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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 Division of Bio-resource, Paper and Coatings Technology, School of Industrial Technology, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysiab Graduate School of Agricultural and Life Science, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo 113-8657, Japanc 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 2011Received in revised form 13 October 2011Accepted 1 November 2011Available online 10 November 2011

Keywords:A. Oil palm trunkC. Response Surface MethodologyD. Compression ratioD. Recovery ratio

a b s t r a c t

The objective of this investigation was to evaluate optimum manufacturing parameters to produce com-pressed lumber from oil palm (Elaeis guineensis) trunks. Experimental samples were made using steamingtime, pressure, pressure time and temperature ranging from 2 to 4 h, from 5 to 12 MPa, from 20 min to60 min and from 100 �C to 200 �C, respectively. Compression and recovery ratios of the specimens weredetermined employing Response Surface Methodology (RSM) approach within the scope of Central Com-posite Design (CCD) computer program. Experimental and calculated values were compared to eachother. Based on the results of the work, the specimens steamed for 2 h before they were compressedusing 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 correla-tion coefficient values, respectively.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Oil palm is one of the largest agricultural plantations in Malay-sia. The life span of oil palm tree is about 25 years before regener-ation is done. During the replanting, abundant of oil palm biomasswas produced which currently is left to rot on the ground. Accord-ing to Shuit et al. [1] 1 hec. of oil palm plantation can produceapproximately 50–70 tonnes of biomass residues making oil palmthe largest biomass resource in Malaysia. It is expected area of oilpalm resource in Malaysia will be growing, and the rate of biomasswill significantly increase in future. Researches on oil palm bio-mass have been carried out to assist in reducing waste of the oilpalm and in the meantime can benefit economic return for thecountry [2]. Crisis of wood shortage also makes this resource as apromising alternative material as a substitute of wood due to itsabundant availability.

One of the potential uses of oil palm trunk would be consideredto convert it into compressed material similar to wood in order toenhance its mechanical properties, as well as better dimensionalstability. Compressed oil palm trunk could be used for variousapplications especially for constructional materials due to its highstrength and easy machinability [3]. The process of compressingthe wood has been found to be an effective way to increase the

density of wood [4]. The products from compressed wood may be-come a substitute for commercial wood products with high densityand strength [5]. Saito [6] has found that application of the heattreatment to the wood after pressing resulted in significancereduction in its springback. By combining high temperature steamtreatment and compressing of wood is believed to improve thedimensional stability of wood because of changes in its chemistry[7].

The process of compressing a wood has been known for dec-ades. Since 1930, the compression wood or with the trade nameLignostone was produced in Germany [8]. In the United States,there are two methods to make compressed wood that have beeninvestigated, namely Compreg [9] and Staypack [8]. Compreg is thetype of compressed wood normally impregnated with water-soluble phenol formaldehyde resin before it is compressed to adesired specific gravity and thickness while Staypack is notimpregnated with resin. During the past decades, research hasbeen conducted to investigate on manufacturing conditions ofcompressed wood but yet, no study has been reported for manu-facturing conditions of compressed products from oil palm trunk.

Recovery ratio is the main factor that should be considered inthe manufacture of compressed wood. Recovery ratio is the per-centage of the compressed material to springback to its originalsize after being compressed. Springback is caused by built up inter-nal stress in the panel and results in excessive thickness swelling[10]. Oil palm is non-wood species therefore, its anatomical

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⇑ Corresponding author. Tel.: +60 4 6535217; fax: +60 4 6573678.E-mail address: hrokiah@usm.my (R. Hashim).

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structure is different from the typical wood species. Oil palmconsists of vascular bundles and parenchyma cells while woodconsists of fibers, tracheids, vessels, parenchyma and ray paren-chyma cells [2]. Hence, special attention has to be applied in usingoil palm due to its inherent density variation and different anatom-ical features which affect overall properties of compressedmaterial.

Based on the findings of a previous research, to prevent spring-back of wood, various chemical treatments have been applied suchas using maleic acid glycerol and impregnation of resin [3]. How-ever, these methods are not really practical due to their certaindisadvantages such as changing the nature of wood, creating haz-ardous material and high cost [3]. Steaming and compressing ofwood would result in reduction of springback phenomena. Bysteaming process, the hemicellulose hydrolyzes, and the lignin willbecome softer. By compressing wood under condition that causesufficient flow of the lignin will also subsequently relief the inter-nal stresses resulting from the compression. Both processes havebeen found possibly to eliminate the springback [11–14]. Thethickness swelling of compressed wood after water absorption de-creased with increasing press temperature and increased with anincreasing amount of compression [15]. Inoue et al. [16] also foundthat the recovery of the compression set after boiling in water de-creased with increasing heating time and temperature. A studycarried out by [17] showed that recovery of the compression setof wood decreased with increasing press temperature and time.

Response Surface Methodology (RSM) is a statistical method forexploring optimum condition in experiment. This program ana-lyzes the effects of several independent variables on the desirableresponses [18]. The objectives of using RSM are to improve quality,including reduction of variability, having an improved process andproduct performance. Therefore, Response Surface Methodology(RSM) is proposed to determine the influence of individual andinteractive parameters as far as properties of compressed samplesare concerned. The main advantage of this method is the reducednumber of experimental trials needed to evaluate multiple param-eters and their interactions [19,20]. Numerous studies on com-pressed wood have been investigated [3,5,6,8] but no suchresearch has been reported by using oil palm trunk as a raw mate-rial. In addition to this, no study has been carried out in optimizingthe manufacturing parameters using RSM for either compressedwood or oil palm trunk lumber. Therefore, the objective of thestudy was to determine the optimum manufacturing conditionsto manufacture compressed lumber from oil palm trunk usingRSM. In order to find the optimum manufacturing conditions formaking compressed oil palm trunk, a set of experiments weredeveloped by employing central composite design. It is expectedthat data from the findings of this work should lead to produce va-lue-added compressed oil palm trunk with low level of recoveryratio and in the future, this material can be used more effectivelyand efficiently.

2. Materials and methods

2.1. Sample preparation compressed lumber manufacture

Oil palm trunks with an approximate age of 28 years old wereharvested from local plantation in Northern Malaysia. After felling,the trunks were immediately cut into small pieces with a dimen-sion of 20 cm � 20 cm � 4 cm in tangential direction. The sampleswere wrapped in a plastic bag and kept in a freezer before anyfurther tests were carried out. Thirty experimental trials weredesigned using Design Expert software. For each experimental trial,the oil palm samples were first steamed using an autoclave at atemperature of 130 �C before they were dried in an oven. In the

next step, the samples were pressed using computer controlledhot press. It is known that the density of the oil palm varies acrossin the radial direction that might affect the quality of the oil palmlumber. The density of oil palm trunk samples used in this researchranged from 0.43 g/cm3 to 0.50 g/cm3 before being compressedand ranged from 0.76 g/cm3 to 0.80 g/cm3. Later, samples werecut and conditioned in a climate chamber with a temperature of20 �C and relative humidity of 65% before their recovery and com-pression ratios were determined.

2.2. Determination of the responses of the samples

Compression and recovery ratios are the responses of the spec-imens. Both tests were performed after the compressed lumberwas produced. The compression ratio for each sample was calcu-lated according to the following equation:

Compression ratio ¼ ðTr1 � T0 � 100Þ=T1 ð1Þ

where Tr1 = thickness after recovery, T1 = thickness before compres-sion, 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, andthe thickness of the samples was measured at four points fromthe corners before and after they were boiled in water [16,21].Recovery ratio was evaluated according to the following equation:

Recovery ratio ¼ ðTr1 � T1 � 100Þ=T0 ð2Þ

where Tr1 = thickness after recovery, T1 = thickness before compres-sion, T0 = Thickness after compression.

2.3. Central composite rotatable design (CCRD)

The experimental plan was carried out using central compositerotatable design (CCRD) of Response Surface Method (RSM) whichis a software developed by the Design Expert Version 6.0.7, Sta-tEase, Inc., USA to determine the best manufacturing conditionsfor compressed lumber from oil palm trunk [18]. Four independentprocess variables were considered including steaming time, pres-sure, pressing time and pressing temperature. The ranges and thedesign levels of the variables investigated in this study are givenin Table 1.

The experiment consists of 30 experimental trials having (N)depending on the number of factors (k) and the number of centerpoints (n). The total number experimental trials are expressed inEq. (3). The design was carried out using (k = 4) factorial with sixstar points (a = 2) and six replicates at center points (n).

N ¼ 2k þ 2kþ n ð3Þ

The experiments were performed according to the central com-posite rotatable design (CCRD) matrix given in Table 2. Optimumexperimental design depends upon the model relating the re-sponse to the factor. The correlation between independent vari-ables with response of recovery ratio (y2) was estimated by alinear model with transformation of the square root expressed by

Table 1The variables and corresponding levels.

Parameters Factor Variable levels

�2 �1 0 1 2

Steaming time (h) A 2 2.5 3.0 3.5 4Pressure (MPa) B 5 6.75 8.5 10.25 12Pressing time (min) C 20 30 40 50 60Pressing temperature (�C) D 100 125 150 175 200

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Eq. (4) while for correlation between independent variables withresponse of compression ratio (y) were also estimated by a qua-dratic equation model expressed by Eq. (5) [18].

Y ¼ b0 þ b1x1 þ b2x2 þ � � � þ bkxk þ e ð4Þ

where y is the response (dependent variable), b0 is a constant coef-ficient, b1 and b2 are coefficient for the linear, X1 and X2 are factors(independent variables), k is the number of factors while e is theerror.

Y2 ¼ b0 þXk

i¼1

b1x1 þXk

i¼1

biix2i þ

X

i¼1

Xk

j¼iþ1

biixixj þ e ð5Þ

where y2 is the response (dependent variable), b0 is a constant coef-ficient, bi, bii and bij are coefficient for the linear, quadratic and

interaction effect, Xi and Xj are factors (independent variables), kis the number of factors while e is the error.

3. Results and discussion

3.1. Experimental design and analysis of variance (ANOVA)

Table 3 shows the statistics for overall responses for linear andquadratic model obtained from the Design Expert software. Fol-lowing three items were considered to have a successful modelin this study.

1. The sequential model sum of squares was in the highest orderpolynomial with the highest F value and low value of probabil-ity (value-p < 0.05).

Table 2Actual and coded parameters for experiments.

Run Actual parameters Coded parameters y (%) y2 (%)

A B C D x0 x1 x2 x3 x4

1 2 5.0 20 100 1 �1 �1 �1 �1 65.50 50.502 4 5.0 20 100 1 1 �1 �1 �1 70.80 52.103 2 12.0 20 100 1 �1 1 �1 �1 57.80 50.484 4 12.0 20 100 1 1 1 �1 �1 63.70 43.405 2 5.0 60 100 1 �1 �1 1 �1 65.00 59.406 4 5.0 60 100 1 1 �1 1 �1 67.00 52.007 2 12.0 60 100 1 �1 1 1 �1 60.00 53.808 4 12.0 60 100 1 1 1 1 �1 68.50 46.709 2 5.0 20 200 1 �1 �1 �1 1 74.70 4.47

10 4 5.0 20 200 1 1 �1 �1 1 76.70 20.5011 2 12.0 20 200 1 �1 1 �1 1 73.30 0.8312 4 12.0 20 200 1 1 1 �1 1 74.30 12.5013 2 5.0 60 200 1 �1 �1 1 1 76.50 1.0014 4 5.0 60 200 1 1 �1 1 1 75.60 4.3515 2 12.0 60 200 1 �1 1 1 1 78.30 1.2016 4 12.0 60 200 1 1 1 1 1 76.50 3.7517 1 8.5 40 150 1 �2 0 0 0 79.50 11.4218 5 8.5 40 150 1 2 0 0 0 67.70 66.1819 3 1.5 40 150 1 0 �2 0 0 75.40 31.6020 3 15.5 40 150 1 0 2 0 0 74.30 29.7021 3 8.5 0 150 1 0 0 �2 0 57.80 46.4022 3 8.5 80 150 1 0 0 2 0 73.50 5.9023 3 8.5 40 50 1 0 0 0 �2 72.40 68.4024 3 8.5 40 250 1 0 0 0 2 76.10 0.7825 3 8.5 40 150 1 0 0 0 0 73.00 43.5026 3 8.5 40 150 1 0 0 0 0 80.60 30.8027 3 8.5 40 150 1 0 0 0 0 78.10 27.6028 3 8.5 40 150 1 0 0 0 0 78.60 36.5029 3 8.5 40 150 1 0 0 0 0 77.90 26.7030 3 8.5 40 150 1 0 0 0 0 73.90 28.20

A = steaming time (h), B = pressure (MPa), C = pressing time (min), D = emperature (�C).

Table 3Fit summary for linear and quadratic model.

Response Compression ratio (y) Recovery ratio (y2)

Model La Qb L Q

Sum of squares F value 4.12 73.34 32.48 5.71Prob > F 0.0106c 0.044c <0.0001c 0.2538

Lack of fit test F value 3.87 3.5 4.1 3.87Prob > F 0.0695 0.0897 0.0619 0.0743

Model summary test Std. dev. 5.33 4.8 1.04 0.95Adjusted R2 0.301 0.4349 0.8128 0.8434Predicted R2 0.137 �0.5225 0.7605 0.5735

a L = linear.b Q = quadratic.c Models are statistically significant (p < 0.05).

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2. The model should have the value of Lack of fit test insignificantwith the low value of F value and highest value of p (value-p > 0.05).

3. The model should have the high value of adjusted R2 and pre-dicted R2 than the other considers models.

Based on criteria above and the data in Table 3, the quadraticmodel were chosen for response of compression ratio and the lin-ear model were used for response of recovery ratio.

Table 4 shows the design of the experiment. The data obtainedfrom the experiment will be analyzed in linear model for recoveryratio and quadratic model for compression ratio. In each individualmodel, the coefficients relationship of the parameters to the re-sponses were calculated and evaluated statistically by the RSM De-sign Expert software based on the regression equation. Thisindividual model will be combined by the RSM in order to getthe optimum model [22,23]. The regression equations for both re-sponses were obtained from the analysis of variances (ANOVA) gi-ven in equation below. The regression equations were in codedfactors. In Eq. (6) shows the quadratic equation represent the re-sponse for compression ratio (y) while in Eq. (7) shows linear equa-tion represent the response for recovery ratio (y2) as a function offour independent variables namely steaming (x1), pressure (x2),pressing time (x3) and pressing temperature (x4). The results indi-cated that the CCRD regression model developed to predict com-pression ratio and recovery ratio were adequate in Table 5 [18].

yð%Þ ¼ þ77:02� 0:067x1 � 0:90x2 þ 1:75x3 þ 3:96x4

� 1:16x21 � 0:85x2

2 � 3:15x23 � 1:00x2

4 þ 0:33x1x2

� 0:40x1x3 � 1:34x1x4 þ 1:11x2x4 þ 0:33x3x4 ð6Þ

Y2ð%Þ ¼ þ4:97þ 0:63x1 � 0:17x2 � 0:53x3 � 2:28x4 ð7Þ

Experimental actual results and predicted values calculated inEqs. (6) and (7) are given in Table 4 and also illustrated in Figs. 1and 2. As can be seen above figures, the predicted values matchedwith the actual values reasonably well, with R2 values of 0.70 and0.83 for compression and recovery ratios, respectively.

Table 4Experimental design for compressed lumber from oil palm trunk.

Trial no. Parameter in uncoded levels Compression ratio (%) Recovery ratio (%)

A B C D y y2

Actual Predicted Actual Predicted

1 2 5.0 20 100 65.50 67.23 7.11 7.322 4 5.0 20 100 70.80 69.92 7.22 8.573 2 12.0 20 100 57.80 60.40 7.10 6.994 4 12.0 20 100 63.70 64.40 6.59 8.245 2 5.0 60 100 65.00 68.65 7.71 6.266 4 5.0 60 100 67.00 69.75 7.21 7.527 2 12.0 60 100 60.00 66.28 7.33 5.938 4 12.0 60 100 68.50 68.67 6.83 7.199 2 5.0 20 200 74.70 75.02 2.11 2.76

10 4 5.0 20 200 76.70 72.36 4.53 4.0211 2 12.0 20 200 73.30 72.50 0.91 2.4312 4 12.0 20 200 74.30 71.14 3.54 3.6913 2 5.0 60 200 76.50 77.75 1.00 1.7114 4 5.0 60 200 75.60 73.49 2.09 2.9615 2 12.0 60 200 78.30 79.67 1.10 1.3716 4 12.0 60 200 76.50 76.71 1.94 2.6317 1 8.5 40 150 79.50 72.52 3.38 3.7218 5 8.5 40 150 67.70 72.25 8.14 6.2319 3 1.5 40 150 75.40 75.43 5.62 5.3020 3 15.5 40 150 74.30 71.83 5.45 4.6421 3 8.5 0 150 57.80 60.93 6.81 6.0322 3 8.5 80 150 73.50 67.93 2.43 3.9223 3 8.5 40 50 72.40 65.12 8.27 9.5324 3 8.5 40 250 76.10 80.95 0.88 0.4225 3 8.5 40 150 73.00 77.02 6.60 4.9726 3 8.5 40 150 80.60 77.02 5.55 4.9727 3 8.5 40 150 78.10 77.02 5.25 4.9728 3 8.5 40 150 78.60 77.02 6.04 4.9729 3 8.5 40 150 77.90 77.02 5.17 4.9730 3 8.5 40 150 73.90 77.02 5.31 4.97

A = steaming time (h), B = pressure (MPa), C = pressing time (min), D = temperature (�C).

Table 5Analysis of variance (ANOVA) of quadratic model for compression ratio and linearmodel for recovery ratio.

Source Compression ratio (y) Recovery ratio (y2)

Model Sum of squares 835.32 141.36DF 14 4Mean square 59.67 35.34F value 2.59 32.48Prob > F 0.0386a <0.0001a

Residual Sum of squares 344.96 27.2DF 15 25Mean square 23 1.09

Lack of fit Sum of squares 301.81 25.64DF 10 20Mean square 30.18 1.28Value F 3.5 4.1Prob > F 0.0897 0.0619

Pure error Sum of squares 43.15 1.56DF 5 5Mean square 8.63 0.31

Std. dev. 4.8 1.04R2 0.7077 0.8386Adjusted R2 0.4349 0.8128Predicted R2 �0.5255 0.7605Adeq precision 6.059 21.397

a Models are statistically significant (p < 0.05).

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The linear and quadratic effects on the independent variablesteaming time, pressure, pressing time and pressing temperatureand their interactions on the responses were obtained by analysisof variance (ANOVA). The significance model, lack of fit and coeffi-cients of determination (R2) are also summarized in Table 5 to eval-uate the validity of the CCRD model. As shown in Table 5 forcompression ratio (y), the model was the significance withp < 0.05. There is only a 3.86% chance for the model that a ‘‘ModelF-value’’ of this large could occur due to noise. The value of R2 is0.70 with no significant lack of fit at p > 0.05. The insignificanceof lack of fits was good. A negative predicted R2 implies that theoverall mean is a better predictor for the response. Adequate pre-cision measures the signal to noise ratio. A ratio greater than 4 isdesirable. The ratio of 6.059 indicates an adequate signal.

For recovery ratio (y2), the model was the significance withp < 0.05. The Model F-value of 32.48 implies the model is signifi-cant. There is only a 0.01% chance that a ‘‘Model F-value’’ this largecould occur due to noise. The value of R2 is 0.8386 with no signif-icant lack of fit at p > 0.05. The ‘‘Lack of Fit F-value’’ of 4.10 implies

there is a 6.19% chance that a ‘‘Lack of Fit F-value’’ this large couldoccur due to noise. The predicted R2 of 0.7605 is in reasonableagreement with the adjusted R2 of 0.8128. The ratio of adequateprecision was 21.397 indicating an adequate signal value.

3.2. Effect of variables on compression ratio based on model term

The three dimensional (3D) response surface plots demonstratethe effects of different variables on compression ratio are given inFig. 3a–f. Fig. 3a shows the effect of steaming time and pressure forpressing process on compression ratio. As can be seen from Fig. 3a,steaming time and the pressure was resulted in a quadratic effecton the compression ratio.

When these two variables were manipulated, the value of com-pression ratio continually increased until reaching one maximumpoint which was the highest value of compression ratio. Above thispoint, the value of compression ratio decreased as a function ofpressure and steaming time. This decrease of compression ratiomaybe due to the prolong steam pressure and higher temperaturethat may have degraded some cellulose component present in oilpalm trunk [14]. This situation will influence to the compressionratio of compressed lumber from oil palm trunk and its properties.The similar situation is shown in Fig. 3b which is steaming timeand pressing time revealed quadratic effect on the compression ra-tio of the samples. The compression ratio depends more on thepress time rather than on steaming time. This is due to longer timefor pressing process will allowed reorientation and crystallinity ofthe cellulose microfibrils to take placed [5]. This will lead to pro-duce high strength properties of compressed lumber from oil palmtrunk. As illustrated in Fig. 3b the compression ratio kept increas-ing until the maximum press time and subsequently decreasedafter such threshold point. In Fig. 3c, the 3D surface graph resultedin a logarithm effect on the compression ratio. An increasing ofpress time and steaming showed on high level of compression ra-tio, but as can be seen in Fig. 3c, the compression ratio dependedmore 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 moreeffect than the other factor. Press time will allowed reorientationand crystallinity of the cellulose microfibrils to take placed.

In Fig. 3d, the 3D surface graph, the quadratic effect on the com-pression ratio after reaction between pressure and press time areillustrated. Fig. 3d shows the center level of pressing time were re-sults on high compression ratio and pressure gave a less effect onthe compression ratio. In Fig. 3e shows a similar relationship tothose of in Fig. 3c. The compression ratio kept steadily increasedwith increasing the press temperature while decreased withincreasing pressure. High pressure will resulted in some degrada-tion to cellulose microfibrils subsequently resulted in decreasingstrength properties of the sample [14]. Fig. 3f shows the effect onpress time and press temperature on compression ratio. As canbe seen in Fig. 3f, the minimum press time and press temperatureresulted on minimum compression ratio. The minimum press timeand press temperature is not sufficient and therefore need to be in-creased. The compression ratio can be increased by increasing thepress time and temperature. Study done by [5] has found that lowpressing temperature resulted in a decreasing of properties to thefinal products.

3.3. Effect of variables on recovery ratio based on model terms

The three dimensional (3D) response surface plots demonstratethe effects of different variables on compression ratio are given inFig. 4a–f. Fig. 4a shows the effect of all 3D surface graphs gave thealgorithm effect on the recovery ratio. For this response, theminimum recovery ratio is required to produce a dimensional

R2= 0.70

80.95

75.16

69.38

63.59

57.80

57.80 63.59 69.38 75.16 80.95

Actual

Pre

dict

ed

Fig. 1. Relationship between actual and predicted compression ratios based on Eq.(6).

R2= 0.83

Actual

Pre

dict

ed

0.42 2.70 4.97 7.25 9.53

0.42

2.70

4.97

7.25

9.53

Fig. 2. Relationship between actual and predicted recovery ratios based on Eq. (7).

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compressed lumber. In Fig. 4a shows the relationship betweensteaming time and pressure on recovery ratio.

As can be seen in Fig. 4a recovery ratio depends more on thesteaming time rather than pressure level. It showed that a mini-mum steaming time resulted on minimum recovery ratio and itincreased with increased steaming time. Steaming process will

allowed hydrolysis of hemicelluloses which increases the com-pressibility of wood and reduces build up of internal stresses incomposites but an excessive of steaming process will hydrolysisall the celluloses which are the main strength of the compressedlumber [11]. Relationship between steaming time and press timeis shown in Fig. 4b. As can be seen in Fig. 4b the maximum press

73.85

74.71

75.56

76.41

77.27

Com

pres

sion

ratio

(%)

2.00

2.50

3.00

3.50

4.00

5.00

6.75

8.50

10.25

12.00

A: Steaming time B: Pressure

73.85

74.71

75.56

76.41

77.27

Com

pres

sion

ratio

(%)

2.00

2.50

3.00

3.50

4.00

20

30

40

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60

A: Steaming time C: pressing time

69.63

72.33

75.02

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80.40

Com

pres

sion

ratio

(%)

2.00

2.50

3.00

3.50

4.00

100

125

150

175

200

A: Steaming time D: Pressing temp.

69.63

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77.71

80.40

Com

pres

sion

ratio

(%)

5.00

6.75

8.50

10.25

12.00

100

125

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175

200

B: Pressure D: Pressing temp.

a b

c

69.63

72.33

75.02

77.71

80.40

Com

pres

sion

ratio

(%)

5.00

6.75

8.50

10.25

12.00

20

30

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B: Pressure C: Pressing time

d

69.63

72.33

75.02

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80.40

Com

pres

sion

ratio

(%)

20

30

40

50

60

100

125

150

175

200

C: Pressing time D: Pressing temp.

fe

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) steamingtime and pressing temperature; (d) pressure and pressing time; (e) pressure and pressing temperature; (f) pressing time and pressing temperature.

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time and the minimum steaming results on minimum recoveryratio. A maximum press time will allowed reorientation and crys-tallinity of the cellulose microfibrils to take placed resulted inminimum recovery ratio.

The relationship between steaming time and pressure tempera-ture which is almost similar with Fig. 4b is illustrated in Fig. 4c. It isclear that pressing temperature had more effect than steamingtime. The maximum pressing temperature resulted on low level

4.18

4.58

4.97

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5.77

Rec

over

y ra

tio (%

)

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A: Steaming time C: Pressing time

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over

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tio (%

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5.00

6.75

8.50

10.25

12.00

A: Steaming time B: Pressure

4.18

4.58

4.97

5.37

5.77

Rec

over

y ra

tio (%

)

5.00

6.75

8.50

10.25

12.00

20

30

40

50

60

B: Pressure C: Pressing time

4.18

4.58

4.97

5.37

5.77

Rec

over

y ra

tio (%

)

20

30

40

50

60

100

125

150

175

200

C: Pressing time D: Pressing temp.

4.18

4.58

4.97

5.37

5.77

Rec

over

y ra

tio (%

)

5.00

6.75

8.50

10.25

12.00

100

125

150

175

200

B: Pressure D: Pressing temp.

4.18

4.58

4.97

5.37

5.77

Rec

over

y ra

tio (%

)

2.00

2.50

3.00

3.50

4.00

100

125

150

175

200

A: Steaming time D: Pressing temp.

a b

c d

fe

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 timeand pressing temperature; (d) pressure and pressing time; (e) pressure and pressing temperature; (f) pressing time and pressing temperature.

994 N. Salim et al. / Composites: Part B 43 (2012) 988–996

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of recovery ratio. High press temperatures will increases themechanical anisotropy. This will resulted in compressed oil palmwith good dimensional stability [3]. In Fig. 4d shows the relation-ship between pressure and press time on recovery ratio. As can beseen in Fig. 4d, pressure resulted in insignificant effect on therecovery ratio as compared to that of press time. It appears thatthe maximum press time resulted on minimum recovery ratio.Fig. 4e shows rapid reduction on recovery ratio at the center levelof press temperature, and it became steady at highly press temper-ature. It was also found that pressure had little effect on recoveryratio in contrast to press temperature. Fig. 4f shows a relationshipbetween press time and press temperature on recovery ratio. Thisfigure is also almost similar to Fig. 4e which is the recovery ratiosubstantially decreased at the center level of press temperatureand kept stable after that point. The recovery ratio depended moreon the effect of press temperature rather than press time. Hightemperature will allow the hydrolysis of lignin and hemicellulosein wood. By removing lignin and partial hydrolysis will reducethe internal stresses in wood subsequently increasing the com-pressibility 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 theoptimum condition for making compressed lumber from oil palmtrunk as displayed in Table 6. These conditions were calculatedbased on Eqs. (4) and (5). The parameters were steaming time,pressure, press time and press temperature. Compression ratiowas adjusted to a maximum level while recovery ratio was ex-pected to be minimum. The optimization suggested around 80.3%compression ratio and 1.53% recovery ratio can be achieved bysteaming 2 h, pressure at 11.16 MPa, pressing for 60 min with atemperature of 200 �C.

It should be noted that the data in Tables 4 and 6 are two differ-ent data which cannot be compared to each other. The data inTable 6 have been analyzed by combining both models in optimi-zation process while data in Table 4 was the data that have beenanalyzed by individual model and not yet been optimized. Thus,the data was taken from the data that has been optimized asshown in Table 6 and not from one time trial run as in run 13 indi-cated in Table 4. As in case of run 13, even if it shows favorableresults, this data cannot be taken because it has not yet been sta-tistically analyzed by the optimum model. The data in Table 5shows that models that were used in this study were statisticallysignificant. As can be seen the results obtained from the experi-ment, were slightly different from those of the predicted by thesoftware. This could be due to some possible error in the experi-ment and limitation of equipment. However, results found in thiswork were still reasonably satisfactory and acceptable.

4. Conclusion

This study demonstrated Response Surface Method (RSM) canbe successfully used for modeling some parameters for optimiza-tion of compressed lumber from oil palm trunk. The predictedvalues 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 pre-sented three-dimensional (3D) response surface graphs. It seemsthat, this program also is an economical way of obtaining the max-imum amount of information in a short period of time and with alimited number of experiments.

Acknowledgments

We would like to acknowledge Universiti Sains Malaysia forUSM-RU-PGRS Grant (1001/PTEKIND/844105). We also extendour gratitude to the Ministry of Science, Technology and Innova-tion for scholarship and Universiti Malaysia Pahang for grantingstudy leave for Nurjannah Salim.

References

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Table 6A set of solution suggested by the Design Expert software.

Run A B C D Compression ratio (%) Difference Recovery ratio (%) Difference

Obtained Predicted Obtained Predicted

1 2 11.16 60 200 77.8 80.3 2.5 2.62 1.53 1.09

A = steaming time (h), B = pressure (MPa), C = pressing time (min), D = temperature (�C).

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