Quality Control Of Gasoline Research Articles

Prediction of octane numbers for commercial gasoline using distillation curves: a comparative regression analysis between principal component and partial least squares methods

In this study, it was found that limiting the number of explanatory distillation curves input variables in a multivariate regression method to the most significant ones (10%, 50%, 90%, and 100% of recovered condensate volumes) provides a reliable model that can be used as a quick and inexpensive method for determining the octane rating of commercial gasoline. Octane numbers were predicted using a combination of distillation curves and multivariate calibration techniques of partial least squares regression (PLSR) and principal component regression (PCR). The root mean square error of prediction (RMSEP) evaluation for calibration and prediction sets for PLSR technique varies between 0.545 and 0.902 for MON and RON, respectively, which is relatively acceptable when compared to earlier studies. Comparing PLSR and PCR results revealed that PCR has slightly lower RMSEP values for MON and RON, 0.527 and 0.899, respectively. The fact that measurements of studied key temperatures are a routine part of gasoline quality control makes the current technique a promising alternative to the suggested PLS distillation curves models in the literature, which require a larger number of explanatory variables. This method can also replace the costly and time-consuming spectral analysis now used to determine the octane rating of gasoline.

Data driven models exploring the combination of NIR and 1H NMR spectroscopies in the determination of gasoline properties

The quality control of a commercial gasoline includes the assessment of several physical-chemical properties. The main objective of the present work is to investigate the simultaneous use of NIR and 1H NMR data to accurately estimate 13 of those characteristics [i.e., density, vapour pressure, percent evaporated at 70 °C, percent evaporated at 100 °C, percent evaporated at 150 °C, final boiling point, research octane number, motor octane number, aromatic content, olefinic content, benzene content, oxygen content and methyl t-butyl ether content]. The data driven models implemented in this work were single PLS using a concatenated block including two data types (i.e., NIR and 1H NMR), and serial PLS using the different data blocks separately. This work also aims at comparing the predictive capability of these regression models with the models calibrated with one type of spectroscopic data only. The optimum complexity of each model (i.e., number of latent variables) has been determined according to the repeated cross-validation strategy, consisting of 100 repetitions of a cross-validation loop. Serial PLS models have been proven effective in property estimation and, for most of the cases, outperform the models developed using a single spectroscopic technique. This proves the complementarity of both spectroscopic techniques, which are based on distinct principles. The developed models yielded accurate predictions of 11 of the 13 gasoline properties within the reproducibility of ISO standards. The implementation of these techniques in industry enables a significant reduction in the total time needed for gasoline quality control and the simultaneous use of two different spectroscopic techniques allows for more reliable estimations of the properties in study.

Новые методы атомно-абсорбционного определения содержания металлов в авиационном и автомобильном бензинах

New methods for direct atomic absorption determination of lead and lithium as antiknock additives components in aviation and automotive gasoline in the concentration ranges 100–1700 (Pb) and 2.5/0.25–100 (Li) mg/L have been developed. To overcome the difference in analytical signals for samples and standard solutions due to concentration values and matrix effects the proper sample dilution and the addition of the blank as pure reagent hydrocarbons mixture in the solutions have been offered. The methods have high precision, are simple and may be recommended for gasoline quality control and identification procedures.

Application of low-field and medium-resolution 1H NMR spectroscopy combined with chemometric methods for automotive gasoline quality control

This work describes the application of low-field and medium-resolution 1H NMR (LF-1H NMR) combined with PLS and SVM multivariate regression techniques for fast prediction of five fundamental quality parameters of commercial Brazilian gasoline (specific gravity, distillation temperatures of 50 and 90 percent of recovered and olefinic and aromatic content). All these five parameters are of paramount importance to predict the fuel quality and the application of the developed PLS and SVM models showed prediction errors compared to the reference methodologies applied for these essays. Nevertheless, it is important to highlight that in a different way of the reference methodologies, such as distillation or chromatographic methods, the application of LF-1H NMR combined with PLS or SVM models enables the fast determination of those five parameters directly from a single LF-1H NMR spectrum, which is acquired without any sample pre-treatment nor dilution in deuterated solvents, and this whole process takes no more than 15 s.

Estimation of gasoline properties by1H NMR spectroscopy with repeated double cross‐validated partial least squares models

AbstractCommercial gasoline must satisfy several product specifications before trading. In the present work, repeated double cross validation using partial least squares regression was applied to create reliable prediction models for 13 physicochemical parameters (eg, density, vapour pressure, evaporate at 70°C, evaporate at 100°C, evaporate at 150°C, final boiling point, research octane number, motor octane number, aromatic content, olefinic content, benzene content, oxygen content, and methyltert‐butyl ether content) of gasoline produced in Matosinhos' refinery. The input variables for the regression are the1H NMR spectral intensities of a total of 448 samples, which were recorded using a picoSpin NMR spectrometer operating at 80 MHz. The output variables are the corresponding property values, which were also measured according to ISO standard methods. A spectral feature elimination before multivariate analysis was done to remove noise and speed up the chemometric analysis. The optimum complexity of each model was achieved by repeated double cross‐validation strategy, consisting of 100 repetitions of two nested cross‐validation loops. Quantitative partial least squares yielded accurate predictions of 11 of 13 properties within the reproducibility of ISO standards. The methodology presented in this work has been proven effective in property estimation and enables a significant reduction in the total time of gasoline quality control.

Reid vapor pressure prediction of automotive gasoline using distillation curves and multivariate calibration

Abstract Partial least squares regression (PLS) in conjunction with distillation curves (ASTM D86) were used successfully to predict the vapor pressure of automotive gasoline. The errors obtained in the calibration and validation models (0.71 and 0.69 kPa) were lower than those reported in the literature. In addition, the proposed method is inexpensive, reduces test time and is easy to implement, making it an alternative method for gasoline quality control. It does be possible because the distillation tests are already routinely performed by ANP as one of the evaluation parameter of the automotive gasoline quality.

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Brazilian commercial gasoline follows a rigid quality control, regulated by Brazilian Government Petroleum, Natural Gas, and Biofuels Agency, ANP, following international analytical protocols, such as ASTM and ABNT, covered by Regulation ANP No. 309. Each property is a complicated function of the gasoline chemical composition, which would be represented by diverse types of mathematical correlations. However, these correlations are not adjusted to Brazilian gasoline, whose chemical composition is modified by anhydrous ethanol addition. The purpose of this work is to find correlations, using partial least-squares (PLS) regressions, between 13C NMR Brazilian gasoline fingerprintings and several physicochemical parameters, such as relative density, distillation curve (temperatures related to 10, 50, and 90% of distilled volume, final boiling point and residue), octane numbers (motor and research octane number and antiknock index), hydrocarbon compositions (olefins, aromatics, and saturated) and anhydrous ethanol and benzene. 150 representative gasoline samples, collected randomly from different gas stations, were analyzed following international analytical protocols. All 13C NMR spectroscopic fingerprintings, reported in parts per million (ppm), FIDs (free induction decays) were zero filled and Fourier transformed. A data matrix, composed of 13C NMR chemical shifts and physicochemical parameters, was constructed and used in PLS regression. 13C NMR fingerprinting of 100 gasoline samples were employed in the training set, and 50 samples formed the prediction set. In 13C NMR-PLS models, root-mean square error of calibration (RMSEC) and prediction (RMSEP) were the mains parameters considered to select the “best model”, which shown results roughly similar in magnitude to the repeatability and reproducibility of ASTM and NBR officials analytical protocols. 13C NMR-PLS multivariate regression, as an alternative analytical methodology, offers an appealing procedure for commercial automotive gasoline quality control.

Fast-HRGC method for quantitative determination of benzene in gasoline

Abstract Gasoline is a very complex mixture of hundreds different components and, from a toxicological point of view, benzene is the most hazardous one. Some of the methods recommended present many drawbacks, such as time-consuming. Thus, the need to develop fast methods for routine analysis of benzene is fundamental to the quality control of gasoline. Therefore, the present work compared two different capillary columns to develop a new method for routine analysis of benzene in gasoline by HRGC–FID. The quantitative analysis was carried out using external standard from 0.1%, 0.5%, 1.0%, 1.5% to 2.0% (v/v) of benzene in ethanol and in spiked gasoline. The results show that, using Fast-HRGC, it is possible to separate benzene and other 245 compounds found in gasoline in analysis time of 8 min with high accuracy.

Multivariate calibrations on 1H NMR profiles for prediction of physicochemical parameters of Brazilian commercial gasoline

► 1 H NMR profiles coupled to chemometric PLS regressions for Brazilian gasoline quality control. ► Usefulness for determination of physicochemical parameters. ► Alternative analytical method for government agency laboratories. ► Prediction capability with accuracy and precision. ► Possible application in routine quality control. Brazilian commercial gasoline follows a rigid quality control, covered by Regulation ANP 309 and following international analytical protocols, such as ASTM and ABNT. Each property is a complicated function of the gasoline chemical composition, which would be represented by diverse types of mathematical correlations. However, these correlations are not adjusted to Brazilian gasoline, whose chemical composition is modified by anhydrous ethanol addition. The purpose of this work is to find correlations using PLS regressions, between 1 H NMR Brazilian gasoline fingerprintings and physicochemical parameters, such as relative density, distillation curve, octane numbers, hydrocarbons compositions (olefins, aromatics and saturated) and anhydrous ethanol and benzene. One hundred and fifty representative gasoline samples, collected randomly from different gas stations, were analyzed following ASTM/ABNT analytical protocols. All 1 H NMR spectroscopic fingerprintings, reported in parts per million (ppm) relative to residual proton signals of CDCl 3 at 7.24 ppm, were acquired on a Varian INOVA spectrometer 500 MHz. FIDs were zero filled and Fourier transformed. Data matrix, composed by 1 H NMR chemical shifts and physicochemical parameters, was constructed and imported into Pirouette® 3.11 software for PLS regression. 1 H NMR fingerprinting of 100 gasoline samples were employed in the training set and 50 samples formed the prediction set. RMSEC and RMSEP were the parameters considered to select the “best model”. 1 H NMR-PLS models results in good prediction capability when compared to repeatability and reproducibility of ASTM/ABNT analytical protocols. 1 H NMR-PLS multivariate regressions supplies an alternative analytical procedure for commercial automotive gasoline quality control.

Prediction of the physicochemical properties of gasoline by comprehensive two-dimensional gas chromatography and multivariate data processing

The estimation of physicochemical parameters such as distillation points and relative densities still plays an important role in the quality control of gasoline and similar fuels. Their measurements according to standard ASTM procedures demands specific equipments and are time and work consuming. An alternative method to predict distillation points and relativity density by multivariate analysis of comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) data is presented here. Gasoline samples, previously tested according to standard methods, were used to build regression models, which were evaluated by external validation. The models for distillation points were built using variable selection methods, while the model for relativity density was built using the whole chromatograms. The root mean square prediction differences (RMSPD) obtained were 0.85%, 0.48%, 1.07% and 1.71% for 10, 50 and 90% v/v of distillation and for the final point of distillation, respectively. For relative density, the RMSPD was 0.24%. These results suggest that GC×GC-FID combined with multivariate analysis can be used to predict these physicochemical properties of gasoline.

Quality control of gasoline by 1H NMR: Aromatics, olefinics, paraffinics, and oxygenated and benzene contents

A simple and fast 1H NMR method, without any pretreatment, was developed for quality control of gasoline. It is based on the average group molecular weight approach and relative-content concept involving aromatics, olefinics and paraffinics, including also ethanol and benzene contents. The ethanol content was evaluated for Brazilian samples, but the method can be easily adapted to any oxygenated compound (ex. MTBE), and to gasoline from other countries. Twenty two laboratory prepared gasoline samples (gasoline from Brazilian refineries plus hydrocarbon solvents) and thirty four real (i.e., Brazilian gas stations) gasoline samples were tested. The routine quality control carried out through the usual physicochemical analyses reached a level of confidence of 75% and 73% in detecting non conformity in laboratory and real gasoline samples, respectively. The NMR method was very superior reaching 100% and 97% of confidence, respectively. It was better suited for laboratories with high sample throughput since measurement time is short and only one NMR experiment is needed per sample.

Improvement of standard analytical methods for gasoline quality control

Improvement of standard analytical methods for gasoline quality control