Determination Of Octane Number Research Articles

Numerical prediction of research octane numbers via a quasi-dimensional two-zone cylinder model

Sustainably produced synthetic fuels offer great potential for a fast reduction of the greenhouse gas emissions of the transport sector. For an immediate application within the existing infrastructure and vehicle fleet, synthetic fuels need to comply with existing standards such as the EN 228 for gasoline. Beyond these standards and with optimized fuel design, certain properties can be improved compared to conventional fuels. For this purpose, methods for evaluating the properties are needed.This work discusses the development of a simplified numerical quasi-dimensional two-zone cylinder model, combined with chemical kinetic models, for the estimation of octane numbers. The model emulates fuel specific operating conditions of the standardized procedure for the determination of octane numbers in the cooperative fuel research engine with variable compression ratios. The two zones represent the burned and unburned in-cylinder volume and are modeled with homogeneous reactors. The octane numbers are determined by the identification of the critical compression ratio, for which premature ignition occurs in the unburned reactor.A two-zone cylinder model is validated against experimental data for various primary reference fuels, blended with toluene, ethanol, isobutanol and ethyl tert-butyl ether. The successful application of different kinetic models is demonstrated and enables the application on a wide range of fuels. It is shown that the simulated research octane numbers are in good agreement with the experimental data.

Investigation on spectral standardization among multi-channel of an on-line near-infrared spectrometer

To tackle the issue that the multivariate calibration model cannot be shared among channels due to the inter-channel spectral differences in the on-line multi-channel spectrometer, taking the determination of octane number (RON) and aromatic hydrocarbon content (AC) of multiple reformulated gasoline by on-line Fourier transform near-infrared (FT-NIR) spectrometer combined with partial least squares (PLS) as an example, a novel approach for spectral standardization by piecewise cosine spectra (cosSSM) is proposed in this paper. A total of 42 reformulated gasoline samples were collected and their RON and AC were determined using standard methods with their NIR absorption spectra being measured using the three channels of an on-line NIR spectrometer, respectively. To prove the effectiveness of cosSSM, the spectra of each channel were standardized using cosSSM, and the PLS models of RON and AC were established using the cosine spectra in one channel, then the predictions of the cosine spectra in the other channels were made. The results suggested that cosSSM effectively eliminated the adverse effect of inter-channel spectral variation on the applicability of the channel-specific PLS model, and the performance was comparable to that of the well-established piecewise direct standardization (PDS) method. Besides, cosSSM is relatively simple and easy-to-implement in practical application.

SDAE-BP Based Octane Number Soft Sensor Using Near-infrared Spectroscopy in Gasoline Blending Process

As the most important properties in the gasoline blending process, octane number is difficult to be measured in real time. To address this problem, a novel deep learning based soft sensor strategy, by using the near-infrared (NIR) spectroscopy obtained in the gasoline blending process, is proposed. First, as a network structure with hidden layer as symmetry axis, input layer and output layer as symmetric, the denosing auto-encoder (DAE) realizes the advanced expression of input. Additionally, the stacked DAE (SDAE) is trained based on unlabeled NIR and the weights in each DAE is recorded. Then, the recorded weights are used as the initial parameters of back propagation (BP) with the reason that the SDAE trained initial weights can avoid local minimums and realizes accelerate convergence, and the soft sensor model is achieved with labeled NIR data. Finally, the achieved soft sensor model is used to estimate the real time octane number. The performance of the method is demonstrated through the NIR dataset of gasoline, which was collected from a real gasoline blending process. Compared with PCA-BP (the dimension of datasets of BP reduced by principal component analysis) soft sensor model, the prediction accuracy was improved from 86.4% of PCA-BP to 94.8%, and the training time decreased from 20.1 s to 16.9 s. Therefore, SDAE-BP is proposed as a novel method for rapid and efficient determination of octane number in the gasoline blending process.

A novel search algorithm for the determination of octane number

ABSTRACTAn extremum search algorithm of bidirectional constant speed scanning is proposed in this article, which can be applied to ATSM-CFR engine for automatic determination of octane number. Firstly, the algorithm searches controlled variable forwardly at a constant speed until a peak appears; secondly, searching it reversely at the same speed until a valley appears; lastly, continue the reverse search and determine next search time based on the time difference between the valley time and the peak time to obtain optimal control. The experiment result shows that the search algorithm has good control performance in the determination of octane number.

A PLS regression model using flame spectroscopy emission for determination of octane numbers in gasoline

This paper presents a method for the prediction of MON (Motor Octane Number) and RON (Research Octane Number) of automotive gasoline using flame emission spectroscopy and multivariate calibration (partial least squares). We obtained low values of RMSEC (root mean square error of calibration) and RMSEP (root mean square error of prediction), of 0.14 and 0.56, respectively for MON, while these values for RON were 0.34 and 0.9. The technique is simple and fast, does not require pretreatment of samples and still produces low octane rating errors, making it a good alternative to the standard method used in quality control of automotive gasoline.

Determination of octane numbers in gasoline by distillation curves and partial least squares regression

In this work, distillation curves (ASTM D86) associated with multivariate calibration (PLS-Partial Least Squares) were used to predict MON (motor octane number) and RON (research octane number) parameters of automotive fuel RMSEC getting values (Root Mean Square Error of Calibration) and RMSEP (Root Mean Square Error of Prediction) smaller than the literature. RMSEC values obtained were 0.051 and 0.078, and RMSEP values were 0.063 and 0.085 for MON and RON, respectively. These low values along with high accuracy, when compared to standard test methods (ASTM D2700 and ASTM D2999), indicated that the PLS models were efficient to predict MON and RON values and can be used as an alternative to control quality for automotive gasoline.

Octane Number Determination of Gasoline with a Phononic Crystal Sensor

The wide range of fuels supplied nowadays has different composition. In order to optimize the combustion process, engine performance and meeting the EPA emissions standards, information about the properties of gasoline should be gained in real time. This study presents a new sensor platform based on a phononic crystal (PnC) sensor and its application as a measuring system for real time gasoline octane number determination. The method is based on the analysis of the transmission spectrum of a phononic crystal sensor filled with the liquid gasoline blend. We could reveal a strong correlation between octane number of the gasoline and the frequency of maximum transmission. Obtained experimental results show that the phononic crystal sensors can be considered as a prospective, competitive and inexpensive device for octane number determination.

Insights relating to the autoignition characteristics of alcohol fuels

A detailed chemical kinetic modeling study was undertaken to characterize the auto-ignition behaviour of a full range of blends of both methanol and ethanol with a PRF 80 base fuel. The overall results were encapsulated in a computationally efficient semi-empirical formulation. The auto-ignition model was combined with an ASTM (CFR) engine simulation to explore the underlying causes for the octane number value exhibited by alcohol fuels. The model predictions were validated against experimental pressure traces obtained with a range of alcohol-PRF blends. These results confirmed the model’s general validity and provide valuable insights relating to the controlling role of the cool flame in the octane number determination and the use of alcohol fuels as octane blending components for use in modern and future gasoline engine technologies.

Contribution of external parameter orthogonalisation for calibration transfer in short waves—Near infrared spectroscopy application to gasoline quality

The octane number rating of a gasoline gives an indication of the gasoline performances, under various engine conditions. Two different ratings are included: Research Octane Number (RON) and Motor Octane Number (MON). The standard laboratory method for octane number determination is the knock engine method in which a gasoline is burned and its combustion characteristics compared to known standards. This method is time consuming and labor intensive, and provides no ability for real time control of production. NIR can be applied in real time directly in process monitoring or as a laboratory procedure. Near infrared spectra of gasoline samples were collected thanks to four different short wavelengths near infrared analysers, built with strictly the same technology. The aim of this study was to transfer the calibration built on one spectrometer to the other ones. We applied the external parameter orthogonalisation (EPO) correction to get rid of the apparatus influence on information contained in spectra. By this method, we managed to improve prediction values of two major gasolines’ properties, i.e. Research and Motor Octane Number.

Determination of Octane Number of Gasoline Using Near Infrared Spectroscopy and Genetic Multivariate Calibration Methods

The feasibility of rating the octane number of gasoline using near infrared (NIR) spectroscopy and three different genetic algorithm-based multivariate calibration methods was demonstrated. The three genetic multivariate calibration methods are genetic regression (GR), genetic classical least squares (GCLS), and genetic inverse least squares (GILS). The sample data set was obtained from the ftp address (ftp://ftp.clarkson.edu/pub/hopkepk/Chemdata/) with the permission of Professor. J. H. Kalivas. This data set contains the NIR spectra of 60 gasoline samples collected using diffuse reflectance as log (1/R) with known octane numbers and covers the range from 900 to 1700 nm in 2 nm intervals. Of these 60 spectra, 20 were used as the calibration set, 20 were used as the prediction set, and 20 were reserved for the validation purposes. Several calibration models were built with the three genetic algorithm-based methods, and the results were compared with the partial least squares (PLS) prediction errors reported in the literature. Overall, the standard error of calibration (SEC), standard error of prediction (SEP), and standard error of validation (SEV) values were in the range of 0.15–0.32 (in the units of motor octane number) for the GR and GILS, which are comparable with the literature. However, GCLS produced relatively large results (0.36 for SEC, 0.39 for SEP and 0.52 for SEV) when compared with the other two methods.

Development of a low-cost piezo film-based knock sensor

It is well known that spark advance is a key parameter in spark ignition engine management. Increasing fuel cost and emission regulation strictness require a higher engine efficiency, which can be improved by an accurate regulation of the spark advance. Under high load conditions, an optimal spark advance choice leads the engine to run next to the knock limit, so the management and control system needs to be equipped with a knock sensor in order to preserve the engine from damage. The authors developed a low-cost knock sensor whose sensing element is a thin washer of polyvinylidine fluoride (PVDF), a fluoropolymer characterized by a great piezoelectric e ect if polarized. The sensor has been tested on a spark ignition CFR engine (the standard single-cylinder test engine used by ASTM for octane number determination of spark ignition engine fuel) and compared with a commercial accelerometer and a pressure sensor, in terms of knocking detection capability, measured knock intensity (KI) and signal-to-noise ratio (SNR). Knocking tests have also been carried out on a Renault series production engine. The collected data show that PVDF ensures a reliable detection of knock, a precise measurement of knock energy and accurate information about the frequency content of the perceived vibration. The sensor worked for several hours without depolarizing and, above all, owing to the great piezoelectric e ect of PVDF, the use of a charge amplifier was unnecessary. PVDF proved to have great potential as a knock detector in spark ignition engines at a very low cost.

Determination of octane numbers of gasoline compounds from their chemical structure by 13C NMR spectroscopy and neural networks

Determination of octane numbers of gasoline compounds from their chemical structure by 13C NMR spectroscopy and neural networks

Determination of octane numbers of gasoline compounds from their chemical structure by 13C NMR spectroscopy and neural networks

A new theoretical model has been developed which explains the association between the molecular structure and the knock resistance of individual gasoline compounds convincingly. The constitutions of more than 300 individual gasoline components were correlated with their knock rating (Blending Research Octane Number, BRON) simultaneously. 13C NMR spectra of all compounds were binned in 28 chemical shift regions of different size. The number of individual carbon signals of the nearly 2500 carbons was counted in each shift region and was combined with the information about the presence or absence of the structure groups Oxygen, Rings, Aromatics, aliphatic Chains and oLefins (ORACL). These numbers were used for the encoding of the chemical structure. The relations between the structure information and the knock ratings were determined using an artificial neural network. For a validation data set of 50 individual chemical compounds from various substance classes consisting only of C, H and O a good agreement was found with their experimentally determined BRON ( R=0.933).

Determination of octane numbers and Reid vapor pressure in commercial gasoline using dispersive fiber-optic Raman spectroscopy

Dispersive fiber-optic Raman spectroscopy utilizing CCD detection and near-IR DBR diode laser excitation is used to remotely analyze 205 petroleum fuels of varying composition for pump octane number, motor octane number (MON), research octane number (RON), and Reid vapor pressure (RVP). Partial least squares regression analysis in tandem with several preprocessing techniques was used to model pump octane, MON, RON, and RVP using 130 selected fuels for the training set. The resulting leave-one-out determined standard error of validation (SEV) values for the mean centered data are 0.542, 0.761 and 0.434 octane units, and 0.740 psi, for pump, RON, MON, and RVP, respectively. The remaining 75 samples were used as a test to further validate the models. For the test set, the standard errors (SE) for the mean centered data are 0.586, 0.773 and 0.492 octane units, and 0.869 psi, for pump, RON, MON, and RVP, respectively.

Determination of octane numbers and Reid vapor pressure in commercial gasoline using dispersive fiber-optic Raman spectroscopy

Determination of octane numbers and Reid vapor pressure in commercial gasoline using dispersive fiber-optic Raman spectroscopy

Determination of Octane Numbers and Reid Vapor Pressure of Commercial Petroleum Fuels Using FT-Raman Spectroscopy and Partial Least-Squares Regression Analysis

A Fourier transform Raman spectrometer was used to collect the Raman spectra of 208 commercial petroleum fuels. The individual motor and research octane numbers (MON and RON, respectively) were determined experimentally using the industry standard ASTM knock engine method. Partial least-squares regression analysis was used to build regression models which correlate the Raman spectra of 175 of the fuels with the experimentally determined values for MON, RON, and pump octane number (the average of MON and RON) of the fuels. Each of the models was validated using leave-one-out validation. The standard errors of validation are 0.415, 0.535, and 0.410 octane units for MON, RON, and pump octane number, respectively. By comparing the standard error of validation to the standard deviation for the experimentally determined octane numbers, it is evident that the accuracy of the Raman determined values is limited by the accuracy of the training set used in creating the models. The Raman regression models were used to predict the octane numbers for the fuels which were not used to build the models. The results compare favorably with the leave-one-outvalidation. Also, it is demonstrated that the experimentally determined Reid vapor pressures are highly correlated with the Raman spectra of the fuel samples and can be predicted with a standard error of 0.568 psi.

Determination of coking gasoline octane number by n.m.r. spectrometry

A correlation between octane number and coking gasoline chemical composition is established on the basis of functional group and hydrocarbon type analysis obtained from 1H n.m.r. data. The method allows determination of octane number on a small sample (0.5 ml) in ~ 30 min. The same analysis provides data on aromatic and paraffinic hydrocarbon content as well as on structural characteristics. The method is particularly applicable to determination of octane number on gasoline samples obtained in laboratory research on the coking process, when the small sample size precludes the use of the standard engine method.

The Application of NIR Spectroscopy for the Prediction of Properties of Australian Refined Reformate

A rapid NIR method for the determination of octane number of reformate samples of Australian refined motor gasoline is described. Results of predictions using calibration sets based on ASTM full blends and actual reformate samples are presented. Interpretation of spectral data using principal component analysis facilitates the selection of a suitable analytical spectral range and demonstrates that the reformate samples may be distinguished on the basis of the geographical origins of the parent crude oils. The described method forms a basis for practical application and is a possible alternative to the standard CFR engine test method.

Determination of reformed gasoline octane number by n.m.r. spectrometry

Octane number has been correlated with reformed gasoline chemical composition, the gasoline functional group and hydrocarbons type analysis being obtained on the basis of 1H n.m.r. spectral data. The research octane number was determined according to the standard method (ASTM D 2699) and the correlation was tested by linear regression analysis. The method presented allows for octane number determination from a small sample quantity (0.5 ml) in approximately 30 min with satisfactory level of accuracy ( s = 0.713). It is particularly applicable to determination of octane number for gasoline samples obtained by laboratory research when small sample quantity prevents the application of the standard engine method.

Determination of octane number based on parameters of reaction of cool-flame oxidation of hydrocarbons

The authors develop a method of determining or monitoring the octane number of a hydrocarbon product in a product stream coming directly from the process unit, this method being based on the dependence of known resistance on the maximum temperature or the time to reach the maximum temperature in the cool-flame oxidation process. Model mixtures of isooctane and n-heptane, reformer naphthas and unleaded A-76 gasoline were used.