovery, T1 = thickness before compression, T0 = Thickness after compression. 2.3. Central composite rotatable design (CCRD) The experimental plan was carried out using central composite rotatable design (CCRD) of Response Surface Method (RSM) which is a software developed by the Design Expert Version 6.0.7, StatEase, Inc., USA to determine the best manufacturing conditions for compressed lumber from oil palm trunk [18]. Four independent process variables were considered including steaming time, pressure, pressing time and pressing temperature. The ranges and the design levels of the variables investigated in this study are given in Table 1. The experiment consists of 30 experimental trials having (N) depending on the number of factors (k) and the number of center points (n). The total number experimental trials are expressed in Eq. (3). The design was carried out using (k = 4) factorial with six star points (a = 2) and six replicates at center points (n).
N ¼ 2k þ 2k þ n
ð3Þ
The experiments were performed according to the central composite rotatable design (CCRD) matrix given in Table 2. Optimum experimental design depends upon the model relating the response to the factor. The correlation between independent variables with response of recovery ratio (y2) was estimated by a linear model with transformation of the square root expressed by
Table 1 The variables and corresponding levels. Parameters
Steaming time (h) Pressure (MPa) Pressing time (min) Pressing temperature (°C)
Factor
A B C D
Variable levels 2
1
0
1
2
2 5 20 100
2.5 6.75 30 125
3.0 8.5 40 150
3.5 10.25 50 175
4 12 60 200
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Table 2 Actual and coded parameters for experiments. Run
Actual parameters
Coded parameters
A
B
C
D
x0
x1
x2
x3
x4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 1 5 3 3 3 3 3 3 3 3 3 3 3 3
5.0 5.0 12.0 12.0 5.0 5.0 12.0 12.0 5.0 5.0 12.0 12.0 5.0 5.0 12.0 12.0 8.5 8.5 1.5 15.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5
20 20 20 20 60 60 60 60 20 20 20 20 60 60 60 60 40 40 40 40 0 80 40 40 40 40 40 40 40 40
100 100 100 100 100 100 100 100 200 200 200 200 200 200 200 200 150 150 150 150 150 150 50 250 150 150 150 150 150 150
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 2 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 2 2 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 2 2 0 0 0 0 0 0
y (%)
y2 (%)
65.50 70.80 57.80 63.70 65.00 67.00 60.00 68.50 74.70 76.70 73.30 74.30 76.50 75.60 78.30 76.50 79.50 67.70 75.40 74.30 57.80 73.50 72.40 76.10 73.00 80.60 78.10 78.60 77.90 73.90
50.50 52.10 50.48 43.40 59.40 52.00 53.80 46.70 4.47 20.50 0.83 12.50 1.00 4.35 1.20 3.75 11.42 66.18 31.60 29.70 46.40 5.90 68.40 0.78 43.50 30.80 27.60 36.50 26.70 28.20
A = steaming time (h), B = pressure (MPa), C = pressing time (min), D = emperature (°C).
Eq. (4) while for correlation between independent variables with response of compression ratio (y) were also estimated by a quadratic equation model expressed by Eq. (5) [18].
interaction effect, Xi and Xj are factors (independent variables), k is the number of factors while e is the error.
Y ¼ b0 þ b1 x1 þ b2 x2 þ þ bk xk þ e
3. Results and discussion
ð4Þ
where y is the response (dependent variable), b0 is a constant coefficient, b1 and b2 are coefficient for the linear, X1 and X2 are factors (independent variables), k is the number of factors while e is the error.
Y 2 ¼ b0 þ
k X
b1 x1 þ
i¼1
k X i¼1
bii x2i þ
k X X
bii xi xj þ e
ð5Þ
i¼1 j¼iþ1
where y2 is the response (dependent variable), b0 is a constant coefficient, bi, bii and bij are coefficient for the linear, quadratic and
3.1. Experimental design and analysis of variance (ANOVA) Table 3 shows the statistics for overall responses for linear and quadratic model obtained from the Design Expert software. Following three items were considered to have a successful model in this study. 1. The sequential model sum of squares was in the highest order polynomial with the highest F value and low value of probability (value-p < 0.05).
Table 3 Fit summary for linear and quadratic model. Response
La
Qb
L
Q
Sum of squares
F value Prob > F
4.12 0.0106c
73.34 0.044c
32.48 <0.0001c
5.71 0.2538
Lack of fit test
F value Prob > F
3.87 0.0695
3.5 0.0897
4.1 0.0619
3.87 0.0743
Model summary test
Std. dev. Adjusted R2 Predicted R2
5.33 0.301 0.137
4.8 0.4349 0.5225
1.04 0.8128 0.7605
0.95 0.8434 0.5735
Model
a b c
Recovery ratio (y2)
Compression ratio (y)
L = linear. Q = quadratic. Models are statistically significant (p < 0.05).
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N. Salim et al. / Composites: Part B 43 (2012) 988–996 Table 4 Experimental design for compressed lumber from oil palm trunk. Trial no.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Parameter in uncoded levels A
B
C
D
2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 1 5 3 3 3 3 3 3 3 3 3 3 3 3
5.0 5.0 12.0 12.0 5.0 5.0 12.0 12.0 5.0 5.0 12.0 12.0 5.0 5.0 12.0 12.0 8.5 8.5 1.5 15.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5
20 20 20 20 60 60 60 60 20 20 20 20 60 60 60 60 40 40 40 40 0 80 40 40 40 40 40 40 40 40
100 100 100 100 100 100 100 100 200 200 200 200 200 200 200 200 150 150 150 150 150 150 50 250 150 150 150 150 150 150
Compression ratio (%)
Recovery ratio (%)
y
y2
Actual
Predicted
Actual
Predicted
65.50 70.80 57.80 63.70 65.00 67.00 60.00 68.50 74.70 76.70 73.30 74.30 76.50 75.60 78.30 76.50 79.50 67.70 75.40 74.30 57.80 73.50 72.40 76.10 73.00 80.60 78.10 78.60 77.90 73.90
67.23 69.92 60.40 64.40 68.65 69.75 66.28 68.67 75.02 72.36 72.50 71.14 77.75 73.49 79.67 76.71 72.52 72.25 75.43 71.83 60.93 67.93 65.12 80.95 77.02 77.02 77.02 77.02 77.02 77.02
7.11 7.22 7.10 6.59 7.71 7.21 7.33 6.83 2.11 4.53 0.91 3.54 1.00 2.09 1.10 1.94 3.38 8.14 5.62 5.45 6.81 2.43 8.27 0.88 6.60 5.55 5.25 6.04 5.17 5.31
7.32 8.57 6.99 8.24 6.26 7.52 5.93 7.19 2.76 4.02 2.43 3.69 1.71 2.96 1.37 2.63 3.72 6.23 5.30 4.64 6.03 3.92 9.53 0.42 4.97 4.97 4.97 4.97 4.97 4.97
A = steaming time (h), B = pressure (MPa), C = pressing time (min), D = temperature (°C).
2. The model should have the value of Lack of fit test insignificant with the low value of F value and highest value of p (valuep > 0.05). 3. The model should have the high value of adjusted R2 and predicted R2 than the other considers models. Based on criteria above and the data in Table 3, the quadratic model were chosen for response of compression ratio and the linear model were used for response of recovery ratio. Table 4 shows the design of the experiment. The data obtained from the experiment will be analyzed in linear model for recovery ratio and quadratic model for compression ratio. In each individual model, the coefficients relationship of the parameters to the responses were calculated and evaluated statistically by the RSM Design Expert software based on the regression equation. This individual model will be combined by the RSM in order to get the optimum model [22,23]. The regression equations for both responses were obtained from the analysis of variances (ANOVA) given in equation below. The regression equations were in coded factors. In Eq. (6) shows the quadratic equation represent the response for compression ratio (y) while in Eq. (7) shows linear equation represent the response for recovery ratio (y2) as a function of four independent variables namely steaming (x1), pressure (x2), pressing time (x3) and pressing temperature (x4). The results indicated that the CCRD regression model developed to predict compression ratio and recovery ratio were adequate in Table 5 [18].
Table 5 Analysis of variance (ANOVA) of quadratic model for compression ratio and linear model for recovery ratio. Source
a
Compression ratio (y)
Recovery ratio (y2)
Model
Sum of squares DF Mean square F value Prob > F
835.32 14 59.67 2.59 0.0386a
141.36 4 35.34 32.48 <0.0001a
Residual
Sum of squares DF Mean square
344.96 15 23
27.2 25 1.09
Lack of fit
Sum of squares DF Mean square Value F Prob > F
301.81 10 30.18 3.5 0.0897
25.64 20 1.28 4.1 0.0619
Pure error
Sum of squares DF Mean square
43.15 5 8.63
1.56 5 0.31
Std. dev. R2 Adjusted R2 Predicted R2 Adeq precision
4.8 0.7077 0.4349 0.5255 6.059
1.04 0.8386 0.8128 0.7605 21.397
Models are statistically significant (p < 0.05).
yð%Þ ¼ þ77:02 0:067x1 0:90x2 þ 1:75x3 þ 3:96x4 1:16x21 0:85x22 3:15x23 1:00x24 þ 0:33x1 x2 0:40x1 x3 1:34x1 x4 þ 1:11x2 x4 þ 0:33x3 x4
ð6Þ
Y 2 ð%Þ ¼ þ4:97 þ 0:63x1 0:17x2 0:53x3 2:28x4
ð7Þ
Experimental actual results and predicted values calculated in Eqs. (6) and (7) are given in Table 4 and also illustrated in Figs. 1 and 2. As can be seen above figures, the predicted values matched with the actual values reasonably well, with R2 values of 0.70 and 0.83 for compression and recovery ratios, respectively.
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there is a 6.19% chance that a ‘‘Lack of Fit F-value’’ this large could occur due to noise. The predicted R2 of 0.7605 is in reasonable agreement with the adjusted R2 of 0.8128. The ratio of adequate precision was 21.397 indicating an adequate signal value.
80.95 2
R = 0.70
75.16
Predicted
3.2. Effect of variables on compression ratio based on model term 69.38
63.59
57.80 57.80
63.59
69.38
75.16
80.95
Actual Fig. 1. Relationship between actual and predicted compression ratios based on Eq. (6).
9.53 R2= 0.83
Predicted
7.25
4.97
2.70
0.42
0.42
2.70
4.97
7.25
9.53
Actual Fig. 2. Relationship between actual and predicted recovery ratios based on Eq. (7).
The linear and quadratic effects on the independent variable steaming time, pressure, pressing time and pressing temperature and their interactions on the responses were obtained by analysis of variance (ANOVA). The significance model, lack of fit and coefficients of determination (R2) are also summarized in Table 5 to evaluate the validity of the CCRD model. As shown in Table 5 for compression ratio (y), the model was the significance with p < 0.05. There is only a 3.86% chance for the model that a ‘‘Model F-value’’ of this large could occur due to noise. The value of R2 is 0.70 with no significant lack of fit at p > 0.05. The insignificance of lack of fits was good. A negative predicted R2 implies that the overall mean is a better predictor for the response. Adequate precision measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of 6.059 indicates an adequate signal. For recovery ratio (y2), the model was the significance with p < 0.05. The Model F-value of 32.48 implies the model is significant. There is only a 0.01% chance that a ‘‘Model F-value’’ this large could occur due to noise. The value of R2 is 0.8386 with no significant lack of fit at p > 0.05. The ‘‘Lack of Fit F-value’’ of 4.10 implies
The three dimensional (3D) response surface plots demonstrate the effects of different variables on compression ratio are given in Fig. 3a–f. Fig. 3a shows the effect of steaming time and pressure for pressing process on compression ratio. As can be seen from Fig. 3a, steaming time and the pressure was resulted in a quadratic effect on the compression ratio. When these two variables were manipulated, the value of compression ratio continually increased until reaching one maximum point which was the highest value of compression ratio. Above this point, the value of compression ratio decreased as a function of pressure and steaming time. This decrease of compression ratio maybe due to the prolong steam pressure and higher temperature that may have degraded some cellulose component present in oil palm trunk [14]. This situation will influence to the compression ratio of compressed lumber from oil palm trunk and its properties. The similar situation is shown in Fig. 3b which is steaming time and pressing time revealed quadratic effect on the compression ratio of the samples. The compression ratio depends more on the press time rather than on steaming time. This is due to longer time for pressing process will allowed reorientation and crystallinity of the cellulose microfibrils to take placed [5]. This will lead to produce high strength properties of compressed lumber from oil palm trunk. As illustrated in Fig. 3b the compression ratio kept increasing until the maximum press time and subsequently decreased after such threshold point. In Fig. 3c, the 3D surface graph resulted in a logarithm effect on the compression ratio. An increasing of press time and steaming showed on high level of compression ratio, but as can be seen in Fig. 3c, the compression ratio depended more on the press time rather on steaming time of the samples. This is due to the same reason as in Fig. 3b as press time gives more effect than the other factor. Press time will allowed reorientation and crystallinity of the cellulose microfibrils to take placed. In Fig. 3d, the 3D surface graph, the quadratic effect on the compression ratio after reaction between pressure and press time are illustrated. Fig. 3d shows the center level of pressing time were results on high compression ratio and pressure gave a less effect on the compression ratio. In Fig. 3e shows a similar relationship to those of in Fig. 3c. The compression ratio kept steadily increased with increasing the press temperature while decreased with increasing pressure. High pressure will resulted in some degradation to cellulose microfibrils subsequently resulted in decreasing strength properties of the sample [14]. Fig. 3f shows the effect on press time and press temperature on compression ratio. As can be seen in Fig. 3f, the minimum press time and press temperature resulted on minimum compression ratio. The minimum press time and press temperature is not sufficient and therefore need to be increased. The compression ratio can be increased by increasing the press time and temperature. Study done by [5] has found that low pressing temperature resulted in a decreasing of properties to the final products. 3.3. Effect of variables on recovery ratio based on model terms The three dimensional (3D) response surface plots demonstrate the effects of different variables on compression ratio are given in Fig. 4a–f. Fig. 4a shows the effect of all 3D surface graphs gave the algorithm effect on the recovery ratio. For this response, the minimum recovery ratio is required to produce a dimensional
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a
b 77.27
Compression ratio (%)
Compression ratio (%)
77.27 76.41 75.56 74.71 73.85
76.41 75.56 74.71 73.85
60
12.00 4.00
10.25
3.50 8.50
B: Pressure
2.50 5.00
C: pressing time
A: Steaming time
2.50 20
2.00
A: Steaming time
d 80.40
Compression ratio (%)
80.40
Compression ratio (%)
3.00 30
2.00
c
77.71 75.02 72.33 69.63
77.71 75.02 72.33 69.63
200
60 4.00
175 150
100
2.00
10.25 40
3.00 2.50
12.00
50
3.50
D: Pressing temp.125
C: Pressing time
A: Steaming time
8.50 30
6.75 20
e
B: Pressure
5.00
f 80.40
Compression ratio (%)
80.40
Compression ratio (%)
3.50 40
3.00 6.75
4.00
50
77.71 75.02 72.33 69.63
77.71 75.02 72.33 69.63
200
200 12.00
175
10.25 150
8.50
D: Pressing temp.125
6.75 100
5.00
B: Pressure
60
175
50 150
40
D: Pressing temp.125
30 100
20
C: Pressing time
Fig. 3. Response surface plots showing the effect of two variables on compression ratio. (a) Steaming time and pressure; (b) steaming time and pressing time; (c) steaming time and pressing temperature; (d) pressure and pressing time; (e) pressure and pressing temperature; (f) pressing time and pressing temperature.
compressed lumber. In Fig. 4a shows the relationship between steaming time and pressure on recovery ratio. As can be seen in Fig. 4a recovery ratio depends more on the steaming time rather than pressure level. It showed that a minimum steaming time resulted on minimum recovery ratio and it increased with increased steaming time. Steaming process will
allowed hydrolysis of hemicelluloses which increases the compressibility of wood and reduces build up of internal stresses in composites but an excessive of steaming process will hydrolysis all the celluloses which are the main strength of the compressed lumber [11]. Relationship between steaming time and press time is shown in Fig. 4b. As can be seen in Fig. 4b the maximum press
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b
5.77
5.77
5.37
5.37
Recovery ratio (%)
Recovery ratio (%)
a
4.97 4.58 4.18
4.97 4.58 4.18
12.00
60 4.00
10.25 8.50
B: Pressure
2.50 5.00
A: Steaming time
3.00 30
2.50 20
A: Steaming time
2.00
d
5.77
5.77
5.37
5.37
Recovery ratio (%)
Recovery ratio (%)
C: Pressing time
2.00
c
4.97 4.58 4.18
4.97 4.58 4.18
200
60 4.00
175 150 2.50 100
10.25 40
3.00 125
12.00
50
3.50
D: Pressing temp.
C: Pressing time
A: Steaming time
8.50 30
2.00
6.75 20
e
B: Pressure
5.00
f 5.77
5.77 5.37
Recovery ratio (%)
Recovery ratio (%)
3.50 40
3.00 6.75
4.00
50
3.50
4.97 4.58 4.18
5.37 4.97 4.58 4.18
200
200 12.00
175
10.25 150
D: Pressing temp.
8.50 125
6.75 100
B: Pressure
5.00
60
175
50 150
D: Pressing temp.
40 125
30 100
C: Pressing time
20
Fig. 4. Response surface plots showing the effect of two variables on recovery ratio. (a) Steaming time and pressure; (b) steaming time and pressing time; (c) steaming time and pressing temperature; (d) pressure and pressing time; (e) pressure and pressing temperature; (f) pressing time and pressing temperature.
time and the minimum steaming results on minimum recovery ratio. A maximum press time will allowed reorientation and crystallinity of the cellulose microfibrils to take placed resulted in minimum recovery ratio.
The relationship between steaming time and pressure temperature which is almost similar with Fig. 4b is illustrated in Fig. 4c. It is clear that pressing temperature had more effect than steaming time. The maximum pressing temperature resulted on low level
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N. Salim et al. / Composites: Part B 43 (2012) 988–996 Table 6 A set of solution suggested by the Design Expert software. Run
A
B
C
D
1
2
11.16
60
200
Compression ratio (%)
Difference
Obtained
Predicted
77.8
80.3
2.5
Recovery ratio (%)
Difference
Obtained
Predicted
2.62
1.53
1.09
A = steaming time (h), B = pressure (MPa), C = pressing time (min), D = temperature (°C).
of recovery ratio. High press temperatures will increases the mechanical anisotropy. This will resulted in compressed oil palm with good dimensional stability [3]. In Fig. 4d shows the relationship between pressure and press time on recovery ratio. As can be seen in Fig. 4d, pressure resulted in insignificant effect on the recovery ratio as compared to that of press time. It appears that the maximum press time resulted on minimum recovery ratio. Fig. 4e shows rapid reduction on recovery ratio at the center level of press temperature, and it became steady at highly press temperature. It was also found that pressure had little effect on recovery ratio in contrast to press temperature. Fig. 4f shows a relationship between press time and press temperature on recovery ratio. This figure is also almost similar to Fig. 4e which is the recovery ratio substantially decreased at the center level of press temperature and kept stable after that point. The recovery ratio depended more on the effect of press temperature rather than press time. High temperature will allow the hydrolysis of lignin and hemicellulose in wood. By removing lignin and partial hydrolysis will reduce the internal stresses in wood subsequently increasing the compressibility in wood and result in low level of recovery ratio [11]. 3.4. Optimization A set of solutions was given by the software to determine the optimum condition for making compressed lumber from oil palm trunk as displayed in Table 6. These conditions were calculated based on Eqs. (4) and (5). The parameters were steaming time, pressure, press time and press temperature. Compression ratio was adjusted to a maximum level while recovery ratio was expected to be minimum. The optimization suggested around 80.3% compression ratio and 1.53% recovery ratio can be achieved by steaming 2 h, pressure at 11.16 MPa, pressing for 60 min with a temperature of 200 °C. It should be noted that the data in Tables 4 and 6 are two different data which cannot be compared to each other. The data in Table 6 have been analyzed by combining both models in optimization process while data in Table 4 was the data that have been analyzed by individual model and not yet been optimized. Thus, the data was taken from the data that has been optimized as shown in Table 6 and not from one time trial run as in run 13 indicated in Table 4. As in case of run 13, even if it shows favorable results, this data cannot be taken because it has not yet been statistically analyzed by the optimum model. The data in Table 5 shows that models that were used in this study were statistically significant. As can be seen the results obtained from the experiment, were slightly different from those of the predicted by the software. This could be due to some possible error in the experiment and limitation of equipment. However, results found in this work were still reasonably satisfactory and acceptable. 4. Conclusion This study demonstrated Response Surface Method (RSM) can be successfully used for modeling some parameters for optimization of compressed lumber from oil palm trunk. The predicted values matched the experimental values reasonably well, with R2 value of 0.70 for compression ratio and R2 value of 0.83 for
recovery ratio of the samples. The predicted models were also presented three-dimensional (3D) response surface graphs. It seems that, this program also is an economical way of obtaining the maximum amount of information in a short period of time and with a limited number of experiments. Acknowledgments We would like to acknowledge Universiti Sains Malaysia for USM-RU-PGRS Grant (1001/PTEKIND/844105). We also extend our gratitude to the Ministry of Science, Technology and Innovation for scholarship and Universiti Malaysia Pahang for granting study leave for Nurjannah Salim. References [1] Shuit SH, Tan KT, Lee KT, Kamaruddin AH. Oil palm biomass as a sustainable energy source. A Malaysian case study. Energy 2009;34:1225–35. [2] Sulaiman O, Salim N, Hashim R, Yusof LHM, Razak W, Yunus NYM. Evaluation on the suitability of some adhesives for laminated veneer lumber from oil palm trunks. Mater Des 2009;30:3572–80. [3] Navi P, Heger F. Combined densification and thermo-hydromechanical processing of wood. DOE:; 2004. [4] Stamm AJ. Wood and cellulose science. New York: Ronald Press; 1964. p. 549. [5] Dwianto W, Inoue M, Tanaka F, Norimoto M. The permanent fixation of compressive deformation in wood by heat treatment. In: Proceeding of 3rd Pacific rim bio-based composite symposium, Kyoto, Japan; 1998. p. 231–9. [6] Saito F. Springback of hot-pressed wood in humidification and water soaking tests. Mokuzai Gakkaishi 1973;19:221–6. [7] Rowell RM, Lange R, McSweeny J, Davis M. Modification of wood fiber using steam. In: Proceedings of 6th Pacific rim bio-based composites symposium; 2002. p. 606–15. [8] Seborg RM, Millett MA, Stamm AJ. Heat-stabilized compressed wood (staypak), FPL. Report No. 1580 (revised); 1962. p. 22. [9] Stamm AJ, Seborg RM. Resin treated, laminated, compressed wood. Trans Am Inst Chem Eng 1941;37:385–97. [10] Wong TS, Paridah MT, Zaidon A, Edi SB, Azmi I. Effects of presteaming and compressing pressure on the dimensional stability of densified wood. In: Proceedings of national conference on forest product, Kuala Lumpur; 2008. p. 183–9. [11] Hsu WE, Schwald W, Schwald J, Shield JA. Chemical and physical changes required for producing dimensionally stable wood-based composites. Wood Sci Technol 1988;22:281–9. [12] Inoue M, Norimoto M, Tanahashi M, Rowell RM. Steam or heat fixation of compressed wood. Wood Fiber Sci 1993;25(3):224–35. [13] Inoue M, Sekino N, Morooka T, Norimoto M. Dimensional stabilization of wood composites by steaming I. Fixation of compressed wood by pre-steaming. In: Proceedings of 3rd Pacific rim bio-based composites symposium, Kyoto, Japan; 1996. p. 240–8. [14] Kawai S, Wang Q, Sasaki H, Tanahashi M. Production of compressed laminated veneer lumber by steam pressing. In: Proceedings of the Pacific rim bio-based composites symposium; 1992. p. 121–8. [15] Suematsu A, Hirai N, Saito F. Properties of hot pressed wood I. Hygroscopicity, water absorption and dynamic viscoelasticity. Mokuzai Gakkaishi 1980;19:581–6. [16] Inoue M, Kadokawa N, Nishio J, Norimoto M. Permanent fixation of compressive deformation by hygro-thermal treatment using moisture in wood. Wood Res Bull 1993;29:54–61. [17] Norimoto M. Transverse compression of wood and its application to wood processing. Wood Res Bull 1994;30:1–15. [18] Myers HM, Montgomery DC. Response surface methodology: process and product optimization using designed experiments. 2nd ed. New York: A WileyInterscience publication; 2002. [19] Gunaraj V, Murugan N. Application of response surface methodologies for predicting weld base quality in submerged arc welding of pipes. J Mater Process Technol 1999;88:266–75. [20] Kincl M, Turk S, Vrecer F. Application of experimental design methodology in development and optimization of drug release method. Int J Pharm 2005;291:39–49.
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[21] Welzbacher CR, Wehsener J, Rapp AO, Haller P. Thermomechanical densification combined with thermal modification of Norway spruce (Piciea a bies Karst) in industrial scale – dimensional stability and durability aspects. Holz Roh Werkst 2008;66:39–49. [22] Aslan N. Application of response surface methodology and central composite rotatable design for modeling the influence of some operating variables of a multi-gravity separator for coal cleaning. Fuel 2007;86:769–76.
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Contents lists available at SciVerse ScienceDirect
Composites: Part B journal homepage: www.elsevier.com/locate/compositesb
Optimum manufacturing parameters for compressed lumber from oil palm (Elaeis guineensis) trunks: Respond surface approach Nurjannah Salim a, Rokiah Hashim a,⇑, Othman Sulaiman a, Mazlan Ibrahim a, Masatoshi Sato b, Salim Hiziroglu c a b c
Division of Bio-resource, Paper and Coatings Technology, School of Industrial Technology, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia Graduate School of Agricultural and Life Science, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, OK 74078-6013, USA
a r t i c l e
i n f o
Article history: Received 11 July 2011 Received in revised form 13 October 2011 Accepted 1 November 2011 Available online 10 November 2011 Keywords: A. Oil palm trunk C. Response Surface Methodology D. Compression ratio D. Recovery ratio
a b s t r a c t The objective of this investigation was to evaluate optimum manufacturing parameters to produce compressed lumber from oil palm (Elaeis guineensis) trunks. Experimental samples were made using steaming time, pressure, pressure time and temperature ranging from 2 to 4 h, from 5 to 12 MPa, from 20 min to 60 min and from 100 °C to 200 °C, respectively. Compression and recovery ratios of the specimens were determined employing Response Surface Methodology (RSM) approach within the scope of Central Composite Design (CCD) computer program. Experimental and calculated values were compared to each other. Based on the results of the work, the specimens steamed for 2 h before they were compressed using a pressure of 11.16 MPa at a temperature of 200 °C for 60 min resulted in optimum conditions. Measured and calculated compression and recovery ratios of the samples showed 0.70 and 0.83 correlation coefficient values, respectively. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction Oil palm is one of the largest agricultural plantations in Malaysia. The life span of oil palm tree is about 25 years before regeneration is done. During the replanting, abundant of oil palm biomass was produced which currently is left to rot on the ground. According to Shuit et al. [1] 1 hec. of oil palm plantation can produce approximately 50–70 tonnes of biomass residues making oil palm the largest biomass resource in Malaysia. It is expected area of oil palm resource in Malaysia will be growing, and the rate of biomass will significantly increase in future. Researches on oil palm biomass have been carried out to assist in reducing waste of the oil palm and in the meantime can benefit economic return for the country [2]. Crisis of wood shortage also makes this resource as a promising alternative material as a substitute of wood due to its abundant availability. One of the potential uses of oil palm trunk would be considered to convert it into compressed material similar to wood in order to enhance its mechanical properties, as well as better dimensional stability. Compressed oil palm trunk could be used for various applications especially for constructional materials due to its high strength and easy machinability [3]. The process of compressing the wood has been found to be an effective way to increase the ⇑ Corresponding author. Tel.: +60 4 6535217; fax: +60 4 6573678. E-mail address: [email protected] (R. Hashim). 1359-8368/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.compositesb.2011.11.002
density of wood [4]. The products from compressed wood may become a substitute for commercial wood products with high density and strength [5]. Saito [6] has found that application of the heat treatment to the wood after pressing resulted in significance reduction in its springback. By combining high temperature steam treatment and compressing of wood is believed to improve the dimensional stability of wood because of changes in its chemistry [7]. The process of compressing a wood has been known for decades. Since 1930, the compression wood or with the trade name Lignostone was produced in Germany [8]. In the United States, there are two methods to make compressed wood that have been investigated, namely Compreg [9] and Staypack [8]. Compreg is the type of compressed wood normally impregnated with watersoluble phenol formaldehyde resin before it is compressed to a desired specific gravity and thickness while Staypack is not impregnated with resin. During the past decades, research has been conducted to investigate on manufacturing conditions of compressed wood but yet, no study has been reported for manufacturing conditions of compressed products from oil palm trunk. Recovery ratio is the main factor that should be considered in the manufacture of compressed wood. Recovery ratio is the percentage of the compressed material to springback to its original size after being compressed. Springback is caused by built up internal stress in the panel and results in excessive thickness swelling [10]. Oil palm is non-wood species therefore, its anatomical
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structure is different from the typical wood species. Oil palm consists of vascular bundles and parenchyma cells while wood consists of fibers, tracheids, vessels, parenchyma and ray parenchyma cells [2]. Hence, special attention has to be applied in using oil palm due to its inherent density variation and different anatomical features which affect overall properties of compressed material. Based on the findings of a previous research, to prevent springback of wood, various chemical treatments have been applied such as using maleic acid glycerol and impregnation of resin [3]. However, these methods are not really practical due to their certain disadvantages such as changing the nature of wood, creating hazardous material and high cost [3]. Steaming and compressing of wood would result in reduction of springback phenomena. By steaming process, the hemicellulose hydrolyzes, and the lignin will become softer. By compressing wood under condition that cause sufficient flow of the lignin will also subsequently relief the internal stresses resulting from the compression. Both processes have been found possibly to eliminate the springback [11–14]. The thickness swelling of compressed wood after water absorption decreased with increasing press temperature and increased with an increasing amount of compression [15]. Inoue et al. [16] also found that the recovery of the compression set after boiling in water decreased with increasing heating time and temperature. A study carried out by [17] showed that recovery of the compression set of wood decreased with increasing press temperature and time. Response Surface Methodology (RSM) is a statistical method for exploring optimum condition in experiment. This program analyzes the effects of several independent variables on the desirable responses [18]. The objectives of using RSM are to improve quality, including reduction of variability, having an improved process and product performance. Therefore, Response Surface Methodology (RSM) is proposed to determine the influence of individual and interactive parameters as far as properties of compressed samples are concerned. The main advantage of this method is the reduced number of experimental trials needed to evaluate multiple parameters and their interactions [19,20]. Numerous studies on compressed wood have been investigated [3,5,6,8] but no such research has been reported by using oil palm trunk as a raw material. In addition to this, no study has been carried out in optimizing the manufacturing parameters using RSM for either compressed wood or oil palm trunk lumber. Therefore, the objective of the study was to determine the optimum manufacturing conditions to manufacture compressed lumber from oil palm trunk using RSM. In order to find the optimum manufacturing conditions for making compressed oil palm trunk, a set of experiments were developed by employing central composite design. It is expected that data from the findings of this work should lead to produce value-added compressed oil palm trunk with low level of recovery ratio and in the future, this material can be used more effectively and efficiently.
2. Materials and methods 2.1. Sample preparation compressed lumber manufacture Oil palm trunks with an approximate age of 28 years old were harvested from local plantation in Northern Malaysia. After felling, the trunks were immediately cut into small pieces with a dimension of 20 cm 20 cm 4 cm in tangential direction. The samples were wrapped in a plastic bag and kept in a freezer before any further tests were carried out. Thirty experimental trials were designed using Design Expert software. For each experimental trial, the oil palm samples were first steamed using an autoclave at a temperature of 130 °C before they were dried in an oven. In the
next step, the samples were pressed using computer controlled hot press. It is known that the density of the oil palm varies across in the radial direction that might affect the quality of the oil palm lumber. The density of oil palm trunk samples used in this research ranged from 0.43 g/cm3 to 0.50 g/cm3 before being compressed and ranged from 0.76 g/cm3 to 0.80 g/cm3. Later, samples were cut and conditioned in a climate chamber with a temperature of 20 °C and relative humidity of 65% before their recovery and compression ratios were determined. 2.2. Determination of the responses of the samples Compression and recovery ratios are the responses of the specimens. Both tests were performed after the compressed lumber was produced. The compression ratio for each sample was calculated according to the following equation:
Compression ratio ¼ ðT r1 T 0 100Þ=T 1
ð1Þ
where Tr1 = thickness after recovery, T1 = thickness before compression, T0 = Thickness after compression. For recovery ratio, the test samples were cut into 20 mm 20 mm dimensions. Each sample was boiled in water for 2 h, and the thickness of the samples was measured at four points from the corners before and after they were boiled in water [16,21]. Recovery ratio was evaluated according to the following equation:
Recovery ratio ¼ ðT r1 T 1 100Þ=T 0
ð2Þ
where Tr1 = thickness after recovery, T1 = thickness before compression, T0 = Thickness after compression. 2.3. Central composite rotatable design (CCRD) The experimental plan was carried out using central composite rotatable design (CCRD) of Response Surface Method (RSM) which is a software developed by the Design Expert Version 6.0.7, StatEase, Inc., USA to determine the best manufacturing conditions for compressed lumber from oil palm trunk [18]. Four independent process variables were considered including steaming time, pressure, pressing time and pressing temperature. The ranges and the design levels of the variables investigated in this study are given in Table 1. The experiment consists of 30 experimental trials having (N) depending on the number of factors (k) and the number of center points (n). The total number experimental trials are expressed in Eq. (3). The design was carried out using (k = 4) factorial with six star points (a = 2) and six replicates at center points (n).
N ¼ 2k þ 2k þ n
ð3Þ
The experiments were performed according to the central composite rotatable design (CCRD) matrix given in Table 2. Optimum experimental design depends upon the model relating the response to the factor. The correlation between independent variables with response of recovery ratio (y2) was estimated by a linear model with transformation of the square root expressed by
Table 1 The variables and corresponding levels. Parameters
Steaming time (h) Pressure (MPa) Pressing time (min) Pressing temperature (°C)
Factor
A B C D
Variable levels 2
1
0
1
2
2 5 20 100
2.5 6.75 30 125
3.0 8.5 40 150
3.5 10.25 50 175
4 12 60 200
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Table 2 Actual and coded parameters for experiments. Run
Actual parameters
Coded parameters
A
B
C
D
x0
x1
x2
x3
x4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 1 5 3 3 3 3 3 3 3 3 3 3 3 3
5.0 5.0 12.0 12.0 5.0 5.0 12.0 12.0 5.0 5.0 12.0 12.0 5.0 5.0 12.0 12.0 8.5 8.5 1.5 15.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5
20 20 20 20 60 60 60 60 20 20 20 20 60 60 60 60 40 40 40 40 0 80 40 40 40 40 40 40 40 40
100 100 100 100 100 100 100 100 200 200 200 200 200 200 200 200 150 150 150 150 150 150 50 250 150 150 150 150 150 150
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 2 2 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 2 2 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 2 2 0 0 0 0 0 0
y (%)
y2 (%)
65.50 70.80 57.80 63.70 65.00 67.00 60.00 68.50 74.70 76.70 73.30 74.30 76.50 75.60 78.30 76.50 79.50 67.70 75.40 74.30 57.80 73.50 72.40 76.10 73.00 80.60 78.10 78.60 77.90 73.90
50.50 52.10 50.48 43.40 59.40 52.00 53.80 46.70 4.47 20.50 0.83 12.50 1.00 4.35 1.20 3.75 11.42 66.18 31.60 29.70 46.40 5.90 68.40 0.78 43.50 30.80 27.60 36.50 26.70 28.20
A = steaming time (h), B = pressure (MPa), C = pressing time (min), D = emperature (°C).
Eq. (4) while for correlation between independent variables with response of compression ratio (y) were also estimated by a quadratic equation model expressed by Eq. (5) [18].
interaction effect, Xi and Xj are factors (independent variables), k is the number of factors while e is the error.
Y ¼ b0 þ b1 x1 þ b2 x2 þ þ bk xk þ e
3. Results and discussion
ð4Þ
where y is the response (dependent variable), b0 is a constant coefficient, b1 and b2 are coefficient for the linear, X1 and X2 are factors (independent variables), k is the number of factors while e is the error.
Y 2 ¼ b0 þ
k X
b1 x1 þ
i¼1
k X i¼1
bii x2i þ
k X X
bii xi xj þ e
ð5Þ
i¼1 j¼iþ1
where y2 is the response (dependent variable), b0 is a constant coefficient, bi, bii and bij are coefficient for the linear, quadratic and
3.1. Experimental design and analysis of variance (ANOVA) Table 3 shows the statistics for overall responses for linear and quadratic model obtained from the Design Expert software. Following three items were considered to have a successful model in this study. 1. The sequential model sum of squares was in the highest order polynomial with the highest F value and low value of probability (value-p < 0.05).
Table 3 Fit summary for linear and quadratic model. Response
La
Qb
L
Q
Sum of squares
F value Prob > F
4.12 0.0106c
73.34 0.044c
32.48 <0.0001c
5.71 0.2538
Lack of fit test
F value Prob > F
3.87 0.0695
3.5 0.0897
4.1 0.0619
3.87 0.0743
Model summary test
Std. dev. Adjusted R2 Predicted R2
5.33 0.301 0.137
4.8 0.4349 0.5225
1.04 0.8128 0.7605
0.95 0.8434 0.5735
Model
a b c
Recovery ratio (y2)
Compression ratio (y)
L = linear. Q = quadratic. Models are statistically significant (p < 0.05).
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N. Salim et al. / Composites: Part B 43 (2012) 988–996 Table 4 Experimental design for compressed lumber from oil palm trunk. Trial no.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Parameter in uncoded levels A
B
C
D
2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 1 5 3 3 3 3 3 3 3 3 3 3 3 3
5.0 5.0 12.0 12.0 5.0 5.0 12.0 12.0 5.0 5.0 12.0 12.0 5.0 5.0 12.0 12.0 8.5 8.5 1.5 15.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5 8.5
20 20 20 20 60 60 60 60 20 20 20 20 60 60 60 60 40 40 40 40 0 80 40 40 40 40 40 40 40 40
100 100 100 100 100 100 100 100 200 200 200 200 200 200 200 200 150 150 150 150 150 150 50 250 150 150 150 150 150 150
Compression ratio (%)
Recovery ratio (%)
y
y2
Actual
Predicted
Actual
Predicted
65.50 70.80 57.80 63.70 65.00 67.00 60.00 68.50 74.70 76.70 73.30 74.30 76.50 75.60 78.30 76.50 79.50 67.70 75.40 74.30 57.80 73.50 72.40 76.10 73.00 80.60 78.10 78.60 77.90 73.90
67.23 69.92 60.40 64.40 68.65 69.75 66.28 68.67 75.02 72.36 72.50 71.14 77.75 73.49 79.67 76.71 72.52 72.25 75.43 71.83 60.93 67.93 65.12 80.95 77.02 77.02 77.02 77.02 77.02 77.02
7.11 7.22 7.10 6.59 7.71 7.21 7.33 6.83 2.11 4.53 0.91 3.54 1.00 2.09 1.10 1.94 3.38 8.14 5.62 5.45 6.81 2.43 8.27 0.88 6.60 5.55 5.25 6.04 5.17 5.31
7.32 8.57 6.99 8.24 6.26 7.52 5.93 7.19 2.76 4.02 2.43 3.69 1.71 2.96 1.37 2.63 3.72 6.23 5.30 4.64 6.03 3.92 9.53 0.42 4.97 4.97 4.97 4.97 4.97 4.97
A = steaming time (h), B = pressure (MPa), C = pressing time (min), D = temperature (°C).
2. The model should have the value of Lack of fit test insignificant with the low value of F value and highest value of p (valuep > 0.05). 3. The model should have the high value of adjusted R2 and predicted R2 than the other considers models. Based on criteria above and the data in Table 3, the quadratic model were chosen for response of compression ratio and the linear model were used for response of recovery ratio. Table 4 shows the design of the experiment. The data obtained from the experiment will be analyzed in linear model for recovery ratio and quadratic model for compression ratio. In each individual model, the coefficients relationship of the parameters to the responses were calculated and evaluated statistically by the RSM Design Expert software based on the regression equation. This individual model will be combined by the RSM in order to get the optimum model [22,23]. The regression equations for both responses were obtained from the analysis of variances (ANOVA) given in equation below. The regression equations were in coded factors. In Eq. (6) shows the quadratic equation represent the response for compression ratio (y) while in Eq. (7) shows linear equation represent the response for recovery ratio (y2) as a function of four independent variables namely steaming (x1), pressure (x2), pressing time (x3) and pressing temperature (x4). The results indicated that the CCRD regression model developed to predict compression ratio and recovery ratio were adequate in Table 5 [18].
Table 5 Analysis of variance (ANOVA) of quadratic model for compression ratio and linear model for recovery ratio. Source
a
Compression ratio (y)
Recovery ratio (y2)
Model
Sum of squares DF Mean square F value Prob > F
835.32 14 59.67 2.59 0.0386a
141.36 4 35.34 32.48 <0.0001a
Residual
Sum of squares DF Mean square
344.96 15 23
27.2 25 1.09
Lack of fit
Sum of squares DF Mean square Value F Prob > F
301.81 10 30.18 3.5 0.0897
25.64 20 1.28 4.1 0.0619
Pure error
Sum of squares DF Mean square
43.15 5 8.63
1.56 5 0.31
Std. dev. R2 Adjusted R2 Predicted R2 Adeq precision
4.8 0.7077 0.4349 0.5255 6.059
1.04 0.8386 0.8128 0.7605 21.397
Models are statistically significant (p < 0.05).
yð%Þ ¼ þ77:02 0:067x1 0:90x2 þ 1:75x3 þ 3:96x4 1:16x21 0:85x22 3:15x23 1:00x24 þ 0:33x1 x2 0:40x1 x3 1:34x1 x4 þ 1:11x2 x4 þ 0:33x3 x4
ð6Þ
Y 2 ð%Þ ¼ þ4:97 þ 0:63x1 0:17x2 0:53x3 2:28x4
ð7Þ
Experimental actual results and predicted values calculated in Eqs. (6) and (7) are given in Table 4 and also illustrated in Figs. 1 and 2. As can be seen above figures, the predicted values matched with the actual values reasonably well, with R2 values of 0.70 and 0.83 for compression and recovery ratios, respectively.
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N. Salim et al. / Composites: Part B 43 (2012) 988–996
there is a 6.19% chance that a ‘‘Lack of Fit F-value’’ this large could occur due to noise. The predicted R2 of 0.7605 is in reasonable agreement with the adjusted R2 of 0.8128. The ratio of adequate precision was 21.397 indicating an adequate signal value.
80.95 2
R = 0.70
75.16
Predicted
3.2. Effect of variables on compression ratio based on model term 69.38
63.59
57.80 57.80
63.59
69.38
75.16
80.95
Actual Fig. 1. Relationship between actual and predicted compression ratios based on Eq. (6).
9.53 R2= 0.83
Predicted
7.25
4.97
2.70
0.42
0.42
2.70
4.97
7.25
9.53
Actual Fig. 2. Relationship between actual and predicted recovery ratios based on Eq. (7).
The linear and quadratic effects on the independent variable steaming time, pressure, pressing time and pressing temperature and their interactions on the responses were obtained by analysis of variance (ANOVA). The significance model, lack of fit and coefficients of determination (R2) are also summarized in Table 5 to evaluate the validity of the CCRD model. As shown in Table 5 for compression ratio (y), the model was the significance with p < 0.05. There is only a 3.86% chance for the model that a ‘‘Model F-value’’ of this large could occur due to noise. The value of R2 is 0.70 with no significant lack of fit at p > 0.05. The insignificance of lack of fits was good. A negative predicted R2 implies that the overall mean is a better predictor for the response. Adequate precision measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of 6.059 indicates an adequate signal. For recovery ratio (y2), the model was the significance with p < 0.05. The Model F-value of 32.48 implies the model is significant. There is only a 0.01% chance that a ‘‘Model F-value’’ this large could occur due to noise. The value of R2 is 0.8386 with no significant lack of fit at p > 0.05. The ‘‘Lack of Fit F-value’’ of 4.10 implies
The three dimensional (3D) response surface plots demonstrate the effects of different variables on compression ratio are given in Fig. 3a–f. Fig. 3a shows the effect of steaming time and pressure for pressing process on compression ratio. As can be seen from Fig. 3a, steaming time and the pressure was resulted in a quadratic effect on the compression ratio. When these two variables were manipulated, the value of compression ratio continually increased until reaching one maximum point which was the highest value of compression ratio. Above this point, the value of compression ratio decreased as a function of pressure and steaming time. This decrease of compression ratio maybe due to the prolong steam pressure and higher temperature that may have degraded some cellulose component present in oil palm trunk [14]. This situation will influence to the compression ratio of compressed lumber from oil palm trunk and its properties. The similar situation is shown in Fig. 3b which is steaming time and pressing time revealed quadratic effect on the compression ratio of the samples. The compression ratio depends more on the press time rather than on steaming time. This is due to longer time for pressing process will allowed reorientation and crystallinity of the cellulose microfibrils to take placed [5]. This will lead to produce high strength properties of compressed lumber from oil palm trunk. As illustrated in Fig. 3b the compression ratio kept increasing until the maximum press time and subsequently decreased after such threshold point. In Fig. 3c, the 3D surface graph resulted in a logarithm effect on the compression ratio. An increasing of press time and steaming showed on high level of compression ratio, but as can be seen in Fig. 3c, the compression ratio depended more on the press time rather on steaming time of the samples. This is due to the same reason as in Fig. 3b as press time gives more effect than the other factor. Press time will allowed reorientation and crystallinity of the cellulose microfibrils to take placed. In Fig. 3d, the 3D surface graph, the quadratic effect on the compression ratio after reaction between pressure and press time are illustrated. Fig. 3d shows the center level of pressing time were results on high compression ratio and pressure gave a less effect on the compression ratio. In Fig. 3e shows a similar relationship to those of in Fig. 3c. The compression ratio kept steadily increased with increasing the press temperature while decreased with increasing pressure. High pressure will resulted in some degradation to cellulose microfibrils subsequently resulted in decreasing strength properties of the sample [14]. Fig. 3f shows the effect on press time and press temperature on compression ratio. As can be seen in Fig. 3f, the minimum press time and press temperature resulted on minimum compression ratio. The minimum press time and press temperature is not sufficient and therefore need to be increased. The compression ratio can be increased by increasing the press time and temperature. Study done by [5] has found that low pressing temperature resulted in a decreasing of properties to the final products. 3.3. Effect of variables on recovery ratio based on model terms The three dimensional (3D) response surface plots demonstrate the effects of different variables on compression ratio are given in Fig. 4a–f. Fig. 4a shows the effect of all 3D surface graphs gave the algorithm effect on the recovery ratio. For this response, the minimum recovery ratio is required to produce a dimensional
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a
b 77.27
Compression ratio (%)
Compression ratio (%)
77.27 76.41 75.56 74.71 73.85
76.41 75.56 74.71 73.85
60
12.00 4.00
10.25
3.50 8.50
B: Pressure
2.50 5.00
C: pressing time
A: Steaming time
2.50 20
2.00
A: Steaming time
d 80.40
Compression ratio (%)
80.40
Compression ratio (%)
3.00 30
2.00
c
77.71 75.02 72.33 69.63
77.71 75.02 72.33 69.63
200
60 4.00
175 150
100
2.00
10.25 40
3.00 2.50
12.00
50
3.50
D: Pressing temp.125
C: Pressing time
A: Steaming time
8.50 30
6.75 20
e
B: Pressure
5.00
f 80.40
Compression ratio (%)
80.40
Compression ratio (%)
3.50 40
3.00 6.75
4.00
50
77.71 75.02 72.33 69.63
77.71 75.02 72.33 69.63
200
200 12.00
175
10.25 150
8.50
D: Pressing temp.125
6.75 100
5.00
B: Pressure
60
175
50 150
40
D: Pressing temp.125
30 100
20
C: Pressing time
Fig. 3. Response surface plots showing the effect of two variables on compression ratio. (a) Steaming time and pressure; (b) steaming time and pressing time; (c) steaming time and pressing temperature; (d) pressure and pressing time; (e) pressure and pressing temperature; (f) pressing time and pressing temperature.
compressed lumber. In Fig. 4a shows the relationship between steaming time and pressure on recovery ratio. As can be seen in Fig. 4a recovery ratio depends more on the steaming time rather than pressure level. It showed that a minimum steaming time resulted on minimum recovery ratio and it increased with increased steaming time. Steaming process will
allowed hydrolysis of hemicelluloses which increases the compressibility of wood and reduces build up of internal stresses in composites but an excessive of steaming process will hydrolysis all the celluloses which are the main strength of the compressed lumber [11]. Relationship between steaming time and press time is shown in Fig. 4b. As can be seen in Fig. 4b the maximum press
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N. Salim et al. / Composites: Part B 43 (2012) 988–996
b
5.77
5.77
5.37
5.37
Recovery ratio (%)
Recovery ratio (%)
a
4.97 4.58 4.18
4.97 4.58 4.18
12.00
60 4.00
10.25 8.50
B: Pressure
2.50 5.00
A: Steaming time
3.00 30
2.50 20
A: Steaming time
2.00
d
5.77
5.77
5.37
5.37
Recovery ratio (%)
Recovery ratio (%)
C: Pressing time
2.00
c
4.97 4.58 4.18
4.97 4.58 4.18
200
60 4.00
175 150 2.50 100
10.25 40
3.00 125
12.00
50
3.50
D: Pressing temp.
C: Pressing time
A: Steaming time
8.50 30
2.00
6.75 20
e
B: Pressure
5.00
f 5.77
5.77 5.37
Recovery ratio (%)
Recovery ratio (%)
3.50 40
3.00 6.75
4.00
50
3.50
4.97 4.58 4.18
5.37 4.97 4.58 4.18
200
200 12.00
175
10.25 150
D: Pressing temp.
8.50 125
6.75 100
B: Pressure
5.00
60
175
50 150
D: Pressing temp.
40 125
30 100
C: Pressing time
20
Fig. 4. Response surface plots showing the effect of two variables on recovery ratio. (a) Steaming time and pressure; (b) steaming time and pressing time; (c) steaming time and pressing temperature; (d) pressure and pressing time; (e) pressure and pressing temperature; (f) pressing time and pressing temperature.
time and the minimum steaming results on minimum recovery ratio. A maximum press time will allowed reorientation and crystallinity of the cellulose microfibrils to take placed resulted in minimum recovery ratio.
The relationship between steaming time and pressure temperature which is almost similar with Fig. 4b is illustrated in Fig. 4c. It is clear that pressing temperature had more effect than steaming time. The maximum pressing temperature resulted on low level
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N. Salim et al. / Composites: Part B 43 (2012) 988–996 Table 6 A set of solution suggested by the Design Expert software. Run
A
B
C
D
1
2
11.16
60
200
Compression ratio (%)
Difference
Obtained
Predicted
77.8
80.3
2.5
Recovery ratio (%)
Difference
Obtained
Predicted
2.62
1.53
1.09
A = steaming time (h), B = pressure (MPa), C = pressing time (min), D = temperature (°C).
of recovery ratio. High press temperatures will increases the mechanical anisotropy. This will resulted in compressed oil palm with good dimensional stability [3]. In Fig. 4d shows the relationship between pressure and press time on recovery ratio. As can be seen in Fig. 4d, pressure resulted in insignificant effect on the recovery ratio as compared to that of press time. It appears that the maximum press time resulted on minimum recovery ratio. Fig. 4e shows rapid reduction on recovery ratio at the center level of press temperature, and it became steady at highly press temperature. It was also found that pressure had little effect on recovery ratio in contrast to press temperature. Fig. 4f shows a relationship between press time and press temperature on recovery ratio. This figure is also almost similar to Fig. 4e which is the recovery ratio substantially decreased at the center level of press temperature and kept stable after that point. The recovery ratio depended more on the effect of press temperature rather than press time. High temperature will allow the hydrolysis of lignin and hemicellulose in wood. By removing lignin and partial hydrolysis will reduce the internal stresses in wood subsequently increasing the compressibility in wood and result in low level of recovery ratio [11]. 3.4. Optimization A set of solutions was given by the software to determine the optimum condition for making compressed lumber from oil palm trunk as displayed in Table 6. These conditions were calculated based on Eqs. (4) and (5). The parameters were steaming time, pressure, press time and press temperature. Compression ratio was adjusted to a maximum level while recovery ratio was expected to be minimum. The optimization suggested around 80.3% compression ratio and 1.53% recovery ratio can be achieved by steaming 2 h, pressure at 11.16 MPa, pressing for 60 min with a temperature of 200 °C. It should be noted that the data in Tables 4 and 6 are two different data which cannot be compared to each other. The data in Table 6 have been analyzed by combining both models in optimization process while data in Table 4 was the data that have been analyzed by individual model and not yet been optimized. Thus, the data was taken from the data that has been optimized as shown in Table 6 and not from one time trial run as in run 13 indicated in Table 4. As in case of run 13, even if it shows favorable results, this data cannot be taken because it has not yet been statistically analyzed by the optimum model. The data in Table 5 shows that models that were used in this study were statistically significant. As can be seen the results obtained from the experiment, were slightly different from those of the predicted by the software. This could be due to some possible error in the experiment and limitation of equipment. However, results found in this work were still reasonably satisfactory and acceptable. 4. Conclusion This study demonstrated Response Surface Method (RSM) can be successfully used for modeling some parameters for optimization of compressed lumber from oil palm trunk. The predicted values matched the experimental values reasonably well, with R2 value of 0.70 for compression ratio and R2 value of 0.83 for
recovery ratio of the samples. The predicted models were also presented three-dimensional (3D) response surface graphs. It seems that, this program also is an economical way of obtaining the maximum amount of information in a short period of time and with a limited number of experiments. Acknowledgments We would like to acknowledge Universiti Sains Malaysia for USM-RU-PGRS Grant (1001/PTEKIND/844105). We also extend our gratitude to the Ministry of Science, Technology and Innovation for scholarship and Universiti Malaysia Pahang for granting study leave for Nurjannah Salim. References [1] Shuit SH, Tan KT, Lee KT, Kamaruddin AH. Oil palm biomass as a sustainable energy source. A Malaysian case study. Energy 2009;34:1225–35. [2] Sulaiman O, Salim N, Hashim R, Yusof LHM, Razak W, Yunus NYM. Evaluation on the suitability of some adhesives for laminated veneer lumber from oil palm trunks. Mater Des 2009;30:3572–80. [3] Navi P, Heger F. Combined densification and thermo-hydromechanical processing of wood. DOE:
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