Chemometrics-Based Analytical Method Using FTIR Spectroscopic

Article pubs.acs.org/EF

Chemometrics-Based Analytical Method Using FTIR Spectroscopic Data To Predict Diesel and Diesel/Diesel Blend Properties Tulay Y. Inan,* Adnan Al-Hajji, and O. Refa Koseoglu SAUDI ARAMCO, Research and Development, 31311 Dhahran, Saudi Arabia ABSTRACT: In the hydrocarbons downstream business, it is very beneficial to quickly and reliably determine the physical and/or chemical properties of fuels. In this context, a nondestructive method was applied using midband Fourier transform infrared (FTIR) spectroscopy in association with multivariate partial least squares (PLS) chemometrics to determine the properties of nine groups of middle distillates (diesels) boiling in the range 180−370 °C. This method enables identification of one single diesel property at a time or a group of properties (32 properties) simultaneously in the spectral data between 4000−650 cm−1; with a minimum number of steps and without any sample preparation. The method was further used for two blends prepared from individual diesel samples. The results showed that using PLS models to process FTIR data is a practical analytical method to predict diesel fuel properties. Statistically, the results obtained showed low standard deviations, a very low root mean square error of cross-validation (RMSECV), low uncertainty values, less than 10 factors, but high correlation coefficient, R2, and performance index (PI) values.

1. INTRODUCTION Petroleum products can be grouped into light distillates (LPG, gasoline, and naphtha), middle distillates (kerosene and diesel), heavy distillates, and residuum (heavy fuel oil, lubricating oils, wax, and asphalt). The middle distillate fraction boiling in the nominal range 180−370 °C is called gas oil or simply diesel. Diesel consists of a very complex mixture of thousands of individual chemicals of different hydrocarbon classes (saturates and aromatics) with a wide range of carbon numbers (C12−C20). The chemical composition of the diesel influences its properties and hence performance in downstream refining operations.1 Standard test methods, such as those from the American Society for Testing and Materials (ASTM), the Institute of Petroleum (IP), or other agencies, are traditionally used to determine petroleum product properties. These methods are reliable, accurate, and widely accepted but they also have some disadvantages. These methods require a large amount of sample, consume time, and may involve the use of toxic or environmentally dangerous reagents.2 In experimental analytical chemistry, an interdisciplinary technique of chemometrics has been widely used to determine patterns and relationships. It uses mathematical methods and computer science for the analysis of results to predict the physical and/or chemical properties of the analyzed samples. For analyzing chemical systems, including fuels, multivariate calibration has been used as one application of chemometrics.1−11 FT-IR spectroscopy is a nondestructive, rapid, and easy analytical method that is based on the measurement of characteristic fundamental resonances for different functional groups. It produces well-defined peaks at wavelengths between 2.5 and 25 μm, corresponding to the 4000−650 cm−1 wavenumber region. Use of near-IR and FT-IR spectroscopy to determine diesel and gasoline fuel properties has been extensive.1−5,7,8 Furthermore, there are some FTIR and/or NIR applications where chemometrics was involved for diesel and diesel/biodiesel quality screening as well as detection of contaminations in gasoline and diesel.1−5,7−11 © 2016 American Chemical Society

FTIR spectral intensities are expected to correlate with diesel properties measured by standard methods. General accepted arguments should be considered in order to illustrate the relationships between the fuel compositions and the spectroscopic features and, hence, the physical and/or chemical properties. High specific gravity, for example, is known to be associated with elevated concentrations of straight chain paraffinic hydrocarbons. On the other hand, the higher the aromatic hydrocarbon content in the fuel is, the higher the octane number, but the lower the cetane number becomes. Thus, all these chemical features are reflected in the FTIR spectrum profile. This study applies nondestructive midband Fourier transform infrared (FT-IR) spectroscopy in association with multivariate

Figure 1. Simplified workflow for the development of the correlation models for the fuel properties from FT-IR data. Received: March 29, 2016 Revised: May 28, 2016 Published: May 31, 2016 5525

DOI: 10.1021/acs.energyfuels.6b00731 Energy Fuels 2016, 30, 5525−5536

Density Refractive index @ 20 °C Viscosity @ 40 °C Hydrogen Carbon Cetane Number Cetane Index Paraffins Non Condensed Naphthenes Condensed Naphthenes Total Naphthenes Total Aromatics Monoaromatics Di+Aromatics Distillation 0 5 10 20 30 40 50 60 70 80 90 95 100 13 C NMR-C/H ratio 13 C NMR-Carbon Aromaticity 1 H NMR-Aromatic proton 1 H NMR-Poly aromatic proton

Code

W% W% W% W% W% W% W% °C W% W% W% W% W% W% W% W% W% W% W% W% W% % % % %

cSt W% W%

g/cm

3

Unit 0.842 1.47 2.893 13.21 85.27 59.5 53.8 44.5 14.5 8.9 23.4 32.1 17.8 14.3 141 188 204 228 249 267 285 301 319 333 351 364 400 0.55 0.167 4.57 1.53

ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 NMR NMR NMR NMR

F-320

ASTM D4052 ASTM D1218 ASTM D445 ASTM D5291 ASTM D5291 ASTM D613 IFP02101 ASTM D2425 ASTM D2425 ASTM D2425 ASTM D2425 ASTM D2425 ASTM D2425 ASTM D2425

Method

149 175 188 211 233 255 273 291 309 328 347 361 394 0.54 0.145 3.77 0.97

0.8346 1.4649 2.54 13.47 84.47 54.6 52.8 46.1 14.9 9.9 24.8 29.1 16.9 12.2

F-318

150 176 190 213 235 256 275 295 314 333 353 366 393 0.54 0.1424 3.81 1.1

0.8346 1.4651 2.608 13.47 84.75 54.3 53.8 47.4 15 9.4 24.4 28.2 15.9 12.3

F-324

Table 1. Diesel Properties as Obtained Using Conventional Standard Methods

152 189 205 229 249 263 278 295 311 329 349 363 392 0.54 0.1435 4.32 1.57

0.8723 1.4815 3.506 12.89 87.16 42.4 43.1 22.4 22.6 26.2 48.8 28.8 17.2 11.6

F-322

167 190 202 223 243 262 279 298 315 333 356 369 397 0.54 0.13 4.11 1.65

0.8402 1.4676 2.95 13.34 86.61 54.6 52.7 40.8 22.2 13.5 35.7 23.5 13.8 9.7

F-327

164 187 199 222 242 262 281 300 319 336 356 368 395 0.54 0.1505 4.2 1.33

0.8445 1.4696 3.295 13.18 85.31 52.8 49.33 38.7 20.7 12.4 33.1 28.2 18.2 10

F-328

141 188 204 228 249 267 285 301 319 333 351 364 400 0.54 0.1463 3.82 1.28

0.8375 1.4665 2.7 13.41 85.06 55.5 53.9 47.5 15 10 25 27.5 15.3 12.2

F-321

151 176 191 216 236 256 275 295 314 333 352 367 400 0.54 0.15 4.01 1.35

0.8427 1.4686 3.19 13.25 85.44 52.8 50.26 40.6 19.7 12.2 31.9 27.5 15.8 11.7

F-323

161.1 180 190.1 207 225.5 243.5 262.9 282.9 304.6 324.8 347.4 362.1 393.6 0.55 0.173 4.88 1.81

0.875 1.485 3.61 12.66 86.82 39.9 39.2 18.5 26.2 21 47.2 34.3 20.5 13.8

F-325

141 175 188 207 225.5 243.5 262.9 282.9 304.6 324.8 347 361 392 0.54 0.13 3.77 0.97

0.835 1.465 2.54 12.66 84.47 39.9 39.2 18.5 14.5 8.9 23.4 23.5 13.8 9.7

Min

167 190 205 229 249 267 285 301 319 336 356 369 400 0.55 0.173 4.88 1.81

0.875 1.485 3.61 13.47 87.16 59.5 53.9 47.5 26.2 26.2 48.8 34.3 20.5 14.3

Max

153.10 183.09 196.92 219.36 239.64 258.36 276.53 294.80 313.47 331.24 351.40 364.92 396.05 0.54 0.15 4.19 1.40

0.8485 1.4717 3.04 13.18 85.68 51.44 49.27 37.50 19.23 14.42 33.32 28.82 16.88 11.98

Avg

26.0000 15.0000 17.0000 22.0000 23.5000 23.5000 22.1000 18.1000 14.4000 11.2000 9.0000 8.0000 8.0000 0.0100 0.0431 1.1100 0.8400

0.0403 0.0199 1.0700 0.8100 2.6900 19.6000 14.7000 29.0000 11.7000 17.3000 25.4000 10.8000 6.7000 4.6000

Range

9.358 6.340 7.112 8.185 8.337 7.384 6.817 5.774 4.977 3.443 3.292 2.746 3.254 0.004 0.013 0.376 0.268

0.01545 0.00719 0.389 0.274 0.963 6.392 5.290 10.741 4.294 5.950 9.723 3.029 1.932 1.510

Std Dev.

Energy & Fuels Article

5526


Article

Energy & Fuels Table 2. Validation Results ASTM Methods Property Density Refractive index @ 20 °C Viscosity @ 40 °C Hydrogen Carbon Cetane Number Cetane Index Paraffins Non Condensed Naphthenes Condensed Naphthenes Total Naphthenes Total Aromatics Monoaromatics Diaromatics Di+Aromatics Distillation 0 5 10 20 30 40 50 60 70 80 90 95 100 13 C NMR-C/H ratio 13 C NMR-Carbon Aromaticity 1 H NMR-Aromatic proton 1 H NMR-Poly aromatic proton

FTIR

Cross-Validation Results

Unit

Method

Min

Max

Min

Max

RMSECV

R2

g/cm3

W% W% W% W% W% W% W% W%

ASTM D4052 ASTM D1218 ASTM D445 ASTM D5291 ASTM D5291 ASTM D613 IFP02101 ASTM D2425 ASTM D2425 ASTM D2425 ASTM D2425 ASTM D2425 ASTM D2425 ASTM D2425 ASTM D2425

0.826 1.462 2.54 12.66 84.47 39.9 39.2 18.5 14 7.8 21.8 23.5 13.8 5.8 9.7

0.8749 1.4848 3.61 13.47 87.16 59.5 53.9 47.5 26.2 26.2 48.8 34.3 20.5 11.5 14.3

0.835 1.4652 2.542 12.66 84.51 39.8 39.3 18.6 14.54 8.9 23.3 23.5 13.86 5.8 9.71

0.877 1.4849 3.613 13.49 87.15 59.6 53.9 47.54 26.2 26.2 47.5 34.33 20.52 11.25 14.36

0.555 × 10−3 0.566 × 10−3 0.00368 0.0306 0.24 0.0589 0.036 0.175 0.532 0.0823 0.198 0.249 0.035 1.02 0.0724

0.99939 0.99604 0.99995 0.99195 0.96707 0.99995 0.99997 0.9998 0.99178 0.99991 0.99974 0.99572 0.9998 0.87444 0.99849

W% W% W% W% W% W% W% W% W% W% W% W% W% % % % %

ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 ASTM D2887 NMR NMR NMR NMR

141 175 188 207 226 244 263 283 306 325 347 361 392 0.54 0.13 3.77 0.97

167 190 205 229 249 267 285 301 319 336 356 369 400 0.55 0.173 4.88 1.81

142 175 188 207 226 244 263 283 305 325 347 361 392 0.541 0.132 3.767 0.97

168 190 205 229 250 267 285 301 319 336 356 369 400 0.554 0.174 4.89 1.8

1.01 0.101 0.116 0.236 0.3 0.193 1.58 0.0968 0.0842 0.094 0.136 0.19 0.1 0.0262 0.0217 0.0386 0.0128

0.99293 0.99986 0.99985 0.99954 0.99928 0.99957 0.96252 0.99984 0.9983 0.9995 0.99902 0.9973 0.99988 0.804 0.98267 0.99368 0.99872

cSt W% W%

partial least-squares (PLS) chemometrics to rapidly and reliably determine a property or a group of properties (in this article 32 properties) of diesel and its blends. The method is not a replacement of a time-consuming standard method, but rather it is a screening technique where fast and reliable information is required. Since only about 2 mL of a homogeneous sample is needed for the FTIR analysis, this entitles simplicity, rapidity, as well as cost reduction.

2.2. FTIR Measurements. Spectroscopic data were collected for all diesel samples using a Nicolet 8700 FT-IR spectrophotometer equipped with a deuterated triglycine sulfate (DTGS) detector (THERMO ELECTRON NICOLET 8700, AN: 00125) and a covered horizontal attenuated total reflectance (HATR) zinc selenide (ZnSe) sample cell. The crystal angle of the HATR cells is 48°, with 12 internal reflections through the sample. Spectroscopic data was collected at 128 scans at a resolution of 4 cm−1 (Figure 2). All spectra were analyzed by the software of the OMNIC TQ Analyst operating system (Version 1.Thermo Electron Nicolet, Nicolet Instrument Corporation Madison, WI) and normalized against an air background. After every measurement, a new reference air background spectrum was taken. In this study, for simplicity and to obtain comparable results, we decided to carry out calibrations on baseline uncorrected spectra within the single spectral region of 4000−650 cm−1, using cross-validations with sample rotations of fuels to avoid problems associated with ZnSe cutoff frequencies. The full spectrum was included in building the calibration models. 2.3. Data Analysis and Software. The FT-IR spectrum was collected for each diesel sample that was previously analyzed according to the standard test methods as shown in Table 1, and the resulting data were then used to establish the calibration models with the corresponding selected property values obtained from the standard methods. Multivariate calibration is an effective calibration method in which the chemical information (absorption, emission, transmission, etc.) of a set of standard mixtures recorded at different variables (wavenumbers) are related to the concentration of the chemical compounds present in the mixtures. The popular calibration approach used in the chemical analysis

2. EXPERIMENTAL SECTION 2.1. Samples. Diesel samples from 9 different sources (5−7 diesel samples from each source) were distilled from different crude oils. A batch distiller was used to recover naphtha and diesel. The nominal diesel cut point range is 180−370 °C. In addition to diesel samples, further synthetic blends were prepared in the lab, and two of the blends results are given in this article. Blend1 was prepared by mixing two different diesel samples (F320+F318) at 50/50 wt/wt, while Blend2 was prepared from 50/50 wt/wt of F324 and F321 diesels to validate the developed method. The properties of the mentioned samples were obtained by following the standard methods at Saudi Aramco Laboratories.12 To select the calibration and validation samples, diesel sample sets were randomly shuffled. The first 20% of each randomized set of samples was designated as the validation set while the remaining samples were specified as the calibration set. The following simplified workflow shows the development of the correlation models for the fuel properties from the FT-IR data (Figure 1). 5527


Article

Energy & Fuels

Figure 2. FT-IR spectra of diesel samples obtained in the range 4,000 to 500 cm−1.

Figure 3. Calibration for density (g/cm3): ASTM D4052 vs FT-IR. is the unvaried calibration in which the chemical information on a set of sample solutions recorded at one variable (i.e., wavenumber) is related to the solute concentration in the solution. The most applied multivariate methods are • • • • •

PLS is a statistical approach to a quantitative analysis. This technique examines the specified region or regions of the calibration spectra to determine which areas are varying statistically as a function of the component concentration or property. The PLS calibration model is developed in one operation using the spectral and fuel property information obtained from the standard methods. The PLS method creates a simplified representation of the spectroscopic data by a process known as spectral decomposition. The PLS algorithm initially calculates a property value (like density, viscosity, etc.), or weighted average spectrum of all spectra of the fuels in the calibration matrix. This statistical analysis requires calibration and validation. In the calibration procedure, the software searches for a relation between the dependent variable, Y (peak height), and the independent variable, X (property), which can be generically written as Y = f(X1, X2, X3, ..., Xp).

Classical least-squares (CLS), Inverse least-squares (ILS), Principal-component regression (PCR), Artificial neural network (ANN), Partial least-squares (PLS) and net-analyte signal (NAS).

In this study, we used the PLS regression to correlate the spectroscopic data to the diesel property values. The PLS algorithm was run from Nicolets’s TQ Anlayst Software package. 5528


Article

Energy & Fuels Table 3. Samples Used in the Partial Least Squares (PLS) Estimation of Density Density, ASTM D4052

Density, ASTM D4052 Index 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

Title DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL DIESEL

F-322_1 F-320_1 F-320_2 F-320_3 F-320_4 F-320_5 F-320_6 F-318_1 F-318_2 F-318_3 F-318_4 F 318_5 F-318_6 F-324_1 F-324_2 F-324_3 F-324_4 F-324_5 F-324_6 F-327_1 F-327_2 F-327_3 F-327_4 F-327_5 F-327_6 F-328_1 F-328_2 F-328_3 F-328_4 F-328_5 F-328_6 F-323_1 F-323_2 F-323_3 F-323_4

3

Usage

g/cm

Calibration Validation Validation Calibration Calibration Calibration Calibration Calibration Calibration Calibration Validation Calibration Calibration Calibration Calibration Calibration Calibration Validation Calibration Calibration Calibration Validation Calibration Calibration Calibration Calibration Calibration Calibration Calibration Validation Calibration Calibration Calibration Calibration Calibration

0.87230 0.84230 0.84231 0.84233 0.84235 0.84236 0.84237 0.83460 0.83470 0.83480 0.83475 0.83465 0.83468 0.83460 0.83465 0.83470 0.83460 0.83465 0.83465 0.84020 0.84030 0.84025 0.84030 0.84045 0.84050 0.84450 0.84460 0.84470 0.84450 0.84450 0.84460 0.84275 0.84280 0.84290 0.84270

Index

Title

Usage

g/cm3

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 61 62 63 64 65 66 67 68 69

DIESEL F-323_5 DIESEL F-323_6 DIESELF-321_1 DIESELF-321_2 DIESELF-321_3 DIESELF-321_4 DIESELF-321_5 DIESELF-321_6 DIESEL F-325_1 DIESEL F-325_2 DIESEL F-325_3 DIESEL F-325_4 DIESEL F-325_5 DIESEL F-325_6 DIESEL F-320_0 DIESEL F-318_0 DIESEL F-324_0 DIESEL F-322_0 DIESEL F-327_0 DIESEL F-328_0 DIESEL F-323_0 DIESEL F-321_0 DIESEL F-325_0 DIESEL F-322_0 DIESEL F-322_1 DIESEL F-322_2 DIESEL F-322_3 DIESEL F-322_4 DIESEL F-323_7 DIESEL F-323_2 DIESEL F-325_0 DIESEL F-322_5 DIESEL F-322_6 DIESEL F-322_7

Validation Calibration Calibration Calibration Validation Calibration Calibration Calibration Validation Calibration Calibration Calibration Calibration Calibration Calibration Validation Calibration Calibration Calibration Calibration Validation Calibration Calibration Calibration Calibration Calibration Calibration Validation Calibration Calibration Calibration Calibration Calibration Calibration

0.84275 0.84276 0.83750 0.83780 0.83790 0.83750 0.83770 0.83780 0.87500 0.87550 0.87600 0.87650 0.87700 0.87800 0.84230 0.83460 0.83460 0.87230 0.84020 0.84450 0.84276 0.83750 0.87750 0.87230 0.87230 0.87230 0.87230 0.87230 0.84275 0.83750 0.87490 0.87230 0.87240 0.87245

Figure 4. Cross-validation results for density: ASTM D4052 vs FT-IR. In practice, an algorithm, based on partial least-squares equations, calculates the regression coefficients of the following equation:

3. RESULTS AND DISCUSSION The FTIR spectra of the standard sample, with known properties, are used in detail by the TQ software, and the results were used to evaluate the fitness of the mathematical model. If the model produces positive results during the validation, it can be used to obtain the property values of unknown samples. In this

Y = b0 + b1X1 + b2X 2 + .... bpXp This defines the mathematical model of the system under investigation. The second step is a so-called “leave-one-out” cross-validation procedure that is used to verify the calibration model. 5529


Article

Energy & Fuels Table 4. Comparison of the Values of 32 Properties of Diesel for a property by Using FTIR-Chemometrics

(RMSEP), the root mean square error of calibration (RMSEC), and the correlation coefficient (R2), and after cross-validation, the root mean square error of the cross-validated error of calibration (RMSECV) and the correlation coefficient (R2) were added as statistical evaluation parameters. In order to assess the capability of the model to fit the calibration data and to calculate the deviation of the model, root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and root mean square error of cross-validated error of calibration (RMSECV) were all used. In evaluating the calibration data, both the data error, as expressed by RMSEP, and the data scatter or “goodness of fit”, i.e., the squared correlation coefficient, R2, must fall within an

study, for simplicity and to obtain comparable results, we decided to carry out calibrations on the baseline uncorrected spectrum within the single spectral region 4000−650 cm−1, using crossvalidations with a sample rotation of fuel. Results for each set of calibration experiments are summarized in Table 2. After the calibration model was established, it was tested by independent validation experiments, in which the calibration model was applied to similar diesel samples that were not part of the calibration training set. The predicted property values were then compared with those derived from the established ASTM procedures. FTIR and multivariable calibration methods accuracy were established by evaluating the root mean square error of prediction 5530


ASTM D4052 ASTM D1218

ASTM D445 ASTM D5291 ASTM D5291 ASTM D613 IFP02101 ASTM D2425 ASTM D2425

ASTM D2425

D2425 D2425 D2425 D2425 D2425

D2887 D2887 D2887 D2887 D2887 D2887 D2887 D2887 D2887 D2887 D2887 D2887 D2887

ASTM ASTM ASTM ASTM ASTM

ASTM ASTM ASTM ASTM ASTM ASTM ASTM ASTM ASTM ASTM ASTM ASTM ASTM NMR NMR

g/cm3

cSt W% W%

W%

W% W% W% W% W% °C W% W% W% W% W% W% W% W% W% W% W% W% W% % %

Density Refractive index @ 20 °C Viscosity @ 40 °C Hydrogen Carbon Cetane Number Cetane Index Paraffins Non Condensed Naphthenes Condensed Naphthenes Total Naphtenes Total Aromatics Monoaromatics Diaromatics Di+Aromatics Distillation 0 5 10 20 30 40 50 60 70 80 90 95 100 13 C NMR-C/H ratio 13 C NMR-Carbon Aromaticity 1 H NMR-Aromatic proton 1 H NMR-Poly aromatic proton

5531

NMR

NMR

%

%

W% W%

Method

Unit

Code

1.53

4.57

141 188 204 228 249 267 285 301 319 333 351 364 400 0.550 0.1667

23.4 32.1 17.8 7 14.3

8.9

2.893 13.21 85.27 59.5 53.8 44.5 14.5

0.8423 1.470

F-320 (measured)

1.536

4.331

146 188 202 224 244 263 281 300 318 335 355 368 398 0.549 0.1668

22.2 32.5 18.5 6.8 14.4

9

2.954 13.12 85.99 57.8 53.5 44.2 14.6

0.8417 1.4739

F-320 (calc.)

0.97

3.77

143 177 191 214 235 254 272 290 309 328 348 363 394 0.540 0.145

24.8 29.1 16.9 5.9 12.2

9.9

2.54 13.47 84.47 54.6 52.8 46.1 14.9

0.8346 1.4649

F-318 (measured)

0.974

3.565

143 177 191 214 235 254 272 290 309 328 348 363 394 0.544 0.1455

24.6 28.3 16.3 5.8 12.1

9.9

2.474 13.4 85.14 55.4 54.3 46.4 14.9

0.8524 1.4687

F-318 (calc.)

1.1

3.81

150 176 190 213 235 256 275 295 314 333 353 366 393 0.540 0.1424

24.4 28.2 15.9 5.8 12.3

9.4

2.608 13.47 84.75 54.3 53.8 47.4 15.0

0.8346 1.4651

F-324 (measured)

1.151

3.909

148 183 197 219 240 259 277 296 314 332 352 366 396 0.547 0.1425

24.5 28.6 16.3 5.7 12.3

9.4

2.753 13.24 85.61 55.6 53.5 47.3 15.4

0.8465 1.4715

F-324 (calc.)

1.57

4.32

152 189 205 229 249 263 278 295 311 329 349 363 392 0.540 0.1435

48.8 28.8 17.2 10.1 11.6

26.2

3.506 12.89 87.16 42.4 43.1 22.4 22.6

0.8723 1.4815

F-322 (measured)

1.578

4.46

159 181 195 217 238 257 275 294 312 331 351 365 396 0.546 0.1435

47.3 30 17.8 10.4 12.1

26.3

3.425 13.25 85.48 43.3 43.1 21.7 22.6

0.8497 1.471

F-322 (calc.)

Table 5. Comparison of the Values of 32 Properties of Diesel, a group of property, by Using FTIR-Chemometrics

1.65

4.11

167 190 202 223 243 262 279 298 315 333 356 369 397 0.540 0.13

35.7 23.5 13.8 8.2 9.7

13.5

2.950 13.34 86.61 54.6 52.7 40.8 22.2

0.8402 1.4676

F-327 (measured)

1.655

4.172

163 188 201 224 244 263 281 299 318 335 355 368 398 1 0.1345

36.6 23.4 13.6 8.4 9.5

13.5

3.014 13.13 85.95 55.7 52.6 40.8 22.3

0.8423 1.4735

F-327 (calc.)

1.33

4.2

164 187 199 222 242 262 281 300 319 336 356 368 395 0.540 0.1505

33.1 28.2 18.2 7.2 10

12.4

3.295 13.18 85.31 52.8 49.3 38.7 20.7

0.8445 1.4696

F-328 (measured)

1.33

4.27

160 186 200 223 243 262 280 298 317 334 354 367 398 0.548 0.15

33.30 27.90 17.30 7.50 11.00

12.40

3.134 13.15 85.85 53.20 50.4 39.20 20.80

0.8438 1.4730

F-328 (calc.)

1.28

3.82

141 188 204 228 249 267 285 301 319 333 351 364 400 0.540 0.1463

25 27.5 15.3 6.1 12.2

10

2.700 13.41 85.06 55.5 53.9 47.5 15

0.8375 1.4665

F-321 (measured)

1.286

4.031

148 181 195 218 238 257 275 293 312 331 351 365 396 0.546 0.151

26.80 29.28 16.30 6.400 12.600

10.50

2.910 13.28 85.46 52.10 50.5 47.3 15.40

0.8488 1.4707

F-321 (calc.)



ASTM D445 ASTM D5291 ASTM D5291 ASTM D613 IFP02101 ASTM D2425 ASTM D2425

ASTM D2425

ASTM ASTM ASTM ASTM ASTM

ASTM ASTM ASTM ASTM ASTM ASTM ASTM ASTM ASTM ASTM ASTM ASTM ASTM NMR NMR

NMR

NMR

cSt W% W%

W%

W% W% W% W% W% °C W% W% W% W% W% W% W% W% W% W% W% W% W% % %

%

%

D2887 D2887 D2887 D2887 D2887 D2887 D2887 D2887 D2887 D2887 D2887 D2887 D2887

D2426 D2425 D2425 D2425 D2425

ASTM D4052 ASTM D1218

g/cm3

Density Refractive index @ 20 °C Viscosity @ 40 °C Hydrogen Carbon Cetane Number Cetane Index Paraffins Non Condensed Naphthenes Condensed Naphthenes Total Naphtenes Total Aromatics Monoaromatics Diaromatics Di+Aromatics Distillation 0 5 10 20 30 40 50 60 70 80 90 95 100 13 C NMR-C/H ratio 13 C NMR-Carbon Aromaticity 1 H NMR-Aromatic proton 1 H NMR-Poly aromatic proton

W% W%

Method

Unit

Code

Table 5. continued

5532

1.35

4.01

151 176 191 216 236 256 275 295 314 333 352 367 400 0.54 0.15

31.9 27.5 15.8 7.1 11.7

12.2

3.19 13.25 85.44 52.8 50.26 40.6 19.7

0.8427 1.4686

F-323 (measured)

1.354

4.047

153 184 197 220 240 259 278 296 315 332 352 366 397 0.547 0.1957

31.6 27.3 15.85 7.1 11.5

12.3

2.965 13.22 85.65 53.7 51.5 41 19.8

0.8462 1.4718

F-323 (calc.)

1.81

4.88

161 180 190.1 207 225.5 243.5 262.9 282.9 304.6 324.8 347.4 362.1 393.6 0.55 0.1731

47.2 34.3 20.5 11.5 13.8

21

3.61 12.66 86.82 39.9 39.2 18.5 26.2

0.8749 1.4848

F-325 (measured)

1.813

4.792

164 182 196 219 239 258 276 295 313 331 351 365 396 0.546 0.1732

47.7 33.5 20.2 11.3 13.4

21.5

3.697 13.2 85.57 39.8 39.6 18.9 26.2

0.84894 1.4716

F-325 (calc.)

0.970

3.770

141 176 190 207 226 244 263 283 305 325 347 362 392 0.540 0.130

23.40 23.50 13.80 5.80 9.70

8.90

2.540 12.66 84.47 39.90 39.20 18.50 14.50

0.835 1.465

Min

1.810

4.880

167 190 205 229 249 267 285 301 319 336 356 369 400 0.550 0.173

48.80 34.30 20.50 11.50 14.30

26.20

3.61 13.47 87.16 59.50 53.90 47.50 26.20

0.875 1.485

Max

1.399

4.166

152 183 197 220 240 259 277 295 314 331 351 365 396 0.542 0.150

32.700 28.800 16.822 7.656 11.978

13.722

3.032 13.209 85.654 51.822 49.877 38.500 18.978

0.847 1.471

Avg

0.268

0.376

9.9 6.0 6.7 7.9 8.2 7.5 6.9 5.8 5.0 3.4 3.2 2.5 3.2 0.004 0.013

9.723 3.029 1.932 1.967 1.510

5.950

0.389 0.274 0.963 6.392 5.290 10.741 4.294

0.015 0.007

Std Dev

ASTM (Measured results)

0.974

3.565

143 177 191 214 235 254 272 290 309 328 348 363 394 0.544 0.135

22.20 23.40 13.60 5.70 9.50

9.00

2.474 13.12 85.14 39.80 39.60 18.90 14.60

0.842 1.469

Min

1.813

4.792

164 188 202 224 244 263 281 300 318 335 355 368 398 0.549 0.196

47.70 33.50 20.20 11.30 14.40

26.30

3.697 13.40 85.99 57.80 54.30 47.30 26.20

0.852 1.474

Max

1.409

4.175

154 183 197 220 240 259 277 296 314 332 352 366 396 0.547 0.156

32.73 28.98 16.91 7.71 12.10

13.87

3.036 13.22 85.63 51.84 49.89 38.53 19.11

0.847 1.472

Avg

0.3

0.3

7.9 3.6 3.5 3.4 3.1 2.9 3.0 3.2 2.9 2.2 2.4 1.9 1.3 0.0 0.0

9.952 3.153 1.967 2.046 1.479

6.274

0.379 0.090 0.278 6.551 5.446 10.966 4.244

0.004 0.002

Std. Dev

FTIR (Calculated results)

0.0

0.2

−1.6 −1.1 −0.8 −6.5 −9.0 −10.5 −9.1 −7.1 −4.4 −3.2 −0.4 −0.4 −2.2 0.0 0.0

0.0

0.1

3.2 1.7 3.2 4.7 4.7 3.9 3.7 1.1 0.8 1.1 0.6 0.6 1.7 0.0 0.0

1.100 0.800 0.300 0.200 −0.100

0.0

0.0

−1.6 0.0 0.3 0.3 0.3 −0.1 −0.3 −0.2 −0.4 −0.5 −0.6 −0.8 −0.4 0.0 0.0

0.0

0.0

2.0 2.5 3.2 4.4 5.1 4.6 3.9 2.6 2.1 1.2 0.8 0.6 1.9 0.0 0.0

−0.2 −0.1 0.0 −0.1 0.0

0.0 0.2 0.7 −0.2 −0.2 −0.2 0.0

−0.033 −0.176 −0.083 −0.056 −0.122

−0.100

−0.100 1.200 0.100 0.200 0.100 0.200

−0.087 0.070 1.170 1.700 −0.400 0.200 0.000 0.066 −0.460 −0.670 0.100 −0.400 −0.400 −0.100

0.0 0.0

−0.3

−0.004 −0.012 0.021 −0.022 −0.013 −0.033 −0.133

0.022 0.011

−0.007 −0.004

Std Dev

−0.144

0.000 −0.001

Max

Min

Avg

Difference (ASTM-FTIR)



Article

Energy & Fuels

Figure 5. Limits of agreement in density: ASTM D4052 vs FT-IR.

Figure 6. PRESS plot (the residue-level virtual angle correlation plots) for density: ASTM D4052 vs FT-IR.

Figure 7. PC Score result for density: ASTM D4052 vs FT-IR.

FT-IR data cannot be better than that on which they are based. Any uncertainty in the standard data will be inherited in the FT-IR data, since the FT-IR spectra are correlated to the data derived from the ASTM methods, without further treatment.

acceptable limit. The presented calibration data relate to the quality of correlations of data derived from standard methods (mainly ASTM) and the spectral data. The correlation coefficient, R2, between the standard methods and the FT-IR values includes the scatter of both methods. 5533


Article

Energy & Fuels

Figure 8. Outliers for density: ASTM D4052 vs FT-IR.

Table 6. Blend Properties of Diesel by Using FTIR-Chemometrics

All but density results are presented in a table format. Density results have been plotted for the purpose of exemplifying the approach and keeping the manuscript at a reasonable length. Multivariate partial least squares (PLS) chemometrics has been applied to determine a property or a group of properties (32 properties of different diesel samples).A group of properties (32 properties) means that the PLS model had a y-block that contained 32 predicted values for the 32 physicochemical properties all in one model prediction, rather than just calibrating and predicting a single physicochemical value. In Table 3, the properties of the diesel samples used for the calibration and validation steps required by the PLS-based chemometric procedure are reported.

The PLS model has a high prediction power for the determination of fuel properties in the calibration and prediction (calculation) sets. Figure 3 shows a cross-plot between the measured and the calculated diesel fuel density. The validation results for density after cross-validation are presented in Figure 4. Data are presented from the standard measurements, the derived FT-IR values, and their residual errors. The minimum, maximum, and average of these values, as well as the sample standard deviation for the residual data, for each set of validation experiments are summarized in Table 2. The PLS method was found to have a high prediction ability for determining diesel properties after cross-validation and R2 value for the density after crossvalidation was obtained as high as 0.99939, as shown in Figure 4. 5534


Article

Energy & Fuels

Two results were used to evaluate correlations between the data from the relevant ASTM method (Table 2) and the applied FTIR method. One method is a simple way of assessing the agreement between the sample properties and the FTIR chemometrics techniques and considers the residual errors (arithmetic differences) between the standard and FTIR methods (Table 4 and 5 differences). Another method uses the limits of agreement. The TQ Analyst software elaborates data from the spectra and, as a result, produces a calibration curve and a validation curve, as reported in Figure 4. Limits of agreement for density are given in Figure 5. This method is generally recommended for cases where the standard methods may not give sufficiently accurate values. Since a number of procedures used in petroleum laboratories are empirical methods, or sometimes they rely on imprecise octane number engine tests, this criterion is generally applicable. To generate the limits of agreement between the values obtained from the relevant ASTM methods and the new values obtained from the FTIR method, the residual error is plotted against the average value of the two methods, and the standard deviation is evaluated. Parity plots are used to compare predicted vs measured values. In addition, the data points in the difference plot will form a horizontal line at exactly zero % difference. A typical % difference plot will show data points distributed randomly above and below the zero line within a narrow range.1−11 In Tables 4 and 5, observed data after cross-validation calculations are presented for a property (Table 4) or a group of properties (Table 5). The cross-validation result with PRESS numbers printed at the right bottom of the difference plot with 4 number of factors is given in Figure 6. The optimum number of factors is important to avoid overfitting by using the one-leaveout cross-validation procedure when using the PLS method. This procedure was repeated until each sample was left out once. A PC score graph for density is given in Figure 7, where the correlation between the standards of the method and the principal component data is created by the TQ software. Datapoint scatter should be random but uniform. The information provided by the PC scores will show any patterns or trends in the data, which may or may not be significant to the application. It can also help to identify standards that may be outliers. The spectrum outlier attempts to isolate any standard sample that does not fit the model statistically.2 It, therefore, analyzes

Table 7. Quantification of a Group of Property for 32 Diesel Properties for the Sample F-320 Index

Property

Result

Unit

Uncertainty

1 2

Density, ASTM D4052 Refractive index @ 20 °C, ASTM D1218 Viscosity @ 40 °C, ASTM D445 Hydrogen, ASTM D5291 Carbon, ASTM D5291 Cetane Number, ASTM D613 Cetane Index, IFP02101 Paraffins, ASTM D2425 Non Condensed Naphthenes, ASTM D2425 Condensed Naphthenes, ASTM D2425 Total Naphthenes, ASTM D2425 Total Aromatics, ASTM D2425 Monoaromatics, ASTM D2425 Diaromatics, ASTM D2425 Di+Aromatics, ASTM D2425 Distillation, ASTM D2887 Initial Boiling Point, (IBP) 5% 10% 20% 30% 40% 50% 60% 70% 80% 90% 95% Final Boiling Point, (FBP) 13 C NMR-C/H ratio 13 C NMR-Carbon Aromaticity 1 H NMR-Aromatic proton 1 H NMR-Poly aromatic proton

0.84074 1.46818

g/cm3

0.009851 0.004264

2.9584

cSt

0.16565

13.1146 86.009 57.5737 53.4201 44.2776 14.5435

W% W%

W% W%

0.20899 1.03047 2.29552 1.92964 0.46741 0.09967

8.9462

W%

0.22702

22.3321

W%

1.14854

32.3985

W%

0.70123

18.5297

W%

0.62092

6.7436 14.3215

W% W% °C

0.33729 0.31605

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

147.3667 188.1718 201.5296 224.0764 244.065 262.826 281.0971 299.7624 318.1288 334.9144 355.4136 368.4569 398.0831 0.548 0.16675 4.3125 1.5258

NMR NMR NMR NMR

6.85031 5.71614 7.11013 8.2075 8.54096 6.90418 6.09253 4.58409 3.81436 2.43551 2.61733 2.55126 3.30761 0.00645 0.015339 0.24625 0.33366

Figure 9. Quantification of density for Blend1 (F-320+F-318): ASTM D4052 vs FT-IR. 5535


Article

Energy & Fuels

Figure 10. Quantification of density for Blend2 (F-324+F-321): ASTM D4052 vs FT-IR.

Notes

variation and reports deviation from the mean spectrum. The outliers graph for the density is given in Figure 8. The method where a single property was determined individually gives better standard deviation results than data obtained with a group of properties (32 property at a time) (Table 4, Table 5 and Table 7). In Table 6, the diesel blend property data are presented with the calibration and validation results. It worth noting the very good uncertainty values and very high performance indexes (PI) with less than 10 number of factors and with low RMSEP and RMSECV values. The calculated properties for the two blends are in line with the individual diesels values. The values for these two diesel blends calculated using the quantification menu of the TQ analyst program for a property are presented in Table 6, and outputs for Blend1 and Blend2 are given in Figure 9 and Figure 10, respectively, using the same calibration model that was applied to produce the results in Table 3 and Table 5.

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS We thank the Saudi Arabian Oil Company (Saudi Aramco) for permission to publish this paper. We extend our appreciation to our colleagues Adel Al-Solami, Amer Al-Tuwailib, Wala Al-Gozeeb, and Ali Al-Yousif for their technical assistance. We also thank Dr. Sedat Inan for his insight in the discussion of the results.

■

4. CONCLUSION Nondestructive midband Fourier transform infrared (FT-IR) spectroscopy in association with multivariate partial least-squares (PLS) chemometrics has been applied to determine one single property at a time or a group of properties (32 properties) simultaneously of diesel samples and two diesel blends. The results show that this rapid and reliable FTIR work involving spectral data between 650 and 4000 cm−1 provided very encouraging results. All calculated values are very similar to the original measured values with ASTM methods with very low uncertainty values, very low RMSECV, but high R2 and PI values and with less than 10 factors showing the applicability of the methods. It is certainly more practical to determine a group of diesel properties rather than a single property at a time; however, the latter will give a more precise prediction for diesel properties. This approach can be used efficiently to screen and identify the middle distillate properties of original samples, their blends, and adulterated samples with high precision. Another beneficial use of this method is that, with portable FTIR, all the results can be obtained in the field. This may lend support to fast decision making processes with a minimum number of steps and without the need for deliberate sample preparation.

■

REFERENCES

(1) Khanmohammadi, M.; Garmarudi, A. B.; Guardi, M. Talanta 2013, 104, 128−134. (2) Fodor, G. E.; Hutzler, S. A. Estimation of Middle Distillate (Diesel) and Diesel/Diesel Blend Fuel Properties by FTIR and Chemometrics, Interim Report; U.S. Army TARDEC: San Antonia, TX, 1997; pp 1−135. (3) Al-Ghouti, M. A.; Al-Degs, Y. S.; Amer, M. Talanta 2008, 76, 1105−1112. (4) Mueller, D.; Ferrão, M. F.; Marder, L.; Costa, A. B.da.; Schneider, R. de C. de S. Sensors 2013, 13, 4258−4271. (5) Teixeira, L. S.G.; Oliveira, F. S.; Santos, H. C. D.; Cordeiro, P. W.L.; Almeida, S. Q. Fuel 2008, 87, 346−352. (6) Accardo, G.; Cioffi, R.; Colangelo, F.; Angelo, R.; Stefano, L. De.; Paglietti, F. Materials 2014, 7, 457−470. (7) Martin, M. C. U.; Schade, P.; Lerch, P. Dumas TrAC, Trends Anal. Chem. 2010, 29/6, 453−463. (8) Pontes, M. J. C. C.; Pereira, F.; Pimente, F. V. C.; Vasconcelos, A. G.; Silva, B. Talanta 2011, 85, 2159−2165. (9) Marques, D. B.; Filho, A. O. B.; Romariz, A. R. S.; Viegas, I. M. A.; Luz, D. A.; Filho, A. K. D. B.; Labidi, S.; Ferraudo, A. S. International Journal of Computer Science and Application (IJCSA) 2014, 3, 97−110. (10) Borecki, M.; Doroz, P.; Prus, P.; Pszczołkowski, P.; Szmidt, J.; KorwinPawlowski, M. L.; Frydrych, J.; Kociubiński, A.; Duk, M. International Journal of Advances in Systems and Measurements 2014, 7, 57−67. (11) Borecki, M.; Korwin-Pawlowski, M. L.; Duk, M.; Kociubiński, A.; Frydrych, J.; Prus, P.; Szmidt, J. Fuel, Sensors & Transducers Journal 2015, 193, 11−22. (12) Rayan, T. W. Diesel Fuel Combustion Characteristics. Lubricants Handbook: Technology, Properties, Performance, and Testing, ASTM Manual Series: MNL37WCD, Totten, G.E, Ed.; ASTM International: 2003, Chapter 27, pp. 717−725.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. 5536


Chemometrics-Based Analytical Method Using FTIR Spectroscopic

Recommend Documents