METHOD FOR DETERMINING THE COMPOSITION AND PROPERTIES OF HYDROCARBON FRACTIONS BY SPECTROSCOPY OR SPECTROMETRY

Abstract

This invention relates to a system and method for the evaluation of samples of a distillate fraction by spectroscopic analysis, followed by the application of chemometrics software to determine physical characteristics of the fraction.

Claims

1. A system for evaluating a sample of a distillate fraction and determining at least one property or composition of the distillate fraction, the system comprising: a spectrometer that performs a spectrographic analysis of the sample, the spectrometer being a selected one of (i) a near infrared (NIR) spectrometer, (ii) a Fourier transform infrared (FTIR) spectrometer, (iii) a nuclear magnetic resonance (NMR) spectrometer, (iv) an ultraviolet visible (UV-Vis) spectrometer, (v) a Fourier transform-ion cyclotron-mass spectrometer (FT-ICR MS), (vi) a time-of-flight mass spectrometer (TOF-MS), (vii) a laser inducted UV spectrometer, and (viii) a fluorescence spectrometer; a non-volatile memory device that stores software program modules and data, the data including spectroscopic data as derived by the analysis of the sample by the spectrometer; a processor coupled to the non-volatile memory device; a first software program module that is stored in the non-volatile memory device and that is executed by the processor, the first software module predicting values of at least two compositional variables of the sample from the spectroscopic data, and storing the predicted values of the compositional variables into the non-volatile memory device, wherein the at least two compositional variables are selected from (i) paraffins, (ii) naphthenes, and (iii) aromatics; a second software program module that is stored in the non-volatile memory device and that is executed by the processor, the second software program module using chemometrics to analyze the at least two compositional variables predicted by the first software program module and to predict a value by correlation of the at least one property or composition, wherein the at least one property is selected of (i) density, (ii) refractive index, (iii) viscosity at 40 C., (iv) cetane number, (v) cetane index, (vi) distillation, (vii) octane number, (viii) cloud point, (ix) pour point, and (x) aniline point, and wherein the at least one composition is selected of (i) hydrogen, (ii) carbon, (iii) paraffins, (iv) naphthenes, and (v) aromatics; and wherein the second software program module will store the predicted value of the at least one property or composition into the non-volatile memory device.

2. The system of claim 1, wherein the spectrometer is a near infrared (NIR) spectrometer, and the wavenumber of the spectrum is in the range of 4,000-12,821 cm.sup.1.

3. The system of claim 1, wherein the spectrometer is a Fourier transform infrared (FTIR) spectrometer, and the wavelength is in the range of 650-2000 cm.sup.1.

4. The system of claim 1, wherein the spectrometer is a nuclear magnetic resonance (NMR) spectrometer, that is carbon- or hydrogen-based.

5. The system of claim 1, wherein the spectrometer is an ultraviolet visible (UV-Vis) spectrometer, and the wavelength is in the range of 220-900 nm.

6. The system of claim 1, wherein the spectrometer is a Fourier transform-ion cyclotron-mass spectrometer (FT-ICR MS), covering masses in the range of 150-1400 m/z.

7. The system of claim 1, wherein the spectrometer is a Fourier transform-ion cyclotron-mass spectrometer (FT-ICR MS), covering carbon numbers in the range 1-60.

8. The system of claim 1, wherein the spectrometer is a time-of-flight mass spectrometer (TOF-MS), operating at 5-20 kHz repetition rates.

9. The system of claim 1, wherein the spectrometer is a fluorescence spectrometer, with a wavelength in the range of 250-800 nm.

10. The system of claim 1, wherein the distillate fraction is a selected one of (i) raw petroleum, (ii) processed petroleum, (iii) coal, (iv) coal liquid, (v) biomaterials, and (vi) synthetic crude oil.

11. A method for evaluating a sample of a distillate fraction and determining at least one property or composition of the distillate fraction, the method comprising: providing a spectrometer, being a selected one of (i) a near infrared (NIR) spectrometer, (ii) a Fourier transform infrared (FTIR) spectrometer, (iii) a nuclear magnetic resonance (NMR) spectrometer, (iv) an ultraviolet visible (UV-Vis) spectrometer, (v) a Fourier transform-ion cyclotron-mass spectrometer (FT-ICR MS), (vi) a time-of-flight mass spectrometer (TOF-MS), (vii) a laser inducted UV spectrometer, and (viii) a fluorescence spectrometer; providing a non-volatile memory device that stores software program modules and data, including at least a first software program module and a second software program module; providing a processor coupled to the non-volatile memory device; conducing a spectrographic analysis of the sample by the spectrometer; executing the first software program module by the processor to predict values of at least two compositional variables of the sample from the spectroscopic data, and storing the predicted values of the at least two compositional variables into the non-volatile memory device, wherein the at least two compositional variables are selected from (i) paraffins, (ii) naphthenes, and (iii) aromatics; executing the second software program module by the processor to use chemometrics to analyze the at least two compositional variables predicted by the first software program module and to predict a value by correlation of the at least one property or composition, and storing the predicted value of the at least one property or composition into the non-volatile memory device, wherein the at least one of the properties are selected of (i) density, (ii) refractive index, (iii) viscosity at 40 C., (iv) cetane number, (v) cetane index, (vi) distillation, (vii) octane number, (viii) cloud point, (ix) pour point, and (x) aniline point, and the at least one of the compositions selected of (i) hydrogen, (ii) carbon, (iii) paraffins, (iv) naphthenes, and (v) aromatics.

12. The method of claim 11, wherein the spectrometer is a near infrared (NIR) spectrometer, and the wavenumber of the spectrum is in the range of 4,000-12,821 cm.sup.1.

13. The method of claim 11, wherein the spectrometer is a Fourier transform infrared (FTIR) spectrometer, and the wavelength is in the range of 650-2000 cm.sup.1.

14. The method of claim 11, wherein the spectrometer is a nuclear magnetic resonance (NMR) spectrometer, that is carbon- or hydrogen-based.

15. The method of claim 11, wherein the spectrometer is an ultraviolet visible (UV-Vis) spectrometer, and the wavelength is in the range of 220-900 nm.

16. The method of claim 11, wherein the spectrometer is a Fourier transform-ion cyclotron-mass spectrometer (FT-ICR MS), covering masses in the range of 150-1400 m/z.

17. The method of claim 11, wherein the spectrometer is a Fourier transform-ion cyclotron-mass spectrometer (FT-ICR MS), covering carbon numbers in the range 1-60.

18. The method of claim 11, wherein the spectrometer is a time-of-flight mass spectrometer (TOF-MS), operating at 5-20 kHz repetition rates.

19. The method of claim 11, wherein the spectrometer is a fluorescence spectrometer, with a wavelength in the range of 250-800 nm.

20. The method of claim 11, wherein the distillate fraction is a selected one of (i) raw petroleum, (ii) processed petroleum, (iii) coal, (iv) coal liquid, (v) biomaterials, and (vi) synthetic crude oil.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Further advantages and features of the present invention will become apparent from the following detailed description of the invention when considered with reference to the accompanying drawings in which:

[0027] FIG. 1 is a process flow diagram of steps of a method in which an embodiment of the invention is implemented;

[0028] FIG. 2 is a schematic block diagram of modules of an embodiment of the invention; and

[0029] FIG. 3 is a block diagram of a computer system in which an embodiment of the invention is implemented.

DETAILED DESCRIPTION OF INVENTION

[0030] A system and a method are provided for analyzing a distillate fraction to determine physical characteristics of the fraction. The fraction is first analyzed by a spectroscope or spectrometer. From the spectroscopic data, chemometrics software is employed to determine physical characteristics such as boiling point, paraffin content, naphthene content, aromatic content, hydrogen content, and carbon content.

[0031] In a refinery or pilot plant that processes a distillate fraction, such as diesel, a very small amount of the distillate fraction, such as 2 ml, is sampled. This sampling is preferably accomplished with an online, continuous measurement. Alternatively, an off-line measurement is used to obtain the sample.

[0032] The sample is then analyzed by a spectroscope or spectrometer, at least the following types of which are suitable: near infrared (NIR) spectrometer, Fourier transform infrared (FTIR) spectrometer, nuclear magnetic resonance (NMR) spectrometer, ultraviolet visible (UV-Vis) spectrometer, Fourier transform-ion cyclotron-mass spectrometer (FT-ICR MS), time-of-flight mass spectrometer (TOF-MS), laser inducted UV spectrometer, or fluorescence spectrometer.

[0033] In developing this invention, diesel samples from nine difference sources were distilled from different crude oils. A batch distiller was used to recover naphtha and diesel. The nominal diesel cut point range is 180-370 C. The properties of these samples were determined using the standard methods. Table 3 summarizes the composition and properties of one of these nine gas oils as an example.

TABLE-US-00003 TABLE 3 Code Unit Method 6880 Density g/cm3 ASTM D4052 0.8423 Refractive index @ 20 C. ASTM D1218 1.470 Viscosity @ 40 C. cSt ASTM D445 2.89 Hydrogen W % ASTM D5291 13.21 Carbon W % ASTM D5291 85.27 Cetane Number ASTM D613 60 Paraffins W % ASTM D2425 44.5 Naphthenes W % ASTM D2425 23.4 Aromatics W % ASTM D2425 32.1 Distillation C. 0 W % ASTM D2887 141 5 W % ASTM D2887 188 10 W % ASTM D2887 204 30 W % ASTM D2887 249 50 W % ASTM D2887 285 70 W % ASTM D2887 319 90 W % ASTM D2887 351 95 W % ASTM D2887 364 100 W % ASTM D2887 400 1H NMR-Aromatic proton % NMR 4.57

[0034] Seven gas oil blends derived from various crude oils by true boiling point distillation were used to predict the paraffins, naphthenes and aromatics contents using an FTIR spectrometer and chemometrics. The FTIR spectrum was collected for each diesel sample that was previously analyzed according to the standard test methods as shown in Table 1, and the resulting spectroscopic data was analyzed by chemometrics to predict the composition of the oil samples. Table 4 tabulates the results for paraffins, naphthenes, and aromatics, showing the actual values (as determined by the ASTM methods), the predicted values (based upon the chemometric analysis of spectroscopic data), and average deviations for each. As seen, the concentration of the oil components can be predicted almost as accurately as measured by the ASTM method. The average absolute deviation (AAD) is calculated as shown in equation 1:

[00001]$\begin{array}{cc}A.Math..Math.A.Math..Math.D=\mathrm{absolute}\left[\frac{\left(\mathrm{actual}.Math..Math.\mathrm{value}-\mathrm{predicted}.Math..Math.\mathrm{value}\right)}{\mathrm{actual}.Math..Math.\mathrm{value}}*100\right];& \left(1\right)\end{array}$

TABLE-US-00004 TABLE 4 Paraffins Naphthenes Aromatics Code Actual Predicted AAD Actual Predicted AAD Actual Predicted AAD F-322 22.4 22.5 0.4 48.8 48.9 0.2 28.8 27.4 4.9 F-328 38.7 38.5 0.4 33.1 33.1 0.0 28.2 28.0 0.7 F-327 40.8 40.9 0.3 35.7 35.3 1.1 23.5 23.3 0.9 F-320 44.5 44.7 0.5 23.4 23.5 0.4 32.1 32.4 0.9 F-318 46.1 46.2 0.2 24.8 24.9 0.4 29.1 28.2 3.0 F-324 47.4 47.2 0.5 24.4 24.5 0.4 28.2 28.6 1.4 F-321 47.5 47.5 0.0 25.0 25.1 0.4 27.5 28.9 5.1 Average 0.3 0.4 2.4 Reproducibility 5 4 3

[0035] The paraffins, naphtenes and aromatics composition of the oil fractions were estimated with high accuracy, AAD of 0.3 W %, 0.4 W % and 2.4 W %, respectively. The average values are well below the method's reproducibility values except that of aromatics. The average AAD for aromatics was 2.4% and the reproducibility for the method is 1.4 W %.

[0036] The correlation is performed against the spectral data through statistical analysis or trends, using statistical methods such as classical least-squares (CLS), inverse least squares (ILS), principal-component regression (PCR), artificial neural network (ANN), partial least-squares (PLS) and net-analyte signal (NAS). When a spectrometer other than the cited FTIR spectrometer is used, the spectra are different, but the same chemometrics methods can be used.

[0037] In this example, the Partial Least Squares (PLS) regression was used to correlate the spectroscopic data to middle distillate (MD) property values (for each property). The PLS algorithm was run from Nicolets's TQ Anlayst Software package.

[0038] The PLS method creates a simplified representation of the spectroscopic data by a process known as spectral decomposition.

[0039] The PLS algorithm initially calculates a property value (naphthenes, aromatics . . . etc.), or weighted average spectrum of all spectra of the MDs in the calibration matrix.

[0040] This statistical analysis requires calibration and validation.

[0041] In the calibration procedure, the software searches for a relation between the dependent variable, Y (peak height), and the independent variable, X (property) which can be generically written as: Y=f(X1, X2, X3 . . . Xp).

[0042] In practice, an algorithm, based on PLS, calculates the regression coefficients of the following equation:

Y=b0+b1X1+b2X2+ . . . bpXp(2);

[0043] This defines the mathematical model of the system under investigation. The second step is a so-called leave-one-out cross-validation procedure that is used to verify the calibration model.

[0044] FTIR and multivariable calibration methods accuracy was established by evaluating Root Mean Square Error of Prediction (RMSEP), Root Mean Square Error of Calibration (RMSEC) and Correlation coefficient (R.sup.2), and after cross-validation, Root Mean Square Error of Cross Validated error of calibration (RMSECV) and the correlation coefficient (R.sup.2) added as statistical evaluation parameters

[0045] These calculations have been done by the software.

[0046] FTIR and multivariable calibration methods accuracy was established by evaluating the root mean square error of prediction (RMSEP), the root mean square error of calibration (RMSEC) and the correlation coefficient (R.sup.2) and after cross-validation, cross validated error of calibration (RMSECV) and the correlation coefficient (R.sup.2) added as statistical evaluation parameters.

[0047] It is critical to establish the correct number of factors to be used in the correlation files, as the predicted MD property values calculated from the model depend on the number of factors used in the model. Too few factors will not adequately model the system, while too many factors will introduce noise vectors in the calibration. These noise vectors will result in less than optimum prediction for samples outside the calibration set. The Nicolet TQ Analyst program provides RMSECV data by plotting the Predicted Residual Error Sum of Squares (PRESS, which is a factor analysis method) versus model factors (1-10) to select the appropriate factor.

[0048] Predicted Residual Error Sum of Squares, PRESS measures how well the calibration model predicts the property value as each factor is added, defined as:

[00002]$\begin{array}{cc}\mathrm{PRESS}=\underset{i=1}{\overset{n}{.Math.}}.Math.{\left(y_i-{\hat{y}}_i\right)}^2=\underset{i=1}{\overset{n}{.Math.}}.Math.e_i^2;& \left(3\right)\end{array}$

[0049] Where y.sub.i is the actual value of y for object i and the y-value .sub.i for object i predicted with the model under evaluation, is the residual for object i (the y difference between the predicted and the actual y-value) and n is the number of objects for which I is obtained by prediction. The PRESS graph produced by TQ analyst software.

[0050] The mean squared error of prediction (MSEP) is defined as the mean value of PRESS:

[00003]$\begin{array}{cc}\mathrm{MSEP}=\frac{\mathrm{PRESS}}{n}=\frac{\underset{i=1}{\overset{n}{.Math.}}.Math.{\left(y_i-{\hat{y}}_i\right)}^2}{n}=\frac{\underset{i=1}{\overset{2}{.Math.}}.Math.e_i^2}{n}& \left(4\right)\end{array}$

[0051] Root mean squared error of prediction (RMSEP) is the MSEPs square root:

[00004]$\begin{array}{cc}\mathrm{RMSEP}=\sqrt{\mathrm{MSEP}}=\sqrt{\frac{\underset{i=1}{\overset{n}{.Math.}}.Math.{\left(y_i-{\hat{y}}_i\right)}^2}{n}}=\sqrt{\frac{\underset{i=1}{\overset{n}{.Math.}}.Math.e_i^2}{n}}& \left(5\right)\end{array}$

[0052] In the chemometrics literature, it seems that RMSEP values are preferred, partly because they are given in the same units as the y-variable.

[0053] Root Mean Standard Error of Calibration for Cross Validation, RMSECV the cross-validation process accompanies the construction of the full model, which uses the complete set of the loaded data. Consequently, once the analysis is executed, the cross-validation results and plots will be made available along with the results and the plots for the full model. For example, the model results window in the analysis will include the Root Mean Square Error of Cross-Validation (RMSECV) value along with the Root Mean Square Error of Calibration (RMSEC) value. The RMSECV is defined as

[00005]$\begin{array}{cc}\mathrm{RMSECV}=\sqrt{\frac{\underset{i=1}{\overset{n}{.Math.}}.Math.{\left(y_i-{\hat{y}}_i\right)}^2}{n}}& \left(6\right)\end{array}$

[0054] Where contains the values of the Y variable that are estimated by cross-validation (where the value for each object i is estimated using a model that was built using a set of objects that does not include object i), y contain the known values of the Y variable, and n is the total number of objects in the data set.

[0055] Square of the Multiple Correlation Coefficient, R.sup.2, The multiple correlation coefficient, R.sup.2, is a measure of how well a linear model fits a given set of data, and has a value between 0 and 1. If the estimated and known values are very similar, then a good fit is achieved and R.sup.2 will be close to 1. A poor fit will give an R.sup.2 closer to 0. R.sup.2 can be interpreted as the total variability in y (MD property values) that is explained by x (spectral data). However, caution must be taken in using R.sup.2 because a large value for R.sup.2 does not necessarily mean a good fit between the model and the data. In Equation 7, for a given MD property of sample y.sub.i, we have the known value from the standard method (ASTM) and the y.sub.i (predicted value by the model) and y (the mean of all y.sub.i), and n (the number of samples).

[00006]$\begin{array}{cc}{R}^2=1-\frac{\underset{i=1}{\overset{}{.Math.}}.Math.{\left(y_i-y_i^{}\right)}^2}{\underset{i=1}{\overset{}{.Math.}}.Math.{\left(y_i-y_i^{}\right)}^2};& \left(7\right)\end{array}$

[0056] Principal component analysis (PCA) is a statistical procedure and PCA is sensitive to the relative scaling of the original variables. PCA is the simplest of the true eigenvector-based multivariate analyses. Often, its operation can be thought of as revealing the internal structure of the data in a way that best explains the variance in the data. By using this method you can observe unknown samples behaviors/distribution in multivariate analysis and these calculations had been done for this work using the mentioned software.

[0057] Finally, additional physical properties and compositional values of the distillate fraction are estimated by correlations as a function of at least two of the three parameters of paraffin, naphthene, and aromatic content. Table 5 shows the AAD for density, cetane number, hydrogen, aromatic hydrogen determined by NMR, viscosity at 40 C., refractive index, and distillation 50 W % point. As seen, the method predicts composition and properties of the oil fractions accurately. Note that aromatic hydrogen determined by NMR is different from the aromatic composition. The aromatic composition refers to the whole aromatic molecule, whereas the aromatic hydrogen refers only to the hydrogen bonded to the aromatic rings.

TABLE-US-00005 TABLE 5 Dist. Cetane Aromatic Viscosity Refractive 50 W % S# Density Number Hydrogen Hydrogen @40 C. Index Point 318 0.05 0.29 0.11 1.13 2.24 0.02 0.42 320 0.01 0.00 0.03 0.11 0.48 0.01 0.06 321 0.04 0.30 0.01 0.74 0.46 0.01 0.21 322 0.01 0.02 0.01 0.01 0.04 0.00 0.00 323 0.17 0.21 0.20 0.21 2.25 0.05 0.20 324 0.04 0.57 0.03 1.59 1.63 0.00 0.49 325 0.01 0.00 0.00 0.00 0.01 0.01 0.00 327 0.02 0.02 0.02 0.03 0.28 0.01 0.03 328 0.13 0.22 0.15 0.02 1.44 0.04 0.09 Average 0.05 0.18 0.06 0.43 0.98 0.14 1.51

[0058] FIG. 1 shows a process flowchart of steps in a method according to one embodiment herein. In step 110, a sample of approximately 2 ml of a distillate fraction is obtained. This sampling is preferably accomplished with an online, continuous measurement. Alternatively, an off-line measurement is used to obtain the sample.

[0059] In step 120, the sample is subjected to spectroscopic analysis by a spectrometer, such as a near infrared (NIR) spectrometer, Fourier transform infrared (FTIR) spectrometer, nuclear magnetic resonance (NMR) spectrometer, ultraviolet visible (UV-Vis) spectrometer, Fourier transform-ion cyclotron-mass spectrometer (FT-ICR MS), time-of-flight mass spectrometer (TOF-MS), laser inducted UV spectrometer, or fluorescence spectrometer. The spectroscopic data is stored into a non-volatile memory device.

[0060] In a preferred embodiment, the spectrometer is a near infrared (NIR) spectrometer, and the wavenumber of the spectrum is in the range of 4,000-12,821 cm.sup.1.

[0061] In a preferred embodiment, the spectrometer is a Fourier transform infrared (FTIR) spectrometer, and the wavelength is in the range of 650-2000 cm.sup.1.

[0062] In a preferred embodiment, the spectrometer is a nuclear magnetic resonance (NMR) spectrometer, that is carbon- or hydrogen-based.

[0063] In a preferred embodiment, the spectrometer is an ultraviolet visible (UV-Vis) spectrometer, and the wavelength is in the range of 220-900 nm.

[0064] In a preferred embodiment, the spectrometer is a Fourier transform-ion cyclotron-mass spectrometer (FT-ICR MS), covering masses in the range of 150-1400 m/z.

[0065] In a preferred embodiment, the spectrometer is a Fourier transform-ion cyclotron-mass spectrometer (FT-ICR MS), covering carbon numbers in the range 1-60.

[0066] In a preferred embodiment, the spectrometer is a time-of-flight mass spectrometer (TOF-MS), operating at 5-20 kHz repetition rates.

[0067] In a preferred embodiment, the spectrometer is a fluorescence spectrometer, with a wavelength in the range of 250-800 nm.

[0068] In step 130, at least two compositional variable parameters of the fraction are predicted from the results of the spectroscopic analysis, with the at least two compositional variable parameters being selected from the paraffins, naphthenes, aromatics. These at least two compositional variable parameters are stored into the non-volatile memory device. As discussed earlier, in one example, several samples that were previously analyzed according to standard test methods were then analyzed by FTIR spectrometer, and the resulting data were then used to establish calibration models with the corresponding compositional variable parameters obtained from the standard methods. In that way, use of an FTIR spectrometer and chemometrics on an unknown sample can be used to predict the composition of the unknown sample.

[0069] Multivariate calibration is an effective calibration method in which the chemical information, such as absorption, emission, transmission, etc., of a set of standard mixtures recorded at different variables (wavenumbers) are related to the concentration of the chemical compounds present in the mixtures. The correlation is performed against the spectral data through statistical analysis or trends, using statistical methods such as classical least-squares (CLS), inverse least squares (ILS), principal-component regression (PCR), artificial neural network (ANN), partial least-squares (PLS) and net-analyte signal (NAS). When a spectrometer other than the cited FTIR spectrometer is used, the spectra are different, but the same chemometrics methods can be used.

[0070] In step 140, the at least two compositional variable parameters are analyzed by chemometrics software, which by correlation identifies at least one property or composition of the sample. The at least one property or composition can include density, with values comparable to those that could conventionally be determined by the ASTM D4052 method; refractive index, with values comparable to those that could conventionally be determined by the ASTM D1218 method; viscosity, with values comparable to those that could conventionally be determined by the ASTM D445 method; hydrogen and/or carbon content, with values comparable to those that could conventionally be determined by the ASTM D5291 method; cetane number, with values comparable to those that could conventionally be determined by the ASTM D613 method; cetane index, with values comparable to those that could conventionally be determined by the ASTM D973 method; distillation, with values comparable to those that could conventionally be determined by the ASTM D2887 method; octane number, with values comparable to those that could conventionally be determined by the ASTM D2699 or D2700 methods; cloud point, with values comparable to those that could conventionally be determined by the ASTM D2500 method; pour point, with values comparable to those that could conventionally be determined by the ASTM D97 method; and aniline point with values comparable to those that could conventionally be determined by the ASTM D611 method.

[0071] In addition, where the at least two compositional variable parameters are paraffins and naphthenes, the aromatics can be identified in step 140. Similarly, where the at least two composition variable parameters are paraffins and aromatics, the naphthenes can be identified in step 140. Similarly, where the at least two composition variable parameters are naphthenes and aromatics, the paraffins can be identified in step 140. For each of these, the values identified would be comparable to those otherwise determinable by the ASTM D2425 method. The properties identified in step 140 are stored into the non-volatile memory device.

[0072] The distillate fractions can be derived from raw or processed petroleum, coal, coal liquid, biomaterials, or synthetic crude oils.

[0073] In a preferred embodiment, the composition and/or property determinations are carried out at ambient temperatures and pressures, such as 20 C. and 1 bar.

[0074] FIG. 2 illustrates a schematic block diagram of modules in accordance with an embodiment of the present invention, system 200. Raw data receiving module 210 receives the spectroscopic data derived from the spectroscopic analysis of the sample of the distillate, and saves the spectroscopic data into the non-volatile memory device.

[0075] First software program module 220 receives the spectroscopic data as input, and predicts values of at least two of the compositional variable parameters of paraffins, naphthenes, aromatics. These predicted values of the at least two compositional variable parameters are stored into the non-volatile memory device.

[0076] Second software program module 230 receives the at least two compositional variable parameters, and applies chemometrics to predict, by correlation, values of at least one property or composition of the distillate, such as discussed above. The predicted values of the at least one property or properties identified are stored into the non-volatile memory device.

[0077] FIG. 3 shows an exemplary block diagram of a computer system 300 in which one embodiment of the present invention can be implemented. Computer system 300 includes a processor 320, such as a central processing unit (CPU), an input/output interface 330 and support circuitry 340. In certain embodiments, where the computer system 300 requires a direct human interface, a display 310 and an input device 350 such as a keyboard, mouse or pointer are also provided. The display 310, input device 350, processor 320, and support circuitry 340 are shown connected to a bus 390 which also connects to a memory 360. Memory 360 includes program storage memory 370 and data storage memory 380. Note that while computer system 300 is depicted with direct human interface components display 310 and input device 350, programming of modules and exportation of data can alternatively be accomplished over the input/output interface 330, for instance, where the computer system 300 is connected to a network and the programming and display operations occur on another associated computer, or via a detachable input device as is known with respect to interfacing programmable logic controllers.

[0078] Program storage memory 370 and data storage memory 380 can each comprise volatile (RAM) and non-volatile (ROM) memory units and can also comprise hard disk and backup storage capacity, and both program storage memory 370 and data storage memory 380 can be embodied in a single memory device or separated in plural memory devices. Program storage memory 370 stores software program modules and associated data, and in particular stores the raw data receiving module 210, first software program module 220, and second software program module 230. Data storage memory 380 stores results and other data generated by the software program modules of the present invention.

[0079] It is to be appreciated that the computer system 300 can be any computer such as a personal computer, minicomputer, workstation, mainframe, a dedicated controller such as a programmable logic controller, a tablet or smart phone, or a combination thereof. While the computer system 300 is shown, for illustration purposes, as a single computer unit, the system can comprise a group of computers which can be scaled depending on the processing load and database size.

[0080] Computer system 300 preferably supports an operating system, for example stored in program storage memory 370 and executed by the processor 320 from volatile memory. According to an embodiment of the invention, the operating system contains instructions for interfacing computer system 300 to the Internet and/or to private networks.

[0081] In alternate embodiments, the present invention can be implemented as a computer program product for use with a computerized computing system. Those skilled in the art will readily appreciate that programs defining the functions of the present invention can be written in any appropriate programming language and delivered to a computer in any form, including but not limited to: (a) information permanently stored on non-writeable storage media (e.g., read-only memory devices such as ROMs or CD-ROM disks); (b) information alterably stored on writeable storage media (e.g., floppy disks and hard drives); and/or (c) information conveyed to a computer through communication media, such as a local area network, a telephone network, or a public network such as the Internet. When carrying computer readable instructions that implement the present invention methods, such computer readable media represent alternate embodiments of the present invention.

[0082] As generally illustrated herein, the system embodiments can incorporate a variety of computer readable media that comprise a computer usable medium having computer readable code means embodied therein. One skilled in the art will recognize that the software associated with the various processes described can be embodied in a wide variety of computer accessible media from which the software is loaded and activated. Pursuant to In re Beauregard, 35 U.S.P.Q.2d 1383 (U.S. Pat. No. 5,710,578), the present invention contemplates and includes this type of computer readable media within the scope of the invention. In certain embodiments, pursuant to In re Nuijten, 500 F.3d 1346 (Fed. Cir. 2007) (U.S. patent application Ser. No. 09/211,928), the scope of the present claims is limited to computer readable media, wherein the media is both tangible and non-transitory.

[0083] The system and method of the present invention have been described above and with reference to the attached figures; however, modifications will be apparent to those of ordinary skill in the art and the scope of protection for the invention is to be defined by the claims that follow.