Journal of the Taiwan Institute of Chemical Engineers xxx (2013) xxx–xxx

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Immobilized lipase-catalyzed transesterification of Jatropha curcas oil:Optimization and modeling

Alireza Zarei a, Nor Aishah Saidina Amin a,*, Amin Talebian-Kiakalaieh a, Nor Azimah Mohd Zain b

a Chemical Reaction Engineering Group (CREG), Faculty of Chemical Engineering, Universiti Teknologi Malaysia, UTM, 81310 Skudai, Johor, Malaysiab Department of Industrial Biotechnology, Faculty of Biosciences and Bioengineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia

A R T I C L E I N F O

Article history:

Received 29 January 2013

Received in revised form 8 May 2013

Accepted 16 May 2013

Available online xxx

Keywords:

Jatropha oil

Biodiesel

Transesterification

Immobilized lipase

Optimization

A B S T R A C T

This study investigates the production of biodiesel from non-edible crude jatropha oil in the presence of

an immobilized lipase catalyst. A modified method was used to immobilize Rhizopus oryzae lipase in the

polyvinyl alcohol–alginate matrix. Response surface methodology (RSM) and artificial neural network

(ANN) were employed to evaluate the relationship between process variables and biodiesel yield and

predict the optimal reaction conditions. The determination coefficient values for RSM and ANN were 0.98

and 0.97, respectively, indicating that both models can accurately predict the experimental results. The

experimental results revealed that the highest biodiesel yield was 87.10% at 40 8C reaction temperature,

5:1 methanol/oil molar ratio, 70 wt% water content, and 17 h reaction time. The immobilized lipase-

catalyst beads exhibited good activity for biodiesel production, as indicated by the major properties that

complied with the American Society for Testing and Materials (ASTM) international D6751 standard.

� 2013 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Contents lists available at SciVerse ScienceDirect

Journal of the Taiwan Institute of Chemical Engineers

jou r nal h o mep age: w ww.els evier . co m/lo c ate / j t i c e

1. Introduction

Fatty acid methyl ester (FAME) compounds, also known asbiodiesel because of their properties similar to diesel fuel, havepotential to be the main fuel source in the near future [1–3].Biodiesel is biodegradable, technically feasible, and environmen-tally friendly renewable fuel [3–5].

Several sources are available as feedstock for biodieselproduction, such as animal fats and vegetable oils [6]. Vegetableoils are more widely used than other sources, and are divided intotwo categories: (1) edible oils, such as soybean [7], palm [8],sunflower [9], and coconut [10]; and (2) inedible oils, such as wastecooking oil (WCO) [11], algae [12], and some bean oils, such asJatropha curcas (Jatropha) [13], Pongamina pinnata (Karanja) [14],Hevea brasiliensis (Rubber) [15], and Calophyllum inophyllum

(Polanga) [16].Biodiesel production using edible oils has been controversial

because it threatens human food sources worldwide [3,17,18],thereby increasing the prices of edible oils and consequently, theprice of biodiesel [19]. About 350 oil-bearing crops have beenrecognized as potential biodiesel sources [20]. Among which,Jatropha, is one of the best candidates that possess resistance

* Corresponding author. Tel.: +60 7 553 5579; fax: +60 7 558 8166.

E-mail addresses: zalireza4@live.utm.my (A. Zarei), noraishah@cheme.utm.my,

profnoraishah@yahoo.com (N.A.S. Amin).

Please cite this article in press as: Zarei, A., et al., Immobilized lipaseand modeling. J. Taiwan Inst. Chem. Eng. (2013), http://dx.doi.org/10

1876-1070/$ – see front matter � 2013 Taiwan Institute of Chemical Engineers. Publis

http://dx.doi.org/10.1016/j.jtice.2013.05.015

against drought and can restore environmental wastelands[18,21].

The most common method for biodiesel production is vegetableoil alcoholysis or transesterification reaction [5,21]. In this method,vegetable oil reacts with alcohol in the presence of a catalyst or insupercritical conditions that result in the production of glyceroland biodiesel [4,5].

Acid [4], base [22], and biocatalyst [1] have been used in thecatalytic transesterification process. Enzymes are considered asbiocatalysts and can improve the chemical reactions within livingcells by lowering the activation energy [23]. Compared withchemical catalysts in the transesterification reaction, biocatalystshave the following advantages: (1) environmentally friendly, (2)exhibit higher reaction rates under ambient conditions, (3) havelittle or no chemical decomposition during the process, (4) requireless alcohol, and (5) have purer final product.

Immobilized forms or free enzymes may be used in industries.Immobilization enhances the stability of enzymes against variousdenaturing forms and also immobilized enzyme separation at theend of the reaction will be direct [24]. In the 1960s, enzymeentrapment techniques were implemented using synthetic poly-mer gels, such as polyacrylamide gel or polyvinyl alcohol (PVA), ornatural polymer derivatives such as silicon elastomers for the sol–gel process, starch, or nitrocellulose [25]. Enzyme-adsorptivecross-linking on membranes and films or beads for the formationof enzyme envelopes has also been developed. Immobilizedbiocatalysts gained stable interests during the past decade,

-catalyzed transesterification of Jatropha curcas oil: Optimization.1016/j.jtice.2013.05.015

hed by Elsevier B.V. All rights reserved.

Table 2Experimental range and levels of independent variables in transesterification

reaction.

Independent variable Range and level

�a �1 0 +1 +a

A: Time (h) 1 5 9 13 17

B: Temperature (8C) 30 35 40 45 50

C: Methanol/oil molar ratio 3:1 4:1 5:1 6:1 7:1

E: Water content (wt%) 10 40 70 100 130

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significantly increasing the number of current scientific reports inbiotechnology related journals. Tamalampudi et al. [26], havecompared R. oryzae immobilized lipase with one of the mostcommon biocatalyst for biodiesel production, Novozym 435. Theyshowed that R. oryzae lipase is more efficient than Novozym 435,regardless of alcohol type.

Process optimization is important and helpful for the industri-alization and development of biodiesel production. RSM-utilizingstatistical methods can provide a research strategy for studyingparameter interaction; thus, biodiesel production in differentconditions, with minor estimation error, can be modeled with RSM[27]. By utilizing this technique, existing data can be used to avoidtrial runs by clarifying the reaction conditions [28].

Compared with the quadratic model in RSM, ANN has also beenused to model the process. ANN is an information processingmodel simulated from the biological neuron system. Theinterrelated network construction of direct processing basicsconducts the data processing with parallel computation [29,30].

In the present study, heterogeneous transesterification of crudejatropha oil using immobilized R. oryzae lipase catalyst has beeninvestigated. This reaction was carried-out in a batch process usingmethanol under different reaction conditions based on theexperimental design to produce biodiesel under ambient reactionconditions. In addition, RSM and ANN were applied to determinethe optimum reaction conditions.

2. Materials and methods

2.1. Materials

R. oryzae lipase and methanol were obtained from Sigma–Aldrich (USA), whereas boric acid and PVA 72000 were supplied byMerck Schuchardt OHG (Germany). Other necessary chemicals,such as calcium chloride and sodium sulphate, were procured fromGCE laboratory Chemicals (UK). Sodium alginate was obtainedfrom Fluka, Switzerland while Jatropha oil was supplied by a localcompany in Malacca, Malaysia. The fatty acid compositions of theoil and its characteristics are reported in Table 1.

2.2. Statistical methods

A five level central composite design with four factors wasemployed to determine the effect of variables, such as reactiontemperature (A), reaction time (B), methanol/oil ratio (C), andwater content (D), on FAME yield. Feasibility experiments wereconducted before the experimental design to determine the rangeof these four variables; the selected ranges are shown in Table 2.

The total number of experimental runs was calculated using theformula: 2K + 2K + 6, where K is the number of variables. Thenumber of experiments for the current study was 30, wherein 24experiments and 6 imitation tests at the design center wereconducted randomly to estimate the error.

Table 1Physical properties and chemical composition of jatropha oil.

Property Test method Results

Viscosity at 40 8C (mm2/s) ASTM D-445 34.20

Density at 15 8C (kg/m3) ASTM D-4052 908

Acid Value (mg KOH/g) ASTM D-974 3.83

Water content (%) ASTM D-2709 0.09

Fatty acid profile (%) ASTM D-1983

Palmitic acid 15.3

Stearic acid 5.8

Oleic acid 43.5

Linoleic acid 33.7

Others 1.7

Please cite this article in press as: Zarei, A., et al., Immobilized lipaseand modeling. J. Taiwan Inst. Chem. Eng. (2013), http://dx.doi.org/10

Eq. (1) is a suitable and simple model for optimization. Itsresponse is based on selected variables by quadratic and linearterms as follows:

h ¼ bo þXk

j¼1

b jx j þXk

j¼1

b j jx2j þ

Xi

Xk

< j¼2

bi jxix j þ ei (1)

where h is the response, bo is the constant coefficient, xi and xj arethe independent factors, bj, bjj, and bij are the coefficients for linear,quadratic, and interaction effects, respectively, and ei is the error.The polynomial quality was fitted by the determination coeffi-cients, R2 and R2

ad j, in Eqs. (2) and (3) [31].

R2 ¼ 1 � SSresidual

SSmodel � SSresidual(2)

R2ad j ¼ 1 � SSresidual=DFresidual

ðSSmodel þ SSresidualÞ=ðDFmodel þ DFresidualÞ(3)

In these equations, DF is the degree of freedom and SS is the sumof squares. Subsequently, the response yield was measured at theend of the experiment. MATLAB 7.11.0 (R2010b) and Design-Expert 8.0.6 softwares were utilized for appropriate modeling,optimization, and experimental design.

2.3. Artificial neural network

The back propagation (BP) network with Marquadt Levenbergalgorithm (MLA) was used for the selected model because of its fasttraining in ANN. Fig. 1 displays that the BP network includes theinput, hidden, and output layers. The input variable values areforwarded to the hidden layers by the input nodes, followed by theestimation of the final model by the output nodes.

In ANN, several important parameters exist in network training,including the learning rate, which influences the speed of training.A moderate learning rate can help obtain a good network in a shorttime. The number of neurons in a hidden layer is another factor fora good network; network performance can be increased with moreneurons. However, a constantly high number of neurons do notindicate better prediction capability.

The data in the training process were divided into three parts,namely, training, validation, and testing. The training data wereused in the network during training; the weights and biases wereadjusted according to their error. Validation data were used tomeasure network generalization. The training can be stoppedwhen generalization stops improving. The two sets of data dictatethe performance of the approximation function and the predictioncapacity. The testing data do not affect the training, they onlyprovide an independent measure of network performance duringand after training. A training algorithm, which is a rule of ANN, wasused to update the weights and biases of the network to reduce theerror caused by the contrast between the values of the simulateddata and the target (experimental data). In the current study, MLAwas adopted to assist these parameters.

-catalyzed transesterification of Jatropha curcas oil: Optimization.1016/j.jtice.2013.05.015

Fig. 1. BP network mode.

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2.4. Catalyst immobilization

The immobilization technique used was based on a previousstudy [32], but sodium sulfate was utilized as a treatment solutionat the end of the immobilization process in this study.

2.5. Surface morphologies

Field emission scanning electron microscopy was used toexamine the surface morphologies and analyze the trace elementin the PVA-alginate beads, as well as the cross-sectional structure ofthe beads. Fig. 2 clearly portrays the formed layers of the cross-linked polymers in the inner surface of the beads, where themicrostructure and stability of the beads with uniformly distributedlayers can be distinguished. The stability of the beads can beattributed to the use of highly concentrated PVA. The beads hadmore compact layers with much denser structure; thus they werenot dissolved in a warm and shaking mixture during the process[33]. The formation of the pores on the outer layer of the beads isrevealed in Fig. 3. The pores were uniformly distributed withaverage sizes. Moreover, the surface morphology of the supportdisplayed that the enzyme was attached to the support layers.

3. Experimental

3.1. Catalyst preparation

A specific amount of lipase was mixed with phosphate andstirred at 140 rpm for 10 min. The mixture was then kept at 4 8C

Fig. 2. Image of lipase immobilized in PVA-Alginate beads taken using FESEM.

Please cite this article in press as: Zarei, A., et al., Immobilized lipaseand modeling. J. Taiwan Inst. Chem. Eng. (2013), http://dx.doi.org/10

before filtration under sterile conditions. PVA and sodium alginatewere utilized for enzyme immobilization. After preparing the PVAsolution, sodium alginate and lipase were blended into thesolution. The mixture was then injected drop wise into a solutioncontaining boric acid and calcium chloride, with continuousstirring for 30–50 min to produce the beads. The beads were keptat 4 8C for 24 h. Subsequently, 10% boric acid solution was addedwhile the beads were stirred for 30 min and treated with 0.5 Msodium sulphate solution for another 30 min. The beads were thenstored at 4 8C for further use. The experiment was conducted understerile conditions.

3.2. Biodiesel production

Biodiesel was produced using a 2 mm thick glass vial as thereactor vessel. The vial was sealed tightly with a cap of silicon rubberto preserve the vaporized compositions. The reactor vessel wasplaced in an incubator shaker for agitation and warming. Thereaction mixture consisted of crude jatropha oil, immobilized lipase,methanol, and water. At a certain reaction interval, a sample reactionmixture was extracted and centrifuged. A 100 mL sample was thenwithdrawn from the supernatant and analyzed for biodiesel yieldusing gas chromatograph–mass spectroscopy (GC–MS). Each testwas carried out in triplicate to ensure reproducibility.

3.3. Sample analysis

The major properties of biodiesel, such as kinematic viscosity,neutralization value, flash point, cloud point, pour point, anddensity, obtained according to the American Society for Testing

Fig. 3. Outer layer of a bead.

-catalyzed transesterification of Jatropha curcas oil: Optimization.1016/j.jtice.2013.05.015

Table 3Fuel properties of obtained biodiesel from jatropha oil.

Property Test method ASTM

specification

Biodiesel

Viscosity at 40 8C (mm2/s) ASTMD-445 1.9 to 6.0 4.39

Density at 15 8C (kg/m3) ASTM D-4052 875 to 900 876

Neutralization value

(mg KOH/g)

ASTM D-664 Max 0.5 0.27

Cloud point (8C) ASTM D-2500 �3 to 12 4

Pour point (8C) ASTM D-97 �15 to 10 5

Flash point (8C) ASTM D-93 Min 130 163

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and Materials International (ASTM) D6751 method are listed inTable 3. The biodiesel product was analyzed using AgilentTechnologies 6890 N GC–MS with an inert mass selective detector5975. The capillary column was an Agilent 19091S-433 HP-5MS(30 mm � 250 mm � 0.25 mm), with helium as the carrier gas. Theinitial temperature of the oven was 80 8C for 30 s, and thensubsequently increased at the rate of 10 8C/min until thetemperature reached 250 8C, which was held constant for 5 min.The analysis was conducted for a total period of 42 min. Thetemperature of the detector was 250 8C, whereas the temperatureof the injector was 325 8C. FAME yield was calculated using Eq. (4).

Yield% ¼ ðFAME area from GCMS � weight of productÞðweight of oil sampleÞ � 100 (4)

4. Results and discussion

4.1. Experimental results

The relationships between the four independent variables(reaction temperature, time of reaction, methanol/oil ratio, and

Table 4Design of experiments and RSM, ANN and experimental results.

Run Temperature (8C) Reaction time (h) Methanol/oil ratio

(mol/mol)

W

1 35.00 5.00 4.00

2 45.00 5.00 4.00

3 35.00 13.00 4.00

4 45.00 13.00 4.00

5 35.00 5.00 6.00

6 45.00 5.00 6.00

7 35.00 13.00 6.00

8 45.00 13.00 6.00

9 35.00 5.00 4.00 1

10 45.00 5.00 4.00 1

11 35.00 13.00 4.00 1

12 45.00 13.00 4.00 1

13 35.00 5.00 6.00 1

14 45.00 5.00 6.00 1

15 35.00 13.00 6.00 1

16 45.00 13.00 6.00 1

17 30.00 9.00 5.00

18 50.00 9.00 5.00

19 40.00 1.00 5.00

20 40.00 17.00 5.00

21 40.00 9.00 3.00

22 40.00 9.00 7.00

23 40.00 9.00 5.00

24 40.00 9.00 5.00 1

25 40.00 9.00 5.00

26 40.00 9.00 5.00

27 40.00 9.00 5.00

28 40.00 9.00 5.00

29 40.00 9.00 5.00

30 40.00 9.00 5.00

Please cite this article in press as: Zarei, A., et al., Immobilized lipaseand modeling. J. Taiwan Inst. Chem. Eng. (2013), http://dx.doi.org/10

water content) and biodiesel yield were studied. The FAME yieldfor each experimental run and the yields from both ANN and RSMmodels are listed in Table 4.

4.2. Regression model and statistical analysis by RSM

The RSM response obtained in Table 4 corresponded to the fourindependent variables utilizing a polynomial equation such as Eq.(1). Least squares regression was employed to fit the acquired datato Eq. (1). The best fit model in terms of actual factors for the FAMEyield is described in Eq. (5).

Yield ¼ �20:83 þ 3:62A þ 1:22B þ 6:36C þ 0:22D þ 0:01AB

� 0:20AC � 2:3 � 10�3AD þ 0:40BC � 2:86 � 10�3BD

þ 9:37 � 10�3CD � 0:04A2 � 0:06B2 � 0:70C2 � 1:05

� 10�3D2 (5)

The ANOVA assessments of the best fit model indicated that themodel is suitable for describing the experimental work, as shownin Table 5. The F-value at 51.88 indicated the significance of thequadratic model. Furthermore, the effect of each term in the modelwas evaluated; the highly significant effect of some variables wasconcluded based on the F-value. The linear terms for reactiontemperature (A), time (B), methanol/oil ratio (C), and all of thevariables in quadratic form, as well as the interaction terms for twofactors (AD and BD) tabulated in Table 5 have significantly affectedthe biodiesel yield because of the high F-values and low p-values(<0.05).

A p-value of greater than 0.05 indicates that the correspondingmodel term is not significant. Table 6 shows that the twointeraction terms, namely, temperature–methanol/oil ratio (AC)and time–methanol/oil ratio (BC), registered higher p-values. Thus,

ater content (wt%) Exp. yield (%) ANN yield (%) RSM yield (%)

40.00 77.50 77.99 77.83

40.00 79.00 78.88 78.77

40.00 84.00 84.20 83.45

40.00 85.00 86.42 85.51

40.00 76.00 76.17 76.43

40.00 77.00 76.84 77.00

40.00 82.00 81.79 82.68

40.00 84.00 85.13 84.37

00.00 79.00 80.24 78.93

00.00 79.00 79.15 78.50

00.00 83.00 83.46 83.18

00.00 84.00 83.93 83.87

00.00 79.00 79.06 78.66

00.00 77.00 77.14 77.85

00.00 83.00 83.45 83.53

00.00 84.00 83.89 83.85

70.00 80.00 80.90 79.64

70.00 81.00 81.18 80.89

70.00 75.00 75.70 75.00

70.00 87.10 87.07 86.62

70.00 82.00 82.34 82.47

70.00 82.00 81.70 81.05

10.00 81.00 81.25 80.47

30.00 81.00 81.05 81.05

70.00 84.00 84.60 84.55

70.00 85.00 84.60 84.55

70.00 84.50 84.60 84.55

70.00 85.00 84.60 84.55

70.00 84.00 84.60 84.55

70.00 84.80 84.60 84.55

MSE 0.28 0.19

R2 0.97 0.98

-catalyzed transesterification of Jatropha curcas oil: Optimization.1016/j.jtice.2013.05.015

Table 5Analysis of variance (ANOVA) for regression model.

Source Sum of squares df Mean Square F value p-Value prob > F

Model 281.4305 14 20.10218 51.87659 < 0.0001 Significant

A-Temp. 2.34375 1 2.34375 6.048387 0.0265

B-time 202.4204 1 202.4204 522.3753 < 0.0001

C-ratio 3.010417 1 3.010417 7.768817 0.0138

D-Water cont. 0.510417 1 0.510417 1.317204 0.2691

AB 1.265625 1 1.265625 3.266129 0.0908

AC 0.140625 1 0.140625 0.362903 0.5559

AD 1.890625 1 1.890625 4.879032 0.0432

BC 0.390625 1 0.390625 1.008065 0.3313

BD 1.890625 1 1.890625 4.879032 0.0432

CD 1.265625 1 1.265625 3.266129 0.0908

A^2 31.51313 1 31.51313 81.32419 < 0.0001

B^2 23.9467 1 23.9467 61.79793 < 0.0001

C^2 13.32027 1 13.32027 34.37488 < 0.0001

D^2 24.5917 1 24.5917 63.46244 < 0.0001

Residual 5.8125 15 0.3875

Lack of Fit 4.7375 10 0.47375 2.203488 0.1983 Not significant

Pure Error 1.075 5 0.215

Cor Total 287.243 29

Table 6ANOVA for response surface reduced quadratic model.

Source Sum of squares df Mean square F value p-Value prob > F

Model 280.8993 12 23.40827 62.72955 <0.0001 Significant

A-Temperature 2.34375 1 2.34375 6.280788 0.0227

B-time 202.4204 1 202.4204 542.4468 <0.0001

C-ratio 3.010417 1 3.010417 8.067323 0.0113

D-Water content 0.510417 1 0.510417 1.367816 0.2583

AB 1.265625 1 1.265625 3.391626 0.0830

AD 1.890625 1 1.890625 5.066502 0.0379

BD 1.890625 1 1.890625 5.066502 0.0379

CD 1.265625 1 1.265625 3.391626 0.0830

A^2 31.51313 1 31.51313 84.44897 <0.0001

B^2 23.9467 1 23.9467 64.17243 <0.0001

C^2 13.32027 1 13.32027 35.69569 <0.0001

D^2 24.5917 1 24.5917 65.9009 <0.0001

Residual 6.34375 17 0.373162

Lack of Fit 5.26875 12 0.439063 2.042151 0.2220 Not significant

Pure Error 1.075 5 0.215

Cor Total 287.243 29

Fig. 4. The Predicted vs. Actual plot.

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they were considered insignificant terms and were removed toimprove the current model and produce an enhanced new model.The determination coefficient (R2) and the regression equationwere evaluated to test the fit of the model [34]. The R2 of thereduced model decreased from 0.9798 to 0.9779 because ofvariable reduction from the regression model [35]. Meanwhile, theadjusted determination coefficient (Radj) increased from 0.9609 to0.9623 after excluding unnecessary terms [36]. The F-value of themodified model increased to 62.73, but its p-value remained<0.0001, indicating that the model improved after deleting the ACand BC terms.

The final practical model after modification based on theANOVA data and statistical parameters is given by Eq. (6) in termsof uncoded factors.

Yield ¼ �18:84 þ 3:53A þ 1:42B þ 5:96C þ 0:22D þ 0:014AB

� 2:30 � 10�3AD � 2:86 � 10�3BD þ 9:38 � 10�3CD

� 0:04A2 � 0:06B2 � 0:70C2 � 1:05 � 10�3D2 (6)

A p-value <0.0001 indicates that there is only a 0.01%probability that a model with a similarly large F-value becomesthe noise product in the data. Moreover, the lack of fit F-value was2.04, denoting that the lack of fit was not significant in pure error[37].

Please cite this article in press as: Zarei, A., et al., Immobilized lipaseand modeling. J. Taiwan Inst. Chem. Eng. (2013), http://dx.doi.org/10

Fig. 4 plots the predicted versus the actual FAME yields andshows that the experimental and predicted values are in proximityto each other. This result indicates the accuracy of the model tocapture the correlation between biodiesel yield and the indepen-dent reaction variables.

-catalyzed transesterification of Jatropha curcas oil: Optimization.1016/j.jtice.2013.05.015

Fig. 5. The effect of reaction temperature and reaction time on FAME yield, for water

content of 70 wt% and Methanol/oil Ratio of 5%.

Table 7Weights and biases of BP network.

W1a W2b

X1 X2 X3 X4 Biasa 1 �0.2295

1 5.4469 �0.1077 �0.1834 5.6632 �2.7542 2 0.2752

2 �2.5956 3.3872 �2.5042 10.8558 3.5282 3 0.1914

3 3.1446 4.1704 0.8810 �1.7310 �1.8694 4 0.0915

4 1.0327 �6.6608 �1.1976 1.2791 �1.9580 5 �0.6393

5 �0.9082 �1.2714 0.0945 0.2958 �0.5990 6 0.2344

6 �3.9774 4.2756 1.1401 �2.6631 2.1926 Biasb �0.4413

a Weights and biases between the input layer and hidden layer.b Weights and biases between the hidden layer and output layer.

Fig. 6. The effects of methanol/oil ratio and water content on FAME yield for a total

time of 17 h and reaction temperature of 40 8C.

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4.3. Prediction with ANN

The weights and biases of the BP network are shown in Table 7.Biodiesel yield under different ranges of input conditions can bepredicted using this table.

The bias and weight values of the hidden, input, and outputlayer neurons were detected based on the maximum amount of R2

and on another important factor, namely, the mean squared error(MSE), which refers to the lower value for better performance, tooptimize the network parameters. These two factors werecalculated using Eqs. (7) and (8) [29]:

MSE ¼ 1

n

Xn

i¼1

y pi � yai

� �2(7)

R2 ¼ 1 �Pn

i¼1 ðy pi � yaiÞ2

Pni¼1 ðy pi � ymÞ

2(8)

where n is the data set number, yai and ypi are the actual and thepredicted output values of the ith set, respectively, and ym is theactual output value mean.

The low MSE (0.28) and high R2 (0.97) given in Table 4 indicatedthe predicted and the actual values of the experimental data fittedwell [38]. However, the slight differences signified that RSMperformance was better than ANN. Nevertheless, the results weregenerally similar and both models are acceptable.

4.4. Influence of reaction temperature and time

The statistical analysis of the experimental data indicated thattemperature (A) and time (B) were important and effectivevariables in response analysis and they have large, linear, andpositive influence on FAME yield. Fig. 5 demonstrates the surfaceplot of the response for the production of biodiesel. The biodieselyield was enhanced with the increase in reaction temperatureand then reduction trend in the yield of biodiesel can be seenafter the optimum temperature under conditions that themethanol/oil molar ratio was 5:1 and water content was 70%(w/w). Similar trend was observed for the entire range of reactiontime. Fig. 5 exhibits that the maximum biodiesel yield obtainedwas 87.1% at temperature of 8C and for 17 h reaction time. Theseconditions are reasonable compared with other reports onbiodiesel production from jatropha oil. Hawash et al. [39]produced biodiesel from jatropha oil under supercritical condi-tion, with 100% yield under 4 min reaction time at 320 8C, with8.4 MPa pressure and 43:1 methanol/oil ratio. These conditionsled to costly biodiesel production process. Tamalampudi et al.

[26] produced biodiesel from jatropha oil using R. oryzae onpolyurethane foam particles with 80% yield under ambientconditions (T = 30 8C and alcohol/oil ratio = 3:1), but with longerreaction time (60 h).

Please cite this article in press as: Zarei, A., et al., Immobilized lipaseand modeling. J. Taiwan Inst. Chem. Eng. (2013), http://dx.doi.org/10

4.5. Methanol/oil ratio and water content effects on FAME yield

Based on the ANOVA results, the methanol/oil molar ratio isconsidered as another important parameter that affects thebiodiesel yield. The effect of methanol/oil ratio (C) and moisturecontent (D) on biodiesel yield under the condition where thereaction temperature and time were constant at 40 8C and 17 h,respectively, is shown in Fig. 6. The maximum FAME yield was87.1% in a methanol/oil molar ratio of 5:1 and water content of70 wt%. The biodiesel yield decreased with the alcohol/oil ratiobeyond 5:1.

Based on the stoichiometry of the transesterification reaction,at least three moles of alcohol are required to produce three molesof ester for a reaction with one mole of oil. The reversibility of thereactions and the methanol/oil ratio produce more methyl esterproducts. However, the excess amount of methanol hindersglycerol recovery because of the effect of methanol on glycerolsolubility [40–42]. The existence of glycerol in the solution drivesthe equilibrium back to the left hand side and consequently lowersthe biodiesel yield [42]. Furthermore, the high amount of methanol

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negatively affects the lipase catalyst activity. Thus, the yieldreduced after the alcohol/oil ratio exceeded the optimum amount.

Water content is another important factor in biocatalytictransesterification reaction by helping to avoid lipase deactivation.The biodiesel yield in Fig. 6 increased with the enhancement inwater content but decreased after the water content reached acertain value. The reduction of the transesterification yield can beattributed to hydrolysis as have been reported in previous studies[43–45].

4.6. Validation of the model

The following optimal reaction conditions: temperature (40 8C),time (17 h), methanol/oil molar ratio (5:1), and water content(70 wt%) were determined using the regression model. Triplicateexperiments were carried out at these optimum conditions toconfirm the accuracy and validation of the regression model. Theaverage yield of biodiesel obtained at 87.10% was close to thepredicted value of 87.07%.

5. Conclusions

The experimental results indicated that 87.1% of biodiesel yieldwas obtained under the reaction conditions of 40 8C temperaturewithin 17 h reaction time, 5:1 alcohol/oil molar ratio, and 70 wt%water content. The predicted FAME yield was experimentallyvalidated. Both the predicted and experimental results fitted well,where the R2 for RSM and ANN models were 0.98 and 0.97,respectively. The models can provide good alternatives tocumbersome laboratory testing. The immobilized lipase catalystexhibited good activity for biodiesel production, as indicated bythe major properties that complied with the ASTM D6751standard. Therefore, immobilized R. oryzae lipase has highpotential to be used in the enzymatic production of biodieselunder ambient reaction conditions.

Acknowledgements

The authors would like to express their sincere gratitude to theMinistry of Higher Education (MOHE), Malaysia for supporting theproject under the Fundamental Research Grant Scheme (FRGS)Vots 78402 and 78681.

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