Predicting Cetane Index, Flash Point, and Content Sulfur of Diesel

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Predicting cetane index, flash point and content sulfur of dieselbiodiesel blend using an artificial neural network (ANN) model Fernanda Maria de Oliveira, Luciene Santos de Carvalho, Leonardo Sena Gomes Teixeira, Cristiano Hora Fontes, Kássio M. G. Lima, Anne Beatriz Figueira Câmara, Heloise Oliveira Medeiros de Araújo, and Rafael Viana Sales Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b00282 • Publication Date (Web): 20 Mar 2017 Downloaded from http://pubs.acs.org on March 21, 2017

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Energy & Fuels

Predicting cetane index, flash point and content sulfur of diesel-biodiesel blend using an artificial neural network (ANN) model Fernanda M. de Oliveira,a* Luciene S. de Carvalhoa*, Leonardo S. G. Teixeira,b Cristiano H. Fontes,b Kássio M. G. Lima,a Anne B. F. Câmara,a Heloise O. M. Araújo,a Rafael V. Salesa

a

Institute of Chemistry, Federal University of Rio Grande do Norte, Natal, 59078-900, Brazil.

b

Institute of Chemistry, Federal University of Bahia, Salvador, 40170-115, Brazil

AUTHOR INFORMATION *Corresponding author: Luciene Santos de Carvalho Tel: +55 84 988285261 Fernanda Maria de Oliveira Tel: +55 84 998652054

ACS Paragon Plus Environment

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Abstract Artificial neural networks (ANNs) were used to predict, not simultaneously flash point, cetane index and sulfur content (S1800) of diesel blends (7% v/v biodiesel) using distillation curves (ASTM D86), specific gravity at 20ºC (ASTM D405), cetane index (ASTM D4737), flash point (ASTM D93) e sulfur content (ASTM D4294). The low error values obtained compared with other chemometric based models described in literature and high correlation coefficients between reference and predicted values showed that ANNs were efficient in determining flash point, cetane index/cetane number and sulfur content (S1800). The constructed model contains diesel samples of different compositions (50, 500 and 1800 mg kg-1), thus revealing the variety of fuel in the Brazilian market. Furthermore, the proposed method has advantages such as low cost and easy implementation, as it applies the results of the routine test to evaluate the quality control of diesel.

KEYWORDS: Artificial neural networks, Diesel-biodiesel blend; Flash point; Cetane index; Sulfur content.


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1.

Introduction In Brazil, the latest energy model is based on cargo transportation using roads. This

has driven the use of diesel as a fuel, having seen a rise in the production sector with average growth in volumetric terms of 7.22% per year in the period from 2010 to 2014. This fact led the Brazilian government, through the National Agency of Petroleum, Natural Gas and Biofuels (ANP),1,2 to intensify the diesel monitoring process, 3 with the evaluation of different physicochemical properties such as flash point,4 cetane number and cetane index,5,6 distillation,7 specific gravity at 20°C8 and sulfur content.9,10 These properties have had their specification limits and analytical methodologies based on standards adopted from American Society for Testing and Materials – ASTM. Although these standards are already consolidated, some methods present restrictions. ASTM D93 used to determine flash point has a high cost and requires significant time to be implemented,11-13 and the cetane number test, according to ASTM D613,5,14 uses elevated sample volume, in addition to needing significant time and having low reproducibility.14 Monitoring sulfur in petroleum fuels according to ASTM D4294 is very important, as sulfur compounds are associated with recurring problems in the storage, processing, transportation, and final quality of diesel, in addition to atmospheric pollution.9,10 According to ANP, diesel has shown a high percentage of non-conformities since 2010. In 2014, 2,347 non-conformities were found, where 31.2% were related to visual appearance which should be clear and free of impurities, according to ASTM D4176; 26.2% with the flash point (ASTM D93); 24.5% with biodiesel content (EN 14078); 10.4% with the sulfur content in the fuel (ASTM D5453); 4.8% with the color (ASTM D1500) and specific gravity at 20°C (ASTM D4052); and 2.8% with the presence of dyes relating to the current ANP Resolution no. 50 in Brazil.1


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According to the experimental/instrumental limitations and the cost of current methods, the development for new practical and reliable prediction methods to estimate the flash point, cetane index/cetane number and the sulfur content of diesel have been sought.11,15 Studies have been developed in order to develop new correlative models to predict the cetane number from fuel properties that can be obtained quickly and reliably. 14 Correlations based on physicochemical properties such as diesel’s API gravity, boiling points and aniline points have found applications in specialized literature.16–18 Chemometric methods like artificial neural networks (ANNs) have also been used and accepted as an alternative for predicting properties of diesel,19-21 such as information processing methodology, inspired by the work of the human brain; being effective in the treatment of non-linear data relations.22 ANNs can be powerful modeling tools for determining the properties of diesel-biodiesel blends and have the capacity to identify highly complex underlying relationships or extract knowledge using only input and output data. 23, 24 Yang et al25 employed a General Regression Neural Network (GRNN) to predict the cetane number and specific gravity of diesel based on its chemical composition. Korres and collaborators23 determined the lubricity of the fuel using a neural network with Radial Basis Function (RBF) having the conductivity, specific gravity, viscosity, sulfur content and 90% distillation point as input variables. Basu and collaborators26 predicted the cetane number of diesel samples using an ANN with a backpropagation algorithm and using spectroscopic data as input. Wu and co-authors27 constructed an ANN model to predict the cold filter plugging point of biodiesel. Pasadakis and co-authors28 performed a distillation prediction profile and cold properties of diesel fuels using mid-infrared spectroscopy and neural networks. Other papers found in the literature apply ANNs in predicting biodiesel and diesel properties. 29-33


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Between different types of ANNs, the feedforward networks of multilayer perceptron (MLP) appear as frequently used models which consist of an input layer, one or more hidden layers and an output layer. 34 This study evaluated the applicability of ANNs, using a feedforward multilayer perceptron (MLP) with the backpropagation training algorithm from Levenberg-Marquardt as an alternative methodology for individual determination of the flash point, cetane index/cetane number and sulfur content of diesel. The importance and viability of developed models are in fact that the input data used are already properties obtained in the routine analysis from diesel quality monitoring in Brazil, such as distillation curves (ASTM-D86), specific gravity at 20ºC (ASTM D405), cetane index (ASTM D4737), flash point (ASTM D93) e sulfur content (ASTM D4294); new methodologies are not required as when using data obtained by spectroscopic techniques,29,35,36 thus minimizing costs of equipment acquisition, technical training and new materials and reagents. The use of models aims to reduce the time and number of analyses and, consequently, reduce the cost of the diesel monitoring process.

2.

Experimental Section

2.1

Samples 162 samples of diesel 7B were used, (7% v/v biodiesel), with sulfur levels of 50, 500

and 1800 mg kg-1 (S50, S500 and S1800). The samples were analyzed in specialized laboratories using the ASTM methods defined by ANP. For each sample, experimental tests were conducted such as distillation (distillation temperatures are denominated as T10%, T50%, T85% and T90%) – ASTM D86,7 specific gravity at 20°C (ME20) and at 15ºC (ME15) – ASTM D40528, cetane index (CI) ASTM D47376 (calculated from ASTM D86 distillation


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and the density at 15 °C data), the flash point ASTM D934 and sulfur content (S) – ASTM D4294.9 Each group of samples consisted of 35 samples of S50, 92 samples of S500 and 35 samples of S1800. In this work, the samples from different suppliers and with the most varied possible characteristics were included in the modeling so that training and modeling from ANN stayed more robust. However, the objective was directed to the diesel/biodiesel B7 blend, which is the commercial mixture sold in Brazil. Table 1 presents information on the database used in this study, being the range for the study parameters comprising the specified limits for the resolutions adopted by ANP to certify diesel quality in Brazil.

Flash Point 162 ºC 38.0/69.0 5.8 57.3 Yes Cetane Index* 162 49.4/56.1 1.5 52.1 Yes 4.00/8.00 1.1 5.0 No S50 35 mg kg-1 Sulfur S500 92 mg kg-1 82.0/1402.0 335.8 149.0 No S1800 35 mg kg-1 226.0/1831.0 271.6 1131.3 Yes 830.0/849.0 3.8 836.0 No Specific Gravity 162 kg m-³ T10% 162 ºC 192.4/220.2 6.2 204.3 Yes T50% 162 ºC 260.7/307.9 6.4 284.5 No Distillation T85% 162 ºC 318.3/363.7 11.2 347.9 No T90% 162 ºC 327.8/383.7 13.2 361.9 No *Cetane index was calculated from data of ASTM D 86 distillation and density at 15°C.

ASTM

Normality Test

Mean or Median

Std Deviation

Minimum/ Maximum

Parameter

Units

Table 1. Diesel samples and specifications of the quality parameters. Number of Samples

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D93 D4737 D4294 D4294 D4294 D4052 D86 D86 D86 D86

2.2 Artificial Neural Networks: Training network ANNs are constituted by simple processing elements called neurons, which are connected to each other and arranged in layers. The adopted network architectures were the feedforward type with 10 neurons in the hidden layer, tangent hyperbolic transfer function in the hidden


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layer and linear in the output layer for all the developed models. The training was conducted by conventional algorithms such as backpropagation and Levenberg-Marquardt. The number of neurons in the hidden layer was determined by trial and error test, where we used the mean square error (RMSE) and correlation coefficient (R) between results from the standard analysis methodology (ASTM), compared to the results predicted by the network. In the training stage, it was found that among the 162 samples used in the modeling, some of them presented some type of error in their input data (called defective samples), and consequently the prediction values differed a lot from the average of expected results. These values were identified as outliers. One neuron was established in the output layer for the models used, corresponding to the predicted quality parameter. In this study, the input layer was formed by properties which are already part of the fuel monitoring routine in Brazil, such as ASTM D86 distillation temperatures (T10%, T50%, T85% and T90% distillate), specific gravity, cetane index and flash point. Table 2 shows the inputs tested in the different networks for each output variable that is predicted. Table 2. Input variables used for each trained network. Target Flash Point

Cetane Index

Network 1 2 3 1 2 3 4 5 6 7 1

S1800

2 3 4

Inputs Distillation D86 (T10, T50, T85, T90) and Specific Gravity. Distillation D86 (T10, T50, T85, T90). Distillation D86 (T10, T50, T85, T90), Specific Gravity and Cetane Index. Distillation D86 (T50) and Specific Gravity. Distillation D86 (T10, T50, T85, T90), Specific Gravity. Distillation D86 (T50). Distillation D86 (T10, T50, T85, T90). Specific Gravity. Distillation D86 (T10, T50) and Specific Gravity. Distillation D86 (T50, T90) and Specific Gravity. Distillation D86 (T10, T50, T85, T90), Specific Gravity, Cetane Index and Flash Point. Distillation D86 (T10, T50, T85, T90), Specific Gravity and Flash Point. DistillationD86 (T10, T50, T85, T90), Specific Gravity and Cetane Index. Distillation D86 (T10, T50, T85, T90) and Specific Gravity.


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Each training used 162 samples, which were randomly divided into three groups: 70% of the samples for training, 15% for validation and 15% for testing. With an objective to improve the generalized capability of the models, a cross-validation procedure was used during training of the network so that the number of iterations in this step was conditioned by the square error in the test samples. Fig. 1 graphically illustrates the example of network architecture used in this work, where x1, x2, x3 and x4 are, respectively, equivalent to the input parameters distillation D86 (T10); distillation D86 (T50); distillation D86 (T85) and distillation D86 (T90), and y1 refers to the output parameter, which one wishes to predict, and flash point, with 4 neurons in the input layer, 10 in the hidden layer and 1 in the output layer.

Figure 1. Architecture of the MLP network used for prediction of specification parameters of diesel.


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The methodology developed in this study used three steps (Scheme 1), which are: i) Obtaining diesel samples and conducting experimental tests based on methodologies recommended by ANP to form a network entry database and to compare the results obtained using ANNs; ii) Identification of ANN models capable of predicting the flash point, cetane index and sulfur content; iii) Comparative study between the predictions and measured/experimental values.

Scheme 1. Flowchart of the training methodology of artificial neural networks.


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2.3 Statistical Analysis The results obtained through artificial neural networks were compared to the values measured by standard methods of analysis. This analysis was performed with the application of statistical parameters, such as the square root of the mean square error (RMSE) (Equation 1), the mean absolute percentage error (MAPE) (Equation 2), the correlation coefficient (R), paired Student's t-test for parametric samples and Wilcoxon test for non-parametric samples, both at the significance level of 0.05. Only the networks that showed the best results were analyzed by Student and Wilcoxon statistical tests. The Kolmogorov Smirnov test was performed at the 0.05 significance level to verify the normality of the data. = ∑ ( ) = ∑ −

(1)

Where " Xi " is the value measured by the standard analysis methods, " " is the value predicted by the network, and N is the sample number. =

() ∑

(2)

Where " Xi " is the value measured by the standard analysis methods, " " is the value predicted by the network, and N is the sample number. Even the mean squared error (MSE) was used as an error threshold in the ANN training process. For presenting the results, the RMSE was considered more suitable for viewing the variation range between the predicted values and actual values, while the MAPE represents the best prediction precision measurement.31 3.

Results and Discussion Data from ASTM D86 distillation temperatures (T10%, T50%, T85% and T90%

distillate), specific gravity, cetane index and flash point were used for constructing the X


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matrices (input layer), where the rows correspond to the samples and the columns to the test results. To predict flash point, cetane index/cetane number and sulfur content, Y matrices (output layer)were built containing different samples in the range of 38.0 °C − 69.0 °C for flash point, 49.4 − 56.1 for cetane index and 226.0 − 1831.0 for S1800, respectively. The feedforward architecture in both commonly used versions (MultiLayer Perceptron, MLP, as in this work; and Radial Basis Network, RBN) is the most popular, simplest and less susceptible to numerical problems of convergence. This architecture is fully suitable for the identification of stationary models, as in this work. Due to its simplicity, it is also widely used even for dynamic modeling by inserting past values of inputs and outputs into the input layer.37 The well-established Levenberg-Marquardt algorithm is a classical, non-heuristic optimization method, widely used for the training of neural networks and presents a better performance than the backpropagation algorithm.38,39,40 The Levenberg-Marquadt tries to directly minimize the mean square error of whole sample, rather than the error of each training example, which provides more robustness in the search for the optimal solution. The values reported in Table 3 show the correlation coefficients (R) and the Root Mean Squared Error (RMSE) for the stages of training, validation, testing and results for the combination between the three steps, as well as the mean absolute percentage error (MAPE) of each network trained for all three predicted physicochemical properties: flash point, cetane index and sulfur content.


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Table 3. Correlation coefficient (R), root mean squared error (RMSE) and mean absolute percentage error (MAPE) of the trained networks to flash point. Training set

Validation set

Test set

Overall

Cetane Index

Flash Point

Network

S1800

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RMSE

R

RMSE

R

RMSE

R

RMSE

R

MAPE(%)

1

3.4

0.813

3.8

0.774

4.2

0.771

3.6

0.789

4.8

2

3.2

0.851

4.8

0.607

4.2

0.676

3.6

0.786

4.9

3

3.2

0.843

3.2

0.775

4.8

0.663

3.5

0.801

4.6

1

0.4

0.966

0.2

0.992

0.4

0.969

0.4

0.971

0.4

2

0.4

0.972

0.3

0.976

0.3

0.977

0.4

0.973

0.4

3

1.3

0.552

1.1

0.522

1.7

0.443

1.3

0.505

1.9

4

1.2

0.653

1.4

0.523

1.2

0.658

1.2

0.631

1.8

5

0.9

0.762

1.0

0.647

1.3

0.607

1.0

0.719

1.5

6

0.3

0.974

0.4

0.964

0.4

0.966

0.4

0.971

0.4

7

0.5

0.955

0.6

0.942

0.4

0.957

0.5

0.952

0.6

1

13.1

0.999

122.7

0.738

97.6

0.894

60.3

0.975

3.3

2

78.4

0.976

273.1

0.779

267.6

0.872

158.9

0.861

12.2

3

144.6

0.912

224.5

0.939

283.4

0.677

183.3

0.823

12.6

4

148.1

0.912

187.5

0.953

62.1

0.961

145.8

0.913

10.4

According to the results in Table 3, the networks trained to obtain the flash point showed similar results, being later evaluated by statistical tests for comparing paired samples. These results are shown in Table 4, along with the results of the normality test, the mean or median of the compared sets of results, standard deviation, and maximum and minimum values of each variable group. Two responses are presented for the normality test; "yes" to groups of samples with normal distribution (parametric) and "no" for sample groups without normal distribution (non-parametric). Regarding results, the outcomes from the mean and ttest were used for groups with normal distribution, while the results of medians and Wilcoxon test were used for non-parametric samples.


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Table 4. Result of the statistical test for the flash point.

Flash Point

Network 1 2 3

Cetane Index

1 2 6 7 1 S1800

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2 3 4

Method

Normality

Measured Predicted Measured Predicted Measured Predicted Measured Predicted Measured Predicted Measured Predicted Measured Predicted Measured Estimated Measured Estimated Measured Estimated Measured Estimated

Yes No Yes No Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Mean or Median 57.3 55.7 57.3 55.5 57.3 56.3 52.1 52.1 52.1 52.1 52.1 52.1 52.1 52.2 1131.3 1135.2 1131.3 1150.5 1131.3 1029.9 1131.3 1079.4

Std Deviation 5.8 4.8 5.8 4.9 5.8 4.8 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.4 271.6 271.0 271.6 43.4 271.6 254.4 271.6 333.9

MinimumMaximum 38.0 - 69.0 42.5 - 69.6 38.0 - 69.0 45.2 - 69.0 38.0 - 69.0 46.5 - 68.6 49.4 - 56.1 49.5 - 55.5 49.4 - 56.1 49.4 - 55.2 49.4 - 56.1 49.3 - 55.9 49.4 - 56.1 49.8 - 55.2 226.0 - 1831.0 237.1 - 1825.9 226.0 - 1831.0 325.8 -1709.9 226.0 - 1831.0 447.3 - 1691.8 226.0 - 1831.0 218.6 - 2150.1

Test

p-value

-2.217

0.027

-2.414

0.016

-0.072

0.942

-0.207

0.837

-0.250

0.803

0.554

0.580

-2.153

0.033

-0.373

0.712

3.706

0.004

3.871

0.000

2.223

0.033

Although the measured results for flash point presented normal distribution, the Wilcoxon test was used to compare the results of measured/predicted methods because the results of both training networks did not present normality. The p-value was obtained and, according to the data presented in Table 4, only network 3 could predict flash point, with pvalue > 0.05. Therefore this model can be considered equal to the standard analysis methodology to a level of 5% tolerance. These results are consistent with those observed in Table 3, where the smallest values for RMSE, MAPE and R were for network 3. Thus, the input variables which can better predict the flash point are distillation (ASTM D86) points


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T10%, T50%, T85% and T90% (distillation temperatures of 10, 50, 85 and 90% v/v distilled), specific gravity and cetane index. The measured and predicted values of flash point were compared to the network that achieved the best results of R, RMSE and MAPE (network 3) using a linear regression model, which can be seen in Figure 2 (a), and in the box plot chart shown in Figure 2 (b), which shows the distribution of the measured results (targets) and those predicted for each trained network.


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(a)

(b)

Figure 2: (a) Scatter of predicted values (output) against measured values (target) for the set of samples of training, validation and test of the netwok3; (b) Boxplot graphical for the different networks of diesel flash point. Using the box plot graph in Figure 2 (b), it can be observed that the results of all the trained networks presented a more centralized distribution and median values which are also lower compared with the results for the distribution of the standard methods of analysis.


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However, the results of network 3 show the nearest distribution of measured values, also presenting a smaller number of outliers. Although only network 3 was approved by the Wilcoxon test, both networks presented similar MAPE as the networks 1 and 2 used less physicochemical properties as input variables. Consequently, these models can be considered viable to use in predicting flash point. Regarding cetane index, the ASTM D86 distillation temperatures of T10%, T50%, T85% and T90% and specific gravity (network 2) were initially applied into the Y matrix. With this network, a good correlation coefficient allowing for the evaluation of the individual influence of each variable on the prediction capacity was obtained. Thus, six networks were created by varying the input matrices as seen in Table 2. Based on the results of correlation coefficient R for the cetane index in Table 4, it is perceived that specific gravity was crucial for network prediction, because the best network performance was obtained by using this parameter. For ASTM D86 distillation temperatures of T10%, T50%, T85% and T90% distillate (network 4), a smaller contribution in the prediction of cetane index was perceived. Thus, it was concluded that the isolated variables (network 3, 4 and 5) do not show good R results, and therefore they were not considered good input variables to describe the cetane index. Networks 1, 2, 6 and 7 showed the best R, RMSE and MAPE results and were evaluated using statistical tests. The Kolmogorov-Smirnov test was initially applied and, posteriorly, the paired t-test for sample comparison. In accordance with the normality test, all analyzed groups showed normal distribution (Table 4). In order to compare the results of the measured and predicted cetane index, the paired t-test at 5% of significance was used. According to Table 4, only network 7 had a p-value


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smaller than 0.05, thus rejecting the hypothesis of equality between the resulting pairs for the tested models, using 5% tolerance. Therefore, the networks that showed similar results to those of standard methodology, considering 5% of tolerance, were networks 1, 2 and 6, which also had smaller errors and larger correlation coefficients. Figure 3(a) indicates the regression graph between the measured values with standard analysis methods and predicted for network 2. It achieved the best results of R, RMSE and MAPE. Figure 3 (b) shows the box plot graph, which presents the results distribution(targets) of the standard analysis methods and for each trained neural network (networks 1-7).

(a)

(b)


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Figure 3. (a) Scatter of predicted values (output) against measured values (target) for the set of samples of training, validation and test of the netwok 2; (b) Boxplot graphical for the different networks of diesel cetane index. The box plot graph of Figure 3 (b) shows the distribution of values for cetane index, where it can be observed that the networks 3, 4, 5 and 7 had higher median and results distributed over a centralized manner around the median when compared with the results measured by standard methods of analysis. These same networks presented highest MAPE and lowest correlation coefficients. Among the models constructed for the cetane index, the network 6 showed the results distributed more similarly when compared with the values of standard analysis methods. The results from sensitivity analysis (R, RMSE and MAPE) for models of networks 1, 2 and 6 showed similar results, and considering that networks 1 and 6 used less input variables, this establishes these models as being more technically viable. Therefore, the input variables that most successfully predicted the cetane index were the combination of physicochemical properties: ASTM D86 distillation temperature of T50% distillate and specific gravity (network 1); and ASTM D86 distillation temperatures of T10% and T50% distillate and specific gravity (network 6). As established by the ANP in Brazil, the cetane number (CN) is obtained experimentally by the test according to ASTM D613. However, in this work the CNs can be obtained indirectly. Knowing that the predicted cetane index (CI) values with trained neural networks, when compared to the results of the standard method of ASTM D 4737, showed an average absolute deviation - AAD (Equation 3) of 0.18. And considering the study by Stratiev and co-authors,41 which compare the cetane number results obtained using the ASTM standard


18

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methodology D4737 with the cetane number results of the ASTM D613 methodology for 140 diesel samples, obtaining an average absolute deviation of 2.4.

AAD = ∑#|CN − CI |

(3)

#

where n is the number of observations. Therefore, for the cetane number, in this work an average absolute deviation of 2.58 (corresponding to the sum of errors 2,4 and 0,18) can be considered in this work. This low error value, allows to perform an indirect relationship between an IC predicted by the ANN and the NC obtained by ASTM D613, mainly, for not performing tests of motors. Thus, the use of this alternative method may lead to a reduction of time and costs in the monitoring process of the disel because the test for NC using ASTM D613 has a high cost. The prediction from sulfur content was performed separately for each type of diesel (TS50, TS500 and TS1800), however, only the models for S1800 were discussed because the others had unsatisfactory results with errors above the maximum established in this work, being 5%. On average errors of 11.45% for S50 and 35.1% for S500 were observed. For the trained network of S1800, only one had an error below 5%; even so, the KolmogorovSmirnov normality test and paired t-test were applied for comparing the methodology results, using all S1800 networks for comparison between them. Using the normality test (Table 4), all resulting groups analyzed showed a normal distribution. Thus, the Student’s paired t-test was used at a 5% significance level to compare the results from the measured and predicted sulfur content. According to the results in Table 4 for the S1800, network 1 was the only evaluated network that accepted the hypothesis of equality between the results obtained by standard analysis methods and those determined by the developed models based on ANNs as being


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true. This network also presented the smallest RMSE and MAPE values, as well as the highest correlation coefficient results for stages of training, validation, testing, and for the three steps combined. Figure 4(a) depicts a regression graph between the values measured by standard and predicted analysis methods for the network that achieved the best results of R, RMSE and MAPE (network 1), and Figure 4 (b) presents a boxplot graph showing the distribution of the results for the standard analysis methods for each trained neural network for sulfur S1800 (networks 1, 2, 3 and 4).


20

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(a)

(b)

Figure 4. (a) Scatter of predicted values (output) against measured values (target) for the set of samples of training, validation and test of the network 2; (b) Boxplot graphical for the different networks of diesel sulfur (S1800).

In analyzing the box plot graph of Figure 4 (b) (distribution of results for both evaluated methods), it is observed that network 3 had a very centralized distribution and consequently presented a higher number of outliers compared with other networks, and also for the results


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for the standard analysis methods. For network 4, more distributed results and higher variation around the median was observed. Network 1 presents a more similar distribution to the results from standard method, with median and range of 25% − 75%, very close to the results of the standard analysis methods (ASTM D4737). From the box plot it was also observed that network 1 could even very well predict the discrepant samples that appear in standard methodology. In this manner, the data presented in the boxplot graph are consistent with the other results of RMSE, MAPE, R and t-test. Therefore, the input variables that most successfully predicted S1800 were the combination of the following physicochemical properties: ASTM D86 distillation temperature for T10%, T50%, T85% and T90% distillate as well as the specific gravity, cetane index and flash point (network 1). In analyzing the input matrices of trained networks for the flash point, cetane index and sulfur content, it was observed that all the networks used ASTM D86 distillation temperatures as input variables. This makes them particularly viable, since these properties are already from the group of tests that are routinely performed for monitoring fuels in Brazil.

4.

Conclusion The feedforward backpropagation ANN showed viability in the proposed method to

reliably predict the flash point, cetane index and sulfur content (S1800), with mean absolute percentage error (MAPE) of 4.6%, 0.4% and 3.3%, respectively. The models have shown to be quite effective because they used in your input matrix, data of physicochemical properties such as distillation curves (ASTM-D86), specific gravity at 20ºC (ASTM D405), cetane index (ASTM D4737), flash point (ASTM D93) e sulfur content (ASTM D4294). that are already part of the diesel specification routine. Therefore, using some of the physicochemical


22

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properties specified in the diesel monitoring routine, it was possible to determine other specification parameters, reducing the number of analyzes of the process. The following benefits of the use of RNAs can be inferred, as such as, cost reduction, lower toxicity due to less solvent and samples to be handled during an analysis, as well as greater speed and flexibility in monitoring diesel quality, allowing an increase In the number of samples monitored annually. This study innovates in the indirect prediction of cetane number (CN) from the predicted cetane index (CI) using ANNs, with an average absolute deviation - AAD of less than 2.6 without performing motor tests. The physicochemical property sets determined for diesel by ANN models in this work are essential for the diesel market as specified by ANP.

Acknowledgements The authors would like to acknowledge CAPES and Program of Chemistry PostGraduation of the Federal University of Rio Grande do Norte. Program of Human Resources Training of Petrobras (PFRH PB 222) for financial support.

References (1) Agência Nacional do Petróleo, Gás Natural e Biocombustíveis. Resolução ANP nº 50, de 23/12/2013, Brazil; 2013. [accessed 22.12.15]. (2) Agência Nacional do Petróleo, Gás Natural e Biocombustíveis. Resolução ANP nº 45, de 20/12/2012, Brazil; 2012. [accessed 20.12.15]. (3) Agência Nacional do Petróleo, Gás Natural e Biocombustíveis. Diretório estatístico brasileiro de petróleo, gás natural e biocombustíveis; 2014.


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(4) ASTM D93, Standard Test Method For Flash Point By Pensky-Martens Closed Cup Tester; 2002. CrossRef (5) ASTM D613, Standard Test Method for Cetane Number of Diesel Fuel Oil; 2015. CrossRef (6) ASTM D4737-09, Standard Test Method for Calculated Cetane Index by Four Variable Equation; 2010. CrossRef (7) ASTM D86, Standard Test Method for Distillation of Petroleum Products and Liquid Fuels at Atmospheric Pressure; 2015. CrossRef (8) ASTM D4052, Standard Test Method for Density, Relative Density, and API Gravity of Liquids by Digital Density Meter; 2015. CrossRef (9) ASTM D4294, Sulfur in Petroleum and Petroleum Products by Energy Dispersive XRay Fluorescence Spectrometry; 2016. CrossRef (10) ASTM D5453, Standard Test Method for Determination of Total Sulfur in Light Hydrocarbons, Spark Ignition Engine Fuel, Diesel Engine Fuel, And Engine Oil by Ultraviolet Fluorescence; 2012. CrossRef (11) Liaw, H. J.; Gerbaud, V.; Li, Y. H. Prediction of miscible mixtures flash-point from UNIFAC group contribution methods. Fluid Phase Equilib 2011, 300 (1-2), 70– 82. CrossRef. (12) Bagheri, M.; Borhani, T. N. G.; Zahedi, G. Estimation of flash point and autoignition temperature of organic sulfur chemicals. Energy Convers Manag 2012, 58, 185– 196. CrossRef. (13) Moghaddam, A. Z.; Rafiei, A.; Khalili, T. Assessing prediction models on calculating the flash point of organic acid, ketone and alcohol mixtures. Fluid Phase Equilib 2012, 316, 117–121. CrossRef. (14) Creton, B.; Dartiguelongue, C.; de Bruin, T.; Toulhoat, H. Prediction of the Cetane Number of Diesel Compounds Using the Quantitative Structure Property Relationship. Energy & Fuels 2010, 24 (10), 5396–5403. CrossRef. (15) Ghosh, P.; Jaffe, S. B. Detailed Composition-Based Model for Predicting the Cetane Number of Diesel Fuels. Ind Eng Chem Res 2006, 45 (1), 346–351. CrossRef. (16) Moser, B. R. Efficacy of specific gravity as a tool for prediction of biodieselpetroleum diesel blend ratio. Fuel 2012, 99, 254–261. CrossRef. (17) Ladommatos, N.; Goacher, J. Equations for predicting the cetane number of diesel fuels from their physical properties. Fuel 1995, 74, 1083–1093. CrossRef.


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(18) Abdelkader, M. F.; Cooper, J. B.; Larkin, C. M. Calibration transfer of partial least squares jet fuel property models using a segmented virtual standards slope-bias correction method. Chemom Intell Lab Syst 2012, 110 (1), 64–73. CrossRef. (19) Jameel, A. G. A.; Naser, N.; Emwas, A.-H.; Dooley, S.; Sarathy, S. M. Predicting fuel ignition quality using 1H NMR spectroscopy and multiple linear regression. Energy Fuels 2016, 30 (11), 9819–9835. CrossRef. (20) Santos, V. H. J. M.; Ketzer, J. M. M.; Rodrigues, L. F. Classification of fuel blends using exploratory analysis by combined data from infrared spectroscopy and stable isotope analysis. Energy Fuels 2017, 31 (1), 523–532. CrossRef. (21) Saldana, D. A.; Starck, L.; Mougin, P.; Rousseau, B.; Ferrando, N.; Creton, B. Prediction of density and viscosity of biofuel compounds using machine learning methods. Energy Fuels 2012, 26 (4), 2416–2426. CrossRef. (22) Kalogirou, S. A. Artificial neural networks in renewable energy systems applications: a review. Renew Sustain Energy Rev 2001, 5 (4), 373–401. CrossRef. (23) Korres, D. M.; Anastopoulos, G.; Lois, E.; Alexandridis, A.; Sarimveis, H.; Bafas, G. A neural network approach to the prediction of diesel fuel lubricity. Fuel 2002, 81 (10), 1243–1250. CrossRef. (24) Balabin, R. M.; Safieva, R. Z. Near-Infrared (NIR) Spectroscopy for Biodiesel Analysis: Fractional Composition, Iodine Value, and Cold Filter Plugging Point from One Vibrational Spectrum. Energy Fuels 2011, 25 (5), 2373–2382. CrossRef. (25) Yang, H.; Ring, Z.; Briker, Y.; McLean, N.; Friesen, W.; Fairbridge, C. Neural network prediction of cetane number and density of diesel fuel from its chemical composition determined by LC and GC–MS. Fuel 2002, 81 (1), 65–74. CrossRef. (26) Basu, B.; Kapur, G. S.; Sarpal, A. S.; Meusinger, R. A neural network approach to the prediction of cetane number of diesel fuels using nuclear magnetic resonance (NMR) spectroscopy. Energy & Fuels 2003, 17 (6), 1570–1575. CrossRef. (27) Wu, C.; Zhang, J.; Li, W.; Wang, Y.; Cao, H. Artificial neural network model to predict cold filter plugging point of blended diesel fuels. Fuel Process Technol 2006, 87 (7), 585–590. CrossRef. (28) Pasadakis, N.; Sourligas, S.; Foteinopoulos, C. Prediction of the distillation profile and cold properties of diesel fuels using mid-IR spectroscopy and neural networks. Fuel 2006, 85 (7-8), 1131–1137. CrossRef. (29) Balabin, R. M.; Lomakina, E. I.; Safieva, R. Z. Neural network (ANN) approach to biodiesel analysis: Analysis of biodiesel density, kinematic viscosity, methanol and


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water contents using near infrared (NIR) spectroscopy. Fuel 2011, 90 (5), 2007– 2015. CrossRef. (30) Ramadhas, A. S.; Jayaraj, S.; Muraleedharan, C.; Padmakumari, K. Artificial neural networks used for the prediction of the cetane number of biodiesel. Renew Energy 2006, 31 (15), 2524–2533. CrossRef. (31) Barradas Filho, A. O.; Barros, A. K. D.; Labidi, S.; Viegas, I. M. A.; Marques, D. B.; Romariz, A. R. S.; de Souza, R. M.; Marques, A. L. B.; Marques, E. P. Application of artificial neural networks to predict viscosity, iodine value and induction period of biodiesel focused on the study of oxidative stability. Fuel 2015, 145, 127– 135. CrossRef. (32) Rocabruno-Valdês, C. I.; Ramírez-Verduzco, L. F.; Hernández, J. A. Artificial neural network models to predict density, dynamic viscosity, and cetane number of biodiesel. Fuel 2015, 147, 9–17. CrossRef. (33) Marques, D. B.; Barradas Filho, A. O.; Romariz, A. R. S.; Viegas, I. M. A.; Luz, D. A.; Barros Filho, A. K. D.; Labidi, S.; Ferraudo, A. S. Recent Developments on Statistical and Neural Network Tools Focusing on Biodiesel Quality. Int J Comput Sci Appl. 2014, 3 (3), 97-110. CrossRef. (34) Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. Learning representations by backpropagating errors. Nature 1986, 323, 533-536. CrossRef. (35) De Oliveira, R. R.; De Lima, K. M. G.; De Juan, A.; Tauler, R. Application of correlation constrained multivariate curve resolution alternating least-squares methods for determination of compounds of interest in biodiesel blends using NIR and UVvisible spectroscopic data. Talanta 2014, 125, 233-241. CrossRef. (36) De Lira, L. F. B.; Vasconcelos, V. F. C.; Pereira, C. F.; Paim, A. P. S.; Stragevitch, L.; Pimentel, M. F. Prediction of properties of diesel/biodiesel blends by infrared spectroscopy and multivariate calibration. Fuel 2010, 89 (2), 405–409. CrossRef. (37) Nogueira I.; Fontes C.; Sartori I.; Pontes K.; Embiruçu M. A model-based approach to quality monitoring of a polymerization process without online measurement of product specifications, Computers & Industrial Engineering 2017, 106C, 123-136. CrossRef (38) Bui D. T.; Pradhan B.; Lofman O.; Revhaug I.; Dick O. B. Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg– Marquardt and Bayesian regularized neural networks, Geomorphology 2012, 12-29, 171-172. CrossRef. (39) Vakili

M.; Karami M.; Delfani S.; Khosrojerdi S. Experimental investigation and modeling of thermal radiative properties of f-CNTs nanofluid by artificial neural


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network with Levenberg–Marquardt algorithm, International Communications in Heat and Mass Transfer 2016, 78, 224–230. CrossRef. (40) Nguyen-Truong H. T.; Le H. M., An implementation of the Levenberg–Marquardt algorithm for simultaneous-energy-gradient fitting using two-layer feed-forward neural networks, Chemical Physics Letters 2015, 629, 40-45. CrossRef.

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Captions for Figures Figure 1. Architecture of the MLP network used for predicting specification parameters of diesel. Scheme 1. Flowchart of the training methodology of artificial neural networks. Figure 2. (a) Scatter of predicted values (output) against measured values (target) for the set of samples of training, validation and testing of network3; (b) Box plot graph for the different networks of diesel flash point. Figure 3. (a) Scatter of predicted values (output) against measured values (target) for the set of samples of training, validation and testing of network 2; (b) Box plot graph for the different networks of diesel cetane index. Figure 4. (a) Scatter of predicted values (output) against measured values (target) for the set of samples of training, validation and testing of network 2; (b) Box plot graph for the different networks of diesel sulfur (S1800).

Captions for Tables

Table 1. Diesel samples and specifications of the quality parameters. Table 2: Input variables used for each trained network. Table 3: Correlation coefficient (R), root mean squared error (RMSE) and mean absolute percentage error (MAPE) of the trained networks for flash point. Table 4: Result of the statistical test for flash point.


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TOC/Abstract graphic


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Figure 1


30

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Scheme 1


31

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Figure 2

(a)

(b)


32

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Figure 3

(a)

(b)


33

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Figure 4

(a)

(b)


34

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Parameter

Unity

Minimum/ Maximum

Std Deviation

Mean or Median

Normality Test

ASTM

Table 1 Number of Samples

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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Flash Point Cetane Index S50 Sulfur S500 S1800 Specific Gravity T10% T50% Distillation T85% T90%

162 162 35 92 35 162 162 162 162 162

ºC mg kg-1 mg kg-1 mg kg-1 kg m-³ ºC ºC ºC ºC

38.0/69.0 49.4/56.1 4.00/8.00 82.0/1402.0 226.0/1831.0 830.0/849.0 192.4/220.2 260.7/307.9 318.3/363.7 327.8/383.7

5.8 1.5 1.1 335.8 271.6 3.8 6.2 6.4 11.2 13.2

57.3 52.1 5.0 149.0 1131.3 836.0 204.3 284.5 347.9 361.9

Yes Yes No No Yes No Yes No No No

D93 D4737 D4294 D4294 D4294 D4052 D86 D86 D86 D86

Table 2 Target

Network Inputs 1 Distillation D86 (T10, T50, T85, T90) and Specific Gravity. Flash 2 Distillation D86 (T10, T50, T85, T90). Point Distillation D86 (T10, T50, T85, T90), Specific Gravity and Cetane 3 Index. 1 Distillation D86 (T50) and Specific Gravity. 2 Distillation D86 (T10, T50, T85, T90), Specific Gravity. 3 Distillation D86 (T50). Cetane 4 Distillation D86 (T10, T50, T85, T90). Index 5 Specific Gravity. 6 Distillation D86 (T10, T50) and Specific Gravity. 7 Distillation D86 (T50, T90) and Specific Gravity. Distillation D86 (T10, T50, T85, T90), Specific Gravity, Cetane Index 1 and Flash Point. 2 Distillation D86 (T10, T50, T85, T90), Specific Gravity and Flash Point. S1800 DistillationD86 (T10, T50, T85, T90), Specific Gravity and Cetane 3 Index. 4 Distillation D86 (T10, T50, T85, T90) and Specific Gravity.


35

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Table 3 Training set

Cetane Index

Flash Point

Network

S180

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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 38

Validation set

Test set

Overall

RMSE

R

RMSE

R

RMSE

R

RMSE

R

1

3.4

0.813

3.8

0.774

4.2

0.771

3.6

0.789

MAPE (%) 4.8

2

3.2

0.851

4.8

0.607

4.2

0.676

3.6

0.786

4.9

3

3.2

0.843

3.2

0.775

4.8

0.663

3.5

0.801

4.6

1

0.4

0.966

0.2

0.992

0.4

0.969

0.4

0.971

0.4

2

0.4

0.972

0.3

0.976

0.3

0.977

0.4

0.973

0.4

3

1.3

0.552

1.1

0.522

1.7

0.443

1.3

0.505

1.9

4

1.2

0.653

1.4

0.523

1.2

0.658

1.2

0.631

1.8

5

0.9

0.762

1.0

0.647

1.3

0.607

1.0

0.719

1.5

6

0.3

0.974

0.4

0.964

0.4

0.966

0.4

0.971

0.4

7

0.5

0.955

0.6

0.942

0.4

0.957

0.5

0.952

0.6

1

13.1

0.999

122.7

0.738

97.6

0.894

60.3

0.975

3.3

2

78.4

0.976

273.1

0.779

267.6

0.872

158.9

0.861

12.2

3

144.6

0.912

224.5

0.939

283.4

0.677

183.3

0.823

12.6

4

148.1

0.912

187.5

0.953

62.1

0.961

145.8

0.913

10.4


36

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Table 4

Flash Point

Network 1 2 3

Cetane Index

1 2 6 7 1 S1800

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 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

2 3 4

Method

Normality

Measured Predicted Measured Predicted Measured Predicted Measured Predicted Measured Predicted Measured Predicted Measured Predicted Measured Estimated Measured Estimated Measured Estimated Measured Estimated

Yes No Yes No Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Mean or Std MinimumMedian Deviation Maximum 57.3 5.8 38.0 - 69.0 55.7 4.8 42.5 - 69.6 57.3 5.8 38.0 - 69.0 55.5 4.9 45.2 - 69.0 57.3 5.8 38.0 - 69.0 56.3 4.8 46.5 - 68.6 52.1 1.5 49.4 - 56.1 52.1 1.5 49.5 - 55.5 52.1 1.5 49.4 - 56.1 52.1 1.5 49.4 - 55.2 52.1 1.5 49.4 - 56.1 52.1 1.5 49.3 - 55.9 52.1 1.5 49.4 - 56.1 52.2 1.4 49.8 - 55.2 1131.3 271.6 226.0 - 1831.0 1135.2 271.0 237.1 - 1825.9 1131.3 271.6 226.0 - 1831.0 1150.5 43.4 325.8 -1709.9 1131.3 271.6 226.0 - 1831.0 1029.9 254.4 447.3 - 1691.8 1131.3 271.6 226.0 - 1831.0 1079.4 333.9 218.6 - 2150.1

Test

p-value

-2.217

0.027

-2.414

0.016

-0.072

0.942

-0.207

0.837

-0.250

0.803

0.554

0.580

-2.153

0.033

-0.373

0.712

3.706

0.004

3.871

0.000

2.223

0.033


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Predicting Cetane Index, Flash Point, and Content Sulfur of Diesel

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