Cetane Number Assessment in Diesel Fuel by 1H or Hydrogen

Article pubs.acs.org/EF

Cetane Number Assessment in Diesel Fuel by 1H or Hydrogen Nuclear Magnetic Resonance-Based Multivariate Calibration Cinthia R. Souza,† Aline H. Silva,† Noemi Nagata,‡ Joaõ Luiz T. Ribas,§ Fabio Simonelli,† and Andersson Barison*,† †

NMR Center, and ‡Development of Advanced Techniques for Residues Treatment Group, Department of Chemistry, Federal University of Paraná (UFPR), 81530-900 Curitiba, Paraná, Brazil § Getúlio Vargas’s President Refinery (REPAR), Petrobras, 83707-440 Araucária, Paraná, Brazil ABSTRACT: The cetane number (CN) is one of the most important parameters regarding diesel fuel oil quality, mainly ignition properties. Traditionally, the CN determination is performed by a quite laborious and high-cost method on an explosion engine. On the other hand, nuclear magnetic resonance (NMR) spectroscopy is increasing as a versatile tool for quality control in several areas, such as petroleum and fuels, which permits the fast and direct investigation on samples. In this work, two NMRbased methods for CN determination based on multivariate calibration were developed with advantages of time saving, without the need of any sample treatment.

1. INTRODUCTION Diesel fuel oil is one of the main petroleum fractionation products, composed of paraffins, olefins, and aromatic compounds containing 6−30 carbon atoms.1 It is the most successful fuel worldwide, mainly for transportation by trucks and ships, because of its higher energetic density. Moreover, diesel fuel has the property of autoignition just by compression, without the need of a spark for ignition start.2 To ensure diesel fuel performance, one of the most important parameters to determine its properties is the ignition quality that can be rated in terms of the cetane number (CN).3−6 The CN essentially depends upon the chemical composition of diesel fuel and represents the total effect of spray formation, heating, vaporization, turbulent mixing, and chemical induction times.5 Diesel fuels with a high CN have the advantage of a short ignition delay and start to combust quickly after they injected into the engine and, as consequence, the fuel is completely burned, with small smoke and atmospheric emissions and noise reduction.3−5 On the other hand, diesel fuels having poor ignition quality may induce some problems, such as engine knock and difficulty of engine ignition starts in cold weather, needing the addition of CN-improving agents.7 Compounds such as n-paraffins have a high CN that increases with the molecular weight, while compounds such as isoparafins as well as aromatics have a lower CN, unless that they have a long n-alkane chain attached to the ring. These compounds have more stable molecular structures, requiring higher temperature and pressure for ignition start.7,8 For this reasons, it is recommended for diesel fuel to have a CN of around 40− 60.9 The CN can be accessed by determining its ignition delay,4,5 in other words, the time between fuel injection and combustion start.5,7 Thus, the higher the CN, the shorter the ignition delay.3,5 Therefore, a scale of CN can be obtained by measuring the ignition delay of fuels with opposite features. For this purpose, two hydrocarbons, one of linear chain, n-hexadecane (cetane), and its ramified isomer, 2,2,4,4,6,8,8-heptamethylno© 2014 American Chemical Society

nane (isocetane) (Figure 1), are employed (ASTM D613 standard test).10 Cetane has a short delay on ignition being

Figure 1. Chemical structures of n-hexadecane (cetane) and its 2,2,4,4,6,8,8-heptamethylnonane isomer (isocetane).

assigned a CN of 100, whereas isocetane has a long delay on ignition being assigned a CN of 15. In this way, a calibration curve is possible by mixing cetane and isocetane in different proportions according to the equation CN = (percent cetane) + 0.15(percent isocetane). According to the ASTM D613 standard test,10 CN of diesel fuel oil is assessed by an internal combustion diesel engine, previously calibrated with cetane and isocetane standards, commercially available as T-23 and U-16, respectively. Because diesel fuel also has some aromatic compounds on its chemical composition and its presence decreases the CN,7,8 only cetane and isocetane could not be taken into account for CN determination. Therefore, standards T-23 and U-16 contain some amount of aromatic compounds as well, to consider its effect on diesel fuel.11 The ASTM D613 approach requires a large fuel amount (∼1 L), along with high-purity hydrocarbon references, is significantly time-consuming (few hours) and shows CN reproducibility errors of around 3−4.10 The large time, required to perform each evaluation, prevent its online application. For this reason, in oil refineries, CN is indirectly estimated by Received: May 5, 2014 Revised: July 8, 2014 Published: July 8, 2014 4958

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Article

2.2. NMR Analyses. 1H NMR spectra of diesel fuel oil and references (T-23 and U-16) were acquired on a Bruker AVANCE 400 NMR spectrometer operating at 9.4 T, observing the 1H nuclei at 400.13 MHz, equipped with a 5-mm multinuclear direct detection probe. For this, aliquots of 30 μL of samples were directly transferred into 5-mm NMR tubes and the volume was filled to 600 μL with CDCl3 containing 0.05% tetramethylsilane (TMS). The 1H NMR spectra were recorded at room temperature (approximately 22 °C) by applying 90° excitation pulses and averaging eight scans for each sample, with a relaxation delay of 1 s, spectral width of 3641 Hz (∼9.10 ppm), and 65,536 data points, providing a digital resolution of 0.05 Hz. The 1H NMR spectra were processed by applying an exponential multiplication of the free induction decays (FIDs) by a factor of 0.3 Hz prior to Fourier transform with zero filling to 131,072 data points. The 1 H NMR spectra were referenced against the TMS signal at 0.00 ppm as the internal reference. 2.3. NMR Data Reduction and Multivariate Calibration. The 1 H NMR spectra are normally acquired with a very large number of data points, such as 65,536, to improve spectra resolution, which is a too large number of variables to handle. For this, initially, all 1H NMR spectra were data-reduced by binning into continuous buckets of 0.05−0.01 ppm width between 0.5 and 8.00 ppm before multivariate analysis with the aid of Bruker AMIX software. Indeed, the region of 1.50−1.65 ppm was excluded, because H2O dissolved in CDCl3 shows a signal in this region.29,30 For example, a 1H NMR spectrum acquired and processed with 65,536 data points (i.e., variables) over a spectral width of 10 ppm can be reduced to 200 buckets of 0.05 ppm width. This process has the advantage of minimizing drifts in the 1H NMR chemical shifts over the NMR spectra set, because of instrumental variations or interferences in the analysis, such as temperature, pH, concentrations, and others.29−31 After the 1H NMR spectra were binned, the areas under the buckets were computed by special integration mode from AMIX, through an algorithm developed by Bruker BioSpin, which is unattached to phase and baseline corrections of the spectra.31,32 Then, all subjectivities from spectra manipulation by the analyst can be eliminated. The areas after being normalized and scaled to overall NMR spectra area were then used as input variables in the PLS regression models with the aid of the PLS Toolbox (Eigenvector Research) functions in the MatLab (MathWorks) software environment. The variables (i.e., 1H NMR chemical shifts) were autoscaled prior to PLS analysis. Hence, the mean of the column elements was subtracted to the individual elements and divided by the standard deviation of the respective column. On the other hand, the CN block was mean-centered because of the small range. The PLS analyses were performed using leave-one-out cross-validation, in which each sample is predicted by the remaining samples and repeated until all samples are estimated.33 The optimal number of latent variables (LVs) in PLS was chosen on the basis of the lowest values of rootmean-square error of cross-validation (RMSECV). The optimized prediction model was applied for CN prediction in diesel fuel oil samples and evaluated by RMSEP values as well as correlation coefficient (R2) of the relation between predicted and real CNs, as determined by the reference method (ASTM D613 standard test).10

means of the cetane index (CI), which provides an idea about fuel ignition quality and can be assessed by density and temperature in distillation towers, according to the ASTM D976 standard test method.12 However, for diesel fuel commercialization, it is necessary to determine its CN by the ASTM D613 standard test. Consequently, there have been several attempts to develop more attractive methods. In this way, better engine tests and correlative methods were developed from bulk properties of diesel fuel that are quicker and more reliable. The most promising in saving time and sample is the ignition quality test. However, the reproducibility errors are higher than those of ASTM D613.4,10 On the other hand, nuclear magnetic resonance (NMR) spectroscopy has been used with success in the quality control of fuels, where NMR spectra have been correlated with several fuel properties, mainly with the aid of statistical analysis. In this way, multivariate calibration is the most successful application that combines chemometrics with spectral data. Partial leastsquares (PLS) analysis is a practical predictive tool for spectral reflective data,13−15 because it can deal efficiently with the multi-collinearity present among the predictors (in this case, resonance frequencies of NMR spectra), analyze spectra when the number of frequencies is either larger or smaller than the number of observations, and deal with noisy spectra.16,17 For example, many papers report the success for the prediction of research octane number (RON) and motor octane number (MON) values in gasoline, with high correlation.18−24 In contrast, few NMR-based methods have been described for CN determination on diesel fuel. Gulder et al. described the prediction of CN by means of 1H NMR spectra, although the relation was only empirical.5 Cookson et al. used 13C{1H} NMR spectra to determine several properties of diesel fuel and found good correlation between real and predicted values.25 Basu et al. developed an artificial neural network approach for CN assessment based on 1H NMR spectra with good predictive capability.3 Kapur et al. used the signal intensities of 1H NMR and multiple linear regression to assess some properties of diesel fuel.6 Moreover, NMR-based multivariate calibration has been used with success in several other related works, such as to evaluate the quality of diesel-biodiesel blends26,27 and gasoline fuel.28 In this work, we propose that the CN on diesel fuel can be assessed by means of 1H NMR spectra profile acquired directly from the samples and the application of multivariate prediction models.

2. MATERIALS AND METHODS 2.1. Samples. Diesel fuel oil samples with the CN previously determined by the ASTM D613 standard test10 (reference method) covering the range from 42.0 to 46.8 were kindly supplied by Refinaria Presidente Getúlio Vargas (REPAR) from Petrobras. The diesel fuel oil samples were without any kind of additives or cetane improvers. Because CN can be indirectly estimated by means of CI according to the ASTM D976 standard test,12 CI is used in oil refineries to setup the distillation process to obtain diesel oil with a CN at least higher than 42. For this reason, the CN interval of diesel fuel oil samples used in this work is narrow, such as 42.0−46.8. The references T-23 (cetane) and U-16 (isoceane), used in the engine calibration according to ASTM D613 standard test,10 were also kindly supplied by REPAR. Then, mixtures of T-23 and U-16 standards (v/v) were prepared to the final volume of 100 μL to cover the same CN interval as for diesel fuel oil samples, indeed, covering the range from 39.2 to 50.5.

3. RESULTS AND DISCUSSION To develop a NMR-based method for CN determination in diesel fuel oil, two different approaches based on multivariate calibration using 1H NMR spectra were evaluated and are described in this work. The first attempt was performed with 1 H NMR spectra from mixtures of cetane and isocetane references that are used in engine calibration for CN determination according to the ASTM D613 standard test.10 The second was achieved using the 1H NMR spectra from diesel fuel samples, with CN previously determined by the reference method (ASTM D613 standard test).10 Then, both calibration models were used to predict the CN on diesel fuel. 4959

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3.1. NMR-Based Multivariate Calibration from Cetane and Isocetane References. The ASTM D613 standard test10 uses a mixture of cetane and isocetane standards in different proportions to calibrate an engine for CN determination on diesel fuel. The main idea of this work was to use the same reference system to elaborate a calibration model based on its 1 H NMR spectra and then use it for CN determination in diesel fuel oil. The simple visual inspection of 1H NMR spectra acquired directly from the references T-23 (cetane) and U-16 (isocetane), covering the CN range of 39.2−50.5, revealed that the spectra profile changes according to its proportions (Figure 2). This finding reveals that a multivariate calibration

Figure 3. 1H NMR spectra from a diesel fuel sample and a mixture of references T-23 (45%) and U-16 (55%), both with a CN of 44.8.

extra information gained when the spectral resolution is much higher than the physical signal resolution of the compounds generating the spectra. Therefore, buckets of 0.05 ppm width were enough to describe the signals and, at same time, to provide the implicit smoothing of the spectra. At same time, the best pre-processing that yields the lowest prediction error in leave-one-out cross-validation and a small number of latent variables and high R2 were evaluated. The first model was built with 12 latent variables, by autoscaling for the sample block (1H NMR spectra) and mean-centering for the CN block and using PLS for regression. However, the excessive number of latent variables can take to a situation of superfit of the model, and then some of them are used only to describe anomalies from the calibration set or instrumental noises.34 The optimal number of latent variables should be as minimal as possible without compromising the prediction power of the model. Fortunately, no significant increases in RMSECV were observed using 5−7 latent variables. A linear correlation between the 1H NMR spectra profile from references and CN was obtained (Figure 4). The high correlation coefficient indicates that the model has high prediction power, at least for the standard mixtures T-23 and U-16.

Figure 2. 1H NMR spectra from cetane (T-23) and isocetane (U-16) references to obtain the respective CN.

model could be obtained to correlate the spectra profile with its respective CN, once each proportion of T-23 and U-16 represents a specific CN, according to the ASTM D613 standard test [CN = (percent cetane) + 0.15(percent isocetane)]. In this way, the higher the concentration of T-23 (cetane) and smaller the concentration of U-16 (isocetane), the higher the CN. The reference T-23 is composed of mainly a linear n-hexadecane alkyl chain (Figure 1), with regard to the signals at 0.88 and 1.25 ppm in the 1H NMR spectra (Figure 2). On the other hand, its isomer has a ramified alkyl chain (Figure 1), and then its 1H NMR spectra show a more complex profile, with several signals from 0.70 to 2.50 ppm (Figure 2). However, the reduction on CN caused by the presence aromatic compounds on diesel fuel (Figure 3) should be taken into account. For this reason, the overall 1H NMR spectra, except the region of the H2O signal (see the Materials and Methods), were taken into account on multivariate calibration models. The influence of the bucket width (from 0.5 to 0.01 ppm) on multivariate calibration models was evaluated on the basis of the high linear correlation coefficient (R2) and the minimum value of the RMSECV. Lower R2 and high RMSECV values were obtained with wider buckets. On the other hand, when the bucket width was reduced, an increase in R2 values and a reduction of RMSECV were observed. Buckets too wide presented low prediction power, possibly because, in this situation, a single bucket may cover more than one real signal in the 1H NMR spectra and, thus, reduce the information content,29 although narrower buckets than 0.05 ppm resulted in similar prediction power. This demonstrates that there is no

Figure 4. CN correlation between real and predicted by multivariate calibration from cetane and isocetane references. 4960

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The idea to use cetane and isocetane references to calibrate the engine is based on the fact that its chemical composition is similar to those from diesel fuel oil. In fact, the 1H NMR spectra of reference blends were similar to those presented by a diesel fuel sample at the same CN (Figure 3). Therefore, the calibration model was used to predict the CN of diesel fuel oil samples with CN previously determined by the reference method.10 The model was able to predict the CN of diesel fuel with good accuracy (Figure 5). Because CN provided by the

Figure 6. CN correlation between real and predicted by multivariate calibration from diesel fuel oil.

This model was then used to predict the CN of the same diesel fuel samples, as predicted by the previous model from the references. A high accuracy was observed. Besides, in this case, all samples were fitted in a ±2.0 limit from CN supplied by the reference method (ASTM D613 standard test)10 (Figure 7). Figure 5. Correlation between CN determined by ASTM D613 (black squares) and multivariate calibration model (red triangles) obtained from cetane and isocetane references. Bars represent the errors of ±2.0.

reference method according to the ASTM D613 standard test10 has a standard deviation of ±2.0, only some samples were set out of this limit (Figure 5), probably because of small differences in the chemical composition of T-23 and U-16 references and diesel fuel oil sample. These findings show that a 1 H NMR-based multivariate calibration model can be obtained from cetane and isocetane references, already used in the engine calibration according to the ASTM D613 standard test,10 to determine the CN of diesel fuel oil. 3.2. NMR-Based Multivariate Calibration from Diesel Fuel. The idea of this work was to use a set of diesel fuel oil samples with CN previously determined by the reference method (ASTM D613 standard test)10 to elaborate a multivariate calibration model based on its 1H NMR spectra. As in previous work, the overall 1H NMR spectra from 0.5 to 8.00 was taken into account to consider the influence of all compounds on CN, mainly aromatic compounds.8 Again, higher R2 and small RMSECV values were found with small bucket width (see early discussion) and using the special integration mode.31,32 Moreover, the optimal multivariate calibration PLS models were achieved with 4−7 latent variables, by autoscaling for the sample block (1H NMR spectra) and mean-centering for the CN block. Therefore, it is reasonable to use 4 latent variables to minimize inserting errors (see early discussion).34 Using this calibration model, a linear correlation between the 1H NMR spectra profile from diesel fuel and the CN was achieved (Figure 6). The high correlation coefficient shows that the model has high predictive power of new diesel fuel oil samples.

Figure 7. Correlation between CN determined by ASTM D613 (black squares) and multivariate calibration model (red triangles) obtained from diesel fuel samples. Bars represent the errors of ±2.0.

Therefore, the multivariate calibration model from diesel fuel was more accurate in predicting CN of new diesel fuel samples than that one built with the references cetane and isocetane. However, this approach needs a calibration set from diesel fuel samples with CN previously determined by the reference method, although just one calibration set is enough to elaborate the calibration model.

4. CONCLUSION Finding the need for quality control of diesel fuels, this work shows that NMR-based spectroscopy in association with multivariate calibration can be used for CN determination. Both strategies presented in this work are able to predict CN in 4961

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(26) Monteiro, M. R.; Ambrozin, A. R. P.; Ferreira, A. G. Fuel 2009, 88, 691−696. (27) Monteiro, M. R.; Ambrozin, A. R. P.; Lião, L. M.; Boffo, E. F.; Pereira-Filho, E. R.; Ferreira, A. G. J. Am. Oil Chem. Soc. 2009, 86, 581−585. (28) Monteiro, M. R.; Ambrozin, A. R. P.; Lião, L. M.; Boffo, E. F.; Tavares, L. A.; Ferreira, M. M. F.; Ferreira, A. G. Energy Fuels 2009, 23, 272−279. (29) Liland, K. H. Trends Anal. Chem. 2011, 30, 827−841. (30) Spraul, M.; Neidig, P.; Klauck, U.; Kessler, P.; Holmes, E.; Nicholson, J. K.; Sweatman, B. C.; Salman, S. R.; Farrant, R. D.; Rahr, E.; Beddell, C. R.; Lindon, J. C. J. Pharm. Biomed. Anal. 1994, 12, 1215−1225. (31) Spraul, M.; Humpfer, S.; Keller, S.; Schäfer, H. Spin Rep. 2005, 26, 154−155. (32) Neidig, K. P. AMIX-Viewer and AMIX Software Manual; Bruker BioSpin GmbH Software: Karlsuhe, Germany, 2008. (33) Efron, B.; Gong, G. Am. Stat. 1983, 37, 36−48. (34) Zamora, P. P.; Cordeiro, G.; Nagata, N. Quim. Nova 2005, 28, 838−841.

diesel fuel, with advantages of time saving, without the need of chemicals and sample treatment. The approach using diesel fuel samples with CN previously determined by the reference method is more efficient. However, this method needs a calibration set of diesel samples with CN previously determined by the reference method. On the other hand, the strategy using reference compounds is a little less efficient on CN prediction, although there is no need for a calibration set, eliminating the need of an engine for CN determination.

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AUTHOR INFORMATION

Corresponding Author

*Telephone: +55-41-3361-3268. Fax: +55-41-3361-3186. Email: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors are grateful to CAPES, CNPq, Finep, UFPR, and Fundaçaõ Araucária for financial support and fellowships. REFERENCES

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