Assessment of Oil Palm Trunk Liquefaction in Glycerol ...

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Journal of the Japan Institute of Energy , 96, 319-325（2017）

Technical Report Special articles: Asian Conference on Biomass Science 特集：アジアバイオマス科学会議

Assessment of Oil Palm Trunk Liquefaction in Glycerol and Ethylene Glycol by 2 4-1 Fractional Factorial Design Mohd Fahmi AWALLUDIN, Othman SULAIMAN †, Rokiah HASHIM, and Mohd Firdaus YHAYA (Received November 15, 2016)

Liquefaction of oil palm trunk (OPT) in ethylene glycol and glycerol, with H2SO4 as a catalyst, at a temperature of 150 °C was conducted based on Design of Experiment (DoE) aided by software Stat-Ease Inc., Design-Expert® Version 7. A 24-1 fractional factorial design was used. The results showed that factors such as types of solvents, percentage of H2 SO4 catalyst and liquefaction time influenced the final liquefaction yield. Liquefaction of OPT in glycerol gave higher amount of liquefied yield. Besides, higher percentage of H2 SO4 catalyst and longer liquefaction time also gave higher liquefaction yields. Key Words Oil palm trunk, Liquefaction, Design of experiment

1.　Introduction

industry as a vital source of national income. Malaysian

Resea rchers a round the world have studied

palm oil industry provides a continuous supply of biomass

liquefaction of biomass from forest and agricultural wastes

material in the form of agricultural wastes. There were

and other lignocellulose products. Among the tree species

several studies about liquefaction of oil palm wastes such

studied were Japanese beech (Fagus crenata Blume) ,

as EFB fibres, mesocarp fibres and palm kernel shells

poplar (Populus ssp.), alder (Alnus ssp.), linden (Tilia ssp.)

used for phenolated EFB resin and as bio oil 23) 24). To our

1)

and chestnut wood (Castanea sativa Mill.) 2) and Chinese

knowledge, no liquefaction of waste oil palm trunk (OPT)

tallow (Triadica sebifera syn. Sapium sebiferum) 3). In terms

ever attempted by any researcher. Therefore, we take

of agricultural crops, among the wastes studied were

this opportunity to study the liquefaction of waste OPT in

corn stover 4), corn bran 5), bagasse and cotton stalks 6),

ethylene glycol and glycerol.

moso bamboo (Phyllostachys angusta) 7), grapevine cane

The study conducted was based on Design of

(Vitis vinisera L.) 8) and oil palm (Elaeis guineensis) empty

Experiment (DoE), 2 4-1 fractional factorial design. This

fruit bunch (EFB) 9). Other studies involving lignocellulose

design is useful for estimating linear model terms, where

products were cotton wool and filter paper 10) and waste

each factor was varied over two levels; highest or lowest

paper .

values. Besides, this design also enables to find the

11)

There are various uses of liquefied biomass. Among the applications that have been tried and evaluated include resol

possible significant variables for the experiment as well as estimating main effects and interactions among factors 25).

‐type phenolic resin used in phenolic foam , novolak phenol 12)

2. Experimental

formaldehyde resin 13), liquefied wood/ polyurethane films , resin for moulding 15), wood ceramics 16), carbon fibres 17) 18),

14)

liquefied wood/epoxy resin

19)

, polymer composites

mesoporous activated carbon fibre

21)

　2.1　Materials

,

A 25 year-old felled OPT during replanting work was

and fuel for gas

procured from an oil palm plantation in Ara Kuda, Kedah,

20)

turbines 22). For years, Malaysia has strengthening its palm oil Division of Bioresource, Paper and Coatings Technology, School of Industrial Technology, Universiti Sains Malaysia 11800 Minden, Penang, Malaysia †Corresponding author: [email protected]

Malaysia. The catalyst used for liquefaction process was sulfuric acid (H 2 SO4) with 95-97% purity grade. Previous studies suggest that H 2 SO4 in the range of 1-5 wt% is the best catalyst for biomass liquefaction compared to hydrochloric and phosphoric acids. At higher concentration,

J. Jpn. Inst. Energy, Vol. 96, No. 8, 2017

320

secondary reaction may occur due to strong oxidation effect of H2 SO4

. Two types of liquefying solvents used were

26) 27)

　2.4 Experimental design (DoE): 2 4 -1 fractional factorial design

reagent grade glycerol and ethylene glycol brand of Sigma-

The liquefaction experiment was carried out based on

Aldrich®. Both solvents are commonly used in biomass

24-1 fractional factorial design to study the most significant

liquefaction . Ethanol was used to wash the liquefied OPT

variables that influence the efficacy of OPT liquefaction.

28)

yield.

The overall process was simplified according to the chart in Fig. 1. The four liquefaction factors were OPT loading (A),

　2.2　Feedstock preparation

liquefaction time (B), percentage of H2 SO4 catalyst (C) and

Five OPT discs were cut out from the middle part

liquefaction solvents (D).

of the trunk. Debarked OPT discs were then endured a

The OPT loading is the amount of OPT feedstock

further downsizing process using a band saw and a chipper.

added into the liquefaction solvents (w/w). It was coded as -1

Air dried OPT chips were ground and screened to specific

and 1 that equals to 30 and 60 g of OPT feedstock in 300 g

sizes in between 250 and 1000 µm and were further oven

liquefaction solvent respectively. The minimum liquefaction time was 10 min while the maximum was 120 min. The

dried for 24 hours before it was used as feedstock.

catalyst percentage was calculated as the weight ratio to 　2.3　Oil palm trunk liquefaction

liquefaction solvent mass (w/w %). The lowest value was

The OPT liquefaction in this study implemented

2%, while the highest was 6%. The liquefaction solvents

the DoE methodology with aid from software by Stat-

used were glycerol and ethylene glycol. The low limit value

Ease, Inc. Design-Expert® Version 7 (Minneapolis, USA).

of the solvent that is 0, meaning that 100% glycerol was

The experimental design adopted a 24-1 fractional factorial

used as liquefaction solvent and no ethylene glycol presented

design. Four independent variables were selected to be

while at 100 high limit value meaning that 100% ethylene

studied that were OPT loading, time, the percentage of

glycol was used with no glycerol presented. The summary

the catalyst and liquefaction solvents. These variables have

of the variables involved is shown in Table 1.

been among widely studied by other researchers in biomass liquefaction

.

Based on fractional factorial designs (24-1), the number of runs generated were eight (16/2 = 8 runs) but each run

29) ～ 32)

The OPT feedstock, liquefaction solvent and catalyst

was duplicated for more precise estimation. Total number of

(H 2 SO4) were mixed according to the formula ratio as

runs was 16. The number of runs, actual and coded values

described in Section 2.4. The liquefaction reaction was

of independent variables is shown in Table 2.

carried out in a 500 ml four-neck glass flask, equipped with an automatic stirrer, a heating mantle, a reflux condenser

3.　Results and Discussion

and an automated temperature probe. The mixture of

　3.1 Effect of multiple variable conditions to the OPT

liquefaction solvent and catalyst was heated first until it

liquefaction

reached 150 °C before the OPT feedstock was added 11)

The amount of liquefied OPT yield obtained as a

33). The reaction time was recorded after all of the OPT

response to the different conditions of OPT loading (A),

feedstock was completely added into the reaction flask.

liquefaction time (B), catalyst (C) and liquefaction solvent (D)

When the reaction was finished, the flask that contained a dark brown mixture (liquefied OPT) was immersed in an ice bath to stop the reaction. The excessive amount of catalyst was neutralized with an equivalent sodium hydroxide (NaOH) aqueous solution. The liquefied OPT was then diluted with excessive amount of ethanol and stirred continuously for 5 min with a magnetic stirrer. The solid residue of unliquefied OPT feedstock was obtained by vacuum-filtration using filter paper. It was then rinsed using ethanol until colourless filtrate was obtained and oven dried overnight at 105 °C. The following Eq. (1) calculated the liquefaction yield: Liquefaction yield (%) = 1−

Weight of solid residue (OD) Weight of OPT feedstock (OD)

×100

(1)

Fig. 1 The experimental design’s flow chart for preliminary OPT liquefaction study


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Table 1　The independent variables used during the OPT liquefaction Actual

Name A B C D

Coded

Low 30 10 2 0

OPT Loading (g) Time (min) Catalyst (%) Liquefaction solvents (%)

High 60 120 6 100 Table 3

Table 2　Factor levels in actual and coded values Factor levels Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

A 60 60 60 30 30 60 60 30 60 30 30 30 60 60 30 30

Actual B C 10 2 120 2 120 6 10 2 10 2 10 2 120 2 120 2 120 6 10 6 120 6 120 6 10 6 10 6 120 2 10 6

D 100 0 100 0 0 100 0 100 100 100 0 0 0 0 100 100

a 1 1 1 -1 -1 1 1 -1 1 -1 -1 -1 1 1 -1 -1

Coded b c -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

d 1 -1 1 -1 -1 1 -1 1 1 1 -1 -1 -1 -1 1 1

A = OPT Loading (g); B = Time (min); C = Catalyst (%); D = Solvent (%)

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

A 60 60 60 30 30 60 60 30 60 30 30 30 60 60 30 30

Low -1 -1 -1 -1

High 1 1 1 1

Liquefied OPT yields as response to the multiple liquefaction variables Actual B C 10 2 120 2 120 6 10 2 10 2 10 2 120 2 120 2 120 6 10 6 120 6 120 6 10 6 10 6 120 2 10 6

D 100 0 100 0 0 100 0 100 100 100 0 0 0 0 100 100

Actual yield (%) 56.50 62.81 64.66 58.83 57.24 54.65 62.29 59.12 67.09 51.36 79.37 81.76 57.70 59.68 59.80 55.28

Predicted yield (%) 55.58 62.55 65.88 58.04 58.04 55.58 62.55 59.46 65.88 53.32 80.56 80.56 58.69 58.69 59.46 53.32

A = OPT Loading (g); B = Time (min); C = Catalyst (%); D = Solvent (%)

is presented in Table 3. The 24-1 fractional factorial design with 16 experimental runs were conducted in randomized order to avoid the effect of lurking variables 34). The final response (liquefied OPT yields) ranges from 51.36% (min) to 81.76% (max). In statistics, generally if the ratio of maximum to minimum value of the response exceeds 10, transformation such as square root, natural log, power and so forth is needed. The calculated ratio between maximum to minimum yield was less than 10, which was 1.59. Therefore, no transformation is needed. The predicted OPT liquefied yields were calculated by the statistical software. It was found that the predicted yield values were in the acceptable range with the experimental value. 　3.2　Evaluation of the significance factor and interaction

Fig. 2 Half-normal plot of a 24-1 fractional factorial design evaluation for preliminary OPT liquefaction study

The half-normal plot graphically and statistically categorizes factors (variables) and interactions into

farthest to right as most important. The closer to the zero

important and unimportant group. As can be seen in Fig. 2,

value the least important the factor/interaction is.

the half-normal plot quantitatively estimates the effect of

The Pareto chart depicted in Fig. 3 was further

a given primary effect and the interactions relative to the

visualized the importance of each factors and some

primary effect of the generated model in DoE. The plot

interactions. In this case, the coefficient of variables with

ranked the factor/interaction importance according to the

t-value of effect above the Bonferroni limit (t-value of


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Fig. 3 Pareto chart of primary variables (A, B, C and D) and interactions (AB, AC and AD)

effect = 3.58) are labelled as indeed significant coefficients. It seems that time (B), solvent (D), catalyst (C) and the interaction factor for AD and AB were found to be

Fig. 4 Interaction plots of variables (a) AB - OPT loading vs. liquefaction time, liquefied OPT yield at 4% H 2 SO 4 and 50% solvent condition and (b) AD - OPT loading vs. liquefaction solvents, liquefied OPT yield at 65 min liquefaction time and 4% H2SO4

statistically significant. Obviously, the effect magnitude by liquefaction time (B) with a t-value of 13.98 has the most

the interaction of OPT loading (A) to the liquefaction time

significant impact on the OPT liquefaction yields. The

implies that AD interaction is more significant compared to

coefficient of OPT loading (A) and OPT loading/catalyst (AC)

AB interaction.

interaction that situated in between t-value limit (t-value of 　3.3　Analysis of variance (ANOVA)

effect = 2.31) and Bonferroni limit was likely less significant.

The analysis of variance (ANOVA) of the model in

The possible interactions between AB and AD for each response were also investigated as shown in Fig. 4.

this study is presented in Table 4. It shows the reliability

Graphically, the significance interaction between factors can

and the significance of the model generated by the

be interpreted by the lack of parallelism in lines 35 . The

statistical software, alongside with the variables involved.

lack of parallelism in the lines in interaction of the OPT

The F-value for the model is of 61.97 indicates that the

loading (A) to the liquefaction solvents (D), compared to

model is significant. With the“Model F-Value”this large,

Table 4　Analysis of variance (ANOVA) for OPT liquefaction model Source

Sum of Squares

df

Mean Square

F Value

Model A (OPT Loading) B (Time) C (Catalyst) D (Solvent) AB AC AD Pure Error Cor Total Standard Deviation Mean C.V. % PRESS

1018.38 18.90 458.61 130.27 163.87 52.59 24.73 169.40 18.78 1037.16 1.53 61.76 2.48 75.13

7 1 1 1 1 1 1 1 8 15

145.48 18.90 458.61 130.27 163.87 52.59 24.73 169.40 2.35

61.97 8.05 195.33 55.49 69.80 22.40 10.53 72.15

(R2) R–Squared (R2Adj.) Adj R–Squared (R2Pred.) Pred R–Squared Adequate Precision

p-value Prob > F < 0.0001 0.0219 < 0.0001 < 0.0001 < 0.0001 0.0015 0.0118 < 0.0001

0.9819 0.9660 0.9276 25.143

Remark Significant


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there is only a 0.01% chance that it could occur due to noise. The model terms were selected or excluded based on the p-value with 95% confidence level. The p-value for the model, factors (A, B, C and D) or their interactions (AB, AC and AD) are less than 0.05 implies that the terms are significant. The predicted R 2 (Pred R-Squared of 0.9276) is in reasonable agreement with the adjusted R 2 (Adj R-Squared of 0.9660), which is good. The adequate precision (Adeq Precision) statistically calculates the signal to noise ratio. A ratio greater than 4 is desirable. The adequate precision of 25.143 obtained from the experiment indicates an adequate signal. This model can be used to navigate the design space. The final empirical models in terms of actual factors for percentage of liquefied OPT yield (y) is shown in Eq. (2). y = 52.06 + 0.02A + 0.2B + 3.30C － 0.26D 　　－ (2.20 × 10 -3)AB－ 0.04AC + (4.34 × 10 -3)AD

(2)

While the final equation in terms of coded factors is shown in Eq. (3): y = 61.76 －1.09a + 5.35b + 2.85c－3.20d－1.81ab－1.24ac + 3.25ad

(3)

Fig. 6 Normal plot of residuals for OPT liquefaction’s model adequacy conformity

correlation between these values is satisfied with R2 value

Therefore, for any A, B, C and D we can predict the

of 0.98. The small differences in comparison between the

percentage of liquefied OPT yield (y). The predicted y is a

actual and predicted values of the liquefied OPT yield can

function of A, B, C and D. For the equation in coded factors,

be seen in Table 3 previously. It indicates that the model is

the coefficients are in unitless.

reliable and the experiment was run carefully.

　3.4 Diagnostic checking of the fitted OPT liquefaction

studentized residuals were also plotted to check the model

Besides, the normal probability versus internally model The predicted versus actual plot for liquefied OPT

adequacy. The normal plot of residuals in Fig. 6 visually inspects whether the residuals follow a straight line. From

yield is presented in Fig. 5. The 45-degree line split the

here, it is concluded that the model had followed a normal

experimental values on the plot evenly, meaning that the

distribution and the model is adequate. There was no abnormality observed in this study. 　3.5　Response surface methodology plots The response surface of the liquefied OPT yield that was generated will help in interpreting the effect of main factor and their interactions to the final yield. In the Fig. 7, the model for liquefied OPT yield in glycerol and ethylene glycol is presented. Meanwhile, the model for liquefied OPT yield in the mixture of both solvents with ratio 1:1 is being presented as a projection generated by the statistical software. Roughly, it can be seen that liquefaction of OPT in glycerol gave higher yield compared in ethylene glycol, while liquefaction of OPT in both solvent mixture is expected to yield somewhere in between the both. Highest yield obtained was 81.76% in liquefaction condition of 30 g OPT loading, 120 min liquefaction time, at 6% H2 SO4 in glycerol. In a study done by Jasiukaitytė et al. 33), they also

Fig. 5　Predicted yield versus actual yield of liquefied OPT

found that lignin has less solubility in ethylene glycol with

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OPT feedstock started to liquefy. At 120 min liquefaction time, 30 g OPT loading gave higher yield due to the higher ratio between solid and liquid. This can be related to the lignin and hemicelluloses started to dissolve 6), thus made the mixture mixed well and permitted the solvent to attack more OPT particles. In ethylene glycol, 60 g OPT loading gave higher yield compared to 30 g OPT loading at 10 min liquefaction time. As the liquefaction time increased, the amounts of yield obtained from 30 g OPT loading also increased and eventually surpassed that of 60 g. This happened due to thinner viscosity of ethylene glycol permitted the OPT feedstock to mixed well, even during the initial phase of the liquefaction process. In terms of catalyst, 6% H 2 SO4 gave higher yield compared to 2%. One thing in common in all the liquefaction conditions was changes in OPT feedstock colour from light to dark brown can be seen as fast as 10 min liquefaction time (observation by naked eyes). This occurrence is in agreement with a study by Kurimoto et al. 36). In a study conducted by Yao 37), he reported that rapid liquefaction in initial phase was due to degradation of lignin components, hemicelluloses and some reachable cellulose. The yield conversion ratio became slow later on because of hard-to-access cellulose. He concluded that the early liquefaction phase affected the conversion rate of the subsequent liquefaction stages. 4.　Conclusion Response surface methodology (RSM) through 2 4-1 fractional factorial design could be used to determine the significant factor that affected the OPT liquefaction process. Factors like liquefaction time, types of solvents used and the percentage of H 2 SO4 catalyst had significantly influenced the final liquefied OPT yield. In comparison, between ethylene glycol and glycerol as solvents, liquefaction of OPT in glycerol gave higher liquefied yield. The use of higher percentage of H2SO4 catalyst and longer liquefaction time gave higher yield. Fig. 7 3D surface view of the yield obtained after OPT liquefaction (a) Liquefaction in glycerol, (b) Liquefaction in ethylene glycol, and (c) Liquefaction in glycerol/ethylene glycol mixture [1:1] (Projected)

　Acknowledgment We acknowledge the Ministry of Higher Education, Malaysia for the MyPhD Scholarship, MyBrain15.

H2 SO4 -catalyzed liquefaction. In glycerol, the OPT loading

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