Property Prediction of Diesel Fuel Based on the Composition Analysis

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Property Prediction of Diesel Fuel Based on the Composition Analysis Data by two-Dimensional Gas Chromatography Ramachandra Chakravarthy, Chhayakanta Acharya, Anilkumar Savalia, Ganesh N. Naik, Asit Kumar Das, Chandra Saravanan, Anurag Verma, and Kalagouda B. Gudasi Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b03822 • Publication Date (Web): 21 Feb 2018 Downloaded from http://pubs.acs.org on February 22, 2018

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Graphical Abstract Title of the Manuscript: Property Prediction of Diesel Based on Composition Analyzed by two-Dimensional Gas Chromatography Authors: Ramachandra Chakravarthy, Chhayakanta Acharya, Anilkumar Savalia, Ganesh N. Naik, Asit Kumar Das, Chandra Saravanan, Anurag Verma, and Kalagouda B. Gudasi

Work Details: Flow modulated reverse phase GC×GC technique with Flame Ionization Detection has been developed for the quantification of PINA components analysis in Kerosene and Diesel samples. Aromatic speciation data obtained by this method for several diesel samples are compared with the results obtained by IP 391 method. A statistical models have been developed for the prediction of the critical diesel properties such as cloud point, pour point and cetane index using composition inputs such as n-Paraffins, Iso-Paraffins, Naphthenes and Aromatics (PINA) obtained by GC×GC technique.

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Property Prediction of Diesel Fuel Based on the Composition Analysis Data by twoDimensional Gas Chromatography Ramachandra Chakravarthy,†,‡ Chhayakanta Acharya, † Anilkumar Savalia,† Ganesh N. Naik,† Asit Kumar Das,† Chandra Saravanan,† Anurag Verma,† and Kalagouda B. Gudasi*,‡ †Reliance Research & Development Centre, Reliance Industries Limited, Reliance Corporate Park, Thane - Belapur Road, Ghansoli, Navi Mumbai - 400701, Maharashtra, India ‡ Department of Chemistry, Karnatak University, Pavate Nagar, Dharwad - 580 003, Karnataka, India * Corresponding author E-Mail: [email protected]

Phone No. +91-836-2215377 +91-9448571368

Abstract The objective of the present study is to develop robust statistical models for the prediction of the critical diesel properties such as cloud point, pour point and cetane index with the composition inputs such as n-Paraffins, Iso-Paraffins, Naphthenes and Aromatics (PINA) obtained by flow modulated two dimensional gas chromatography with flame ionization detection (GC×GC-FID). A single gas chromatographic measurement coupled with models to predict the key physical properties is attractive for refiners to make quick decisions in optimizing diesel blending. We present a partial least squares (PLS) linear regression statistical model that has been developed using 41 data sets of diesel samples with different composition out of which 33 samples were used for the calibration, and 8 samples for validation of the model. The R2 values obtained for cloud point, pour point and cetane index were 0.92, 0.93 and 0.92 with standard deviations of 1.20, 1.50 and 0.40 respectively. The average relative errors for predicted values of cloud point, pour point, and cetane index are found to be 0.86, 1.02 and 0.25 respectively. The PINA analysis of diesel and kerosene samples were carried out using flow modulated GC×GC with flame ionization detection (FID). The technique adapts reverse

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phase gas chromatography with two capillary chromatographic columns; the columns differ in length, diameter, stationary phase and film thickness to get maximum peak resolution. The gravimetric blends of high purity reference standards of paraffins, naphthenes and aromatic compounds (PINA) with variable carbon numbers were used for identification, and to draw the boundaries for group types. Mono aromatic and poly aromatic content obtained for diesel and kerosene samples by flow modulated GC×GC method were comparable to the results obtained by High Performance Liquid Chromatographic (HPLC) method as per IP 391 or ASTM D 6591. Repeatability and reproducibility of the GC×GC analysis was performed for several samples to validate the method. It has been found that HPLC method for the determination of aromatics content using single calibration standard for each type such as mono, di and poly aromatics causes a small error in the quantification in some of the samples as the refractive indices of all the aromatic species present in the diesel and kerosene samples vary depending on the addition of alkyl side chains, presence of hetero atoms such as sulfur, nitrogen, and oxygen etc. Key Words: Cloud Point, Pour Point, Cetane Index, PINA analyses, GC×GC FID, Flow Modulation 1. Introduction The parameters such as total sulfur, cetane number, cetane index, pour point, cloud point, flash point, poly aromatic content, density, viscosity, total ash and water content are some of the key parameters that determine the quality of diesel fuels

1-11

. The desired

specifications of these properties of diesel fuel also vary region to region globally depending on the climatic conditions and government regulations 12-19. In refineries, also, a quick method of determining these properties of diesel fuels in blending various process streams can help to optimize the diesel product as per its specifications. Conventional methods of analyses of individual properties, however, are laborious, time consuming, uneconomical and often not

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practical

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. In this article, we propose an analytical methodology where a single

compositional analysis can be used to predict these diesel properties by a composition based statistical model. We demonstrate this capability of the statistical models by predicting cloud point, pour point and cetane index for diesel samples. Composition of diesel fuels can be represented by groups of hydrocarbon types such as paraffins, naphthenes, and aromatics. Analysis of these group types are generally performed by liquid chromatography (LC) 21-36 super critical fluid chromatography (SFC) 28, spectroscopy 37-42

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, combination chromatography (LC-GC, SFC-GC etc.) 43 and gas chromatography (GC) 44-

. Most of these analytical techniques have focused on determining aromatic group types.

ASTM D 5186-03 is a SFC based method specified for the determination of mono-aromatic and polycyclic aromatic (two or more ring aromatic hydrocarbons) content 28. However, it is not suitable for biodiesel where fatty acid methyl esters are present. The HPLC methodology of IP 391 can provide analysis of aromatic compounds

33

for fuels having boiling range from

150 °C to 400 °C. This method is specific only for aromatic hydrocarbons determination. Furthermore, the HPLC technique is not suitable for very low concentrations of aromatics 33,74. ASTM D-2425 uses mass spectrometry for hydrocarbon type analysis 47. However, use of this method requires separation of saturates and aromatic fractions prior to mass spectrometric analyses which is time intensive process. NMR based method ASTM D-5292 can be used to determine core aromatics with its aromatic hydrogen and carbon identification and quantification purposes

37

. The alkyl side

chains present in aromatic rings cannot be detected by this method and hence there will be a variation in the quantification of aromatics species when comparing the results with chromatographic methods. ASTM D-1840 specifically determines naphthalene content in jet fuels 40 by using the UV absorptivity of C10 and C13 naphthalenes. In this methodology, two fuels with the same volume percent naphthalenes but different distribution of isomers may give

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different results. Fourier transform ion cyclotron resonance-mass spectroscopy (FTICR-MS) 48-53

can identify the molecular composition, double-bond equivalents, and carbon number

based on ultrahigh-resolution and accurate mass measurements. However, FTICR-MS instrument requires a huge capital investment and quantification by this technique needs highly skilled operators; this restricts its widespread applicability for routine analysis. Owing to restrictions faced in above analytical methods, recent advancements in gas chromatography coupled with vacuum ultraviolet detection for the group type analyses is promising 55. Compared to all the above available techniques, two dimensional gas chromatography offers a much simple, high accurate (due to two dimensional separation capacity), and enhanced sensitivity

55-73

in determining the various hydrocarbon group types. Literature

provides application note for Paraffin, Isoparaffin, Naphthenes and Aromatics (PINA) composition analyses using flow modulated two-dimensional gas chromatography 73, 85. Even though several limitations have been reported for the flow modulation GC×GC technique, with a proper care and validations of the analytical data, this methodology can be used successfully for the determination of PINA composition in petroleum middle distillates. Further, the data collected by GC×GC technique for diesel samples are used to build chemometric model for the prediction of some of the critical properties of diesel fuels such as cloud point, pour point and cetane index. The PINA composition along with cloud point, pour point and cetane index helps refinery to make quick decision during various steps of refining processes. The accuracy and simple analyses steps observed in GC×GC technique compared to HPLC method made the technique more reliable and easy accessible in any quality control laboratory. In the following sections, we describe the application of flow modulated twodimensional GC for PINA analysis. We have also described a (PINA) composition based model

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for the prediction of some of the critical properties of diesel fuels such as cloud point, pour point and cetane index 75-83. 2.1. Materials and Methods To capture the broad range of variations in the compositions in the petroleum middle distillate fuels, both kerosene and diesel samples were collected from Refinery Unit, Reliance Industries Limited, Jamnagar, Gujarat, India. Group type analysis was performed using flow modulated GC×GC technique. Reference standards of kerosene, diesel and gravimetric blends used were obtained from PAC, The Netherlands. Aromatic standards such as orthoxylene, fluorene, anthracene and phenanthrene were obtained from Sigma Aldrich. The gravimetric blends and reference standards of kerosene and diesel were stored in 1mL sealed ampoules to prevent evaporation. HPLC grade solvents (minimum of 99.9% purity) obtained from Merck chemicals were used for the analyses. Cloud point and pour points were measured as per the standard ASTM methods75-76 using Pour Point & Cloud Point Analyzer (Model: MPP 5G2S/V22402) procured from ‘Instrumentation Scientific de Laboratories’. The cetane index for the all diesel samples were calculated based on ASTM method 77. Analytical gases (helium, nitrogen, hydrogen, and air) used for GC analysis were of 99.995 % purity. Composition parameters of diesel such as n-Paraffins, Iso-paraffins, Naphthenes, Monoaromatics, and polyaromatics are taken as primary inputs from GC×GC analysis to develop cloud point, pour point and cetane index prediction models. Property prediction models were developed by applying partial least squares (PLS) linear regression method using R-software. 2.2. Instrumental parameters The GC×GC instrument is composed of Agilent 7890 “B” Gas Chromatograph coupled with flow modulation technique, two columns of different selectivity, polarity, diameter, film thickness & length and two flame ionization detectors 70,72. All flows are EPC controlled and the sample was introduced (0.1 uL) through split/splitless inlet (inlet temperature: 350 oC).

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ALS assembly (Agilent model : G 4513A) was used for the injection of samples with split/split less mode. First dimension column is connected to a second dimension column by “AC Flow modulator”. The eluent collected from the first dimension column is quickly transferred to the second dimension column by retaining the separation that has happened in the first column through the flow modulator positioned between them. Further separation takes place in the second dimension column and detected by Flame Ionization Detector. The method is optimized by tuning column lengths, column phase, column coating, column flows, modulation period, injection time and GC oven programming. Reverse phase gas chromatography technique was adapted for the current group type analyses such as n-paraffins, iso-paraffins, naphthenes and aromatic (PINA) components due to its better resolution. The primary column (PAC Part No: 10.73.168 ) of 30 m length and 0.25 mm ID with polar stationary phase and secondary column (PAC Part No:10.73.167) of 10 m length and 0.32 mm ID, with non-polar (100% poly dimethyl siloxane) stationary phase was used for the present study (from PAC, The Netherlands). This combination along with flow modulation allows the mixture of components to get separated according to their polarity and volatility. Oven temperature was programmed between 40 to 300 0C. The helium flow rate for primary column was maintained at 1.0 mL/min and for secondary column at 30.0 mL/min. Flame Ionization Detector temperature was maintained at 350 0C and modulation time was maintained at 3.5 s (optimized by several trials and best separation was achieved at 3.5 Sec). Data handling, two dimensional images and other chromatographic calculations were performed using “GC Image” software provided by Zoex. Quantification of paraffins, naphthenes and aromatics present in the kerosene and diesel samples were performed based on theoretical response factors that conforms to ASTM D 6730 84

and the same calculations were extended to diesel range (carbon number up to 30) molecules.

The concentration of each component (mass %) were determined by normalization of the peak areas (blob volumes in GC image software by automatic integration with set parameters) after

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correction with the detector response factors (Blob detection parameters: (i) column smoothing, first column- 0.1 and 2nd column- 1.5, (ii) Blob filter: minimum area- 15, minimum volume: 100). In qualitative measurements, the retention times of components were determined by analyzing known reference mixtures or samples under identical conditions. In quantitative determination of hydrocarbons, theoretical response factors were used for correcting the detector response. The response of an FID to hydrocarbons is determined by the ratio of the molecular weight of the carbon in the analyte to the total molecular weight of the analyte. The response factors are relative to that calculated for heptane. Calculations are based on the following equation: 𝑅𝑅𝐹 = [(𝐶𝑎𝑤 ∗ 𝐶𝑛) + (𝐻𝑎𝑤 ∗ 𝐻𝑛)] ∗ 0.83905 Where: RRF = relative response factor for a hydrocarbon type group of a particular carbon number Caw = atomic mass of carbon, 12.011 Cn = number of carbon atoms in the hydrocarbon type group Haw = atomic mass of hydrogen, 1.008 Hn = number of hydrogen atoms in the hydrocarbon type group 0.83905 = factor to normalize the result to a heptane response of unity For total PINA analysis, an average response factor for the particular group is used. The quantification methodology was validated by analyzing “gravimetric blend” reference sample that contains paraffins, naphthenes and mono- and polycyclic aromatic hydrocarbon components of known composition. A kerosene / diesel reference material was analyzed in order to determine the template chromatographic boundaries for chromatographic peak identification. GC×GC FID separation was plotted in two dimensions, where x-axis shows a primary column separation and y-axis shows the secondary column separation. The intensity of the signals is presented on a colour scale.

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The HPLC analysis was performed as per the details mentioned in IP 391 method33. A known mass of the sample was diluted with n-heptane and from which 10 µL of this solution was injected into a high performance liquid chromatograph fitted with a polar amino bonded column and Cyano column in series (ZORBAX NH2: 4.6 x 250 mm x 5 µm, part No. 880952-708 and ZORBAX CN: 4.6 x 250 mm x 5 µm, Part No. 880952-705) and refractive index detector. This column set has little affinity for non-aromatic hydrocarbons, whilst exhibiting a strong selectivity for aromatic hydrocarbons. As a result of this selectivity, the aromatic hydrocarbons are separated from the non-aromatic hydrocarbons into distinct bands according to their ring structure, i.e. mono-aromatics, di-aromatics, poly-aromatic compounds. Prior to sample analysis, the instrument was calibrated with known reference standards as illustrated in IP 391 method. The response factors for various components were measured based on standards and used for the quantification of aromatic components present in various diesel and kerosene samples. 2.3. Composition based modeling The prediction models for cloud point, pour point and cetane index properties of diesel fuels were developed using partial least squares (PLS) regression method 80-82 . Compositions of n-paraffins, iso-paraffins, naphthenes, mono-aromatics and poly-aromatics obtained from GC×GC analysis were used as predictors in these models. The analysis data of compositional and physical properties were compiled for 41 different samples of diesel fuels from which 80% of data was used for calibration and 20% for validation of the models. Validation data set was also selected so that it samples entire range of physical properties for best validation. The diesel samples considered had cloud point in the range of -10oC to 12oC, pour point in range of -24oC to 12oC and cetane index from 50.10 to 59.10. 2.4. Preparations

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The reference standards and gravimetric blends for GC×GC analysis were used directly as procured. The mono aromatic and poly aromatic standards used for HPLC were prepared by dissolving known quantity of p-xylene, naphthalene and phenanthrene in known volume of the solvent and series of dilutions were made to get proper calibration standards. Neat diesel samples were used for the determination of pour point and cloud point. 3. Results and Discussion 3.1. GC×GC Analysis The GC×GC analysis helps in getting detailed information on the sample composition than that provided by a simple group-type analysis measured by HPLC. The elution zones of hydrocarbons of the same chemical family and with the same number of carbon atoms were plotted into clusters in GC×GC analysis that helps to identify the individual components along with their group types. Many of the components of the samples were identified by comparing with the two dimensional retention times of model components. A gravimetric blend was prepared using Sigma Aldrich standards of paraffins, naphthenes and aromatics to cover the kerosene range carbon numbers. The chromatogram for kerosene and diesel samples are presented in Figure 1 to 8 (supporting information). The image presented in Figure 1 and 2 (supporting information) depicts the details of the components used as QC standard that was used to fix the boundary for group type analyses. The gravimetric blend for diesel was prepared by accurately weighed paraffins, FAME, naphthenes and aromatic hydrocarbon components and all the reference standards and gravimetric blends with certificate of analysis were procured from PAC, Netherlands. All the components are identified based on their elution profile and group type boundaries were specified based on the QC standards. The GC×GC method was validated by analysing 15 diesel samples and 10 kerosene samples. The same samples were also analysed by HPLC (based on IP 391) method and the results obtained by two different methods are compared. (The samples used for the GC×GC method validations are different

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than the samples used for the chemometric model development). Each sample analysis was carried out in triplicate (n=3) injections and the average value of the three experimental data were considered for the comparison of the two methodologies for the measurement of total aromatic content and its speciation. The results obtained by 2D GC analysis for various group type components such as paraffins, naphthenes, monocyclic aromatic hydrocarbons, and polycyclic aromatic hydrocarons for diesel and kerosene samples are presented in Table 1 and 2 respectively (supporting information). The three repetitive analysis was done for all the samples and standard deviation was found to be less than 0.3 in all the components analysis such as paraffins, naphthenes, and aromatics. Just to check the applicability of the GC×GC method for variable composition of diesel and kerosene samples, various samples have been collected from refinery units and analysed. The obtained data suggests that the method can be successfully used for variable composition of diesel and kerosene samples. The 2D plot for typical kerosene and diesel samples are presented in Figure 1 and 2 respectively. The typical 3D images for diesel and kerosene samples are presented in Figure 3 and 4 respectively. The total composition details (PINA) of various diesel and kerosene samples were presented in Figure 5. The total paraffins are classified into two categories such as n-paraffin (straight chain alkanes) and iso-paraffins (branched-chain alkanes). All diesel samples show n-paraffins concentration in the range of 17.3% to 25.9 %, except samples 9 and 12. The high percentage of n-paraffin content was observed in sample 9 (50.1%) and 12 (48.3%) respectively. In kerosene samples, the n-paraffin concentration varied between 22.3% to 33% except sample 9 and sample 1. The n-paraffin content in these samples is 9.8% and 37.9% respectively. The iso-paraffin content varied in the range of 12.3 % to 28.8 % in diesel samples and 14.6% to 34.2 % in kerosene samples. The sum of the n-paraffin and iso-paraffin content is considered as total paraffins. The GC×GC analyses provides n-paraffin and total paraffin content based on

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template creation using chromatographic boundaries and iso-paraffins were calculated by subtracting n-paraffins from total paraffins. Two-dimensional gas chromatographic technique plays a key role in identification and quantification of group type components and also helps for the identification of individual components with mass detection, if needed. Paraffins are the most favourable components for diesel fuel, which enhances the combustion properties. Whereas, other contents of diesel fuels such as naphthenes and aromatics are not so favorable. The paraffin content is controlled by limiting the aromatic and olefin contents in the diesel fuel. Cetane numbers of paraffinic fuels depend on the ratio of n-, iso- and cyclo-paraffins. Paraffinic content also decides the cold flow properties as the relative solubility of n-paraffins and isoparaffins having carbon numbers C18 to C28 in hydrocarbon varies with temperature and are complex to determine. Naphthenes are an important component of liquid petroleum refinery products. The Table 1 and 2 (supporting information) depicts the percentage of naphthenes in various kerosene and diesel samples. Most of the diesel samples are having naphthenes content in the range of 21% to 30 %, and very few samples have shown even up to 48 %. Kerosene fractions of individual crude oil samples were having naphthenes in the broad range starting from 23 % to 50 %. The aromatic components are major part of crude oil and its fractions boils at high temperatures. The aromatic series of hydrocarbons are chemically and physically very different from the paraffins and naphthenes. Aromatic components are classified into two major categories such as monocyclic aromatics and polycyclic aromatics and these mainly include C3-benzenes, C4-benzenes, C5+-benzenes, 1- naphthalene, and C2-naphthalenes. The C8 group includes two-carbon substituted benzenes and C9+ group includes many structural isomers of one and two ring aromatic compounds. Quantitative analysis of all the monocyclic and polycyclic aromatic hydrocarbons in petroleum or petroleum derivatives is rather difficult task due to the high complexity of these matrices.

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The experimental data presented in Table 1 and 2 (supporting information) depicts that, in diesel samples aromatic content varies in the range of 11 to 41 % and in kerosene it was 10 to 23 %. The composition analysis of aromatic components in various refinery streams is an essential parameter as it helps to route them to various refinery processes to get a desired product. The total compositional analysis of diesel samples also helps us to assess the cetane number and to optimize dosage of chemicals to improve cetane number of the product, if needed. The exact composition of diesel streams of refinery and crude oil helps to optimize the blending processes without compromising the quality of product. In the GC×GC image, from our primary observation, it has been observed that, the separation space (peak capacity) expanded as compared to normal phase GC×GC separations; however, further studies are required for the conclusion. In non-polar followed by polar system (normal phase), two analytes having the same vapour pressure are co-eluted after the first separation but may be separated in the second dimension on the basis of the differences in their partition coefficient. In polar/non-polar system (reverse phase), the first dimension separation is governed by both volatility and molecular specific interactions. In the present GC×GC methodology, iso-paraffins, saturates and aromatics are clearly separated in primary as well as in short secondary column. 3.2. Comparison of GC×GC quantification results for monocyclic and polycyclic aromatic hydrocarbons with IP 391 method The results obtained by flow modulated GC×GC method for kerosene and diesel samples were compared with the results obtained by HPLC methodology using refractive index detection as per IP 391 method. The comparison data for mono-aromatics and poly-aromatics for diesel samples are tabulated in Table 1. Some variation in total aromatic content has been observed between HPLC analysis data and GC×GC analysis data. The major limitation of HPLC method for the aromatic speciation and quantification is that the average refractive index

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of each group type such as monocyclic and polycyclic aromatic hydrocarbons is considered for quantification

33, 74

. The refractive index of all aromatic components during quantification is

based on the standards which are used during calibration (results are presented in Table 3A, Table 3B and Table 3C in the supporting information). In IP 391 method, the refractive index of all the molecules are measured based on refractive index of n-heptane. It has been learnt from the literature that the addition of alkyl side chains, carbon numbers and hetero atoms such as sulfur and nitrogen alters the refractive index of the compound and hence the average response calculation based on a single standard for monocyclic and polycyclic aromatic quantification by HPLC method introduces inaccuracy in the quantification 33, 74. The alkyl side chain containing double bond also alters the refractive index. The results tabulated in Table 1 displays that the results of some of the samples are in close agreement between the two methods and in some of the cases the variation is up to 2 percent. The conjugated di-alkenes and polyalkenes strongly interferes with aromatic quantification and makes considerable variations during HPLC quantification 33,74. Similarly olefins elute between the boundaries of aromatics and naphthenes in GC×GC reverse phase chromatography and may mislead the aromatics quantification data. 3.3. Linearity studies Linearity study was performed to determine the linear reportable range for an analyte. The linear response of GC×GC instrument was tested by injecting standard ‘anthracene’ at various concentrations. The calibration graph was drawn using an instrumental response vs concentration of the anthracene solutions. Good linearity (R2=0.9999) and precision was obtained in the range of 0.01 to 0.11 % suggests the sensitivity of the instrument for aromatic quantification even at lower concentrations. 3.4. Repeatability, Reproducibility and Robustness of GC×GC measurements The repeatability study was performed by analysing one of the kerosene and diesel

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samples by injecting multiple times. Table 2 depicts the results obtained for group type analysis of kerosene sample with analytical variations such as standard deviation (SD) and percentage relative standard deviation (RSD). The repeatability studies for diesel samples are performed by 3 times and data compilation is provided in Table 4 (supporting information). The reproducibility studies were performed by analyzing the standard kerosene and diesel samples by two different instruments, different column sets and different operators. The standard samples were sealed in 1ml glass ampoules using automatic ampoule filling and sealing machine and stored in a refrigerator for the period of 18 months. Table 5 (supporting information) displays the group-type analysis of kerosene and diesel fuel samples for reproducibility. The data obtained for the samples used for reproducibility are in close agreement. This indicates that the method is robust enough for the analyses of kerosene and diesel samples and can be considered as an alternate method to ASTM and European standard methods for the product certification of diesel and jet fuels. 3.5. Effect of concentration on GC×GC measurements for kerosene and diesel samples The concentration effect on GC×GC analyses was performed by injecting the same sample at three different injection volumes such as 0.1 µL, 0.02 µL and 0.01 µL. Table 6 (supporting information) depicts the group type analysis results obtained for one of the diesel fuels. The results obtained are well within the analytical variation and are within the acceptable limits. This indicates that, the flame ionization detector has wide range of linearity in GC×GC analyses and hence analysis can be performed at any concentration by direct sample injection of diesel fuel up to 0.1 µL injection volume. 3.6. Prediction model for cloud point, pour point and cetane index Using the GC×GC method described in sections above and the ASTM methods on physical properties of diesel, the composition details, cloud point, pour point and cetane index of 41 samples were compiled. The compiled data are presented in Table 3 to Table 5. GC×GC

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compositions were grouped into n-paraffins, iso-paraffins, naphthenes, mono-aromatics and poly-aromatics. These compositions were used as predictors for the physical properties of diesel. Partial Least Squares (PLS) based models were developed for each of the physical properties using n-paraffins, iso-paraffins, naphthenes, mono-aromatics and poly-aromatics as predictors. 33 calibration and 8 validation samples were used. The scaled equation coefficients are shown in Table 6 and prediction values of each property are shown in Tables 3-5. 3.6.1 Cloud point model The instrumentally measured cloud point, predicted cloud point and the difference between actual and predicted values are displayed in the Table 3. The Table 3 clearly indicates the good accuracy of the model for the prediction of cloud point. The graphical representation of measured cloud point and predicted cloud point for model calibration (33 samples), validation (8 samples), cloud point prediction test results, error plot, cloud point test, and plot for prediction vs difference are displayed in Figure 6. The difference between the measured cloud point and the predicted results are within the acceptable limit. The R2 value for calibration and validation results are 0.90 and 0.92 respectively, indicates the good accuracy for the prediction of cloud point of unknown samples. The ASTM experimental repeatability is shown as upper and lower bound in the parity plot, the maximum error in the test case was found to be within experimental repeatability range. The standard deviation in the test case was found to be 1.20. From the cloud point fit equation in Table 6, all the coefficients have the same direction (+/-) compared to their correlation with the cloud point indicating a realistic fit equation. During the PLS fitting, all predictors were mean-centered and scaled with their standard deviations. Therefore, the magnitudes of fit coefficients indicate each predictor. In case of cloud point, the predictors were iso-paraffins, poly-aromatics and n-paraffins. These findings corroborate with the literature as paraffins are known to be waxy in nature and can affect cloud point of a fuel.

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Depending on the relative solubility and concentration of n-paraffins of carbon number ranges from C18 to C28 at lower temperatures in lower paraffins, naphthenes, and aromatic components; cloud point and pour point of diesel samples varies and is complex in nature. In the fit equation, however, we can further identify that iso-paraffins tend to decrease the cloud point and n-paraffins tend to increase the cloud point. On the other hand, the poly-aromatics tend to increase the cloud point analogous to n-paraffins. We envisage that due to branched structure of iso-paraffins the stearic hindrance between molecules increases and prevents close-pack structure formations that happen during a cloud point. Therefore, much lower temperatures are required for attaining cloud point with isoparaffin rich fuels. The n-paraffins and poly-aromatics do not have these steric hindrances and therefore are able to pack themselves in close structures with appreciably strong moleculemolecule interactions at higher temperatures. Thus their contribution towards cloud point is positive and that for iso-paraffins is negative as per the fit equation in Table 6. 3.6.2 Pour point model Similar to cloud point case, the data used for pour point model is presented in Table 4. Graphical representation of measured and predicted pour point details, prediction test results, error plot, pour point test, and plot for prediction vs difference, are displayed in Figure 7. The R2 value for calibration and validation results are 0.93 and 0.93 respectively, with standard deviation of 1.50 in the validation samples. From the pour point equation in Table 6, the direction of coefficient and correlations are same indicating a realistic model. Upon comparing the scaled equation coefficient magnitudes, we find that iso-paraffins, poly-aromatics and n-paraffins are predictors of pour point as well. From the equation, iso-paraffins decrease the pour point and n-paraffins and polyaromatics increase the pour point. The reason for these components being predictors for pour point is again envisaged to be the steric-hindrances caused by their molecule types. Presence

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of iso-paraffins makes it easy to induce liquid state at lower pour point due to their higher intermolecular distances causing low molecule-molecule interactions. On the contrary, the nparaffins and poly-aromatics require higher temperature to induce liquid state as they are more susceptible close packed structures with the high molecule-molecule interactions. 3.6.3 Cetane index model Data for cetane index model is presented in Table 5. The graphical representation of measured and predicted cetane index, prediction test results, error plot, cetane index test, and plot for prediction vs difference, are displayed in Figure 8. The R2 value for calibration and validation results are 0.93 and 0.93 respectively with standard deviation of 1.50 in the test case. The predictions for cetane index also lie within the repeatability bounds of ASTM method. Cetane index is fundamentally a different property compared to cloud and pour points. This property depends on distillation and density of diesel fuels. Cetane index model has been developed for the samples wherein we have density and distillation data from standarad reference methods, and hence the numbers of samples used for modelling are less as compared to the samples used for cloud point and pour point measurements. The composition based model for cetane index, therefore, should account for the complex interactions between the molecules that give distillation and density. The fit equation for cetane index is given in Table 6 shows n-paraffins and mono-aromatics as primary predictors of cetane index. However, some of the coefficients do not have the same direction as the correlation. The reason why this equation is able to fit within the ASTM reproducibility is that the present analysis has been conducted over product diesel samples. Therefore, the distillation profiles of all the samples would be similar as they need to meet the distillation specifications. However the molecular distributions in these diesel streams will provide differences in density which can be responsible for the range of cetane index (50.0 to 59.0) considered in this modelling exercise. The fit equation for cetane index therefore captures the non-linear behaviours of density for

17


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Energy & Fuels

diesel fuels and is expected to work well in the range where cetane index does not vary too far from 50.0 to 59.0 range. 4. Conclusion In this article we have demonstrated the use of flow modulated GC×GC with flame ionization detector (FID) to provide quantitative analysis of n-paraffins, iso-paraffins, naphthenes, mono aromatics and poly aromatics in diesel fuels. The GC×GC results are comparable with HPLC IP 391 method in most of the cases and have also been validated for robustness using repetitive analysis. Compositional analysis of diesel samples was then used to make PLS based predictive models for physical properties of diesel (cloud point, pour point and cetane index). The model equations of PLS further provide insight into the molecules that affect the diesel physical properties. From the model equations the cloud point and pour point properties depend mostly on n-paraffins, iso-paraffins and poly-aromatics species whereas n-paraffins and mono-aromatics are the prime contributors for cetane index model. Molecular interactions and steric-hindrances of iso-paraffins are responsible for the inverse correlation with the cloud and pour points. On the other hand, the absence of steric hindrances in n-paraffins and polyaromatic sheets provide a positive correlation with cloud and pour points. The cetane index model, however, is expected to be a fit for the non-linear relation of density in the product diesel samples considered and is expected to perform well for cetane indices within 50.0 to 59.0 range. The conclusions based on molecular interactions for cloud point and pour point accompanied with GC×GC analysis can assist refining operations to optimize the molecular composition in their product diesel and / or troubleshoot any off-spec production. Acknowledgement We are greatly thankful to Reliance Industries Limited, Jamnagar, Gujarat, India for providing Kerosene and Diesel stream samples. We acknowledge the support provided by Mr. Rik

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Suijker, Rob de Jong, Vac Hanemaaijer and Marijn Van Harmelen of PAC, Netherlands for their continuous support during the method validation. The presented GC×GC method is validated with the help of PAC, The Netherlands.

Supporting Information The following contents are provided in the supporting information: Table 1. Composition analyses results for diesel samples obtained by GC×GC experiments Table 2. Composition analyses for kerosene samples obtained by GC×GC experiments Table 3A, 3B and 3C. Response factor for aromatics Table 4. Repeatability studies for diesel Table 5. Reproducibility results for typical kerosene and diesel sample analysed by GC×GC method Table 6. Concentration effect on GC×GC analyses conducted for diesel samples Figure 1. Two Dimensional image of Kerosene Reference Standard for the determination of FID response and to fix group boundaries / template creation Figure 2. Two Dimensional image for Diesel Reference Standard for the determination of FID response and to fix group boundaries / template creation Figure 3. Typical chromatogram for Kerosene sample 1 (K-1) Figure 4. Typical chromatogram for Kerosene sample 2 (K-3) Figure 5. Typical chromatogram for Kerosene sample 3 (K-9) Figure 6. Typical chromatogram for Diesel sample 1 Figure 7. Typical 3D chromatogram for Kerosene sample Figure 8. Typical 3D chromatogram for Diesel sample 3

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References (1)

Santos, R. G.; Loh, W.; Bannwart, A. C.; Trevisan, O. V. An overview of heavy oil properties and its recovery and transportation methods, Braz. J. Chem. Eng. 2014, 31, 571-590.

(2)

Chevron, Diesel Fuels Technical Review, 2007, https://www.chevron.com//media/chevron/operations/documents/diesel-fuel-tech-review.pdf

(3)

Suslick, S. B.; Schiozer, D. J.; Nepomuceno, F.; Furtado, R. Forecasting the development of heavy-oil reserves in ultra-deep waters using technological risk models. SPE Hydrocarbon Economics and Evaluation Symposium, 2003, April 5-8, Dallas, Texas, USA.

(4)

Thomas, S. Enhanced oil recovery-an overview. Oil Gas Sci. Technol.-Rev. IFP, 2008, 63, 9-19.

(5)

Speight, J. G. The Chemistry and Technology of Petroleum. Marcel Dekker, Inc. The University of Michigan, 1999, pp 1-760.

(6)

Speight, J.G. The Chemistry and Technology of Petroleum, 4th Edition, CRC Press, Taylor & Francis Group, Boca Raton, Florida, 2007.

(7)

Speight, J.G. Handbook of Petroleum Product Analysis, 2nd Ed. John Wiley & Sons, Hoboken, New Jersey, 2015, pp. 368. ISBN: 978-1-118-36926-5

(8)

Speight, J. G.; Ozum, B. Petroleum Refining Processes, CRC Press, 2001, pp. 728, ISBN 148227096X, 9781482270969

(9)

Blad, F.W.; Davidson, R.L. Petroleum processing handbook, 4th ed., William Clowers and Sons limited, New York, 1983, pp. 30-70.

(10) Watt, M.; Roussis, S. Crude Assay, Practical Advances in Petroleum Processing, 2006, Springer New York CY -New York, NYSN- 978-0-387-25789-1 UR, pp. 103-116.

20


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 44

(11) Allinson, J.P. Criteria for quality of petroleum products, 4th ed., Gilf Publishing company, Huston, 1980, pp. 15-62. (12) Air Trends, National Air Pollutant Emissions Estimates for Major Pollutants,” U.S. EPA, http://www.epa.gov/airtrends/econ-emissions.html (13) Standard Specification for Diesel Fuel Oils, ASTM D 975-17, ASTM International, West Conshohocken, PA, 2017. (14) Worldwide Fuels Standards Overview of specifications and regulations on (bio) fuels, NEN-the

Netherlands

Standardization

Institute

Ortwin

Costenoble.

http://task39.sites.olt.ubc.ca/files/2013/05/Worldwide-fuels-standards-Overview-ofspecifications-and-regulations-on-biofuels.pdf (15) Worldwide

Winter

Diesel

Fuel

Quality

Survey,

2014,

https://www.infineum.com/media/80722/wdfs-2014-full-screen.pdf. (16) 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. (17) Liaw, H. J.; Gerbaud, V.; Li, Y. H. Prediction of miscible mixtures flash-point from UNIFAC group contribution methods. Fluid Phase Equilib. 2011, 300 (12), 70-82. (18) Bagheri, M.; Borhani, T. N. G.; Zahedi, G. Estimation of flash point and autoignition temperature of organic sulfur chemicals. Energy Convers. Manage. 2012, 58, 185-196. (19) Creton, B.; Dartiguelongue, C.; de Bruin, T.; Toulhoat, H. Prediction of the Cetane Number of Diesel Compounds Using the Quantitative Structure Property Relationship. Energy Fuels, 2010, 24 (10), 5396-5403. (20) Odebunmi, E. O.; Ogunsakin, E.A.; Ilukhor, P.E.P. Characterisation of crude oils and petroleum products, (I) Elution liquid chromatographic separation and gas

21


Page 23 of 44 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

chromatographic analysis of crude oils and petroleum products, Bull. Chem. Soc. Ethiop. 2002, 16 (2), 115. (21) Lundanes, E.; Greibrokk, T. Separation of fuels, heavy fractions, and crude oils into compound classes: A review, J. High Resol. Chromatogr. 1994, 17, 197- 202. (22) Suatoni, J. C.; Garber, H. R. Hydrocarbon Group-Type Analysis of Petroleum Fractions [b.p. 190°-360°C] by High Performance Liquid Chromatography, J. Chromatogr. Sci. 1976, 14 (11), 546-548. (23) Bollet, C.; Escalier, J.C.; Souteyrand, C.; Caude, M.; Rosset, R. Rapid separation of heavy petroleum products by high-performance liquid chromatography, J. Chromatogr.1981, 206, 289-300. (24) Dark, W. A. Crude Oil Hydrocarbon Group Separation Quantitation, J. Liq. Chromatogr, 1982, 5, 1645-1652. (25) Ali, M. A.; Nofal, W. A. Application of high performance liquid chromatography for hydrocarbon group type analysis of crude oils, Fuel Sci. Technol. Int. 1994, 12, 21-33. (26) Radke, M.; Willsch, H.; Welte, D. H. Preparative hydrocarbon group type determination by automated medium pressure liquid chromatography, Anal. Chem., 1980, 52 (3), 406-411. (27) Standard Test Method for Hydrocarbon Types in Liquid Petroleum Products by Fluorescent Indicator Adsorption, ASTM D 1319-15, ASTM International, 2015, West Conshohocken, PA. (28) Standard Test Method for Determination of Aromatic Content and Polynuclear Aromatic Content of Diesel Fuels and Aviation Turbine Fuels by Supercritical Fluid Chromatography, ASTM D 5186-03, ASTM International, 2003, West Conshohocken, PA.

22


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 44

(29) Maximilian K. J.; Sutherland, A.C.; Eshner, M.; Groger, T.; Wilharm,T.; Zimmermann, R.; Quantitative analysis of modern fuels derived from middle distillates-The impact of diverse compositions on standard methods evaluated by an offline hyphenation of HPLC-refractive index detection with GC×GC -TOFMS; Fuel, 2017, 187,16-25. (30) Standard Test Method for Determination of Aromatic Hydrocarbon Types in Middle Distillates-High Performance Liquid Chromatography Method with Refractive Index Detection, ASTM D 6591-11, ASTM International, 2011, West Conshohocken, PA (31) Petroleum products. Determination of aromatic hydrocarbon types in middle distillates. High performance liquid chromatography method with refractive index detection, EN 12916, 2016, 2016. ISBN: 978 0 580 872778 (32) Determination of aromatic hydrocarbon types in middle distillates - High performance liquid chromatography method with refractive index detection, IP 548, 2007, REF/ISBN: IP 548-2936450. (33) Petroleum products-Determination of aromatic hydrocarbon types in middle distillatesHigh performance liquid chromatography method with refractive index detection, IP 391, REF/ISBN: IP 391-2934869 (34) Standard Test Method for Determination of Aromatic Hydrocarbon Types in Aviation Fuels and Petroleum Distillates-High Performance Liquid Chromatography Method with Refractive Index Detection, ASTM D 6379-11, ASTM International, 2011, West Conshohocken, PA (35) Standard Test Method for Separation of Representative Aromatics and Non aromatics Fractions of High-Boiling Oils by Elution Chromatography, ASTM D 2549-02, ASTM International, 2012, West Conshohocken, PA

23


Page 25 of 44 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

(36) Grizzle, P. L.; Sablotny, D. M. Automated liquid chromatographic compound class group-type separation of crude oils and bitumens using chemically bonded silica-NH2. Anal. Chem. 1986, 58 (12), 2389-2396. (37) Standard Test Method for Aromatic Carbon Contents of Hydrocarbon Oils by High Resolution Nuclear Magnetic Resonance Spectroscopy, ASTM D 5292-99, ASTM International, 2014, West Conshohocken, PA (38) Standard Test Method for Hydrogen Content of Aviation Turbine Fuels by Low Resolution Nuclear Magnetic Resonance Spectrometry, ASTM D 3701-01, ASTM International, 2012, West Conshohocken, PA (39) Standard Test Methods for Hydrogen Content of Light Distillates, Middle Distillates, Gas Oils, and Residua by Low-Resolution Nuclear Magnetic Resonance Spectroscopy, ASTM D 4808-01, ASTM International, 2012, West Conshohocken, PA (40) Standard Test Method for Naphthalene Hydrocarbons in Aviation Turbine Fuels by Ultraviolet Spectrophotometry, ASTM D 1840-07, ASTM International, 2013, West Conshohocken, PA (41) Aske, N.; Kallevik, H.; Sjöblom, J. Determination of Saturate, Aromatic, Resin, and Asphaltenic (SARA) Components in Crude Oils by Means of Infrared and NearInfrared Spectroscopy, Energy Fuels, 2001, 15 (5), 1304-1312 (42) Riazi, M. R. Characterization and properties of petroleum fractions, ASTM stock number: MNL50, ASTM 100, Barr Harbor, West Conshohocken, PA (43) Adam, F.; Thiébaut, D.; Bertoncini, F.; Courtiade, M.; Hennion, M. Supercritical fluid chromatography hyphenated with twin comprehensive two-dimensional gas chromatography for ultimate analysis of middle distillates J. Chromatogr. A, 2010, 1217, 1386-1394.

24


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 44

(44) Lissitsyna, K.; Huertas, S.; Quintero, L.C.; Polo, L.M. PIONA analysis of kerosene by comprehensive two-dimensional gas chromatography coupled to time of flight mass spectrometry, Fuel, 2014, 116, 716-722. (45) Maximilian K.J.; Eschner, M.; Groger, T.; Wilharm,T.; Zimmermann, R. Complete Group-Type Quantification of Petroleum Middle Distillates Based on Comprehensive Two-Dimensional Gas Chromatography Time-of-Flight Mass Spectrometry (GC×GC TOFMS) and Visual Basic Scripting, Energy Fuels, 2014, 28(9), 5670-5681 (46) Hübschmann, H.J. Handbook of GC/MS: Fundamentals and Applications, John Wiley & Sons, Science-719, 2009, ISBN: 352731427X, 9783527314270 (47) Standard Test Method for Hydrocarbon Types in Middle Distillates by Mass Spectrometry, ASTM D 2425-04, ASTM International, 2009, West Conshohocken, PA. (48) Smith, D.F. Petroleomics, Applications of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry: Crude Oil and Bitumen Analysis, The Florida State University, ProQuest, 2007, 198, ISBN: 0549467947, 9780549467946. (49) Marshall A.G.; Hendrickson C.L.; Jackson G.S. Fourier transform ion cyclotron resonance mass spectrometry: A primer. Mass Spectrom. Rev. 1998, 17, 1-35. (50) Marshall, A. G. Fourier transform ion-cyclotron resonance mass spectrometry, Acc. Chem. Res. 1985, 18, 316-322 (51) Nibbering, N.M.M. Principles and applications of Fourier transform ion cyclotron resonance mass spectrometry plenary lecture, Analyst. 1992, 117, 289-293. (52) Russel, D.H. An evaluation of fourier transform ion cyclotron resonance mass spectrometry for high mass applications. Mass Spectrom Rev., 1986, 5, 167-189. (53) Hughey, C.A.; Rodgers R.P.; Marshall, A.G. Resolution of 11000 compositionally distinct components in a single electrospray ionisation fourier transform ion cyclotron resonance mass spectrum of crude oil. Anal. Chem., 2002, 74, 4145-4149.

25


Page 27 of 44 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

(54) Hsu,

C.

S.

Analytical

Advances

for

Hydrocarbon

Research

Modern Analytical Chemistry, Springer Science & Business Media, 2012, pp. 463, ISBN: 144199212X, 9781441992123 (55) Weber, B.M.; Walsh, P.; Harynuk, J. J. Determination of Hydrocarbon Group-Type of Diesel Fuels by Gas Chromatography with Vacuum Ultraviolet Detection, Anal. Chem. 2016, 88, 5809-5817 (56) Dijkmans, T.; Van Geem, K.M.; Djokic, M.R.; Marin, G.B. Combined Comprehensive Two-Dimensional

Gas

Hydrocarbons/Polyaromatic

Chromatography Sulfur-Containing

Analysis

of

Hydrocarbons

Polyaromatic

(PAH/PASH)

in

Complex Matrices, Ind. Eng. Chem. Res. 2014, 53, 15436-15446 (57) Davis, J.M.; Giddings, J.C. Statistical theory of component overlap in multicomponent chromatograms, Anal. Chem. 1983, 55, 418. (58) Giddings, J.C. Sample dimensionality: A predictor of order-disorder in component peak distribution in multidimensional separation, J. Chromatogr. A, 1995, 703, 3-15 (59) Dallüge, J.; Beens, J.; Brinkman, U.A.T. Comprehensive two-dimensional gas chromatography: a powerful and versatile analytical tool, J. Chromatogr. A, 2003, 1000, 69-108. (60) Lee, A.L.; Bartle, K.D.; Lewis, A.C. A Model of Peak Amplitude Enhancement in Orthogonal Two-Dimensional Gas Chromatography, Anal. Chem. 2001, 73, 1330. (61) Vendeuvre, C.; Ruiz-Guerrero, M.; Bertoncini F.; Duval, L.; Thiébaut, D.; Hennion, M.C. Characterisation of middle-distillates by comprehensive two-dimensional gas chromatography (GC×GC): A powerful alternative for performing various standard analysis of middle-distillates, J. Chromatogr. A, 2005, 1086, 21-28. (62) Adam, F.; Bertoncini, F.; Brodusch, N.; Durand, E.; Thiébaut, D.; Espinat, D.; Hennion, M.C. New benchmark for basic and neutral nitrogen compounds speciation in middle

26


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 44

distillates using comprehensive two-dimensional gas chromatography, J. Chromatogr. A, 2007, 1148, 55-64. (63) Von Muehlen, C.; Zini, C.A.; Caramao, E.B.; Marriott, P.J. Characterization of petrochemical samples and their derivatives by comprehensive two-dimensional gas chromatography, Quim. Nova, 2006, 29, 765. (64) Vendeuvre, C.; Bertoncini, F.; Espinat, D.; Thiébaut, D.; Hennion, M.C. Multidimensional gas chromatography for the detailed PIONA analysis of heavy naphtha: Hyphenation of an olefin trap to comprehensive two-dimensional gas chromatography, J. Chromatogr. A, 2005, 1090, 116-125. (65) Liu, Z.; Phillips, J.B. Comprehensive Two-Dimensional Gas Chromatography using an On-Column Thermal Modulator Interface, J.Chrom.Sci. 1991, 29, 227-231. (66) Diehl, J.W.; Di-Sanzo, F.P. Determination of aromatic hydrocarbons in gasolines by flow modulated comprehensive two-dimensional gas chromatography, J. Chromatogr. A, 2005, 1080 (2), pp. 157-165 (67) Egeness, M.J. Comprehensive Two-Dimensional Gas Chromatography: method development and verification by characterization of petroleum fractions, Norwegian University of Science and Technology, NTNU, Trondheim (68) Vendeuvre, C.; Ruiz-Guerrero, R.; Bertoncini, F.; Duval, L.; Thiebaut, D. Comprehensive Two-dimensional Gas Chromatography for detailed characterization of petroleum products. Oil & Gas Science & Technology- Rev. IFP, 2007, 62, 1, pp. 4355 (69) Beens, J.; Adahchour, M.; Vreuls, R.J.J.; van Altena, K.; Brinkman, U.A.T. Simple, non-moving

modulation

interface

for

comprehensive

two-dimensional

gas

chromatography, J. Chromatogr. A, 2001, 919, 127-132.

27


Page 29 of 44 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

(70) Application Note, Group-type analysis (PiPNA) in diesel and Jet Fuel by flow modulated GC×GC FID, AC, Analytical Control. (71) Griffitha,J.F.; Winniforda, W.L.; Suna,K.; Edamb, R.; Luong,J.C. A reversed-flow differential flow modulator for comprehensive two-dimensional gas chromatography, J. Chromatogr. A, 2012, 1226, 116-123. (72) Application Note, Group-type analysis (PiPNA) in diesel and Jet Fuel by flow modulated GC×GC FID, AC, Analytical Control. (73) Griffitha,J.F.; Winniforda, W.L.; Suna,K.; Edamb, R.; Luong,J.C. A reversed-flow differential flow modulator for comprehensive two-dimensional gas chromatography, J. Chromatogr. A, 2012, 1226, 116-123. (74) Forziati, A.F. Refractive Index as a Function of Wavelength for Sixty API-NBS Hydrocarbons, J. Res. 1950, 44. (75) Standard Test method for Standard Test Method for Pour Point of Petroleum Products (Automatic Pressure Pulsing Method), ASTM D 5949-16, ASTM International, West Conshohocken, PA (76) Standard Test Method for Cloud Point of Petroleum Products and Liquid Fuels (Linear Cooling Rate Method), ASTM D 5772-17, ASTM International, West Conshohocken, PA (77) Standard Test Method for Cetane Number of Diesel Fuel Oil, ASTM D 613-17B, ASTM International, West Conshohocken, PA (78) Unavane, M. Statistical Tools for Managing and Manipulating Crude Oil Data, Exploration & Production, 2010, 8, 135-138 (79) Saine Aye, M.M.; Zhang, N. A Novel Methodology in Transforming Bulk Properties of Refining Streams into Molecular Information, Chem. Eng. Sci. 2005, 60, 6702-6717

28


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 44

(80) Karonis, A.; Lois, E.; Zannikos, F.; Alexandridis, A.; Sarimveis, H. A Neural Network Approach for the Correlation of Exhaust Emissions from a Diesel Engine with Diesel Fuel Properties, Energy Fuels, 2003, 17, 1259-1265. (81) Cookson, D. J.; Lloyd, C. P.; Smith, B. E. Investigation of the chemical basis of diesel fuel properties, Energy Fuels, 1988, 2, 854-860. (82) Cookson, D. J.; Iliopoulos, P.; Smith, B. E. Composition-property relations for jet and diesel fuels of variable boiling range, Fuels, 1995, 74, 70-78. (83) Harumi, H.; Yuhji, M.; Hiroshi, U.; Yu, N. Supply and demand analysis on petroleum products and crude oils for Asia and the world. The energy data and modelling center (EDMC) and The institute of energy economics, Japan (IEEJ), 2008. (84) Standard Test Method for Determination of Individual Components in Spark Ignition Engine Fuels by 100 Metre Capillary (with Precolumn) High-Resolution Gas Chromatography, ASTM D 6730-01, ASTM International, 2016, West Conshohocken, PA (85) Boczkaj, G.; Jaszczolt, M.; Przyjazny, A.; Kamiński, M. Application of normal-phase high-performance liquid chromatography followed by gas chromatography for analytics of diesel fuel additives, Anal. Bioanal. Chem., 2013, 405, 18, 6095-6103.

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Mono Aromatics, % Sample

Poly Aromatics, %

Absolute

Relative

Absolute

Relative

ID

GC×GC

IP 391

Difference

Difference

GC×GC

IP 391

Difference

Difference

1.00

23.38

23.89

0.51

2.13

9.20

10.71

1.52

14.19

2.00

11.29

12.24

0.95

7.76

0.45

0.58

0.13

22.41

3.00

22.80

22.30

0.50

2.24

18.22

16.65

1.57

9.43

4.00

16.05

14.81

1.24

8.37

10.91

9.39

1.52

16.19

5.00

21.84

20.49

1.34

6.54

15.99

15.21

0.77

5.06

6.00

18.92

18.59

0.32

1.72

15.78

15.29

0.49

3.20

7.00

17.60

15.95

1.65

10.34

6.59

6.63

0.04

0.60

8.00

11.00

12.57

1.57

12.49

5.49

5.58

0.09

1.61

9.00

7.79

7.72

0.07

0.91

4.22

4.50

0.29

6.44

10.00

21.34

21.08

0.26

1.23

15.76

16.19

0.43

2.66

11.00

22.93

22.81

0.12

0.53

18.20

17.19

1.00

5.82

12.00

8.14

6.98

1.16

16.62

4.87

3.83

1.04

27.15

13.00

15.94

14.86

1.08

7.27

10.86

10.62

0.23

2.17

14.00

19.00

19.06

0.06

0.31

16.00

15.55

0.45

2.89

15.00

10.44

12.33

1.90

15.41

6.07

5.98

0.09

1.51

Table 1. Comparison of mono-aromatics and poly aromatics results obtained by GC×GC and IP 391 method for diesel samples.

30


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Components

Total

Page 32 of 44

n-

Iso-

Mono

Poly

Paraffins

paraffins

(%)

(%)

(%)

(%)

(%)

(%)

(%)

Rep 1

23.63

25.05

48.68

31.54

19.67

0.11

19.78

Rep 2

23.62

24.85

48.47

31.52

19.88

0.14

20.02

Rep 3

23.53

25.14

48.67

31.60

19.60

0.12

19.72

Rep 4

23.58

25.09

48.67

31.61

19.61

0.12

19.73

Rep 5

23.63

24.99

48.62

31.53

19.72

0.13

19.85

Rep 6

23.56

25.03

48.59

31.56

19.71

0.13

19.84

Rep 7

23.69

24.98

48.67

31.60

19.60

0.13

19.73

Rep 8

23.62

25.05

48.67

31.64

19.63

0.14

19.77

Rep 9

23.58

25.17

48.75

31.48

19.69

0.12

19.81

Rep 10

23.61

25.08

48.67

31.54

19.60

0.12

19.72

Rep 11

23.59

25.08

48.67

31.61

19.68

0.13

19.77

Average, %

23.60

25.05

48.65

31.57

19.67

0.13

19.79

SD

0.04

0.09

0.07

0.05

0.08

0.01

0.09

RSD, %

0.18

0.34

0.15

0.15

0.42

7.32

0.44

Paraffins Naphthenes Aromatics aromatics

Total aromatics

Table 2. Repeatability results for typical kerosene sample analyzed by GC×GC method.

31


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Energy & Fuels

1

nParaffin (%) 17.30

IsoParaffin (%) 20.10

Mono Aromatics (%) 21.37

Poly Aromatics (%) 2.19

39.02

Cloud Point measured (oC) -10

2

17.90

19.52

21.24

2.27

39.07

-10

Cal.

-6.84

-3.16

3

16.00

21.35

21.62

2.12

38.90

-10

Cal.

-11.22

1.22

4

19.07

21.41

20.21

2.55

36.74

-8

Cal.

-7.58

-0.42

5

18.07

18.44

23.24

2.13

38.12

-8

Cal.

-5.55

-2.45

6

18.17

18.85

22.78

1.74

38.46

-7

Cal.

-6.05

-0.95

7

19.92

20.93

10.21

0.49

48.45

-7

Cal.

-6.40

-0.60

8

19.91

19.97

11.29

0.45

48.37

-7

Cal.

-5.35

-1.65

9

19.72

20.34

11.67

0.41

47.86

-6

Cal.

-6.04

0.04

10

17.62

18.12

24.57

2.16

37.53

-6

Cal.

-5.72

-0.28

11

19.46

20.88

10.54

0.51

48.61

-6

Cal.

-6.86

0.86

12

20.02

19.88

14.09

0.28

45.73

-6

Cal.

-5.34

-0.66

13

17.30

21.20

21.28

15.81

24.42

-5

Cal.

-3.80

-1.20

14

18.31

18.22

24.54

2.09

36.83

-5

Cal.

-5.09

0.09

15

17.81

17.97

24.63

2.31

37.29

-5

Cal.

-5.28

0.28

16

18.99

18.33

24.04

1.89

36.75

-5

Cal.

-4.52

-0.48

17

18.98

18.26

24.26

2.11

36.39

-5

Cal.

-4.36

-0.64

18

19.99

19.57

14.34

0.46

45.64

-4

Cal.

-4.94

0.94

19

18.88

19.66

24.13

2.02

36.31

-4

Cal.

-6.21

2.21

20

19.13

18.68

23.76

1.89

36.54

-4

Cal.

-4.75

0.75

21

19.23

17.88

24.63

2.10

36.16

-4

Cal.

-3.66

-0.34

22

19.12

18.54

24.14

1.98

36.22

-4

Cal.

-4.58

0.58

23

19.01

19.22

22.57

1.78

37.42

-4

Cal.

-5.51

1.51

24

17.25

21.22

21.34

15.76

24.44

-3

Cal.

-3.90

0.90

25

18.55

18.06

24.57

2.08

36.74

-3

Cal.

-4.65

1.65

26

19.44

18.61

23.53

2.03

36.38

-3

Cal.

-4.26

1.26

27

19.93

19.24

22.59

1.70

36.54

-3

Cal.

-4.54

1.54

28

18.37

16.71

22.80

18.22

23.91

2

Cal.

3.64

-1.64

29

21.74

21.10

19.00

16.00

22.17

3

Cal.

1.53

1.47

30

18.28

16.69

22.93

18.20

23.92

3

Cal.

3.54

-0.54

31

22.24

17.69

21.84

15.99

22.26

5

Cal.

5.95

-0.95

32

25.85

12.33

17.60

6.59

37.64

10

Cal.

12.61

-2.61

33

20.95

14.04

16.05

10.91

38.07

12

Cal.

6.97

5.03

34

16.23

19.00

22.33

2.09

40.33

-11

Val.

-8.24

-2.76

35

17.78

18.72

23.19

1.74

38.56

-8

Val.

-6.36

-1.64

36

18.34

18.83

22.74

1.79

38.29

-6

Val.

-5.81

-0.19

37

19.88

20.49

9.95

0.26

49.42

-6

Val.

-6.00

0.00

38

19.33

19.37

22.58

1.73

36.98

-5

Val.

-5.34

0.34

39

18.86

17.98

24.75

2.08

36.33

-4

Val.

-4.21

0.21

Sl. No.

40 41

Naphthenes (%)

Cal.

Cloud point Predicted (oC) -8.24

Relative Diff. (oC) -1.76

Data Type

19.47 19.36 22.54 1.77 36.86 -3 Val. -5.17 2.17 21.78 21.31 18.92 15.78 22.23 3 Val. 1.23 1.77 Table 3. Prediction model for cloud point (oC); Components used for model development are; n paraffin, Isoparaffin, monoaromatics, polyaromatics, Naphthenes and cloud point, [calibration (sample 1-33), R2=0.90 and validation (sample 34-41) R2=0.92], [Cal. = Calibration, Val. = Validation]

32


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Sl. No.

Iso Paraffin (%) 20.10

Mono Aromatics (%) 21.37

Poly Aromatics (%) 2.19

Naphthenes (%)

1

nParaffin (%) 17.30

2

17.90

19.52

21.24

3

16.00

21.35

4

19.07

5

Page 34 of 44

Data Type

39.02

Pour point measured (oC) -15

Cal.

Pour point Predicted (oC) -14.60

Relative Diff. (oC) -0.40

2.27

39.07

-15

Cal.

-12.72

-2.28

21.62

2.12

38.90

-24

Cal.

-18.57

-5.43

21.41

20.21

2.55

36.74

-12

Cal.

-13.24

1.24

18.07

18.44

23.24

2.13

38.12

-12

Cal.

-11.36

-0.64

6

18.17

18.85

22.78

1.74

38.46

-12

Cal.

-12.01

0.01

7

19.92

20.93

10.21

0.49

48.45

-12

Cal.

-10.91

-1.09

8

19.91

19.97

11.29

0.45

48.37

-12

Cal.

-9.75

-2.25

9

19.72

20.34

11.67

0.41

47.86

-12

Cal.

-10.69

-1.31

10

17.62

18.12

24.57

2.16

37.53

-12

Cal.

-11.81

-0.19

11

19.46

20.88

10.54

0.51

48.61

-9

Cal.

-11.60

2.60

12

20.02

19.88

14.09

0.28

45.73

-9

Cal.

-10.09

1.09

13

17.30

21.20

21.28

15.81

24.42

-6

Cal.

-5.88

-0.12

14

18.31

18.22

24.54

2.09

36.83

-12

Cal.

-10.92

-1.08

15

17.81

17.97

24.63

2.31

37.29

-12

Cal.

-11.20

-0.80

16

18.99

18.33

24.04

1.89

36.75

-9

Cal.

-10.07

1.07

17

18.98

18.26

24.26

2.11

36.39

-9

Cal.

-9.86

0.86

18

19.99

19.57

14.34

0.46

45.64

-9

Cal.

-9.60

0.60

19

18.88

19.66

24.13

2.02

36.31

-9

Cal.

-12.23

3.23

20

19.13

18.68

23.76

1.89

36.54

-9

Cal.

-10.31

1.31

21

19.23

17.88

24.63

2.10

36.16

-9

Cal.

-8.99

-0.01

22

19.12

18.54

24.14

1.98

36.22

-9

Cal.

-10.12

1.12

23

19.01

19.22

22.57

1.78

37.42

-12

Cal.

-11.15

-0.85

24

17.25

21.22

21.34

15.76

24.44

-3

Cal.

-6.03

3.03

25

18.55

18.06

24.57

2.08

36.74

-9

Cal.

-10.33

1.33

26

19.44

18.61

23.53

2.03

36.38

-9

Cal.

-9.58

0.58

27

19.93

19.24

22.59

1.70

36.54

-9

Cal.

-9.81

0.81

28

18.37

16.71

22.80

18.22

23.91

3

Cal.

3.91

-0.91

29

21.74

21.10

19.00

16.00

22.17

3

Cal.

1.83

1.17

30

18.28

16.69

22.93

18.20

23.92

3

Cal.

3.75

-0.75

31

22.24

17.69

21.84

15.99

22.26

3

Cal.

7.06

-4.06

32

25.85

12.33

17.60

6.59

37.64

12

Cal.

14.26

-2.26

33

20.95

14.04

16.05

10.91

38.07

12

Cal.

7.59

4.41

34

16.23

19.00

22.33

2.09

40.33

-15

Val.

-14.92

-0.08

35

17.78

18.72

23.19

1.74

38.56

-15

Val.

-12.50

-2.50

36

18.34

18.83

22.74

1.79

38.29

-12

Val.

-11.66

-0.34

37

19.88

20.49

9.95

0.26

49.42

-12

Val.

-10.45

-1.55

38

19.33

19.37

22.58

1.73

36.98

-9

Val.

-10.90

1.90

39

18.86

17.98

24.75

2.08

36.33

-9

Val.

-9.76

0.76

40

19.47

19.36

22.54

1.77

36.86

-9

Val.

-10.64

1.64

41

21.78

21.31

18.92

15.78

22.23

3

Val.

1.42

1.58

Table 4. Prediction model for Pour point (oC); Components used for model development are; n paraffin, Isoparaffin, monoaromatics, polyaromatics, Naphthenes and Pour point, [Calibration (sample 1-33), R2=0.93 and validation (sample 34-41) R2=0.93], [Cal. = Calibration, Val. = Validation]

33


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Energy & Fuels

Sl.

n-

Iso-

Mono

Poly

Naphthenes

Cetane Index

Data

Cetane Index

Relative

No.

Paraffin

Paraffin

Aromatics

Aromatics

(%)

Measured

Type

Predicted

Diff.

(%)

(%)

(%)

(%)

1

18.17

18.85

22.78

1.74

38.46

51.70

Cal.

51.49

0.21

2

17.62

18.12

24.57

2.16

37.53

50.50

Cal.

49.95

0.55

3

17.30

21.20

21.28

15.81

24.42

52.20

Cal.

51.69

0.51

4

18.31

18.22

24.54

2.09

36.83

50.70

Cal.

50.43

0.27

5

18.99

18.33

24.04

1.89

36.75

51.20

Cal.

51.20

0.00

6

19.33

19.37

22.58

1.73

36.98

52.30

Cal.

52.45

-0.15

7

18.98

18.26

24.26

2.11

36.39

51.10

Cal.

51.05

0.05

8

18.88

19.66

24.13

2.02

36.31

51.10

Cal.

51.22

-0.12

9

19.23

17.88

24.63

2.10

36.16

50.50

Cal.

50.93

-0.43

10

19.12

18.54

24.14

1.98

36.22

50.80

Cal.

51.25

-0.45

11

19.01

19.22

22.57

1.78

37.42

52.30

Cal.

52.22

0.08

12

17.25

21.22

21.34

15.76

24.44

52.20

Cal.

51.63

0.57

13

18.55

18.06

24.57

2.08

36.74

50.70

Cal.

50.55

0.15

14

19.44

18.61

23.53

2.03

36.38

51.90

Cal.

51.83

0.07

15

19.93

19.24

22.59

1.70

36.54

52.20

Cal.

52.81

-0.61

16

18.37

16.71

22.80

18.22

23.91

50.30

Cal.

50.76

-0.46

17

21.74

21.10

19.00

16.00

22.17

55.40

Cal.

55.94

-0.54

18

18.28

16.69

22.93

18.20

23.92

50.30

Cal.

50.62

-0.32

19

22.24

17.69

21.84

15.99

22.26

54.50

Cal.

54.08

0.42

20

25.85

12.33

17.60

6.59

37.64

59.10

Cal.

58.50

0.60

21

20.95

14.04

16.05

10.91

38.07

55.90

Cal.

56.30

-0.40

22

17.78

18.72

23.19

1.74

38.56

51.10

Val.

50.97

0.13

23

17.81

17.97

24.63

2.31

37.29

50.60

Val.

50.00

0.60

24

19.13

18.68

23.76

1.89

36.54

51.30

Val.

51.51

-0.21

25

19.47

19.36

22.54

1.77

36.86

52.00

Val.

52.55

-0.55

26

21.78

21.31

18.92

15.78

22.23

55.40

Val.

56.05

-0.65

27

20.95

14.12

15.94

10.86

38.13

55.90

Val.

56.39

-0.49

Table 5. Prediction model for cetane index; Components used for model development are; n paraffin, Isoparaffin, monoaromatics, polyaromatics, Naphthenes and cetane index, [Calibration (sample 1-21), R2=0.93 and validation (sample 22-27) R2=0.93], [Cal. = Calibration, Val. = Validation]

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Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Components

Page 36 of 44

Cloud Point

Pour Point

Cetane Index

Coefficient Correlation

Coefficient

Correlation

Coefficient

Correlation

n-Paraffins

1.78

0.71

2.42

0.70

1.32

0.88

iso-Paraffins

-2.50

-0.69

-3.20

-0.63

0.37

-0.41

mono-

-0.69

-0.11

-1.64

-0.14

-1.40

-0.90

1.85

0.64

3.35

0.73

-0.06

0.37

-0.74

-0.43

-1.30

-0.48

0.12

-0.14

Aromatics polyAromatics Naphthenes

Table 6. Equation coefficients for cloud point, pour point and cetane index based on composition analysis. The coefficients are given for mean centered and standard deviation scaled composition values. Correlation values show how well the components correlate with physical property data.

35


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Energy & Fuels

*X axis: Retension Time (RT), Y axis: Modulation preiod (3.5 sec)

Figure 1. Typical Two Dimensional GC×GC image for Kerosene sample

36


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 44


Figure 2. Typical Two Dimensional GC×GC image for Diesel sample

37


Page 39 of 44 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels


Figure 3. Typical GC×GC -3D image for Kerosene sample

38


Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 40 of 44


Figure 4. Typical GC×GC- 3D image for Diesel sample

39


Page 41 of 44

n-Paraffin

i-Paraffin

Naphthenes

Mono-aromatics

Poly-aromatics

100 90

Percentage distribution

80 70 60 50 40 30 20 10

K-10

K-9

K-8

K-7

K-6

K-5

K-4

K-3

K-2

K-1

D-15

D-14

D-13

D-12

D-11

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Figure 5. Graphical representation of n-Paraffins, i-paraffins, total paraffins, naphthenes, mono-aromatics, poly-aromatics, and total aromatics distribution in various diesel and kerosene samples

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Figure 6. Cloud point model plots (1) Experiment (black) vs predicted (red) plot for prediction on calibration data. (2) Parity plot of experimental and predicted values in calibration data. The boundaries show experimental error margins. (3) Parity plot for validation data. (4) Cross validation error for best principal component in PLS analysis. (5) Experiment (black) vs predicted (red) plot for prediction on validation data set, and (6) Error plot for prediction vs actual data.

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Energy & Fuels

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Figure 7. Pour point model plots (1) Experiment (black) vs predicted (red) plot for prediction on calibration data. (2) Parity plot of experimental and predicted values in calibration data. The boundaries show experimental error margins. (3) Parity plot for validation data. (4) Cross validation error for best principal component in PLS analysis. (5) Experiment (black) vs predicted (red) plot for prediction on validation data set, and (6) Error plot for prediction vs actual data.

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Figure 8. Cetane index plots (1) Experiment (black) vs predicted (red) plot for prediction on calibration data. (2) Parity plot of experimental and predicted values in calibration data. The boundaries show experimental error margins. (3) Parity plot for validation data. (4) Cross validation error for best pricipal component in PLS analysis. (5) Experiment (black) vs predicted (red) plot for prediction on validation data set, and (6) Error plot for prediction vs actual data.

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Property Prediction of Diesel Fuel Based on the Composition Analysis

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