energy conversion and management · 2018. 1. 6. · extra purity oleic acid (p97%) was purchased...

Energy Conversion and Management 76 (2013) 818–827

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Energy Conversion and Management

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Optimization of oleic acid esterification catalyzed by ionic liquidfor green biodiesel synthesis

0196-8904/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.enconman.2013.08.029

⇑ Corresponding author. Tel.: +60 7 553 5579; fax: +60 7 558 8166.E-mail address: [email protected] (N.A. Saidina Amin).

Ahmad Hafiidz Mohammad Fauzi , Nor Aishah Saidina Amin ⇑Chemical Reaction Engineering Group (CREG), Faculty of Chemical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia

a r t i c l e i n f o

Article history:Received 24 March 2013Accepted 13 August 2013

Keywords:EsterificationIonic liquidOptimizationRecycleGreen

a b s t r a c t

In order to improve the efficiency of biodiesel production from esterification of free fatty acids, an alter-native to sulfuric acid has been explored. In this study, esterification of oleic acid was performed using1-butyl-3-methylimidazolium hydrogen sulfate ([BMIM][HSO4]) ionic liquid for green biodiesel produc-tion. Response surface methodology (RSM) based on central composite design (CCD) was employed tostudy the effect of independent parameters on the process and also for single-objective optimization,while artificial neural network–genetic algorithm (ANN–GA) was utilized to simultaneously optimizethe responses of the reaction (methyl oleate yield and oleic acid conversion). From the results, the pre-dicted mathematical models for both methyl oleate yield and oleic acid conversion covered more than80% of the variability in the experimental data. Furthermore, the linear temperature coefficient was iden-tified as the most influential coefficient towards both responses. Higher responses were predicted formulti-objective optimization using ANN–GA compared to single-objective optimization using RSM. Theoptimum responses predicted using multi-objective optimization were 81.2% and 80.6% for methyl oleateyield and oleic acid conversion, respectively. The conditions to achieve optimum response were metha-nol–oleic acid molar ratio of 9:1, catalyst loading (0.06 mol), reaction temperature (87 �C), and reactiontime (5.2 h). Furthermore, there was only small decrease in the catalytic activity of the IL after beingrecycled for five successive runs.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Primary energy sources such as petroleum, natural gas and coalare all non-renewable, and relying on them for energy generationis not sustainable. According to U.S. Energy Information Adminis-tration (EIA), in 2011, transportation is the second largest energyconsumer in the US after the industrial sector, and it is forecastedthat the consumption will continue to grow until year 2040, wherethe transportation sector depends mostly on petroleum products[1]. On top of that, Maggio and Cacciola [2] forecasted that the peakproductions of oil was estimated to be between year 2009 and2021, after which the oil production was expected to decline. Inorder to secure sustainable energy source, particularly for trans-portation sector, the search for alternatives to fossil fuel hasbecome a top priority.

Biodiesel can be produced by transesterification of triglyceridesor esterification of free fatty acids (FFAs). Alcohols, such asmethanol and ethanol, are usually used as the acyl acceptor dueto cheaper cost and wide availability [3]. Esterification of FFAs isan important process in biodiesel synthesis. Non-edible or low

costs feedstocks usually contain high FFAs [4], which needs to bereduced to less than 1% to prevent saponification of FFA fromoccurring, especially when alkali catalysts are employed. Conven-tional esterification method is conducted in the presence of sulfu-ric acid (H2SO4) for catalyzing the reaction [5–7]. However, thereare some drawbacks associated with the utilization of H2SO4. Theseinclude equipments are susceptible to corrosion problems andacidic wastewater generated from neutralizing biodiesel [8]. Othertypes of catalysts that have been used for esterification processesare sulfated zirconia [9,10], heteropoly acids [11,12], ion exchangeresin [13], and ionic liquids [14–16]. In enzymatic biodiesel pro-duction, ionic liquids are used as co-solvents and not as catalysts,where ionic liquids improved the interaction between reactantsof different phases, whereas the catalysts for the reactions arethe enzymes.

In response to the development of more environmental benignesterification process, ionic liquids (ILs) seem to be a good option.Ionic liquids, defined as molten salts, consist of only cations andanions, and usually appear as liquids under 100 �C. ILs are consid-ered as replacements for volatile organic solvents (VOCs) due tonegligible vapor pressure [17], which eliminate problems associ-ated with volatility and flammability. Biodiesel synthesis can beconducted in the presence of ionic liquids as catalysts due to theirtunable acidic or alkaline behavior. This characteristic depends on

Table 1Experimental range and factor level of process variables.

Factors Symbol Range and levels

�1 0 1

Methanol to oleic acid molar ratio X1 5:1 8:1 12:1Catalyst loading (mol) X2 0.025 0.004 0.055Reaction temperature (�C) X3 30 50 75Reaction time (h) X4 2.5 4.0 6.0

A.H. Mohammad Fauzi, N.A. Saidina Amin / Energy Conversion and Management 76 (2013) 818–827 819

the type of anion attached to the bulky cation, which can be fromBrønsted acid, Lewis acid or alkali groups. Fang et al. [18]conducted esterification of free fatty acids using dicationic ionicliquids, and found that they performed better in terms of catalyticactivity compared to monocationic ionic liquids. Guo et al. [19]added metal chlorides to the ionic liquids in order to provide Lewisacidic sites for enhancing the transesterification of Jatropha oil. Theutilization of ionic liquids as catalysts in biodiesel synthesis hasbeen reviewed recently [20]. The review highlighted different useof ionic liquids for biodiesel synthesis, especially as the catalyst,and the prospect of switchable ionic liquids (SILs) as green solventsthat can switch between the neutral and ionic state with the intro-duction of an external stimulus has been discussed.

Recently, Brønsted acidic ILs are preferred for green biodieselsynthesis via esterification method. The catalytic performance iscomparable or better than those of conventional catalysts [8,14],which can be influenced by the different combination of cationsand anions in ILs. Apart from that, the ILs can also be recycledand reused for subsequent runs. 1-butyl-3-methylimidazoliumhydrogen sulfate ([BMIM][HSO4]) is a Brønsted acid ionic liquidwith acidic counterion, which influence its catalytic performancein reactions. Among all the ionic liquids, [BMIM][HSO4] prevail asthe catalyst with highest catalytic activity in esterification of aceticanhydride [21]. In the reaction, acetates and acetic acid were suc-cessfully produced from the reaction between acetic anhydridewith alcohols. Furthermore, the higher acidity of [BMIM][HSO4]led to higher conversion of crude palm oil (CPO) to biodiesel in atwo-stage process compared to other ionic liquids [15]. FFAs con-tent in the CPO was reduced in the pre-treatment step prior totransesterification process to obtain biodiesel.

Response surface methodology (RSM) is a technique for design-ing experiment, evaluating the effects of process parameters on theresponse, and also optimizing the process. It is widely used formultivariable optimization studies, including biodiesel synthesisvia transesterification process [12,22]. RSM does not only reducedthe number of experiments needed to provide sufficient informa-tion for statistically acceptable result, but it is also a less expensivemethod for obtaining experimental data compared to classicalmethod in terms of time and materials involved. On the otherhand, artificial neural network (ANN) is a non-linear statisticaltechnique, where it can be used to solve problems that are not eli-gible for conventional statistical methods. It has the ability to mod-el linear and non-linear systems without the need to makeassumptions implicitly as compared to most statistical approaches.An advantage of ANN over RSM is that it can approximate almostall kinds of non-linear functions, but the latter is limited only forquadratic approximations [23].

Genetic algorithm (GA) is known to be one of the popular strat-egies to optimize non-linear systems with a number of variables. Itis useful when there are multiple optimum solutions in the rangeof the study, as the technique can identify the global optimumwhen there are multiple maxima present [24]. Furthermore, thecombination of artificial neural network with genetic algorithmproduces a useful tool for predicting and optimizing process thatinvolves multivariable parameters that is cost effective and alsoless time consuming [25]. GA technique is often integrated withartificial neural network method, where GA is applied for optimiz-ing the process parameters to find the optimum solution of a pro-cess [25,26].

Herein, we report for the first time the multi-objective optimi-zation of oleic acid esterification catalyzed by [BMIM][HSO4] usingartificial neural network–genetic algorithm (ANN–GA). Due to thehigh activity exhibited by [BMIM][HSO4] in catalyzing esterifica-tion of long chain FFAs, the catalyst was chosen for conversion ofoleic acid to methyl oleate for biodiesel synthesis. The study isessential for determining the optimum conditions for better

utilization of ionic liquid at industrial scale process, which usuallyrequires large amount of ionic liquids. The significance of processvariables and the interaction between variables on the processwere also studied using response surface methodology. Single-objective optimization was conducted using RSM, while multi-objective optimization was carried out by employing ANN–GA,and their performance in predicting the best conditions for opti-mum responses (methyl oleate yield and oleic acid conversion)were compared. Finally, the IL was recycled to observe its perfor-mance for a few successive runs. It is important to study the activ-ity of recycled IL, as it is several times more expensive compared toother conventional catalysts and needs to be used efficientlyespecially for industrial processes.

2. Materials and methods

2.1. Reagents and materials

Extra purity oleic acid (P97%) was purchased from QReC (NewZealand), while methanol was obtained from Merck (Germany).Oleic acid was chosen as the model compound for biodieselsynthesis as it is the most common components of many naturaloils. The acid value of oleic acid was 236 mg KOH/g, as determinedby potassium hydroxide (KOH) titration method. Meanwhile, theionic liquid, 1-butyl-3-methylimidazolium hydrogen sulfate([BMIM][HSO4]) with purity of P94.5% and water content of61.0% was purchased from Sigma Aldrich (Switzerland). All thechemicals were used as received.

2.2. Experimental design

The study was performed using an experimental design meth-od. STATISTICA 8.0 was used to conduct the statistical analysis ofmethyl oleate yield and oleic acid conversion to methyl oleate.The effect of four process variables (methanol to oleic acid molarratio, catalyst loading, reaction temperature, and reaction time)on the responses of the process was studied using response surfacemethodology based on a factorial central composite design (CCD).The CCD factorial design allows the estimation of all the regressionparameters required to fit a second order model to a given re-sponse. Table 1 represents the experimental range and factor levelfor the esterification process. Twenty-six runs were required,which included full 24 factorial designs (four factors each at twolevels), eight star points, and two center points.

2.3. General procedure for esterification of oleic acid

The process was carried out in a 100 ml three-necked flask at-tached with reflux condenser, thermometer and stopper. In a typ-ical reaction, 0.05 mol (15.8 ml) oleic acid, 10.1–24.3 ml methanol(correspond to 5:1–12:1 M ratio of methanol to oleic acid), and0.025–0.055 mol IL were charged into the flask. Experiments wereconducted at 30–75 �C for 2.5–6 h. Reaction mixture was stirredvigorously at constant stirring rate for all runs. Methanol to oleic

820 A.H. Mohammad Fauzi, N.A. Saidina Amin / Energy Conversion and Management 76 (2013) 818–827

acid molar ratio, catalyst loading, reaction time and reaction tem-perature were varied according to the experimental design for eachrun.

After the designated time for the reaction has been reached, theflask was removed from the oil bath and the reaction was stoppedby quickly submerging the flask in a water bath to cool the mixtureto room temperature. Then, the reaction mixture was transferredto a separating funnel to allow separation of products from by-products, and left to settle overnight. When the mixture becamebiphasic, the upper and the lower layers could be isolated bydecantation. The upper layer, which mainly comprised of methyloleate, was later washed with distilled water to remove any impu-rities before heating in the oven at 105 �C for 24 h to remove tracesof water and unreacted methanol.

2.4. Product analysis

Methyl oleate content (C) was determined by using a gas chro-matograph (GC) equipped with a flame ionization detector (FID)(Perkin Elmer, USA). The analysis was conducted using a DB-Waxcolumn (30 m � 0.53 mm, 0.5 lm), where 1 ll of sample was in-jected into the GC for each run. The injector temperature was setto 250 �C, while the detector temperature was kept at 270 �C.The oven temperature was maintained at 60 �C for 1 min, and thenincreased to 220 �C in 10 min as the final oven temperature. Nitro-gen was used as the carrier gas for the GC. Methyl oleate contentwas expressed as a relative percentage of the total peak area. Themethyl ester was identified by comparing its retention time tothe retention time of methyl oleate standard. Methyl oleate yield(Y1) was then calculated using methyl oleate content and the ratioof the weight of methyl oleate to the weight of oleic acid used inthe feed, as shown in:

Methyl oleate yield; Y1 ð%Þ ¼C� Weight of product ðgÞWeight of oleic acid in feed ðgÞ100% ð1Þ

On the other hand, the conversion of oleic acid (Y2) was ob-tained by determining the acid number of oleic acid and the prod-uct, and calculated using Eq. (2)[15]. AVi is the initial acid value(i.e. acid value of oleic acid), while AVt is the final acidic value ofmethyl oleate produced. Potassium hydroxide (KOH) titrationwas carried out to determine the acid value.

Oleic acid conversion; Y2 ð%Þ ¼AVi � AVt

AVi

� �� 100% ð2Þ

2.5. Data analysis

2.5.1. Response surface methodology (RSM)After the completion of experimental works and related data

were obtained, STATISTICA 8.0 was used once again to conductdata analysis related to the study. Mathematical models that de-scribed the relationship and interaction between independent vari-ables and responses were produced for both methyl oleate yieldand oleic acid conversion. The model, in the form of a quadraticpolynomial equation, was developed for predicting the responseas a function of independent variables and their interactions. Theoverall general equation can be written as in Eq. (3)[27]:

Y ¼ b0 þX4

j¼1

bjXj þX4

j¼1

bjjX2j þ

Xi<j

bijXiXj ð3Þ

In this equation, Y is the predicted response for the process, b0 is theintercept coefficient (offset), bj are the linear terms, bjj are thequadratic terms, bij are the interaction terms, and Xi and Xj are theuncoded independent variables [27].

Analysis of variance (ANOVA) was carried out to check the ade-quacy of the models obtained earlier, conducted at 5% level of sig-nificance. The analysis included Fisher F-test to examine the fitnessof the model by comparing the F-value from the experiment andalso the tabulated F-value. Fisher F-test is often adopted in ANOVAto check the adequacy of the predicted model [28,29]. Generally,the calculated F-value must be greater than the F-value obtainedfrom the standard distribution table to indicate that the modelgives good prediction of the experimental results [22].

Pareto chart was plotted to study the significance of eachcoefficient and the interactions between variables on the response.Pareto chart of effects is a useful tool to represent the results of anexperiment, where the ANOVA effect estimates of parameters aresorted from the largest absolute value to the smallest absolutevalue [30]. The magnitude of each effect is represented by its indi-vidual column, where the effect going across the p = 0.05 line indi-cates the effect is statistically significant at 95% confidence interval[30]. Student’s t-test and p-value test were used to check the sig-nificance of the equation parameters for a response, where theparameter with the smallest p-value is defined as the most signif-icant parameter in the esterification of oleic acid.

Response surface plots were used to visualize the interactions ofindependent variables on the responses. The three-dimensionalplots were produced by keeping two of the four independent vari-ables constant at their center points, while the other two variableswere varied along the x- and y-axis, with the response was as-signed to the other axis.

Single-objective optimization was carried out for both re-sponses separately. The optimum response with their correspond-ing conditions was predicted using STATISTICA 8.0, where thesoftware predicted the optimum point using the method of steep-est ascent based on the mathematical models predicted. Finally,canonical analysis was performed to examine the characteristicof the stationary point, where the nature of the point can be deter-mined as maximum, minimum or saddle, based on the state ofeigenvalues (i.e. positive or negative) obtained from the analysis.Detailed explanations and derivations regarding canonical analysisare available elsewhere [27,31].

2.5.2. Artificial neural network–genetic algorithm (ANN–GA)Fig. 1 represents the flow chart of methodology to simulta-

neously find optimum conditions for both responses using ANN–GA. The development of network and optimization of processparameters were conducted using MATLAB software. The type ofnetwork used was a feed-forward back propagation network(FFBP). The first phase involved the determination of number ofneurons in hidden layer (1–20), and the number of neurons thatgave the lowest mean square error (MSE) of the experimental datawas chosen for the next phase. Then, the training of the networkwas conducted to search for the network with the lowest MSE.Finally, the best network was used to simultaneously find the opti-mum process parameters for methyl oleate yield and oleic acidconversion using the genetic algorithm tool available from MAT-LAB software.

2.6. Recycling of ionic liquid

Ionic liquid was recycled after withdrawing the lower layerfrom the separating funnel. The lower phase was a mixture ofthe ionic liquid, water and unreacted methanol. The mixture wasleft in the oven overnight to remove water and unreactedmethanol. The oven was set to 105 �C to ensure these elementswere completely removed, especially water. The remaining ionicliquid was later reused for biodiesel synthesis for few subsequentcycles.

Fig. 1. Flow chart for ANN–GA methodology.


3. Results and discussions

3.1. Methyl oleate yield for esterification of oleic acid

3.1.1. ANOVA for methyl oleate yieldResponse surface methodology was employed to study the

interaction between the operating parameters and the responses.

Table 2Corresponding experimental and predicted responses for esterification of oleic acid.

No. Process variables

X1 X2 (mol) X3 (�C) X4 (h)

1 8 0.040 96.3 42 12 0.055 75 33 5 0.055 30 64 5 0.055 75 35 5 0.025 30 36 0.8 0.040 50 47 12 0.025 75 38 15.2 0.040 50 49 8 0.009 50 4

10 8 0.040 50 0.411 12 0.025 30 312 8 0.071 50 413 5 0.025 30 614 12 0.055 30 615 5 0.055 30 316 5 0.025 75 317 8 0.040 50 418 5 0.055 75 619 8 0.040 3.7 420 12 0.055 75 621 8 0.040 50 422 12 0.055 30 323 12 0.025 30 624 12 0.025 75 625 8 0.040 50 7.626 5 0.025 75 6

The design of experiments, experimental results and predicted re-sponses are given in Table 2. The predicted quadratic model thatrelates methyl oleate yield (Y1) with independent variables isshown in:

Y1 ¼ �35:17þ 10:22X1 þ 655:93X2 þ 1:42X3 � 5:34X4

� 0:28X21 � 953:22X2

2 � 0:01X23 � 0:34X2

4 � 148:23X1X2

� 0:08X1X3 þ 0:86X1X4 þ 9:54X2X3 þ 35:92X2X4

þ 0:02X3X4 ð4Þ

The fitness between the experimental and predicted values canbe verified using coefficient of determination (R2). By using STAT-ISTICA 8.0 to analyze the data, the value of R2 for the model pre-dicting methyl oleate yield was found to be 0.832. This indicatesthat less than 20% of the total variations did not fit the model,i.e. 83.2% of the total variation in the response was justified bythe fitted model. The obtained R2 showed that the model was reli-able in predicting the response as more than 80% of the variabilityin the experiments were covered, and the value of R2 obtained islarger than the minimal R2 of 0.75 for adequate explanation onthe variability in the experiments [22].

The model was then tested for its adequateness by analysis ofvariance (ANOVA), and the statistical analysis is represented in Ta-ble 3. The mathematical model was tested with 5% significance le-vel. From the calculation, it was found that the calculated F-value(3.90) model for methyl oleate yield was higher than the F-valuefrom the distribution table (F0.05,14,11 = 2.74), which confirmed thatthe model was significant.

The relative importance of regression coefficients for the math-ematical model representing methyl oleate yield was examined byplotting Pareto chart, as depicted in Fig. 2. It is clearly shown thatthe linear term for reaction temperature (X3) has the largest effecton methyl oleate yield, based on the largest t-value, with 100%significant level for methyl oleate yield. The positive coefficientalso indicates that it has a positive effect towards the response.Guo et al. [19] also determined that reaction temperature as themost significant variable for esterification of oleic acid using

Yield, Y1 (%) Conversion, Y2 (%)

Experiment Predicted Experiment Predicted

67.6 67.9 82.1 74.437.3 36.7 67.7 70.127.9 23.3 35.3 36.198.7 75.7 58.7 68.135.5 35.5 32.0 36.155.1 53.4 65.3 57.665.4 64.6 65.5 62.031.5 43.8 34.3 50.955.7 56.4 59.4 61.254.0 53.7 51.6 47.950.1 50.4 45.7 48.560.1 59.0 62.5 60.438.2 22.0 47.0 53.254.3 53.2 39.5 31.233.3 30.9 31.0 39.257.9 61.1 55.6 58.864.5 59.1 66.8 61.087.6 85.7 63.2 66.122.7 21.2 5.3 7.160.5 59.6 75.1 73.759.2 59.1 66.4 61.024.8 25.1 29.2 26.549.4 50.7 46.2 44.780.5 79.9 72.5 71.253.8 51.9 62.4 61.961.0 62.2 59.8 62.1

Table 3ANOVA results for methyl oleate yield.

Sources Sum ofSquares(SS)

Degree ofFreedom(d.f.)

MeanSquares(MS)

F-value(calculated)

F-tabulated(a = 0.05)

Methyl oleate yieldRegression 7305.20 14 521.80 3.90 2.74Residual 1472.53 11 133.87Total 8777.73 25

Fig. 2. Pareto chart for methyl oleate yield.


1-butyl-3-methylimidazolium tosylate ([BMIM][CH3SO3]) as cata-lyst. Another coefficient that is significant at 95% confidence levelis the interaction between methanol–oil molar ratio and catalystloading (X1X2), but it gives negative effect on the response, basedon the negative t-value (i.e. �2.6998). Other terms in the mathe-matical model were found to be insignificant at 95% confidence le-vel, with two of the most insignificant coefficients being linearterm of catalyst term (X2) and quadratic term of catalyst loading

X22

� �.

Other factors were significant as well, but at lower significancelevel than 95%, and the model was well represented by these fac-tors. There are other publications that also have two or three fac-tors that are significant at 95% significance level, but the modelswere generally acceptable. These include ethanol production fromthick juice [32], supercritical extraction of bio-oils from Germanbeech wood [33], and ultrasound-assisted transesterification ofsoybean oil for biodiesel production [34].

3.1.2. Response surface plots for methyl oleate yieldThe graphical representation of interaction between indepen-

dent variables and methyl oleate yield are presented as responsesurface plots in Fig. 3. Fig. 3a relates methanol–oleic acid molar ra-tio and reaction temperature with methyl oleate yield. The yieldincreased significantly when reaction temperature reached 40 �C,but there was not much improvement in the response for reactiontemperature above 80 �C. On the other hand, increasing molar ratiodid not significantly improve the response for reaction tempera-ture less than 40 �C. The yield only went up to nearly 50% whenlower molar ratio until 16:1 was employed at reaction temperatureof 40 �C. Meanwhile, the interaction between methanol–oleic acidmolar ratio and reaction time is depicted in Fig. 3b. Methyl oleateyield decreased in shorter reaction time and at higher molar ratio;also in longer reaction time and at lower molar ratio.

Fig. 3c illustrates the response surface plot for interaction be-tween catalyst loading and reaction temperature. The figure clearly

depicts that the yield did not exceed 60% for reaction temperaturelower than 40 �C even by increasing the catalyst loading. This sup-ports earlier finding that reaction temperature is the most influen-tial variable for methyl oleate yield in the esterification reaction.Once the temperature passed 40 �C, there was not much variationin the response, especially after 80 �C. Long et al. [35] also foundout that increasing the temperature resulted in higher yield oftransesterification of soybean oil to biodiesel, with small changein the response was observed for the temperature in the range be-tween 80 �C and 90 �C.

Fig. 3d shows the interaction between reaction time and reac-tion temperature with respect to methyl oleate yield. Reactiontime was insignificant towards the response for temperature lowerthan 40 �C, where the yield did not exceed 40%. Even prolongingthe reaction time to 8 h did not improve the yield for temperaturebelow than 40 �C. Higher temperature was required to reach theoptimum yield (i.e. 100 �C), with reaction time of approximately7 h.

3.1.3. Single-response optimization and canonical analysis of methyloleate yield

The optimization of the operating variables for methyl oleateyield was conducted in order to improve the efficiency of the pro-cess using STATISTICA 8.0. The software predicted that optimummethyl oleate yield for esterification of oleic acid by [BMIM][HSO4]was 77.7%, which can be obtained by using molar ratio methanol–oleic acid of 13.2:1, catalyst loading (0.05 mol), reaction tempera-ture of 93.2 �C and reaction time of 14.0 h.

Canonical analysis was carried out in order to examine the nat-ure of the optimum response at stationary points, whether it is amaximum, minimum or saddle point [31]. By conducting the anal-ysis of the methyl oleate yield (Eq. (4)), the eigenvalues obtainedwere [�959.3; �0.2; �0.1; 5.6]. The following equation showsthe canonical form of the fitted methyl oleate yield model:

y1 ¼ 174:5� 959:3w21 � 0:2w2

2 þ 0:1w23 þ 5:6w2

4 ð5Þ

As the eigenvalues signs of the model were mixture betweenpositive and negative signs, hence it can be concluded that theoptimum point for methyl oleate yield was a saddle point.

3.2. Oleic acid conversion for esterification of oleic acid

3.2.1. ANOVA for oleic acid conversionThe mathematical equation that predicted the relationship be-

tween oleic acid conversion (Y2) and independent variables is rep-resented in:

Y2 ¼ �24:76þ 3:72X1 þ 185:23X2 þ 1:33X3 þ 8:26X4

� 0:29X21 � 6363:24X2

2 � 0:01X23 � 0:75X2

4 � 16:83X1X2

þ 0:03X1X3 þ 0:08X1X4 þ 8:44X2X3 � 5:23X2X4

� 0:02X3X4 ð6Þ

The coefficient of determination (R2) for Eq. (6)is 0.854, as givenfrom data analysis of experimental results using STATISTICA 8.0.Less than 15% of the total variation in the response did not fitthe model, which highlighted that the model is adequate to coverabout 85% of the variability in the experiments. Furthermore, themodel can predict the response with greater accuracy than the pre-viously obtained model for methyl oleate yield (Eq. (4)), based onthe higher value of R2.

In addition, the calculated F-value for oleic acid conversion(4.59) was also greater than the F-value from the distribution table(F0.05, 14, 11 = 2.74), as shown in Table 4. This means that the qua-dratic model is significant and can gives good predictions of oleicacid conversion at high confidence level (95%).

Fig. 3. Response surface plots of methyl oleate yield as a function of (a) methanol–oleic acid ratio and reaction temperature, (b) methanol–oleic acid ratio and reaction time,(c) catalyst loading and reaction temperature, and (d) reaction time and reaction temperature.

Table 4ANOVA results for oleic acid conversion.

Sources Sum ofSquares(SS)

Degree ofFreedom(d.f.)

MeanSquares(MS)

F-value(calculated)

F-tabulated(a = 0.05)

Oleic acid conversionRegression 6632.81 14 473.77 4.59 2.74Residual 1134.75 11 103.16Total 7767.56 25

Fig. 4. Pareto chart for oleic acid conversion.


The Pareto chart for oleic acid conversion in the esterificationreaction is illustrated in Fig. 4. Similar to the previous finding formethyl oleate yield, the linear term of reaction temperature (X3)was also identified as the term with the largest effect on oleic acidconversion. Increased in the temperature enhanced the conversion,based on the positive t-value (i.e. 7.1005). Furthermore, the secondmost influential coefficient in the conversion is quadratic term ofreaction temperature X2

3

� �, at 97% confidence level. Other coeffi-

cients were not significant at 95% confidence level. However, thesecoefficients are still significant at lower confidence level and givewell represented model, as discussed in Section 3.1.1.

3.2.2. Response surface plots of oleic acid conversionFig. 5a shows the interaction between methanol–oleic acid mo-

lar ratio and reaction temperature with respect to oleic acid con-version. The conversion increased significantly once the reactiontemperature reached 40 �C, but insignificant change was observedfor reaction temperature above 65 �C. Increasing the temperatureto higher than 65 �C decreased oleic acid conversion, as moremethanol was vaporized when the reaction temperature exceeded

methanol boiling point. Theoretically, increasing reaction temper-ature enhanced the esterification reaction since at higher temper-ature, mass transfer rates among the reactants accelerated as themolecules gained more kinetic energy, and eventually led to higherconversion in a shorter period [36]. Zhang et al. [14] determinedthat the maximum conversion of oleic acid occurred at reactiontemperature of 70 �C, with no obvious change in the conversionabove this temperature.

The change in oleic acid conversion for the interaction betweenmethanol–oleic acid molar ratio and reaction time is shown inFig. 5b. There was considerable interaction between the variableswith oleic acid conversion due to the elliptical nature of the con-tour plot [22]. The conversion increased significantly from the start

Fig. 5. Response surface plots of oleic acid conversion as a function of (a) methanol–oleic acid ratio and reaction temperature, (b) methanol–oleic acid ratio and reaction time,(c) catalyst loading and reaction temperature, and (d) reaction temperature and reaction time.


up to 2 h of reaction at molar ratio of 8:1, and remained constantbeyond that. The optimum conversion was achieved when for mo-lar ratio of 8:1 and reaction time of 4.5 h, and decreased for highermolar ratio and reaction time. The same trend was also reported byElsheikh et al. [15] in esterification of crude palm oil using[BMIM][HSO4] to catalyze the reaction, as the conversion of FFAsremained virtually unchanged after reaction time of 2 h. However,there was slight decrease in the conversion after 6 h of reaction inthis study. Leung et al. [37] mentioned that it was related to thereaction time, where prolonged time favoured the backward reac-tion (i.e. hydrolysis of esters), which reduced the conversion.

Response surface plot for interaction between catalyst loadingand reaction temperature on oleic acid conversion is illustratedin Fig. 5c. The conversion did not exceed 60% for reaction temper-ature lower than 40 �C regardless of any catalyst loading. Man et al.[38], using triethylammonium hydrogen sulfate ([Et3N][HSO4])ionic liquid as the catalyst, showed that temperature played anintegral role for higher FFA conversion in esterification of crudepalm oil, where the conversion increased from 20% at 120 �C to85% at 180 �C.

The interaction between reaction time and reaction tempera-ture with respect to oleic acid conversion is shown in Fig. 5d.The conversion for temperature lower than 40 �C remained almostconstant where the conversion did not exceed 60%. Higher temper-ature was required to reach the optimum conversion (i.e. 80 �C).Zhao et al. [39] previously reported that 80% oleic acid conversionwas achieved at the temperature of 80 �C. Reduction in oleic acidconversion was observed at longer reaction time once the opti-mum conversion was achieved, due to the reversible nature ofthe esterification reaction, where hydrolysis of ester may have oc-curred and reduced the conversion.

3.2.3. Single-response optimization and canonical analysis of oleic acidconversion

The single-response optimization of oleic acid conversion pre-dicted the optimum response of 78.1%, which can be achieved byconducting the esterification reaction for 4.9 h at the temperatureof 89.6 �C, with molar ratio methanol–oleic acid of 9.4:1 and cata-lyst loading of 0.06 mol. Following the canonical analysis for themathematical model of oleic acid conversion (Eq. (6)), it was deter-mined that the optimum point for oleic acid conversion is a maxi-mum point, based on the eigenvalues obtained ([�6363.3; �0.8;�0.3; �6.7 � 106]). The equation that represents the canonicalform of the oleic acid conversion model is as follows:

y2 ¼ 91:0� 6363:3w21 � 0:8w2

2 � 0:3w23 � 6:7� 106w2

4 ð7Þ

3.3. Multi-objective optimization of responses in esterification of oleicacid

Experimental data were used to train the network beforeoptimization of methyl oleate yield and oleic acid conversionwas performed simultaneously. After the network with lowestMSE was obtained, the genetic algorithm tool (gatool) availablefrom MATLAB software was employed, and the solver option formulti-objective optimization was chosen (gamultiobj). At the endof the simulation, several optimum conditions for optimum re-sponses were produced. The conditions and their respective re-sponses are listed in Table 5, while the Pareto-optimal solutionsfor multi-objective optimization are depicted in Fig. 6.

The optimal point from the set of solutions was selected basedon the smallest differences between methyl oleate yield and oleic

Table 5Multi-objective optimization using ANN–GA for esterification of oleic acid.

No. Process variables Predicted responses

X1 X2 (mol) X3 (�C) X4 (h) Yield, Y1 (%) Conversion, Y2 (%)

1 8.6 0.053 87.6 4.7 69.4 80.92 8.6 0.055 87.6 5.5 71.9 80.63 8.6 0.061 87.5 5.0 76.3 80.64 8.6 0.063 87.3 5.2 81.8 80.45 8.6 0.063 87.3 5.3 83.9 80.46 8.6 0.031 86.5 5.8 88.5 80.27 8.6 0.062 87.3 5.7 92.4 80.28 8.6 0.063 86.5 5.7 93.3 80.29 8.5 0.064 86.9 5.8 99.2 80.1

80.0

80.1

80.2

80.3

80.4

80.5

80.6

80.7

70 75 80 85 90 95 100

Con

vers

ion

(%)

Yield (%)

Optimum point

Fig. 6. Pareto front plot of simultaneous multi-objective optimization for methyloleate yield and oleic acid conversion.

Table 6Predicted optimum responses for single- and multi-objective optimization.

Optimization method Independent variables Optimum responses

X1 X2

(mol)X3

(�C)X4

(h)Yield(%)

Conversion(%)

Single-objective(yield)

13:1 0.05 93 14.0 77.7 –

Single-objective(conversion)

9:1 0.06 90 4.9 – 78.1

Multi-objective 9:1 0.06 87 5.2 81.8 80.4


acid conversion. It was determined that the optimum yield andconversion were 81.8% and 80.4%, respectively. These responsescan be achieved by conducting the esterification reaction at tem-perature being 87.3 �C for 5.2 h, with methanol–oleic acid molarratio of 8.6:1 and catalyst loading of 0.063 mol.

3.4. Comparison between single- and multi-objective optimization foresterification of oleic acid

In this study, RSM was utilized for single-objective optimizationof methyl oleate yield and oleic acid conversion, while ANN–GA

Table 7Catalytic performance of different catalysts for esterification of oleic acid.

Catalyst Alcohol Reaction time (h)

H2SO4 Ethanol 6.0H2SO4 Ethanol 8.0H3PW12O40 Methanol 10.0Sulfated zirconia Methanol 1.4[(CH2)4SO3HMIM][CF3SO3] Methanol 6[BMIM][HSO4] Methanol 5.2

was employed for simultaneous multi-objective optimization ofresponses. Both methods predicted the desired response togetherwith the optimum conditions, and the results are shown in Table 6.By using ANN–GA, the optimum value for methyl oleate yield was81.8%, while the predicted optimum oleic acid conversion was80.4%. Both responses correspond to the process parameters of9:1 M ratio methanol–oleic acid, 0.06 mol catalyst loading, 87 �Creaction temperature, and also 5.2 h reaction time. A lower methyloleate yield was predicted (77.7%) for single-objective optimiza-tion. It also required higher methanol–oleic acid molar ratio andslightly higher reaction temperature, but the most noticeable dif-ference is the reaction time as 14.0 h was required to achieve theoptimum yield. This could be costly in terms of operating timefor the process, as well as the possibility of the backward reactionfor esterification process (i.e. hydrolysis of esters), which could de-crease the yield due to extended reaction time. Meanwhile, therewas not much difference for oleic acid conversion between the val-ues predicted for single-objective optimization (78.1%) with theone given by multi-objective optimization (80.4%).

3.5. Catalytic activity of different catalysts in esterification of oleic acid

The performance of various catalysts in esterification of oleicacid is summarized in Table 7, where the response is reported asoleic acid conversion. It is obvious that the IL used for this studyexhibited higher catalytic activity compared to sulfuric acid as con-ventional homogeneous catalyst for esterification of fatty acids[8,18]. The optimum conversion in this study was even better thanthe conversion achieved using heterogeneous catalysts, where het-eropolyacid (H3PW12O40) [11] and sulfated zirconia [40] were pre-viously employed in esterification of oleic acid.

High conversion was achieved using [BMIM][HSO4] in shortertime. This is because of its high Brønsted acidity (Hammett acidityfunction (H0) = 0.73), based on the measurement to determine theHammett acidity function using UV–visible spectroscopy [41]. Theperformance of this catalyst was comparable to another ionicliquid, 1-(4-sulfobutyl)-3-methylimidazolium trifluoromethane-sulfonate [(CH2)4SO3HMIM][CF3SO3] employed for esterificationof long chain aliphatic acid [39] where the conversion obtainedwas 80%.

3.6. Catalytic activity of recycled ionic liquid

In order to circumvent the high price of ionic liquid, the perfor-mance of recycled ionic liquid for esterification of oleic acid is stud-ied. Recycled IL was used in each cycle after removal of water andunreacted methanol. The results for methyl oleate yield and oleicacid conversion using recycled IL are exhibited in Fig. 7. The reduc-tion in the catalytic performance after five successive runs wasbarely noticeable. Similar trend was also reported as the catalyticactivity of [BMIM][HSO4] decreased after being recycled for fourtimes [42]. The reduced performance of the ionic liquid might havebeen attributed to the retention of IL in the biodiesel even afterdecantation during the phase separation step [43].

Reaction temperature (�C) Conversion (%) Refs.

70 61.0 [18]70 66.9 [8]25 75.0 [11]65 69.8 [40]80 80.0 [39]87 80.4 This study

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

1 2 3 4 5

Res

pons

es

Runs

Yield (%)

Conv. (%)

Fig. 7. Catalytic performance of recycled [BMIM][HSO4].


4. Conclusions

The performance of [BMIM][[HSO4] ionic liquid has been testedfor catalyzing the esterification of oleic acid for green synthesis ofbiodiesel. The process could become a viable alternative to theconventional process due to the distinctiveness of IL as a greensolvent. The mathematical models produced from the data analysisgave good predictions at 95% significance level. Moreover, it wasdiscovered that linear coefficient of reaction temperature for themathematical models was the most influential variables for bothresponses in esterification of oleic acid. Multi-objective optimiza-tion using ANN–GA was determined to be more effective thanthe single-objective optimization using RSM. At optimum condi-tions, methyl oleate yield was 81.8%, while oleic acid conversionwas 80.4%, as predicted using ANN–GA method. The ionic liquiddisplayed stable catalytic activity even after five successive cyclessince not much changed in oleic acid conversion was observed.

Acknowledgements

The authors would like to express their sincere appreciation toUniversiti Teknologi Malaysia (UTM) for providing Research Uni-versity Grant (RUG, Vote No.: Q.J130000.2644.05J08) and ZamalahScholarship to one of the authors (AHMF).

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