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Modeling of ultrasound assisted intensification of biodiesel production from neem (Azadirachta indica) oil using response surface methodology and artificial neural network
Fuel 143 (2015) 262–267
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Modeling of ultrasound assisted intensification of biodiesel production from neem (Azadirachta indica) oil using response surface methodology and artificial neural network J. Prakash Maran a,⇑, B. Priya b a b
Department of Food Technology, Kongu Engineering College, Perundurai, Erode 638052, Tamil Nadu, India Department of Food Process Engineering, SRM University, SRM Nagar, Kattankulathur, Chennai 603203, Tamil Nadu, India
h i g h l i g h t s First report on ultrasound assisted biodiesel production from neem oil. Response surface and artificial neural network approach is used for modeling. Comparison of prediction and generalization abilities of RSM and ANN model. ANN was superior to RSM for predicting FAME conversion from neem oil.
a r t i c l e
i n f o
Article history: Received 25 July 2014 Received in revised form 11 November 2014 Accepted 17 November 2014 Available online 27 November 2014 Keywords: Biodiesel Neem oil Ultrasound RSM ANN
a b s t r a c t In the present work, modeling of ultrasound-assisted intensification of biodiesel production from neem (Azadirachta indica) oil was investigated using four factors three level central composite rotatable design (CCRD) of response surface methodology (RSM). The experimental data obtained through CCRD was used to train the artificial neural network (ANN) model. RSM and ANN models were developed and compared for their predictive and generalization abilities. To evaluate the accuracy of results, additional experiments were conducted which does not belong to experimental design. The results showed that, both models having the ability to predict the experimental data, but ANN was found to be more reliable and superior than RSM. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction The energy needs of the world are currently derived from petroleum, mineral coal and natural gas. The world is presently confronted with the two main crises of fossil fuel depletion and environmental degradation. Therefore, there is an increasing worldwide concern for environmental protection and for the conservation of non-renewable natural resources [1]. Among the different possible resources for the production of alternative fuels, biodiesel appears to be the most promising alternative to petroleum based diesel fuel because it is renewable in nature and can be produced locally, as well as being environmentally friendly. Biodiesel has received significant attention in all countries since it is nontoxic, biodegradable and renewable diesel fuel. Biodiesel is generally produced from cooking vegetable oils. Using high-quality ⇑ Corresponding author. Tel.: +91 4294 226606; fax: +91 4294 220087. E-mail address: [email protected] (J. Prakash Maran). http://dx.doi.org/10.1016/j.fuel.2014.11.058 0016-2361/Ó 2014 Elsevier Ltd. All rights reserved.
virgin oils makes biodiesel more expensive than diesel fuel and it causes to increase in vegetable oil prices. Therefore, selecting the best feedstock is vital for ensuring low production costs and should be used in biodiesel production. Thus, there is an urgent need to find an alternative, cheaper feedstock, non-edible, readily available and in large quantities. Biodiesel is among the promising alternatives for fossil fuels and is mainly produced from animal fats and vegetable oils. There are many ways and procedures to biodiesel fuel from vegetable oil such as pyrolysis, dilution, microemulsion and transesterification [2]. Biodiesel produced via transesterification process has proven to be a viable, economic and alternative fuel with similar characteristics to diesel fuel [3]. Different intensification methods such as ultrasonic, microwave irradiation, hydrodynamic cavitation, addition of co-solvents or mass transfer catalysts and application of supercritical synthesis conditions have been tried out to eliminate or minimize the mass transfer limitation in order to improve the biodiesel production process over the traditional methods.
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Cavitational reactors can offer a useful energy-efficient intensification approach for biodiesel production as compared to other approaches for intensification such as microwave irradiation, oscillatory flow reactor, microchannel reactor, addition of co-solvent and supercritical uncatalyzed transesterification [4]. Ultrasonic irradiation causes cavitation bubbles to form near the phase boundary between the alcohol and oil phases. The collapse of the cavitation bubbles disrupts the phase boundary and causes emulsification by ultrasonic jets that impinge from one liquid upon the other. Cavitation may also lead to a localized increase in temperature at the phase boundary enhancing the transesterification reaction [5]. Ultrasound assisted transesterification gives the advantages of shorter reaction period and hence less energy consumption along with using an effective molar ratio of methanol to oil as compared to conventional mechanical stirring [6]. Ultrasound has been successfully employed to produce biodiesel from vegetable edible and non-edible oils are mostly used although oil-bearing materials such as seeds [7–9], rice bran [10], waste cooking oils [11–14], fish oils [15,16], animal fats [17] and sidestreaming products from edible oil production [18]. Neem oil (Azadirachta indica) is non-edible oil available in huge surplus quantities in South Asia. Annual production of neem oil in India is estimated to be 30,000 tons. It has been used as insecticide, lubricant and in medicine to various kinds of diseases. Traditionally; it has been used as fuel in lamps for lighting purpose in rural areas and is used on an industrial scale for manufacturing of soaps, cosmetics, pharmaceuticals and other non-edible products. Muthu et al. [19], Aransiola et al. [20] and Dhar et al. [3] synthesized biodiesel from NO using a two-step transesterification process. However, application of ultrasound for biodiesel production from NO was not reported in the literature. Therefore, the present work is aimed to produce biodiesel from NO using ultrasonication by two-step in situ (acid-catalyzed esterification followed by base-catalyzed transesterification) process. The alkali catalyzed transesterification process was evaluated and compared through experimental design and artificial neural network methodology approach. Four factors (methanol to oil molar ratio, catalyst concentration, reaction temperature and reaction time) five level central composite rotatable design (CCRD) and multi-layer perceptron (MLP) neural network with three layers (input (4 neurons), hidden (9 neurons) and output (1 neuron) based models were developed to predict the experimental data on conversion of NO to fatty acid methyl esters (FAME) for alkali catalyzed in situ transesterification process. 2. Materials and methods 2.1. Materials Neem oil (NO) was purchased from local suppliers near Chennai, Tamil Nadu, India. The free fatty acid (FFA) content of the oil was found to be 5.87 wt%. Analytical grade of Sulfuric acid (H2SO4), potassium hydroxide (KOH), and methanol were obtained from the Merck Chemicals, Mumbai.
Conversion ð%Þ ¼
263
NO. The stainless steel ultrasonic batch reactor (1000 ml) equipped with a thermocouple probe and a sampling port was immersed in a temperature water bath. The ultrasonic transducer (horn with a diameter of 10 mm and a length of 120 mm) was submerged and used to transmit the ultrasound into ultrasonic batch reactor contained the solution (in the methanol phase). The higher FFA content of NO is much higher than the safe limit for direct transesterification reaction [1] using an alkaline catalyst. Hence, two-step transesterification process (in situ esterification-acid catalyzed esterification followed by in situ transesterification-alkaline catalyzed transesterification) was adopted in this study to obtain high amount of biodiesel.
2.2.1. In situ esterification and transesterification process The esterification reaction was carried out with a methanol to oil molar ratio of 6:1, catalyst (sulfuric acid (3 wt%)) and ultrasonic irradiation time of 30 min at room temperature (30 °C). After the acid catalyzed esterification reaction, the product was allowed to settle down for 8 h, the unreacted methanol and water fraction in the bottom layer were removed. From the results of acid value of the product, the FFA content of the oil was found to be 0.86 wt% and it was used for transesterification process. The alkali (KOH) catalyzed transesterification process was performed using ultrasonic irradiation at 20 kHz frequency and supported by a power of 400 W. The experiments were carried out with different molar ratio of methanol to oil, catalyst concentration, reaction temperature and reaction time (Table 1). At selected time, the product was taken from the reactor and allowed to separate overnight by gravity in a separating funnel. The upper phase in the separation funnel consisted of methyl ester or biodiesel and the lower phase being glycerol. The lower phase was removed from the funnel. The upper phase was washed several times with a small amount of fresh hot water until the washing water was found to be neutral. Finally, the methanol and water content was evaporated by means of heating.
2.3. Determination of FAME content Gas chromatograph equipped with flame ionized detector (FID) was used to find out the composition of fatty acid methyl esters (FAME) or biodiesel and helium was used as the carrier gas with the flow rate of 3 ml/min, while n-hexane was used as the solvent. The oven temperature was set at 110 °C and then increased to 220 °C at a rate of 10 °C/min. The temperature of the detector and injector were set at 220 and 250 °C, respectively. Comparing the retention time of each component in the samples with the peaks of pure methyl ester standard compound, the composition of the FAME was determined. Triglycerides to FAME conversion was calculated by using the following equation [21]
P P ð Mono; di; tri glyceride in oilÞ ð Mono; di; tri glyceride in FAMEÞ P 100 ð Mono; di; tri glyceride in oilÞ
ð1Þ
2.2. Experimental set up and procedure
2.4. Response surface methodology (RSM) modeling
An ultrasonic processor (VCX 400, Sonics and Materials, USA, 20 kHz, 400 W) was utilized for the production of biodiesel from
In this study, four factors five level central composite rotatable design (CCRD) was employed for alkali catalyzed in situ
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Table 1 CCRD with experimental and predicted values. Independent variables
Unit
Symbols
Level of factors a (2)
1
0
1
a (2)
Methanol to oil molar ratio Catalyst concentration Reaction temperature Reaction time
wt% °C min
X1 X2 X3 X4
3.0 0.50 30 10
4.5 0.75 35 20
6.0 1.00 40 30
7.5 1.25 45 40
9.0 1.50 50 50
Run order
X1
X2
X3
X4
Conversion to biodiesel (%) Observed
RSM
ANN
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
0 0 0 1 1 0 0 1 2 2 1 0 0 1 1 1 0 0 1 1 1 1 0 0 1 1 1 0 1 1
2 0 0 1 1 0 0 1 0 0 1 0 0 1 1 1 0 0 1 1 1 1 0 0 1 1 1 2 1 1
0 0 0 1 1 0 0 1 0 0 1 2 0 1 1 1 0 0 1 1 1 1 0 2 1 1 1 0 1 1
0 0 0 1 1 2 0 1 0 0 1 0 2 1 1 1 0 0 1 1 1 1 0 0 1 1 1 0 1 1
87.45 95.38 94.14 93.36 94.36 95.70 95.40 88.15 89.62 92.21 89.69 92.66 92.23 94.03 93.58 95.06 94.72 95.44 93.92 94.23 95.63 90.25 95.05 94.69 94.28 90.04 90.95 93.36 90.66 87.85
86.69 95.02 95.02 92.95 93.87 95.12 95.02 87.89 89.45 92.39 89.95 93.16 92.83 93.72 93.73 94.51 95.02 95.02 93.55 94.75 95.18 89.82 95.02 94.20 94.31 90.67 91.65 94.13 91.33 88.05
87.39 95.37 94.11 93.35 94.32 95.68 95.43 88.16 89.63 92.22 89.67 92.69 92.21 94.08 93.56 95.05 94.73 95.45 93.93 94.19 95.61 90.28 95.03 94.68 94.26 90.06 90.92 93.34 90.63 87.92
transesterification process in order to study the effect of process variables (methanol to oil molar ratio, catalyst concentration, reaction temperature and reaction time) on the conversion of oil to FAME. A total number of 30 experiments (Table 1) includes 16 factorial points, 8 axial points and 6 center point replications was carried out in this study. The number of the axial points on the axis of each design factor at a distance of ±a (a = 2k/4 = 2; k is number of independent variables) [22]. The relationship between independent variables and response (FAME conversion) was exhibited by a second order polynomial equation and its generalized form was given below
Y ¼ b0 þ
k k k X X XX bj xj þ bjj x2j þ bij xi xj þ ei j¼1
j¼1
ð2Þ
i
where Y is the response; xi and xj are variables (i and j range from 1 to k); b0 is the model intercept coefficient; bj, bjj and bij are interaction coefficients of linear, quadratic and the second-order terms, respectively; k is the number of independent parameters (k = 4 in this study); ei is the error [23]. The construction of experimental design and analysis of experimental data was carried out with the help of statistical package software (Design Expert 8.0.7.1, Statease, USA).
(MLP) neural network with logistic sigmoid transfer function at hidden layers and a purelin transfer function at output layers were selected and trained. Different back-propagation (BP) algorithms (Scaled Conjugate Gradient (SCG), Levenberg–Marquardt (LM), Gradient Descent with variable learning rate back propagation (GDX) and Resilient back Propagation (RP)) were used to train the MLP network in order to select a suitable BP algorithm. To achieve high homogenous results with reduced network error, the inputs and outputs were normalized in the range 0–1. The experimental data obtained from CCRD matrix was used to train, test and validate the neural network model in order to predict the FAME conversion from NO. The sum of squared error between predicted and actual data was computed according to the following equation
SSE ¼
n X
Yi Yi
2
ð3Þ
i¼1
where Yi and Y i are actual and predicted data. The ANN modeling was done with the help of MATLAB 6.5 (The Mathworks Inc.) software with neural network toolbox. 3. Results and discussion
2.5. Artificial neural network modeling 3.1. RSM modeling Artificial neural network (ANN) is one of the tools used to model the complex nonlinear relationship between experimental data and responses [24]. In this study, a multilayered perceptron
Experiments were carried out according to the CCRD matrix and the results were shown in Table 1. The experimental data was
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J. Prakash Maran, B. Priya / Fuel 143 (2015) 262–267 Table 2 Adequacy of model tested and ANOVA analysis of RSM model. Adequacy of the model tested Source
R2
Std. Dev.
Linear 2FI Quadratic Cubic
1.77 1.84 0.68 0.48
Adjusted R2 0.573 0.652 0.963 0.991
Predicted R2
0.505 0.469 0.928 0.964
0.415 0.366 0.816 0.757
p-Value 0.0037 0.0028 0.2116 0.6011
PRESS
Remarks
107.62 116.62 33.92 44.67
Suggested Aliased
ANOVA Source
Coefficient estimate
Sum of squares
Degree of freedom
Standard error
Mean square
F value
p-Value
Model X1 X2 X3 X4 X12 X13 X14 X23 X24 X34 X21 X22 X23 X24 Residual Std. Dev. Mean C.V.% Ade. Pre
95.02 0.74 1.86 0.26 0.57 0.26 0.17 0.14 0.38 0.70 0.39 1.02 1.15 0.33 0.26
177.18 12.99 82.99 1.62 7.87 1.11 0.48 0.30 2.31 7.94 2.38 28.77 36.38 3.06 1.88 6.87
14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15
0.28 0.14 0.14 0.14 0.14 0.17 0.17 0.17 0.17 0.17 0.17 0.13 0.13 0.13 0.13
12.66 12.99 82.99 1.62 7.87 1.11 0.48 0.30 2.31 7.94 2.38 28.77 36.38 3.06 1.88 0.46
27.61 28.35 181.06 3.53 17.16 2.41 1.04 0.66 5.04 17.33 5.19 62.78 79.39 6.69 4.10
<0.0001 <0.0001 <0.0001 0.0800 0.0009 0.1410 0.3229 0.4293 0.0403 0.0008 0.0378 <0.0001 <0.0001 0.0207 0.0611
0.68 92.80 0.73 17.73
fitted to various models (linear, interactive, quadratic and cubic) [25] and the results showed that quadratic model incorporating linear, interactive and quadratic terms exhibited higher R2, adjusted R2, predicted R2 and also low p-values, when compared to other models (Table 2). The relationship between independent variables and response was exhibited by an empirical quadratic model developed through multiple regression analysis [26] of experimental data and the final model acquired in terms of uncoded factors is given below
FAME conversion ð%Þ ¼ 10:595 þ 6:007X 1 þ 69:116X 2 þ 1:055X 3 þ 0:243X 4 0:701X 1 X 2 þ 0:023X 1 X 3 9:166E 003X 1 X 4 0:304X 2 X 3 0:281X 2 X 4 þ 7:71E
precision measures the signal-to-noise ratio and the ratio greater than 4 is desirable [33]. In this study, the adequate precision was found to be 17.13, which indicates the best fitness of the developed model. The sum of square (SS) value of each process variables acquired from the ANOVA table (Table 2) was used to investigate the effect of process variables (linear, interaction and quadratic) [34] on FAME conversion from NO and the results are shown in Fig. 1. From the figure, it was found that, significant effect was exhibited by the linear effect of process variables (54.93%) on the conversion process, followed by quadratic effect of process variables (36.518%). The interactive effects of process variables (7.56%) showed negligible effect on the conversion process compared to other terms. The very low effect (0.97%) was exhibited by the residual error (measure of amount of variation unexplained by the model).
003X 3 X 4 0:455 3.2. ANN modeling
X 21
18:428X 22
0:133X 23
2:617E
003X 24
ð4Þ
The developed quadratic model was evaluated through Pareto analysis of variance (ANOVA) and the results were shown in Table 2. ANOVA of the regression model (Eq. (4)) showed that the developed quadratic model was highly significant, as was evident from the Fisher’s F-test (F model = 27.61) with a very low probability value (<0.0001) [27]. The goodness of fit of the model was evaluated by the determination co-efficient (R2), adjusted determination co-efficient (R2a ) and co-efficient of variance (CV) and signal to noise ratio (S/N). In this study, the value of determination coefficient (R2 = 0.962) indicated that only 0.38% of the total variations were not explained by the developed regression model [28,29]. In addition, the value of adjusted determination coefficient (R2a ¼ 0:927) was also very high, indicating a high significance of the model developed through experimental data [30,31]. Furthermore, a very high degree of precision and a good deal of the reliability of the conducted experiments were indicated by a low value of the coefficient of variation (CV = 0.73%) [32]. Adequate
In ANN modeling, appropriate selection of network size, the choice of number of hidden layers and neurons is one of the important tasks for constructing suitable ANN model to predict the experimental data [35,36]. In this study MLP network was tested with 4–10 neurons on the one hidden layer. Different training algorithms (SCG, LM, GDX and RP) were used and tested by varying the number of neurons (4–10) in order to train MLP network and select the optimal architecture based on the minimization of the performance function. The experimental data obtained from the experiments were divided into three categories (training (60%), testing (20%) and validation (20%)) in order to measure the performance of the neural network for the prediction of unseen data and to assess the generalization capability of ANN. From the results of training and testing errors, LM training algorithm with nine neurons in the hidden layer was found to have higher performance. Hence, in this study, MLP network with three layers (input (4 neurons), hidden (9 neurons) and output (1 neuron) was used to estimate the FAME conversion from NO and the results were
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Fig. 1. Effect of process variables (a) individual (b) combined.
shown in Table 1. The residual error was calculated from the experimental and predicted data by RSM and ANN. From the results, it was found that, the residual error between experimental and predicted data by ANN was very lower when compared to RSM model (Fig. 2).
3.3. Comparison of RSM and ANN To test and compare the predictive ability of RSM and ANN models, a new set of ten experiments were performed in the experimental process variables range, which does not belongs to the training, testing and validation data and the results were shown in Table 3. Three statistical measures such as root mean square error (RMSE), co-efficient of determination (R2) and absolute average deviation (AAD) [37–39] were used to determine the performance and predictive capacity of developed ANN and RSM models. From the results (Table 3), it was found that, both models have the ability to predict the experimental data. However, the predictive capability of ANN model was higher than the RSM Fig. 2. Residual errors between experimental and predicted data by RSM and ANN.
Table 3 Validation of RSM and ANN model for additional experimental data. Run order
X1
X2
X3
X4
1 2 3 4 5 6 7 8 9 10
3.5 5 4 8 6.5 7 4 6.5 5.5 3
0.5 3 1.25 1.3 0.8 1.2 0.5 1.3 0.5 0.8
40 35 50 45 40 50 45 50 35 50
15 20 25 50 35 30 20 30 45 30
Conversion to biodiesel (%) Actual
RSM
ANN
74.29 41.36 86.25 89.31 89.94 92.72 82.26 91.51 84.68 87.27 RMSE R2 AAD
77.84 44.30 90.76 92.84 93.48 94.47 80.83 94.00 86.74 85.90 4.196 0.919 2.872
74.23 41.42 86.23 89.36 89.86 92.64 82.31 91.53 84.67 87.19 0.001 0.999 0.009
Fig. 3. Comparison of prediction capacity of RSM and AAN for unseen experimental data.
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models. The ANN model predictions are lie much closer to the line of perfect prediction than the RSM model (Fig. 3). Thus, the ANN model shows a significant and higher generalization capacity than the RSM model. This higher predictive accuracy of the ANN can be attributed to its universal ability to approximate the nonlinearity of the system, whereas the RSM is restricted to a second-order polynomial. Generation of ANN model requires a large number of iterative calculations, whereas it is only a single step calculation for a response surface model. ANN model may require a high computational time to create and more costly than a response model. But ANN is flexible and permits to add new experimental data to build a trustable ANN model, inherently capture almost any form of non-linearity, easily overcome the limitation of RSM [39,40] and this methodology does not require a standard experimental design to build the model.
4. Conclusion In this study, ultrasound assisted intensification of biodiesel production from NO was evaluated through RSM and ANN. Four factors five level RSM-CCRD and ANN-MLP network (LM training algorithm) with nine neurons in hidden layer were used to construct model in order to predict the experimental data and also the predictive capabilities of both model was compared statistically. From the results, it was observed that, the ANN model was more robust and accurate in predicting the values of dependent variables when compared with RSM model.
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Response surface optimization of ultrasound assisted extraction of pectin from pomegranate peel. Int J Biol Macromol 2015;72:1323–8. [35] Badkar DS, Pandey KS, Buvanashekaran G. Development of RSM and ANNbased models to predict and analyze the effects of process parameters of laserhardened commercially pure titanium on heat input and tensile strength. Int J Adv Manuf Technol 2013;65:1319–38. [36] Ghoreishi SM, Hedari E. Extraction of Epigallocatechin-3-gallate from green tea via supercritical fluid technology: neural network modeling and response surface optimization. J Supercrit Fluid 2013;74:128–36. [37] Bingöl D, Hercan M, Elevli S, Kılıç E. Comparison of the results of response surface methodology and artificial neural network for the biosorption of lead using black cumin. Bioresour Technol 2012;112:111–5. [38] Geyikçi F, Kılıç E, Çoruh S, Elevli S. Modelling of lead adsorption from industrial sludge leachate on red mud by using RSM and ANN. Chem Eng J 2012;183:53–9. 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Fuel journal homepage: www.elsevier.com/locate/fuel
Modeling of ultrasound assisted intensification of biodiesel production from neem (Azadirachta indica) oil using response surface methodology and artificial neural network J. Prakash Maran a,⇑, B. Priya b a b
Department of Food Technology, Kongu Engineering College, Perundurai, Erode 638052, Tamil Nadu, India Department of Food Process Engineering, SRM University, SRM Nagar, Kattankulathur, Chennai 603203, Tamil Nadu, India
h i g h l i g h t s First report on ultrasound assisted biodiesel production from neem oil. Response surface and artificial neural network approach is used for modeling. Comparison of prediction and generalization abilities of RSM and ANN model. ANN was superior to RSM for predicting FAME conversion from neem oil.
a r t i c l e
i n f o
Article history: Received 25 July 2014 Received in revised form 11 November 2014 Accepted 17 November 2014 Available online 27 November 2014 Keywords: Biodiesel Neem oil Ultrasound RSM ANN
a b s t r a c t In the present work, modeling of ultrasound-assisted intensification of biodiesel production from neem (Azadirachta indica) oil was investigated using four factors three level central composite rotatable design (CCRD) of response surface methodology (RSM). The experimental data obtained through CCRD was used to train the artificial neural network (ANN) model. RSM and ANN models were developed and compared for their predictive and generalization abilities. To evaluate the accuracy of results, additional experiments were conducted which does not belong to experimental design. The results showed that, both models having the ability to predict the experimental data, but ANN was found to be more reliable and superior than RSM. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction The energy needs of the world are currently derived from petroleum, mineral coal and natural gas. The world is presently confronted with the two main crises of fossil fuel depletion and environmental degradation. Therefore, there is an increasing worldwide concern for environmental protection and for the conservation of non-renewable natural resources [1]. Among the different possible resources for the production of alternative fuels, biodiesel appears to be the most promising alternative to petroleum based diesel fuel because it is renewable in nature and can be produced locally, as well as being environmentally friendly. Biodiesel has received significant attention in all countries since it is nontoxic, biodegradable and renewable diesel fuel. Biodiesel is generally produced from cooking vegetable oils. Using high-quality ⇑ Corresponding author. Tel.: +91 4294 226606; fax: +91 4294 220087. E-mail address: [email protected] (J. Prakash Maran). http://dx.doi.org/10.1016/j.fuel.2014.11.058 0016-2361/Ó 2014 Elsevier Ltd. All rights reserved.
virgin oils makes biodiesel more expensive than diesel fuel and it causes to increase in vegetable oil prices. Therefore, selecting the best feedstock is vital for ensuring low production costs and should be used in biodiesel production. Thus, there is an urgent need to find an alternative, cheaper feedstock, non-edible, readily available and in large quantities. Biodiesel is among the promising alternatives for fossil fuels and is mainly produced from animal fats and vegetable oils. There are many ways and procedures to biodiesel fuel from vegetable oil such as pyrolysis, dilution, microemulsion and transesterification [2]. Biodiesel produced via transesterification process has proven to be a viable, economic and alternative fuel with similar characteristics to diesel fuel [3]. Different intensification methods such as ultrasonic, microwave irradiation, hydrodynamic cavitation, addition of co-solvents or mass transfer catalysts and application of supercritical synthesis conditions have been tried out to eliminate or minimize the mass transfer limitation in order to improve the biodiesel production process over the traditional methods.
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Cavitational reactors can offer a useful energy-efficient intensification approach for biodiesel production as compared to other approaches for intensification such as microwave irradiation, oscillatory flow reactor, microchannel reactor, addition of co-solvent and supercritical uncatalyzed transesterification [4]. Ultrasonic irradiation causes cavitation bubbles to form near the phase boundary between the alcohol and oil phases. The collapse of the cavitation bubbles disrupts the phase boundary and causes emulsification by ultrasonic jets that impinge from one liquid upon the other. Cavitation may also lead to a localized increase in temperature at the phase boundary enhancing the transesterification reaction [5]. Ultrasound assisted transesterification gives the advantages of shorter reaction period and hence less energy consumption along with using an effective molar ratio of methanol to oil as compared to conventional mechanical stirring [6]. Ultrasound has been successfully employed to produce biodiesel from vegetable edible and non-edible oils are mostly used although oil-bearing materials such as seeds [7–9], rice bran [10], waste cooking oils [11–14], fish oils [15,16], animal fats [17] and sidestreaming products from edible oil production [18]. Neem oil (Azadirachta indica) is non-edible oil available in huge surplus quantities in South Asia. Annual production of neem oil in India is estimated to be 30,000 tons. It has been used as insecticide, lubricant and in medicine to various kinds of diseases. Traditionally; it has been used as fuel in lamps for lighting purpose in rural areas and is used on an industrial scale for manufacturing of soaps, cosmetics, pharmaceuticals and other non-edible products. Muthu et al. [19], Aransiola et al. [20] and Dhar et al. [3] synthesized biodiesel from NO using a two-step transesterification process. However, application of ultrasound for biodiesel production from NO was not reported in the literature. Therefore, the present work is aimed to produce biodiesel from NO using ultrasonication by two-step in situ (acid-catalyzed esterification followed by base-catalyzed transesterification) process. The alkali catalyzed transesterification process was evaluated and compared through experimental design and artificial neural network methodology approach. Four factors (methanol to oil molar ratio, catalyst concentration, reaction temperature and reaction time) five level central composite rotatable design (CCRD) and multi-layer perceptron (MLP) neural network with three layers (input (4 neurons), hidden (9 neurons) and output (1 neuron) based models were developed to predict the experimental data on conversion of NO to fatty acid methyl esters (FAME) for alkali catalyzed in situ transesterification process. 2. Materials and methods 2.1. Materials Neem oil (NO) was purchased from local suppliers near Chennai, Tamil Nadu, India. The free fatty acid (FFA) content of the oil was found to be 5.87 wt%. Analytical grade of Sulfuric acid (H2SO4), potassium hydroxide (KOH), and methanol were obtained from the Merck Chemicals, Mumbai.
Conversion ð%Þ ¼
263
NO. The stainless steel ultrasonic batch reactor (1000 ml) equipped with a thermocouple probe and a sampling port was immersed in a temperature water bath. The ultrasonic transducer (horn with a diameter of 10 mm and a length of 120 mm) was submerged and used to transmit the ultrasound into ultrasonic batch reactor contained the solution (in the methanol phase). The higher FFA content of NO is much higher than the safe limit for direct transesterification reaction [1] using an alkaline catalyst. Hence, two-step transesterification process (in situ esterification-acid catalyzed esterification followed by in situ transesterification-alkaline catalyzed transesterification) was adopted in this study to obtain high amount of biodiesel.
2.2.1. In situ esterification and transesterification process The esterification reaction was carried out with a methanol to oil molar ratio of 6:1, catalyst (sulfuric acid (3 wt%)) and ultrasonic irradiation time of 30 min at room temperature (30 °C). After the acid catalyzed esterification reaction, the product was allowed to settle down for 8 h, the unreacted methanol and water fraction in the bottom layer were removed. From the results of acid value of the product, the FFA content of the oil was found to be 0.86 wt% and it was used for transesterification process. The alkali (KOH) catalyzed transesterification process was performed using ultrasonic irradiation at 20 kHz frequency and supported by a power of 400 W. The experiments were carried out with different molar ratio of methanol to oil, catalyst concentration, reaction temperature and reaction time (Table 1). At selected time, the product was taken from the reactor and allowed to separate overnight by gravity in a separating funnel. The upper phase in the separation funnel consisted of methyl ester or biodiesel and the lower phase being glycerol. The lower phase was removed from the funnel. The upper phase was washed several times with a small amount of fresh hot water until the washing water was found to be neutral. Finally, the methanol and water content was evaporated by means of heating.
2.3. Determination of FAME content Gas chromatograph equipped with flame ionized detector (FID) was used to find out the composition of fatty acid methyl esters (FAME) or biodiesel and helium was used as the carrier gas with the flow rate of 3 ml/min, while n-hexane was used as the solvent. The oven temperature was set at 110 °C and then increased to 220 °C at a rate of 10 °C/min. The temperature of the detector and injector were set at 220 and 250 °C, respectively. Comparing the retention time of each component in the samples with the peaks of pure methyl ester standard compound, the composition of the FAME was determined. Triglycerides to FAME conversion was calculated by using the following equation [21]
P P ð Mono; di; tri glyceride in oilÞ ð Mono; di; tri glyceride in FAMEÞ P 100 ð Mono; di; tri glyceride in oilÞ
ð1Þ
2.2. Experimental set up and procedure
2.4. Response surface methodology (RSM) modeling
An ultrasonic processor (VCX 400, Sonics and Materials, USA, 20 kHz, 400 W) was utilized for the production of biodiesel from
In this study, four factors five level central composite rotatable design (CCRD) was employed for alkali catalyzed in situ
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Table 1 CCRD with experimental and predicted values. Independent variables
Unit
Symbols
Level of factors a (2)
1
0
1
a (2)
Methanol to oil molar ratio Catalyst concentration Reaction temperature Reaction time
wt% °C min
X1 X2 X3 X4
3.0 0.50 30 10
4.5 0.75 35 20
6.0 1.00 40 30
7.5 1.25 45 40
9.0 1.50 50 50
Run order
X1
X2
X3
X4
Conversion to biodiesel (%) Observed
RSM
ANN
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
0 0 0 1 1 0 0 1 2 2 1 0 0 1 1 1 0 0 1 1 1 1 0 0 1 1 1 0 1 1
2 0 0 1 1 0 0 1 0 0 1 0 0 1 1 1 0 0 1 1 1 1 0 0 1 1 1 2 1 1
0 0 0 1 1 0 0 1 0 0 1 2 0 1 1 1 0 0 1 1 1 1 0 2 1 1 1 0 1 1
0 0 0 1 1 2 0 1 0 0 1 0 2 1 1 1 0 0 1 1 1 1 0 0 1 1 1 0 1 1
87.45 95.38 94.14 93.36 94.36 95.70 95.40 88.15 89.62 92.21 89.69 92.66 92.23 94.03 93.58 95.06 94.72 95.44 93.92 94.23 95.63 90.25 95.05 94.69 94.28 90.04 90.95 93.36 90.66 87.85
86.69 95.02 95.02 92.95 93.87 95.12 95.02 87.89 89.45 92.39 89.95 93.16 92.83 93.72 93.73 94.51 95.02 95.02 93.55 94.75 95.18 89.82 95.02 94.20 94.31 90.67 91.65 94.13 91.33 88.05
87.39 95.37 94.11 93.35 94.32 95.68 95.43 88.16 89.63 92.22 89.67 92.69 92.21 94.08 93.56 95.05 94.73 95.45 93.93 94.19 95.61 90.28 95.03 94.68 94.26 90.06 90.92 93.34 90.63 87.92
transesterification process in order to study the effect of process variables (methanol to oil molar ratio, catalyst concentration, reaction temperature and reaction time) on the conversion of oil to FAME. A total number of 30 experiments (Table 1) includes 16 factorial points, 8 axial points and 6 center point replications was carried out in this study. The number of the axial points on the axis of each design factor at a distance of ±a (a = 2k/4 = 2; k is number of independent variables) [22]. The relationship between independent variables and response (FAME conversion) was exhibited by a second order polynomial equation and its generalized form was given below
Y ¼ b0 þ
k k k X X XX bj xj þ bjj x2j þ bij xi xj þ ei j¼1
j¼1
ð2Þ
i
where Y is the response; xi and xj are variables (i and j range from 1 to k); b0 is the model intercept coefficient; bj, bjj and bij are interaction coefficients of linear, quadratic and the second-order terms, respectively; k is the number of independent parameters (k = 4 in this study); ei is the error [23]. The construction of experimental design and analysis of experimental data was carried out with the help of statistical package software (Design Expert 8.0.7.1, Statease, USA).
(MLP) neural network with logistic sigmoid transfer function at hidden layers and a purelin transfer function at output layers were selected and trained. Different back-propagation (BP) algorithms (Scaled Conjugate Gradient (SCG), Levenberg–Marquardt (LM), Gradient Descent with variable learning rate back propagation (GDX) and Resilient back Propagation (RP)) were used to train the MLP network in order to select a suitable BP algorithm. To achieve high homogenous results with reduced network error, the inputs and outputs were normalized in the range 0–1. The experimental data obtained from CCRD matrix was used to train, test and validate the neural network model in order to predict the FAME conversion from NO. The sum of squared error between predicted and actual data was computed according to the following equation
SSE ¼
n X
Yi Yi
2
ð3Þ
i¼1
where Yi and Y i are actual and predicted data. The ANN modeling was done with the help of MATLAB 6.5 (The Mathworks Inc.) software with neural network toolbox. 3. Results and discussion
2.5. Artificial neural network modeling 3.1. RSM modeling Artificial neural network (ANN) is one of the tools used to model the complex nonlinear relationship between experimental data and responses [24]. In this study, a multilayered perceptron
Experiments were carried out according to the CCRD matrix and the results were shown in Table 1. The experimental data was
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J. Prakash Maran, B. Priya / Fuel 143 (2015) 262–267 Table 2 Adequacy of model tested and ANOVA analysis of RSM model. Adequacy of the model tested Source
R2
Std. Dev.
Linear 2FI Quadratic Cubic
1.77 1.84 0.68 0.48
Adjusted R2 0.573 0.652 0.963 0.991
Predicted R2
0.505 0.469 0.928 0.964
0.415 0.366 0.816 0.757
p-Value 0.0037 0.0028 0.2116 0.6011
PRESS
Remarks
107.62 116.62 33.92 44.67
Suggested Aliased
ANOVA Source
Coefficient estimate
Sum of squares
Degree of freedom
Standard error
Mean square
F value
p-Value
Model X1 X2 X3 X4 X12 X13 X14 X23 X24 X34 X21 X22 X23 X24 Residual Std. Dev. Mean C.V.% Ade. Pre
95.02 0.74 1.86 0.26 0.57 0.26 0.17 0.14 0.38 0.70 0.39 1.02 1.15 0.33 0.26
177.18 12.99 82.99 1.62 7.87 1.11 0.48 0.30 2.31 7.94 2.38 28.77 36.38 3.06 1.88 6.87
14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15
0.28 0.14 0.14 0.14 0.14 0.17 0.17 0.17 0.17 0.17 0.17 0.13 0.13 0.13 0.13
12.66 12.99 82.99 1.62 7.87 1.11 0.48 0.30 2.31 7.94 2.38 28.77 36.38 3.06 1.88 0.46
27.61 28.35 181.06 3.53 17.16 2.41 1.04 0.66 5.04 17.33 5.19 62.78 79.39 6.69 4.10
<0.0001 <0.0001 <0.0001 0.0800 0.0009 0.1410 0.3229 0.4293 0.0403 0.0008 0.0378 <0.0001 <0.0001 0.0207 0.0611
0.68 92.80 0.73 17.73
fitted to various models (linear, interactive, quadratic and cubic) [25] and the results showed that quadratic model incorporating linear, interactive and quadratic terms exhibited higher R2, adjusted R2, predicted R2 and also low p-values, when compared to other models (Table 2). The relationship between independent variables and response was exhibited by an empirical quadratic model developed through multiple regression analysis [26] of experimental data and the final model acquired in terms of uncoded factors is given below
FAME conversion ð%Þ ¼ 10:595 þ 6:007X 1 þ 69:116X 2 þ 1:055X 3 þ 0:243X 4 0:701X 1 X 2 þ 0:023X 1 X 3 9:166E 003X 1 X 4 0:304X 2 X 3 0:281X 2 X 4 þ 7:71E
precision measures the signal-to-noise ratio and the ratio greater than 4 is desirable [33]. In this study, the adequate precision was found to be 17.13, which indicates the best fitness of the developed model. The sum of square (SS) value of each process variables acquired from the ANOVA table (Table 2) was used to investigate the effect of process variables (linear, interaction and quadratic) [34] on FAME conversion from NO and the results are shown in Fig. 1. From the figure, it was found that, significant effect was exhibited by the linear effect of process variables (54.93%) on the conversion process, followed by quadratic effect of process variables (36.518%). The interactive effects of process variables (7.56%) showed negligible effect on the conversion process compared to other terms. The very low effect (0.97%) was exhibited by the residual error (measure of amount of variation unexplained by the model).
003X 3 X 4 0:455 3.2. ANN modeling
X 21
18:428X 22
0:133X 23
2:617E
003X 24
ð4Þ
The developed quadratic model was evaluated through Pareto analysis of variance (ANOVA) and the results were shown in Table 2. ANOVA of the regression model (Eq. (4)) showed that the developed quadratic model was highly significant, as was evident from the Fisher’s F-test (F model = 27.61) with a very low probability value (<0.0001) [27]. The goodness of fit of the model was evaluated by the determination co-efficient (R2), adjusted determination co-efficient (R2a ) and co-efficient of variance (CV) and signal to noise ratio (S/N). In this study, the value of determination coefficient (R2 = 0.962) indicated that only 0.38% of the total variations were not explained by the developed regression model [28,29]. In addition, the value of adjusted determination coefficient (R2a ¼ 0:927) was also very high, indicating a high significance of the model developed through experimental data [30,31]. Furthermore, a very high degree of precision and a good deal of the reliability of the conducted experiments were indicated by a low value of the coefficient of variation (CV = 0.73%) [32]. Adequate
In ANN modeling, appropriate selection of network size, the choice of number of hidden layers and neurons is one of the important tasks for constructing suitable ANN model to predict the experimental data [35,36]. In this study MLP network was tested with 4–10 neurons on the one hidden layer. Different training algorithms (SCG, LM, GDX and RP) were used and tested by varying the number of neurons (4–10) in order to train MLP network and select the optimal architecture based on the minimization of the performance function. The experimental data obtained from the experiments were divided into three categories (training (60%), testing (20%) and validation (20%)) in order to measure the performance of the neural network for the prediction of unseen data and to assess the generalization capability of ANN. From the results of training and testing errors, LM training algorithm with nine neurons in the hidden layer was found to have higher performance. Hence, in this study, MLP network with three layers (input (4 neurons), hidden (9 neurons) and output (1 neuron) was used to estimate the FAME conversion from NO and the results were
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Fig. 1. Effect of process variables (a) individual (b) combined.
shown in Table 1. The residual error was calculated from the experimental and predicted data by RSM and ANN. From the results, it was found that, the residual error between experimental and predicted data by ANN was very lower when compared to RSM model (Fig. 2).
3.3. Comparison of RSM and ANN To test and compare the predictive ability of RSM and ANN models, a new set of ten experiments were performed in the experimental process variables range, which does not belongs to the training, testing and validation data and the results were shown in Table 3. Three statistical measures such as root mean square error (RMSE), co-efficient of determination (R2) and absolute average deviation (AAD) [37–39] were used to determine the performance and predictive capacity of developed ANN and RSM models. From the results (Table 3), it was found that, both models have the ability to predict the experimental data. However, the predictive capability of ANN model was higher than the RSM Fig. 2. Residual errors between experimental and predicted data by RSM and ANN.
Table 3 Validation of RSM and ANN model for additional experimental data. Run order
X1
X2
X3
X4
1 2 3 4 5 6 7 8 9 10
3.5 5 4 8 6.5 7 4 6.5 5.5 3
0.5 3 1.25 1.3 0.8 1.2 0.5 1.3 0.5 0.8
40 35 50 45 40 50 45 50 35 50
15 20 25 50 35 30 20 30 45 30
Conversion to biodiesel (%) Actual
RSM
ANN
74.29 41.36 86.25 89.31 89.94 92.72 82.26 91.51 84.68 87.27 RMSE R2 AAD
77.84 44.30 90.76 92.84 93.48 94.47 80.83 94.00 86.74 85.90 4.196 0.919 2.872
74.23 41.42 86.23 89.36 89.86 92.64 82.31 91.53 84.67 87.19 0.001 0.999 0.009
Fig. 3. Comparison of prediction capacity of RSM and AAN for unseen experimental data.
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models. The ANN model predictions are lie much closer to the line of perfect prediction than the RSM model (Fig. 3). Thus, the ANN model shows a significant and higher generalization capacity than the RSM model. This higher predictive accuracy of the ANN can be attributed to its universal ability to approximate the nonlinearity of the system, whereas the RSM is restricted to a second-order polynomial. Generation of ANN model requires a large number of iterative calculations, whereas it is only a single step calculation for a response surface model. ANN model may require a high computational time to create and more costly than a response model. But ANN is flexible and permits to add new experimental data to build a trustable ANN model, inherently capture almost any form of non-linearity, easily overcome the limitation of RSM [39,40] and this methodology does not require a standard experimental design to build the model.
4. Conclusion In this study, ultrasound assisted intensification of biodiesel production from NO was evaluated through RSM and ANN. Four factors five level RSM-CCRD and ANN-MLP network (LM training algorithm) with nine neurons in hidden layer were used to construct model in order to predict the experimental data and also the predictive capabilities of both model was compared statistically. From the results, it was observed that, the ANN model was more robust and accurate in predicting the values of dependent variables when compared with RSM model.
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