Energy & Fuels 2008, 22, 2743–2747
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Identification of the Biodiesel Source Using an Electronic Nose D. S. Giordani,* A. F. Siqueira, M. L. C. P. Silva, P. C. Oliveira, and H. F. de Castro Engineering School of Lorena, UniVersity of Sa˜o Paulo, Post Office Box 116, 12600-970, Lorena, SP, Brazil ReceiVed December 14, 2007. ReVised Manuscript ReceiVed March 31, 2008
Biodiesel is an important new alternative fuel. The feedstock used and the process employed determines whether it fulfills the required specifications. In this work, an identification method is proposed using an electronic nose (e-nose). Four samples of biodiesel from different sources and one of petrodiesel were analyzed and well-recognized by the e-nose. Both pure biodiesel and B20 blends were studied. Furthermore, an innovative semiquantitative method is proposed on the basis of the smellprints correlated by a feed-forward artificial neural network. The results have demonstrated that the e-nose can be used to identify the biodiesel source and as a preliminary quantitative assay in place of expensive equipment.
1. Introduction Biodiesel is a mixture of alkyl esters obtained from several lipid-based feedstock by transesterification reactions in the presence of a suitable catalyst. Homogeneous alkaline catalysts are commonly used as industrial catalysts, because they are relatively cheap. Nevertheless, many attempts have been made to use heterogeneous catalyst systems, from which a number of studies have shown that the enzyme (lipase) holds promise as a catalyst.1 The lipid source (vegetable oils or fats) for biodiesel production is chosen according to its availability in each region or country. From a chemical point of view, oils from different sources have different fatty acid compositions. The fatty acids are different in relation to the chain length, degree of unsaturation, or presence of other chemical functions. Therefore, biodiesel properties are strongly influenced by the properties of the individual fatty esters.2 The advantages of using biodiesel compared to petrodiesel are related to its derivation from a renewable resource, reducing dependence on petroleum, biodegradability, reduction of most exhaust emissions (with the exception of nitrogen oxides, NOx), and higher flash point, leading to safer handling and storage and excellent lubricity, a fact that is steadily gaining importance with the advent of low-sulfur petrodiesel fuels, which have greatly reduced lubricity. Adding biodiesel at low levels (1-2%) restores the lubricity.1 Some problems associated with biodiesel are concerning its higher price, which in many countries is offset by legislative regulatory incentives or subsidies in the form of reduced excise taxes, cold flow properties that are especially relevant in cold countries, stability when exposed to air (low oxidative stability), and slightly increased NOx exhaust emissions.1 However, the air-quality effect of 100% market penetration of B20 into onroad heavy-duty fleets in several major urban areas were examined in a study that employed pollutant inventory and air* To whom correspondence should be addressed. E-mail: giordani@ dequi.eel.usp.br. (1) Khote, G. Introduction. In Biodiesel Handbook; AOCS Press: Champaign, IL, 2005; p 9. (2) Pinto, A. C.; Guarieiro, L. L. N.; Rezende, M. J. C.; et al. J. Braz. Chem. Soc. 2005, 16, 1313.
quality models; the results suggest that the NOx increase does not have serious air-quality implications.3 Many countries already have specific legislation to regulate the use of biodiesel and its blends with petrodiesel as a commercial fuel. In Brazil, since 2005, the use of biodiesel as a fuel has been legally authorized, allowing up to a volume fraction of 2% in petrodiesel (blend commonly named as B2). This level became mandatory in 2008, expecting an increase in the participation of biodiesel to 5% (blend B5) in 2013 and then up to 20% (blend B20) in 2020. The worldwide use of biodiesel is, of course, associated with a long certification process to ensure the quality of the fuel. The characterization of biodiesel has been largely studied and reported in the literature. Some authors have reported chromatographic and spectroscopic methods to classify biodiesel according to composition in terms of esters and mono-, di-, or triacylglycerol.4–6 To determine the concentration of biodiesel in blends with petrodiesel, different methods have been used, including 1H nuclear magnetic resonance (NMR) spectroscopy, chromatography, and infrared spectroscopy.7–9 Recently, multivariate near-infrared spectroscopy was used to predict methanol and water content in biodiesel.10 However, there are a few papers in the literature reporting the determination of the parent oils from which a biodiesel has been produced. There are some important reasons to characterize a biodiesel in terms of its parent oils; probably the most important is fiscal law. Some countries apply different policies depending upon the feedstock. Another important reason is (3) Morris, R. E.; Polack, A. K.; Mansell, G. E.; Lindhjem, C.; Jia, Y.; Wilson, G. Summary Report. National Renewable Energy Laboratory, Golden, CO, NREL/SR-54033793, 2003. (4) Freedman, B.; Kwolek, W. F.; Pryde, E. H. J. Am. Oil Chem. Soc. 1986, 63, 1370. (5) Foglia, T. A.; Jones, K. C. J. Liq. Chromatogr. Relat. Technol. 1997, 20, 1829. (6) Freedman, B.; Pryde, E. H.; Kwolek, W. F. J. Am. Oil Chem. Soc. 1984, 61, 1215. (7) Knothe, G. J. Am. Oil Chem. Soc. 2001, 78, 1025. (8) Foglia, T. A.; Jones, K. C.; Phillips, J. G. Chromatographia 2005, 62, 115. (9) Pimentel, M. F.; Ribeiro, G. M. G. S.; Cruz, R. S.; Stragevitch, L.; Pacheco Filho, J. G. A.; Teixeira, L. S. G. Microchem. J. 2006, 82, 201. (10) Felizardo, P.; Baptista, P.; Menezes, J. C.; Correia, M. J. N. Anal. Chim. Acta 2007, 595, 107.
10.1021/ef700760b CCC: $40.75 2008 American Chemical Society Published on Web 05/28/2008
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related to the fact that each oil determines the biodiesel-specific physicochemical properties, which are essential data to use biodiesel in appropriate ways. The quantitative analysis in terms of the amount of biodiesel in the blend with petrodiesel is also important to monitor the correct application of the environmental regulations. The use of an electronic nose (e-nose) associated with artificial neural networks (ANNs) seems to be a good tool to estimate the composition of such oils. E-nose measurements are based on a change in resistance in an array of chemical sensors when exposed to a chemical vapor. Its use has been reported in the literature to determine the origin of a variety of items, such as honey,11 cigarettes,12 wine,13 olive oil,14 spoiled beef,15 sesame oil, and gasoline.16 ANNs are computational models designed to simulate the way by which the human brain processes information; they gather information from data by detecting relationships and patterns. Therefore, they are able to learn from experiences. The major capabilities of the ANNs are their ability to recognize patterns and data correlation (regression) when traditional mathematical models are not available.17 Some authors have successfully used this tool to enhance the potential of the e-nose.13,18–20 In this work, a 32 sensor-based e-nose was used to distinguish four samples of biodiesel and one of commercial petrodiesel. Each sensor produces a value of maximum electrical resistance; hence a vector of 32 values forms the smellprint of the analyzed substance. For classification purposes, principle component analysis (PCA) was used to reduce the dimensionality of the pattern space, leading to a better visualization of data clustering. The ANN was used to correlate the biodiesel amount in petrodiesel blends with their smellprints in such a way that was possible to obtain a semiquantitative assay by regression. 2. Materials and Methods E-nose. A Smiths Detection Cyranose 320 e-nose was used in this work. Its measurements are based on a change in the electrical resistance of each chemical sensor in the 32-sensor array when exposed to a chemical vapor. This is a differential measurement with the sensor response measured as a function of (Rmax - Ro)/ Ro, with Ro being the resistance during a baseline gas flow and Rmax being the maximum resistance during exposure to the sample vapor, as shown in Figure 1. The chemical sensors act in response to the vapor headspace to which they are exposed. Across the array of unique sensors, the responses are different and a response pattern is obtained that represents each particular headspace. The sensor materials are thin films deposited across two electrical leads on alumina substrate, creating the conducting chemiresistors. When the composite film is exposed to a vapor-phase analyte, the polymer matrix resembles a sponge and swells while absorbing the analyte. The increase in volume causes an increase in resistance (11) Benedetti, S.; Mannino, S.; Sabatini, A. G.; Marcazzan, L. G. Apidologie 2004, 35, 1. (12) Dehan, L.; Gholam Hosseini, H.; Stewart, J. R. Sens. Actuators, B 2004, 99, 253. (13) Lozano, J.; Santos, J. P.; Horrillo, M. C. Talanta 2005, 67, 610. (14) Cosio, M. S.; Ballabio, D.; Benedetti, S.; Gigliotti, C. Anal. Chim. Acta 2006, 567, 202. (15) Panigrahia, S.; Balasubramaniana, S.; Gua, H.; Logueb, C.; Marchelloc, M. LWT 2006, 39, 135. (16) Sobanski, T.; Szczurek, A.; Nitsch, K.; Licznerski, B. W.; Radwan, W. Sens. Actuators, B 2006, 116, 207. (17) Haykin, S. Neural networkssA ComprehensiVe Foundation, 2nd ed.; Macmillan Col. Pub. Co.: New York, 1999. (18) Martı´n, Y. G. M.; Oliveros, M. C. C.; Pavon, J. L. P.; Pinto, C. G.; Cordero, B. M. Anal. Chim. Acta 2001, 449, 69. (19) Onkal-Engina, G.; Demira, I.; Engin, S. N. EnViron. Modell. Software 2005, 20, 843. (20) Hai, Z.; Wang, J. Sens. Actuators, B 2006, 119, 449.
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Figure 1. Typical sensor response through the (A) baseline purge, (B) sample exposure, and (C) sensor refresh.
because the conductive carbon-black pathways through the material are disrupted. When the analyte is removed, the polymer releases the analyte and shrinks back to its original size, restoring the conductive pathways. Each polymer used in the array is chemically unique and absorbs the analyte gases to a different degree, creating a pattern of differential response across the array. Figure 2 shows a typical sensor response to petrodiesel. A variety of samples may be exposed to the e-nose that gathers their smellprints and groups them using mathematical techniques; this is called the training step of the e-nose. After this, in the working step, the e-nose will be able to recognize the samples to which it was trained. In this work, the exposure of the headspace of the samples to the e-nose was tested with two different procedures: with closed vials and with open vials. The main reason to test the exposure with open vials is to reproduce possible field conditions, where no sample preparing would be required. PCA. As can be seen, a great amount of data is collected for each sample; it is not always practical to work with all of them because important information may be hidden inside the data set. Therefore, it is necessary to use a mathematical technique to reduce the data set to a fewer but significant amount of data. The resource often used for this purpose is the PCA. This method consists of expressing the response vectors in terms of linear combinations of orthogonal vectors along a new set of coordinate axes; it is sometimes referred to as vector decomposition and thus helps to display multivariate data in two or three dimensions. A loading plot of a PCA shows to what degree the different sensors contribute to the principal components. In this plot, sensors with similar contributions (i.e., that contain similar information) will be close together. Sensors that are close to the origin have comparably small variance and, therefore, probably contain little information.13,21 In this work, the first three components were used, which often represented more than 99% of the total variance. Consequently, it was possible to plot three-dimensional graphics to show the clustering effect of similar data to form recognizable classes. To have a valid measure, validation and qualification tests were proposed. They were planned to measure the applicability of the experimental procedure with the e-nose used. Validation studies were performed by repeating, exhaustively, recognition tests using the same headspace technique used in training step at normal operating conditions, i.e., with closed vials. The qualification test was carried out by testing samples at the boundaries of operating conditions, which was accomplished by aspirating the sample headspace with open vials to reproduce possible field conditions. Identifications were performed by the e-nose that uses PCA to classify the samples and standard algorithms to rate this identification based on the underlying structure of the data. For this purpose, the identification quality was adjusted to the maximum level, which means that positive recognition only occurs if the analyzed sample is positioned inside the cluster formed by the recognized class. (21) Duda, R. O.; Hart, P. E.; Stork, D. G. Pattern Classification; WileyInterscience: New York, 2001; pp 115-117.
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Figure 2. Response of the 32-based sensor array when exposed to the petrodiesel sample.
ANNs. There are many types of neural networks, but all of them have the same basic principle. Each neuron in the network is able to receive input signals, process them, and generate an output signal. Each neuron is connected to at least another neuron, and each connection is represented by a real number, called weight coefficient, which reflects the degree of importance of a given connection to the neural network. ANNs can be applied to perform regression and data classification; however, in this work, they were used only to the former purpose, because PCA has some limitations for the classification task. One of the most popular ANNs is the feed-forward backpropagation type, in which the structure is based on an input layer containing all variables that feed the network and an output layer that contains the ANN responses to the desired problem. All layers between input and output are called hidden layers. There is no limit to the number of hidden layers, but one hidden layer with an arbitrarily large number of processing elements (neurons) is generally enough to solve the majority of problems, although some rare functions require two hidden layers to be well-modeled.17 The output value of theith neuron xi is determined by eqs 1 and 2, which holds xi ) f(yi)
(1)
n
yi ) bi +
∑w x
(2)
ij
j)1
where yi is the potential of the ith neuron, n is the number of input connections on the ith neuron, wij is the weight coefficient of the connection between the input j and the ith neuron, xj is the value of the input j, and bi is the bias coefficient and can be understood as a weight coefficient of the connection formally added to the neuron. The function f(yi) is the so-called transfer function. One of the most widely used transfer functions is the sigmoidal, but there are others, such as hyperbolic tangent and linear. In the supervised training, i.e., the one whose targets are already known for a given input data set, biases and weight coefficients are varied to minimize the sum of the squared differences between the computed and required output values (targets), which are performed by minimization of an objective function E n
E)
∑ 21 (x - xˆ ) j
j)1
j
2
(3)
Table 1. Viscosities of Biodiesel and Petrodiesel Samples Used in This Work parent oil
viscosity (mm2/s)
palm babassu chicken grease beef tallow petrodiesel
4.00 4.50 6.90 5.38 3.74
where xj and xˆj are vectors composed, respectively, of the computed and target activities of the output neurons and n is the number of neurons. In the feed-forward back-propagation ANN, the error E is back-propagated through the network to correct biases and weight coefficients until a predetermined value of E is attained. Although back-propagation is one of the most popular training algorithms, it is known that sometimes it is slow to converge and tends to fall into local minima; there exist some variations of this algorithm developed to optimize convergence. The LevenbergMarquardt algorithm that was designed to approach second-order training speed22 seems to be the fastest method for training moderate-sized feed-forward neural networks (up to several hundred weights). It also has a very efficient MATLAB implementation. This algorithm has proven to be very efficient in papers where it has been used.23,24 The main advantage of neural networks is that they are able to use some information that may be hidden in data sets. The process of capturing the unknown information is performed during the training step of the ANN, when one may say that the ANN is learning how to output a satisfactory response for an input data set. In mathematical language, the learning process is the adjustment of the set of weight coefficients in such a way that some conditions are fulfilled. Biodiesel and Petrodiesel Samples. Four different samples of biodiesel and one of petrodiesel were used; the viscosities for all samples are shown in Table 1. The viscosities were determined in Brookfield Model DV-II+pro viscometer at 40 °C and may furnish a reasonable idea whether the biodiesel contains parent oil remaining from uncompleted transesterification. Palm and babassu samples (22) Marquardt, D. J. Soc. Ind. Appl. Math. 1963, 11, 431. (23) Laugier, S.; Richon, D. Fluid Phase Equilib. 2003, 210, 247. (24) Yu, D. L.; Gomm, J. B. Control Eng. Pract. 2003, 11, 1315.
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Table 2. Outline of the Test Performed in This Worka
test 1 test 2 test 3 a
test type
mathematical method used
biodiesel source used
classification
PCA
regression
ANN
babassu, palm, chicken grease, and beef tallow babassu
concentrations pure B20 petrodiesel, B2, B5, B10, and B20
number of replications of each sample 5 (read twice) 10 10
Observed BX means X% of biodiesel blended with petrodiesel.
were obtained by enzymatic catalysis, and the chicken grease sample was obtained by alkaline catalysis, all at laboratory scale.25–27 The beef tallow sample (BIOMAX) was obtained industrially, cordially furnished by Fertibom Ltd., and used as received. Table 2 outlines the tests carried out in this work. For test 1, five samples (1 mL) of each pure biodiesel or petrodiesel studied were placed into 40 mL headspace vials and were left for enough time to attain liquid-vapor equilibrium at room temperature. The headspace of each vial was then exposed to the e-nose to generate a data set large enough for statistical treatment. To obtain a data set containing 10 exposures of each sample, each vial was exposed twice to the e-nose. E-nose was adjusted to run for 10 s in purging air to form the baseline and 30 s sucking the vapors of the headspace; this time was enough to reach a steady state for the majority of sensors. Purge and sucking were at a sampling speed of 120 mL/min. Six sensors were disabled because their resistance failed to attain a steady state. In test 2, four B20 blends (20% of biodiesel with 80% of petrodiesel) were prepared with each biodiesel sample. A total of 10 samples (1 mL) of each B20 were exposed to the e-nose. The reason for not preparing five samples and exposing them twice as performed in the former study was to obtain a more homogeneous data set, given that, when working with a blend, where one of the component is much more volatile than the other one, the time to attain headspace equilibrium is greater, which could make the second exposure different from the first one. Blend exposure was regulated to have 10 s of air purging at 120 mL/min to construct the baseline and 60 s of headspace aspiration at 180 mL/s. To remove the effects of response size on the smellprint pattern, the responses of the 26 used sensors were normalized using a simple weighting method type ∆R R0 i ∆R ) R0 i ∆R Σ R0 j
( ) ( ) ( )
(4)
∆R ) Rmax - R0
(5)
Finally, for test 3, B2, B5, B10, and B20 blends with babassu biodiesel were prepared and 10 samples of each were exposed to the e-nose. The baseline of the smellprints had to be conditioned with the pure petrodiesel; i.e., the baseline was not formed by pure air purging but by pure petrodiesel headspace to enhance the sensitiveness of the sensors regarding the biodiesel presence in the mixture. No normalization techniques were used here to keep the original resistances registered by sensors, because in this case, the response sizes of the smellprints are used to be correlated to the concentration.
3. Results and Discussion Pure Biodiesel. On the basis of PCA techniques, the first three principal components were used to describe most of the variability presented by the sensors, as shown in Figure 3. As (25) Moreira, A. B. R.; Perez, V. H.; Zanin, G. M.; de Castro, H. F. Energy Fuels 2007, 21, 3689–3694. (26) Paula, A. V.; Urioste, D.; Santos, J. C.; de Castro, H. F. J. Chem. Technol. Biotechnol. 2007, 82, 281. (27) Amaral, T. B.; Gonc¸alves, A. R.; Almeida, C. R. O.; Rocha, G. J. M. 29th Symposium on Biotechnology for Fuels and Chemicals, Denver, CO, 2007; abstracts, p 178.
Figure 3. PCA projecting plot for different samples of pure biodiesel samples and petrodiesel.
Figure 4. PCA projecting plot for four different B20 blends.
a consequence, the e-nose was able to recognize the smellprint of each sample. These results lead to assume that the e-nose is a suitable equipment to recognize with efficiency and in a fast way any of the presented samples. Furthermore, they indicate that other biodiesel samples could be well-recognized because the distances among the groups are large enough to hold more groups. Validation studies to this analysis attained 100% success, and the qualification test attained approximately 93% success. B20 Blends. In a first assay, the four blend samples were exposed to the e-nose according to the procedure described in the Materials and Methods. As can be seen in Figure 4, beef tallow and chicken grease B20 blends had almost the same pattern and were grouped by the PCA technique very close to each other; in this way, it is not possible to recognize them. The recognition only could be performed in terms of the biodiesel source (animal fat or vegetable oil). Nevertheless, in such situations, it is recommended to perform the assay in two steps, which is commonly called tiered analysis. Then, a second assay was carried out, this time using only chicken grease and beef tallow B20 blends. As a result, both samples could be distinguished by the e-nose as shown in Figure 5. Thus, the strategy recommended to this set of B20 is tiered methodology. This fact may occur with other sets of blends, and therefore, in this case, the same approach should be used. The validation of test 2 was carried out using the same methodology for test 1. The results were 88% of correct assays.
Biodiesel Source Using an Electronic Nose
Figure 5. PCA projecting plot for beef tallow and chicken grease B20 blends.
Qualification of the method, with aspiration of open vial headspace, achieves only 60% of correct determination, which indicates that, for B20, the assays should be performed with the strict methodology of closed vials. Semiquantitative Analysis. For test 3, a set of pure petrodiesel and blends with babassu biodiesel (B2, B5, B10, and B20) was submitted to the e-nose; results of the PCA projecting plot were not stimulating, as can be observed in Figure 5, where no group separation can be found because of the great proximity of the smellprints of the blends. A cross-validation test using the leave-one-out method showed a 68% of correct predictions, which was not considered reasonable. However, a meticulous analysis of the smellprints and the response intensities of the sensors, when no data regularization was applied, has shown two important facts: there were slight differences among them, and these differences could be greater if the baseline was not formed by air purging but by pure petrodiesel headspace. When the e-nose is compared to the human nose, it could be noticed that the e-nose is more sensible to a little quantity of babassu biodiesel in the blend if it is previously “adapted” to the petrodiesel smell. Because other authors have successfully used ANN resources to enhance the power of the e-nose, it was thus decided to apply this methodology with the regression approach, instead of classification.13,18–20 The smellprint of each sample exposure to the e-nose was collected to form the training data set of a feed-forward backpropagation ANN. Because there were 10 samples of pure petrodiesel and 10 of each blend (B2, B5, B10, and B20), a set of 50 assays was performed. Five assays were separated to form a testing group. A 26:3:1 structure was tested, where 26 is the amount of neurons in the input layer, corresponding to the number of activated sensors, 3 is the amount of neurons in the hidden layer, and 1 is number of neurons in the output layer, corresponding to the babassu biodiesel concentration in the blend. The network was trained using the Levenberg-Marquardt algorithm. The trained ANN was tested by introducing the testing group to the network. To test the prediction capacity of the ANN, two new blends were added in this testing group: B7 and B15. As shown in Figure 6, predictions were very close to the real values. It can also be observed that for B7 and B15 (0.07 and 0.15, respectively) the predicted values were not as accurate as the remaining group, which indicated that the generalization capacity of the ANN needs to be improved (Figure 7). The average error to the testing set was 7.42%. The use of a larger amount of
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Figure 6. PCA projection plot of babassu B2, B5, B10, B20, and petrodiesel blend samples.
Figure 7. Correlation of real values and ANN predicted values.
training samples and changes in the structure of the ANN probably would have improved this approach. On the other hand, the semiquantitative potential of the method has already been demonstrated. 4. Conclusions The ability of the e-nose to recognize pure biodiesel samples has been demonstrated even under slightly different conditions than those used to train it. This indicates that the equipment is suitable to perform assays in a very rapid and practical way. The electronic nose was also tested to recognize the origin of the biodiesel in B20 blends. The study revealed that it is also suitable to this task. However, the assays must always be performed under the same training conditions, i.e., using closed headspace vials. In some cases, it is necessary to apply the tiered method to recognize samples. A semiquantitative method was successfully developed to determine the amount of biodiesel in blends of babassu biodiesel with petrodiesel using ANNs to enhance the e-nose capacity. This technique is worth further studies to improve the generalization of the network predictions. E-nose coupled to ANNs is a very promising technique to biodiesel characterization, especially when considering the portability and the price of the equipment compared to traditional techniques. EF700760B
