Optimization of sunflower oil ethanolysis catalyzed by calcium oxide: RSM versus ANN-GA

Energy Conversion and Management 105 (2015) 1149–1156

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Optimization of sunflower oil ethanolysis catalyzed by calcium oxide: RSM versus ANN-GA Jelena M. Avramovic´ a, Ana V. Velicˇkovic´ a, Olivera S. Stamenkovic´ a, Katarina M. Rajkovic´ b, Petar S. Milic´ c, Vlada B. Veljkovic´ a,⇑ a b c

University of Niš, Faculty of Technology, Bulevar oslobodjenja 124, 16000 Leskovac, Serbia High Chemical and Technological School for Professional Studies, Kruševac, Serbia ´ uprija, Serbia High Medical School of Professional Studies, C

a r t i c l e

i n f o

Article history: Received 25 June 2015 Accepted 21 August 2015 Available online 14 September 2015 Keywords: Artificial neural network Ethanolysis Fatty acid ethyl esters Full factorial design Genetic algorithm Response surface methodology

a b s t r a c t The ethanolysis of sunflower oil catalyzed by calcium oxide was modeled and optimized in terms of the following operating conditions: reaction temperature (65–75 °C), ethanol:oil molar ratio (6:1–18:1), catalyst loading (10–20% based on oil weight) and reaction time (360–480 min). Response surface methodology (RSM) and artificial neural network (ANN) approaches were used for modeling the content of fatty acid ethyl esters (FAEE) and optimizing the four process variables. Both models were determined to be reliable in terms of predicting the FAEE content, but the ANN model was found to be more accurate than the RSM model. The highest FAEE content of 99.2% was determined using the ANN model combined with a genetic algorithm optimization method, which agreed well with the experimental value (97.8%). A good agreement between the predicted and actual maximum FAEE contents was observed for both models. The generalization of the ANN model developed for heterogeneously catalyzed alcoholysis was also tested on several oily feedstocks. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Consistently growing negative effects caused by fossil fuel usage on the climate, as well as severe consequences toward society, the environment and the world economy, have focused efforts to develop technologies based on renewable energy sources. New sources could partially replace fossil fuels, reduce the emission of pollutants and minimize any possible impacts on the environment. One possibility is to use biodiesel, a non-toxic, renewable, safe-to-handle and biodegradable fuel that would produce less air, water and soil pollution, as well as cause minimal impact on human health and help reduce greenhouse gas emissions. Biodiesel production generally involves the alcoholysis reaction between triacylglycerols (TAGs) from vegetable oils, animal fats or algal oils and alcohol in the presence of a catalyst. The most commonly used alcohols for the production of biodiesel are methanol and ethanol, each with their own advantages and disadvantages. The most important positive characteristics of methanol include its suitable physicochemical properties, low cost, mild reaction conditions, fast reaction time and easy phase separation. However, because of its low boiling point, the explosion risk ⇑ Corresponding author. Tel.: +381 16 247 203; fax: +381 16 242 859. E-mail address: [email protected] (V.B. Veljkovic´). http://dx.doi.org/10.1016/j.enconman.2015.08.072 0196-8904/Ó 2015 Elsevier Ltd. All rights reserved.

associated with methanol vapors and the extreme toxicity of both methanol and methoxide [1], new trends in the area of biodiesel production are oriented toward the use of ethanol. Compared to methanol, ethanol is characterized by its superior vegetable oil dissolving power, lower toxicity and biodegradability [2]. The drawbacks of ethanol’s use in biodiesel production are related to the difficult separation of ethyl esters caused by the stable emulsion formed during the ethanolysis reaction, significant dependence of fatty acid ethyl ester (FAEE) yield on the presence of water in the reaction mixture and a greater hindrance effect of ethoxide ions located on the active surface sites in the case of a heterogeneously catalyzed reaction [3,4]. There are several benefits of using FAEEs instead of fatty acid methyl esters (FAMEs). Due to the extra carbon atom (from the ethanol molecule), FAEEs have slightly higher values for heat content, a higher cetane number and improved storage properties [2,5]. Another advantage of FAEEs are lower cloud and pour points, which improve engine start at low temperatures [2,6,7]. In addition, FAEEs are more environmentally friendly compared to FAMEs because of lower nitrogen oxide and carbon monoxide emissions, as well as lower smoke density [1,8]. Moreover, FAEE-based biodiesel is a completely biorenewable fuel because both reactants (vegetable oil and ethanol) can be produced from biorenewable resources.

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To date, homogeneously catalyzed ethanolysis of different oily feedstocks has been more frequently studied than heterogeneously catalyzed ethanolysis [4]. However, the use of heterogeneous catalysts improves the ethanolysis reaction, as separation from the reaction mixture is easy and catalysts can be reused; this makes the process environmentally and economically more favorable [9]. The primary deficiency of heterogeneous catalysts is the formation of three phases leading to diffusion limitations and the reduction of the reaction rate [10]. The reuse possibility of the catalyst is a benefit, but until now only a few catalysts are recycled without loss of activity [11,12]. A literature survey on the application of ethanolysis in biodiesel production is shown in Table 1. A limited number of edible (sunflower and soybean oils) and waste frying oils have been used as oily feedstocks in the presence of various solid catalysts. Although calcium-based catalysts have been most frequently employed to date, pure calcium oxide has not been applied yet. Only CaO loaded with Ca(OH)2 has been used to catalyze the conversion of soybean oil to FAEEs [16]. To increase biodiesel production processes’ efficiency and to reduce costs, these processes should be conducted under optimal reaction conditions, which are usually determined with the help of statistical and mathematical methods, such as response surface methodology (RSM) and artificial neural network (ANN). However, these methods have not previously been employed for modeling and optimization of heterogeneously catalyzed ethanolysis reaction conditions. RSM has more frequently been employed [21– 26] than ANN [27,28], and both methods have been proven to be powerful tools for modeling and optimizing the methanolysis of various vegetable oils. ANN is generally demonstrated to have better generalization capability than RSM, which is attributed to its universal ability to simulate non-linear variations. Until recently, there were several investigations comparing the efficiencies of the two methods for transesterification [29–35] and esterification [36] of various feedstocks by applying different catalysts. These methods have also been employed in the modeling and optimization of two-step processes, where esterification is followed by transesterification [37]. Both methods are not mechanistic, so they do not contribute to any deeper insight into the chemical reaction under the study. However, their application will expand knowledge about the effects of the process factors and their interactions with FAEE content, as well as help identify (1) the optimum properties that ensure maximum FAEE content and (2) the capabilities of RMS and ANN as mathematical tools for modeling and optimization of the ethanolysis reaction. In this way, they bring valuable results that help us better understand and reliably improve biodiesel production processes with less cost, effort and time. To make a

general conclusion on their modeling and optimization capabilities, these methods should be tested and validated for each combination of feedstock, alcohol, catalyst, process and operating condition [30]. It is worth mentioning that both RMS and ANN have not previously been employed for the modeling and optimization of heterogeneously catalyzed ethanolysis reaction conditions. Moreover, the present study is the first attempt to apply RSM and ANN combined with a genetic algorithm (GA) for modeling the FAEE content and optimization of CaO-catalyzed sunflower oil ethanolysis reaction conditions in a batch reactor. Additionally, to the best of the authors’ knowledge, the ethanolysis of vegetable oils in the presence of a solid catalyst has not been modeled and optimized to date using RSM and ANN methods. Thus, the novelty of the present study is in both the reaction used for biodiesel production and the comparison of the efficiencies of the RSM and ANN methods as modeling and optimization tools for this reaction. Furthermore, the evaluation of generalization ability of the developed ANN model for other oils, alcohols and catalysts is also a valuable approach that has rarely been employed in the modeling of alcoholysis reactions. Both models give energy efficient process conditions leading to the maximum FAEE content. Main goals of the study were to evaluate the effect of the reaction conditions on the FAEE content through analysis of variance (ANOVA); correlate the FAEE content with the process variables; and optimize the process variables to achieve the maximum FAEE content using RSM and ANN combined with GA (ANN-GA). Additionally, developed RSM and ANN models were compared against each other with respect to the accuracy of the FAEE content prediction to better select a mathematical tool for modeling and optimization of the CaO-catalyzed sunflower oil ethanolysis. Finally, physicochemical properties of the sunflower oil ethyl esters obtained under the optimum reaction conditions were determined.

2. Experimental 2.1. Materials Refined, edible sunflower oil (Sunce, Sombor, Serbia) was used, and its physicochemical properties can be found elsewhere [21]. Ethanol (99%) and calcium oxide (min. 96% purity) were obtained from Fisher Chemical (Leicestershire, UK) and Lachema (Neratovice, Czech Republic), respectively. Hydrochloric acid (36%) was from Centrohem (Belgrade, Serbia), while HPLC grade 2-propanol and n-hexane were purchased from Lab-Scan (Dublin, Ireland). The HPLC standards for esters of palmitic, stearic, oleic and linoleic

Table 1 Review of heterogeneously catalyzed ethanolysis with different feedstocks. Oily feedstock

Reaction temperature (°C)

Ethanol/oil molar ratio (mol/mol)

Catalyst/loading (%)

Yield (%)/time (h)

Reference

Sunflower Sunflower Sunflower Sunflower

65–75 200 78 70–90a 80b 70 70 120 60–200 100–200c 50

6:1–15 12:1 20:1 6:1–15:1a 4:1–8:1b 20:1 9:1 20:1 40:1 40:1c 3:1–10:1

CaO/10-20 ZrX-MCM/14.6 Calcium zincate/3 Ca(OCH2CH3)2/0.5–4a Ca(OCH2CH3)2/0.25–1b La50SBA-15/1 Ca(OH)2/CaO/3.7% SO4/ZrO2/5 ZnAl2O4/1–10 ZnAl2O4/1–10c Ion-exchange resin/20–40d

98.1/8 91.5/– 95/3 31.4–80.5a/2.5 96.5 80/6 96.3/10 92/1 95/2 >96.5/2c 100/1.5

Present work [13] [14] [2]

oil oil oil oil

Soybean oil Soybean oil Soybean oil Waste frying oil Waste frying oil Triolein a b c d

First stage. Second stage. Supercritical CO2. Weight percent on the total reaction mixture.

[15] [16] [17] [18] [19] [20]

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acids, triolein, diolein and monoolein were obtained from Sigma Aldrich (St. Louis, USA). 2.2. Reaction conditions and FAEE characterization 2.2.1. Ascertainment of external and internal diffusion limitations First, the influence of both external liquid–solid mass transfer and intraparticle diffusion limitations was ascertained. Using the correlation of Dossin et al. [38], the minimum agitation speed that ensured perfect mixing, complete suspension of the catalyst particles and no external mass transfer limitation was calculated to be in the range between 692 and 868 rpm for the reaction conditions applied; hence, the reaction was carried out at 900 rpm for all experiments. Existence of internal mass transfer resistance was checked by calculation of the Thiele modulus, which indicated if the pore diffusion offered a resistance [39]. Considering all reaction conditions applied and limited values for catalyst particle porosity [40] and tortuosity [41], the highest Thiele modulus was calculated to be 0.009 or 0.003 for the diffusion of TAG through TAG and ethanol, respectively. Being significantly smaller than the limit one (
0.4), these values of Thiele modulus confirmed the ignorable internal diffusion resistance in the investigated system.

hydrochloric acid (5 mol/L) and washed with distilled water (14% based on the FAEE weight) to remove the remaining impurities (catalyst salts, ethanol and glycerol). Water and FAEE phases were separated by centrifugation, and the latter phase was dried at 120 °C for 4 h. The physical and chemical properties of the purified biodiesel were determined according to the appropriate standard methods. The physical and chemical properties of the obtained biodiesel were determined according to the appropriate standard methods: density (EN ISO 3675:1988), kinematic viscosity (EN ISO 3104:2003), flash point (EN ISO 2719:2002), iodine value (EN 14111:2003), acid value (EN 14104:2003), water content (EN ISO 12937:2000), FAME content (EN 14103:2003), as well as the content of mono-, di- and tri-acylglycerols (EN 14105:2003). 2.3. Modeling and optimization 2.3.1. RSM modeling and optimization The quadratic equation was applied in modeling the FAEE content as a function of process variables and for optimizing the process conditions:

Y ¼ a0 þ

X i

2.2.2. Equipment and reaction conditions Experimental work was performed in accordance with a 34 factorial design with five center points (with a total of 86 experimental runs). Ranges of the process variables in this set of experiments are given in Table 2. This set of data was used for modeling and optimization of the FAEE content by the RSM. In addition, the FAEE content was monitored at 30 min intervals up to the third hour of the reaction, then hourly up to the end of the reaction (with a total of 330 data points). This set of data was used in the ANN-GA modeling and optimization of the FAEE content. The reaction was carried out in a 250 mL glass three-neck, round-bottom flask equipped with a condenser and a twobladed, flat paddle agitator. The reactor was immersed in a glass chamber filled with water circulating from a thermostated bath (Dema, Ilirska bistrica, Slovenia) by means of a pump. Agitation intensity, measured with an optoelectronic counter, was controlled with a voltage regulator. Samples of the reaction mixture (0.5 mL) were taken during the progress of the ethanolysis reaction to determine the FAEE content. After being withdrawn, samples were instantly neutralized by adding the required amount of hydrochloric acid (5 mol/L) in aqueous solution and centrifuged at 3500 rpm for 10 min. The FAEE-oil layer was separated, dissolved in a solution of 2-propanol and nhexane (5/4 ml/ml), filtered through a Millipore filter (0.45 lm) and analyzed by the HPLC method described elsewhere [22]. 2.2.3. Physicochemical characterization of FAEEs The FAEEs obtained under the optimal reaction conditions determined by the RSM were characterized according to the prescribed standards. The FAEE fraction (the upper layer), obtained after the centrifugal separation, was neutralized with the Table 2 Process factors and their levels for the RSM modeling and optimization. Notation for factor

Process factor

Unit

Lower level (1)

Middle level (0)

Upper level (1)

A

Ethanol:oil molar ratio Catalyst loadinga Reaction temperature Reaction time

mol/mol

6:1

12:1

18:1

% °C min

10 65 360

15 70 420

20 75 480

B C D a

Based on the oil weight.

1151

ai X i þ

XX X aij X i X j þ aii X 2i i

j>i

ð1Þ

i

where Y is the FAEE content (response), X is the process (independent) variable (ethanol:oil molar ratio, catalyst loading, reaction temperature and reaction time, i.e., A, B, C and D, respectively), a0 , ai , aij and aii are regression coefficients (i = 1, 2, 3, 4 and j > i). The set of data used in the RSM modeling and optimization can be found in the Supplementary material (Table S1). Statistical significance of the process variables was estimated using analysis of variance (ANOVA). For optimizing the factors, maximum FAEE content was selected as the goal of optimization. The Design-Expert 7.0.0 Trial software (Stat-Ease Inc., Minneapolis, MN) program was used for the purpose of modeling and optimization. 2.3.2. ANN modeling and ANN-GA optimization ANN and ANN-GA combination were applied in non-parametric modeling and the optimization of the FAEE content, respectively using the MATLAB’s Neural Network and Genetic Algorithm Toolboxes (MATLAB 8.1.0.604). A feedforward, back-propagation multilayer ANN was performed using the Levenberg–Marquardt (LM) algorithm because of its power in function modeling and fast training. Hyperbolic tangent transfer function (tan h) was chosen for both input and output hidden layers. The selected ANN had an input layer with four neurons (ethanol:oil molar ratio, catalyst loading, reaction temperature and reaction time), a hidden layer and an output layer with one neuron (FAEE content). The number of hidden neurons was defined by testing different numbers of neurons until the mean relative percentage deviation (MRPD) value of the output data was minimized. Optimal number of hidden neurons was found to be 10 (see Fig. S1 in Supplementary material) because further increasing the number of these neurons made the model less accurate. It might be worth mentioning that this increase would simply increase the complexity of the model and should not decrease the accuracy unless the optimization/fitting is incomplete or stuck in a local minimum. Moreover, with additional optimization efforts, it should be possible to improve the fit of the model with more neurons. However, for practical reasons, selection of 10 hidden neurons was correct, as the additional computation effort required might be notably large. The main characteristics of the ANN model are presented in Supplementary material (Table S2). The set of input–output data points (330 in total) mutually interrelated with the FAEE content and process variables and was divided into three subsets: training (70% of the

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total data points), testing (15% of the total data points) and validating (15% of the total data points). Input interval of the developed ANN model was optimized to search for optimal operating conditions and lead to the highest FAEE content using GA. The main characteristics of GA are presented in the Supplementary material (Table S2). The objective function used to maximize the FAEE content in the ranges of the process variables was applied. GA parameters, such as population size, number of generations, crossover rate and mutation probability, were first optimized using sensitivity analysis [42], and their values were as follows: 20, 50, 0.8 and 0.01, respectively. Then, the optimum set of the process variables was determined by applying GA over the ANN model. The evolution profile of optimal search by the ANN-GA model is shown in the Supplementary material (Fig. S2), which depicts the values of the FAEE content in terms of the best and mean values at each generation. Fifty generations are enough for GA to reach the accurate optimization. 2.3.3. Evaluation of the mathematical models The performances of RSM and ANN models were statistically measured by F-and p-values of the model and the lack of fit, as well as the coefficient of determination (R2) and the MRPD. 3. Results and discussion 3.1. RSM modeling Results of ANOVA are summed up in Table 3 in terms of the sum and means of squares of residuals, corresponding degrees of freedom and F- and p-values. Statistical significance of the model, individual factors, their squares and interactions was estimated from their F- and p-values. As the p-value is less than 0.050, catalyst loading (B), reaction temperature (C), reaction time (D), two-way interactions of catalyst loading with reaction temperature and reaction time (BC and BD), reaction temperature and reaction time (CD), and the square of the reaction temperature (C2) have statistically significant effects on the FAEE content at the 95% confidence level, as can be observed in Table 3. Among the individual process variables, the most important was reaction temperature, followed by reaction time and catalyst loading. However, the ethanol:oil molar ratio, as well as their square and interactions with other process factors did not have statistically significant effects on the FAEE

Table 3 The results of ANOVA. Source of variation

Sum of squares

Degree of freedom

Mean square

F value

p-value

Modela A B C D AB AC AD BC BD CD A2 B2 C2 D2 Lack of fit Pure error Corrected total

17926.05 327.92 2713.63 6571.23 3706.46 37.31 24.35 100.84 1762.60 527.39 943.82 4.08 120.34 804.54 87.12 6791.53 175.09 24892.67

14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 66 5 85

1280.43 327.92 2713.63 6571.23 3706.46 37.31 24.35 100.84 1762.60 527.39 943.82 4.08 120.34 804.54 87.12 102.90 35.02

13.05 3.34 27.66 66.97 37.77 0.38 0.25 1.03 17.96 5.37 9.62 0.042 1.23 8.20 0.89 2.94

<0.0001 0.072 <0.0001 <0.0001 <0.0001 0.539 0.620 0.314 <0.0001 0.023 0.003 0.839 0.272 0.006 0.349 0.113

a A – ethanol:oil molar ratio; B – catalyst loading; C – reaction temperature and D – reaction time.

content. These observances are valid only for the ranges of operating conditions applied in the present study. Because no study on statistical optimization of heterogeneously catalyzed ethanolysis has been reported so far, the effects of the process variables on heterogeneously catalyzed methanolysis of various vegetable oils have briefly been discussed. The FAME yield in the methanolysis of jatropha oil using calcium oxide [43], a mixed oxide (CaO–MgO) catalyst [23], KSF clay, Amberlyst 15 [24] and aluminum oxide modified Mg–Zn catalyst [44] was influenced by all process variables. The same was observed in the methanolysis of waste cooking palm oil over Sr/ZrO2 [25] and the methanolysis of soybean oil over CaO from low-cost mussel shells [45]. The multiple regression method was applied to the experimental data of the FAEE content to fit the quadratic equation, Eq. (1), using coded levels of the process variables:

Y ¼ 92:0 þ 2:46A þ 7:09B þ 11:03C þ 8:28D þ 1:02AB  0:82AC  1:67AD  7:00BC  3:83BD  5:12CD  0:46A2  2:48B2  6:41C 2  2:11D2

ð2Þ

Individual process variables, the interaction of the ethanol:oil molar ratio and catalyst loading have positive effects on the FAEE content, while other interactions and squares influenced the FAEE content negatively. The Fmodel-value (13.05), which was higher than the critical value (F0.05,14,71 = 1.86) and the very low pmodel-value (<0.0001), indicated that the model was significant at the 95% confidence level. Value of the correlation coefficient (R = 0.850) showed strong correlation between FAEE content and the process variables. According to the value of the coefficient of determination (R2 = 0.720), mathematical prediction of the FAEE content was not the best because only 72.0% of variation in the FAEE content could be explained by the fitted model. In addition, the lack of fit was insignificantly relevant to the pure error because its p-value (0.133) was higher than 0.050, meaning that the model was adequate for the prediction of the FAEE content. The MRPD between the predicted and actual values of the FAEE content of  10.1% (based on 86 data points) indicated their good agreement. Deviations between the predicted and actual values higher than ±30% were observed if the FAEE content was lower than approximately 50% (only 6 data points), and if these ‘‘bad” data points were not considered, the MRPD was reduced to ±6.9%. There was no problem with the normality in the distribution of experimental data, so the ANOVA results were valid. In addition, the Cook’s distances values were lower than 0.15 (far from the limit value of 0.8), suggesting that there were no outliers in analyzed data set. By removing the insignificant model terms that have a p-value higher than 0.05 (Table 3), Eq. (2) can be simplified as follows:

Y ¼ 92:0 þ 7:09B þ 11:03C þ 8:28D  7:00BC  3:83BD  5:12CD  6:41C 2

ð3Þ

with the following values of the most important statistical criteria: the insignificant lack of fit (p = 0.113 > 0.050) relative to the pure error, R2 = 0.691 and MRPD = ±10.5%. Response surfaces and contour plots show the influence of the ethanol:oil molar ratio on the FAEE content as less significant and dependent on the reaction time (Fig. 1a). The FAEE content increases slightly with increasing ethanol:oil molar ratio at shorter reaction times, which could be attributed to the positive effect of ethanol amount on the conversion rate. As the reaction progresses, the effect of the ethanol:oil molar ratio on the FAEE content is not significant. This was explained by the presence of FAEEs and intermediate products, which contributed to the increase of liquid–liq-

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Fig. 1. Response surface and contour plots for FAEE content (experimental data points: d) as a function of (a) ethanol:oil molar ratio and reaction time (catalyst loading: 15% and reaction temperature: 70 °C) and (b) catalyst loading and reaction temperature (ethanol:oil molar ratio: 12:1 and reaction time: 420 min).

uid interfacial area, thus reducing the importance of ethanol solubility in the oil. The same effect was observed in the sunflower oil methanolysis [46]. Additionally, formation of FAEEs in the CaOZnO-catalyzed sunflower oil ethanolysis was negligible at molar ratios less than 20:1, while it decreased in the range from 20:1 to 50:1 [14]. Similarly, the soybean oil conversion varied slightly as the ethanol:oil molar ratio increased from 9:1 to 15:1, but decreased once reaching the ratio of 18:1 [16]. The effect of catalyst loading and reaction temperature on the FAEE content is shown in Fig. 1b. The high slope of the contours indicated great influence of both process factors on the FAEE content, although the effect of reaction temperature was higher. The effect of reaction temperature was more pronounced at lower catalyst loadings, but became less important at higher catalyst loadings (approximately >18%). Catalyst loading significantly improved the FAEE content at low reaction temperatures, but its effect was less significant at high reaction temperatures. Additionally, the shape of contours confirmed significance of the interaction between the catalyst loading and the reaction temperature.

Obtained results were in agreement with the results of Alves and coworkers [18,19]. 3.2. ANN modeling Correlation between the FAEE content and process variables was determined using ANN with 4-10-1 topology. Good agreement between the predicted and experimental values of FAEE content was indicated by the MRPD value, which was only ±6.9% (330 data points). R-value of the ANN model was 0.973, indicating its better capability of FAEE content prediction compared to the RSM model. Surface and contour plots obtained by the ANN model that present the predicted FAEE content as a function of reaction temperature and reaction time at an ethanol:oil molar ratio of 12:1 and catalyst loading of 15% are shown in Fig. 2. It can be noted that the FAEE content increases with the progress of reaction, reaches a plateau and remains almost constant at longer reaction times. The effect of the reaction temperature is more significant in the initial reaction period and

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Fig. 2. Response surface and contour plots for the predicted FAEE content (experimental data points: d) as a function of reaction temperature and time (ethanol:oil molar ratio: 12:1 and catalyst loading: 15%).

becomes insignificant at reaction times above 420 min, which can be explained by achieving the reaction equilibrium; therefore, further increase of the temperature has no significant effect on the FAEE content. Contour plot shows that reaction time has more significant influence on the FAEE content than reaction temperature. Plateau is reached at reaction times and temperatures higher than 420 min and 70 °C, respectively. Generalization ability of the developed ANN model was judged with unseen data of heterogeneous methanolysis reaction systems taken from the literature [31,47]. The type of vegetable oil, solid catalyst and alcohol, ranges of the process variables as well as MRPD values are presented in Table 4. MRPD values for three different reaction systems demonstrate significant generalization ability of the developed ANN for predicting the ester yield achieved by both ethanolysis and methanolysis of various oily feedstocks, which is imputed to its universal capability to simulate nonlinearity of studied reaction systems. 3.3. Optimization of process variables by RSM and ANN-GA Maximum predicted values of FAEE content found through optimization using both the RSM and ANN-GA model are compared in Table 5. As it can be observed, the RSM model predicts the highest FAEE content at a higher ethanol:oil molar ratio, as well as larger catalyst loading, longer reaction time and somewhat lower reaction temperature than ANN-GA model. Both models predict almost the same best FAEE contents and agree with experimental values determined under optimum reaction conditions.

Table 5 Comparison of optimal process conditions and maximum FAAE contents determined by RSM and ANN-GA optimization. Process variable

RSM optimization

ANN-GA optimization

Ethanol:oil molar ratio Catalyst loading (%) Reaction temperature (°C) Reaction time (min) FAEE content, model (%) FAEE content, actual (%)

14:1 17 72 440 98.8 98.2

12:1 15 75 415 99.2 97.8

3.4. Physicochemical properties of FAEEs Investigated physicochemical properties of the FAEEs obtained under the optimum reaction conditions, along with the data reported in previous studies and limits of the quality properties prescribed by the EN14214 and USA ASTM D6751 biodiesel quality standards, are shown in Table 6. Except for the iodine value regarding the EN14214 standard, all properties of sunflower FAEEs are within the limits of both standards. According to the EN14214 standard, maximum allowed iodine value is 120, while the USA ASTM D6751 standard does not specify its value. Additionally, some researchers argue that further limitation of iodine value by the EN14214 standard is not necessary [50]. Moreover, required oxidation stability can be achieved by adding antioxidants to the biodiesel product. Hence, an iodine value that is somewhat higher than the standard specification will not limit the use of these

Table 4 Generalization ability of the developed ANN model.

a b c

Oil

Catalyst

Alcohol

Design of experimentsa

Alcohol-to-oil molar ratio (mol/mol)

Catalyst loading (%)

Reaction temperature (°C)

Reaction time (h)

Number of data points

MRPD (%)

Ref.

Sunflower Waste cooking oil Yellow oleander

Calcium oxide H3PW12O.406H2O Calcinated plantain peels

Ethanol Methanol Methanol

FFD CCD BBFD

6–18 30–90 0.09–0.30

10–20b 5–12.5 1–5c

65–75 55–75 60

Up to 16 6–22 0.5–1.5

330 30 17

±6.9 ±8.8 ±2.1

Present study [47] [31]

BBFD – Box–Behnken factorial design; CCD – central composite design; FFD – full factorial design. Based on the oil weight. w/v.

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Table 6 Fuel characteristics of FAEEs. Fuel characteristic

Unit

Density at 15 °C Kinematic viscosity at 40 °C Flash point Iodine value Acid value Water content Cetane number FAEE MAG DAG TAG Calcium and magnesium Reference

kg/m3 mm2/s °C g I2/100 g oil mg KOH/g mg/kg 51 % % % %

Type of oil

Standard

Sunflower

Mustard

Sunflower

Rapeseed

Olive

Used frying

EN14214

ASTM D6751

874.7 3.7 175 131.9 0.38 227

899.5 8.33 120 105 0.33 – – 91.0 – – – – [48]

882.7 4.63 178 – 0.15 154 – 96.7 0.478 0.152 0.087 – [49]

881.2 4.84 181 – 0.35 189 – 97.2 0.512 0.095 0.275 –

881.5 4 182 – 0.19 208 – 97.8 0.644 0.130 0.116 –

888.5 5.81 188 – 0.46 376 – 93.2 1.367 2.228 3.132 –

860–900 3.5–5.0 min 101 max 120 max 0.5 max 500 min 51 min 96.5 max 0.8 max 0.2 max 0.2 max 5

– 1.9–6.0 93 min – max 0.5 max 500 min 47 – – – –

96.8 0.3 2.5 0.4 2.1 Present work

FAEES as biodiesel. Obtained FAEEs have similar fuel properties as the biodiesels produced from other vegetable and used frying oils that make use of homogeneous catalysts (KOH [48] and NaOH [49]).

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.enconman.2015. 08.072.

4. Conclusions RSM and ANN models were developed for modeling and optimization of the FAEE content in the ethanolysis of sunflower oil using CaO as a catalyst to increase the process efficiency and to define the energy efficient process conditions, leading to maximum FAEE content. Both models provide good quality prediction of the FAEE content in terms of reaction temperature, ethanol: oil molar ratio, catalyst loading and reaction time. However, based on the R and MRPD values (RSM: 0.850 and ±10.1%; and ANN: 0.973 and ±6.9%), the ANN model was found to be more accurate in predicting FAEE content than the RSM model. This was attributed to better predictive ability of ANN as a modeling technique for representing nonlinearities rather than the quadratic equation used in RSM. It should be emphasized that the excellent fitting of experimental data was achieved with a rather simple back propagation ANN performed using the LM algorithm. RSM and ANN (in combination with GA) models are also proved to be acceptable tools for optimizing the ethanolysis reaction conditions to achieve the highest FAEE content. RSM model predicts the FAEE content of 98.8% in 440 min under the following conditions: reaction temperature of 72 °C, ethanol:oil molar ratio of 14:1 and catalyst loading of 17%. According to the ANN-GA model, the FAEE content of 99.2% corresponds to an ethanol:oil molar ratio of 12:1, catalyst loading of 15%, reaction temperature of 75 °C and reaction time of 415 min. With both models, a good agreement between the predicted and actual maximum FAEE contents was observed. Generalization ability of the developed ANN model was well documented for a couple of heterogeneously catalyzed alcoholysis reactions. Although ANN is superior over RSM in terms of accuracy, these models complement each other in interpreting the experimental FAEE content. While ANN is more reliable in capturing the nonlinear relationship between the FAEE content and process variables, RSM notes the statistical importance of the individual process variables and their interactions via ANOVA. Acknowledgment This work has been funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia – Serbia (Project III 45001).

References [1] Leung DYC, Wu X, Leung MKH. A review on biodiesel production using catalyzed transesterification. Appl Energy 2010;87:1083–95. [2] Anastopoulos G, Dodos GS, Kalligeros S, Zannikos F. Biodiesel production by ethanolysis of various vegetable oils using calcium ethoxide as a solid base catalyst. Int J Green Energy 2013;10:468–81. [3] Kim M, Di Maggio C, Yan S, Salley SO, Ng KYS. The synergistic effect of alcohol mixtures on transesterification of soybean oil using homogeneous and heterogeneous catalysts. Appl Catal A Gen 2010;378:134–43. [4] Stamenkovic´ OS, Velicˇkovic´ AV, Veljkovic´ VB. The production of biodiesel from vegetable oils by ethanolysis: current state and perspectives. Fuel 2011;90:3141–55. [5] Clark S, Wagner JL, Schrack MD, Piennar PG. Methyl and ethyl soybean esters as renewable fuels for diesel engines. J Am Oil Chem Soc 1998;61:1632–8. [6] Encinar JM, Gonzalez JF, Rodriguez-Reinares A. Ethanolysis of used frying oil. Biodiesel preparation and characterization. Fuel Process Technol 2007;88:513–22. [7] Joshi H, Moser BR, Toler J, Walker T. Preparation and fuel properties of mixtures of soybean oil methyl and ethyl esters. Biomass Bioenergy 2010;34:14–20. [8] Makareviciene V, Janulis P. Environmental effect of rapeseed oil ethyl ester. Renew Energy 2003;28:2395–403. [9] Dossin TF, Reyniers MF, Berger RJ, Marin GB. Simulation of heterogeneously MgO-catalyzed transesterification for fine-chemical and biodiesel industrial production. Appl Catal B Environ 2006;67:136–48. [10] Mbaraka IK, Shanks BH. Conversion of oils and fats using advanced mesoporous heterogeneous catalysts. J Am Oil Chem Soc 2006;83:79–91. [11] Li E, Xu ZP, Rudolph V. MgCoAl–LDH derived heterogeneous catalysts for the ethanol transesterification of canola oil to biodiesel. Appl Catal B Environ 2009;88:42–9. [12] Sreeprasanth PS, Srivastava R, Srinivas D, Ratnasamy P. Hydrophobic, solid acid catalysts for production of biofuels and lubricants. Appl Catal A Gen 2006;314:148–59. [13] Jiménez-Morales I, Santamaría-González J, Maireles-Torres P, Jiménez-López A. Calcined zirconium sulfate supported on MCM-41 silica as acid catalyst for ethanolysis of sunflower oil. Appl Catal B Environ 2011;103:91–8. [14] Rubio-Caballero JM, Santamaria-Gonzalez Merida-Roble JJ, Moreno-Tost R, Alonso-Castillo ML, Vereda-Alonso E, Jimenez-Lopez A, et al. Calcium zincate derived heterogeneous catalyst for biodiesel production by ethanolysis. Fuel 2013;105:518–22. [15] Quintella SA, Saboya RMA, Salmin DC, Novaes DS, Araújo AS, Albuquerque MCG, et al. Transesterification of soybean oil using ethanol and mesoporous silica catalyst. Renew Energy 2012;38:136–40. [16] Watcharathamrongkul K, Jongsomjit B, Phisalaphong M. Calcium oxide based catalysts for ethanolysis of soybean oil. Songklanakarin J Sci Technol 2010;32:627–34. [17] Garcia CM, Teixeira S, Marciniuk LL, Schuchardt U. Transesterification of soybean oil catalyzed by sulfated zirconia. Bioresour Technol 2008;99: 6608–13. [18] Alves CT, de Oliveira AS, Carneiro SAV, Santos RCD, Vieira de Melo SAB, Andrade HMC, et al. Transesterification of waste frying oils using ZnAl2O4 as heterogeneous catalyst. Proc Eng 2012;42:1928–45.

1156

J.M. Avramovic´ et al. / Energy Conversion and Management 105 (2015) 1149–1156

[19] Alves CT, Andrade HMC, Santos RCD, Vieira de Melo SAB, Torres EA. Transesterification reaction using zinc aluminate as heterogeneous catalyst and supercritical carbon dioxide. Chem Eng Trans 2013;32:883–8. [20] Shibasaki-Kitakawa N, Honda H, Kuribayashi H, Toda T, Fukumura T, Yonemoto T. Biodiesel production using ion-exchange resin as heterogeneous catalyst. Bioresour Technol 2007;98:416–21. [21] Avramovic´ JM, Stamenkovic´ OS, Todorovic´ ZB, Lazic´ ML, Veljkovic´ VB. The optimization of the ultrasound-assisted base-catalyzed sunflower oil methanolysis by a full factorial design. Fuel Process Technol 2010;91:1551–7. [22] Velicˇkovic´ AV, Stamenkovic´ OS, Todorovic´ ZB, Veljkovic´ VB. Application of the full factorial design to optimization of base-catalyzed sunflower oil ethanolysis. Fuel 2013;104:433–42. [23] Lee HV, Yunus R, Juan JC, Taufiq-Yap YH. Process optimization design for jatropha-based biodiesel production using response surface methodology. Fuel Process Technol 2011;92:2420–8. [24] Zanette AF, Barella RA, Pergher SBC, Treichel H, Oliveira D, Mazutti MA, et al. Screening, optimization and kinetics of Jatropha curcas oil transesterification with heterogeneous catalysts. Renew. Energy 2011;36:726–31. [25] Wan Omar WNN, Amin NAS. Optimization of heterogeneous biodiesel production from waste cooking palm oil via response surface methodology. Biomass Bioenergy 2011;35:1329–38. [26] Liu M, Niu S, Lu C, Cheng S. An optimization study on transesterification catalyzed by the activated carbide slag through the response surface methodology. Energy Convers Manage 2015;92:498–506. [27] Moradi GR, Dehghani S, Khosravian F, Arjmandzadeh A. The optimized operational conditions for biodiesel production from soybean oil and application of artificial neural networks for estimation of the biodiesel yield. Renew Energy 2013;50:915–20. [28] Badday AS, Abdullah AZ, Lee K-T. Artificial neural network approach for modeling of ultrasound-assisted transesterification process of crude Jatropha oil catalyzed by heteropolyacid based catalyst. Chem Eng Process 2014;75:31–7. [29] Stamenkovic´ OS, Rajkovic´ KM, Velicˇkovic´ AV, Milic´ PS, Veljkovic´ VB. Optimization of base-catalyzed ethanolysis of sunflower oil by regression and artificial neural network models. Fuel Process Technol 2013;114:101–8. [30] Rajkovic´ KM, Avramovic´ JM, Milic´ PS, Stamenkovic´ OS, Veljkovic´ VB. Optimization of ultrasound-assisted base-catalyzed methanolysis of sunflower oil using response surface and artificial neural network methodologies. Chem Eng J 2013;215–216:82–9. [31] Betiku E, Ajala SO. Modeling and optimization of Thevetia peruviana (yellow oleander) oil biodiesel synthesis via Musa paradisiacal (plantain) peels as heterogeneous base catalyst: a case of artificial neural network vs. response surface methodology. Ind Crops Prod 2014;53:314–22. [32] Betiku E, Omilakin OR, Ajala SO, Okeleye AA, Taiwo AE, Solomon BO. Mathematical modeling and process parameters optimization studies by artificial neural network and response surface methodology: a case of nonedible neem (Azadirachta indica) seed oil biodiesel synthesis. Energy 2014;72:266–73. [33] Sarve A, Sonawane SS, Varma MN. Ultrasound assisted biodiesel production from sesame (Sesamum indicum L.) oil using barium hydroxide as a heterogeneous catalyst: comparative assessment of prediction abilities

[34]

[35]

[36]

[37]

[38] [39] [40] [41] [42]

[43]

[44]

[45] [46]

[47]

[48]

[49]

[50]

between response surface methodology (RSM) and artificial neural network (ANN). Ultrason Sonochem 2015;26:218–28. Betiku E, Okunsolawo SS, Ajala SO, Odedele OS. Performance evaluation of artificial neural network coupled with generic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tree (Vitellaria paradoxa) nut butter. Renew Energy 2015;76:408–17. Stamenkovic´ OS, Velicˇkovic´ AV, Kostic´ MD, Jokovic´ NM, Rajkovic´ KM, Milic´ PS, et al. Optimization of KOH-catalyzed methanolysis of hempseed oil. Energy Convers Manage 2015;103:235–43. Fauzi AHM, Amin NAS. Optimization of oleic acid esterification catalyzed by ionic liquid for green biodiesel synthesis. Energy Convers Manage 2013;76:818–27. Maran JP, Priya B. Comparison of response surface methodology and artificial neural network approach towards efficient ultrasound-assisted biodiesel production from muskmelon oil. Ultrason Sonochem 2015;23:192–200. Dossin TF, Reyniers MF, Marin GB. Kinetics of heterogeneously MgO catalyzed transesterification. Appl Catal B 2006;61:35–45. Levenspiel O. Chemical reaction engineering. 3rd ed. New York: John Wiley and Sons; 1999. Aris R. Introduction to the analysis of chemical reactors. New York: PrenticeHall; 1965. Carberry J. Chemical and catalytic reaction engineering. New York: McGrawHill; 1976. Rajendra M, Jena PC, Raheman H. Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA. Fuel 2009;88:868–75. Zhu H, Wu Z, Chen Y, Zhang P, Duan S, Liu X, et al. Preparation of biodiesel catalyzed by solid super base of calcium oxide and its refining process. Chin J Catal 2006;27:391–6. Olutoye MA, Hameed BH. Synthesis of fatty acid methyl ester from crude jatropha (Jatropha curcas Linnaeus) oil using aluminum oxide modified Mg–Zn heterogeneous catalyst. Bioresour Technol 2011;102:6392–8. Rezaei R, Mohadesi M, Moradi GR. Optimization of biodiesel production using waste mussel shell catalyst. Fuel 2013;109:534–41. Stamenkovic´ OS, Lazic´ ML, Todorovic´ ZB, Veljkovic´ VB, Skala DU. The effect of agitation intensity on alkali-catalyzed methanolysis of sunflower oil. Bioresour Technol 2007;98:2688–99. Talebian-Kiakalaieh A, Amin NAS, Zarei A, Noshadi I. Transesterification of waste cooking oil by heteropoly acid (HPA) catalyst: optimization and kinetic model. Appl Energy 2013;102:283–92. Fadhil AB, Abdulahad WS. Transesterification of mustard (Brassica nigra) seed oil with ethanol: purification of the crude ethyl ester with activated carbon produced from de-oiled cake. Energy Convers Manage 2014;77:495–503. Anastopoulos G, Zannikou Y, Stournas S, Kalligeros S. Transesterification of vegetable oils with ethanol and characterization of the key fuel properties of ethyl esters. Energy 2009;2:362–76. Costenoble O, Mittelbach M, Schober S, Fischer J, Haupt J. Improvements needed for the biodiesel standard EN 14214. EC project TREN/D2/44-LOT 1/ S07.54676, NEN, University of Graz and AGQM; 2008. [March, 2014].