Property determination

Description

This invention relates to a method of determining or predicting by near infra red (NIR) spectroscopy properties of feeds or products and/or yields in physical or chemical processes or separations, in particular involving hydrocarbons, especially in hydrocarbon refineries
BACKGROUND OF THE INVENTION
NIR spectroscopy has many advantages over other methods of analysis in refineries and can cover a large number of repetitive applications accurately, quickly and on line. The NIR region between 800 and 2500 nm contains the totality of molecular information in the form of combinations and overtones from polyatomic vibrations, but Mathematical techniques are needed to exploit this information and to calculate the desired parameters. U.S. Pat. Nos. 5,490,085; 5,452,232; and 5,475,612 the disclosure of which is hereby incorporated by reference, describe the use of NIR for determining octane number, yields and/or properties of a product of a chemical process or separation process from analysis on the feeds to that process, and yields and/or properties of a product of a blending operation again from analysis on the feed thereto.
At present, numerical methods described for modelling physicochemical properties based on NIR spectra all are of a correlative nature and involve relations of a regressional character between the property(ies) studied. Among these multivariable analyses are multilinear regression (MLR), Principle Component Regression (PLR), Canonic regression, and regression by Partial Least Squares (PLS). In all cases there is sought between the property and the NIR spectrum a relation which may be linear but is usually quadratic or of higher algebraic form involving regression coefficients applied to each absorption. The establishment of any regression requires a progressive calibration, as the approach is empirical and not supported by a theory.
These techniques have disadvantages, the chief of which is the need for establishing a strong correlation between the spectrum and the property, and their difficulty in dealing with positive or negative synergy between components contributing to that property. For example for determining chemical composition e.g. LINA (linear, isoparaffin, Naphthenic, Aromatics) in a hydrocarbon feed to a catalyst reformer, a PLS technique based on the NIR spectra has been described for use. The model works well on the calibration set but the response of the models when pure hydrocarbons are added e.g. cyclohexane is not satisfactory, as the model predicts changes in isoparaffins and naphthenes the reverse of that found experimentally Furthermore there are other practical difficulties, mainly in the need to identify samples of families having the same kind of relation between the spectra and the properties to be modelled. Thus the model may be limited especially with a non linear relation between spectrum and property. Especially when at the edges of the available data the accuracy of the model diminishes. The stability of the model is also a problem, as is the need when adding new standards to do laborious revisions to give the new model, especially when adjusting to a new feedstock for a process; thus testing 6 properties on 4 products leaving a distillation unit requires 24 models, each of which has to be changed for each change of the feed not included in the calibration.
We have discovered a new approach avoiding the above problems with correlations, and regression calculations, and being capable of being expanded automatically with use of a new product of different quality.
SUMMARY OF THE INVENTION
The present invention provides a method of determining or predicting a value Px, of a property of a material X or a property of a product of a process from said material or yield of said process, which method comprises measuring the absorption D.sub.i x of said material at more than one wavelength in the region 600-2600 nm, comparing the said absorptions or a derivative thereof with absorptions D.sub.i m or derivatives thereof at the same wavelengths for a number of standards S in a bank for which the said property or yield P is known, and choosing from the bank at least one and preferably at least 2 standard S.sub.m with property P.sub.m, said standard S.sub.m having the smallest average values of the absolute values of the difference at each wavelength i between the absorption D.sub.i x (or derivative thereof) for the material and the absorption D.sub.i m (or derivative thereof) for the standard S.sub.m to obtain value P.sub.x, and with averaging of said properties or yields Pm, when more than 1 standard S.sub.m is chosen.
The above method can be performed without regression or correlation techniques.

BRIEF DESCRIPTION OF THE DRAWINGS
The invention is illustrated in the accompanying Figures in which:
FIG. 1 represents a schematic diagram showing apparatus for use in the invention.
FIG. 2 represents a schematic block flow diagram for the method of the invention.

In FIG. 1, an optical fibre or tube 3 links a spectrometer 2 and a probe 6 in or at process line 1. The spectrophotometer 2 produces absorbance signals at more than 1 wavelength, which signals are passed via line 4 to computer 5, where the signals as such or after conversion to one or more derivative signals, are used to enable the computer to access the databank 7 of standard absorptions and properties/yields therein. The signals are compared to those of the standard absorptions as described above and one or more standard absorption(s) and its/their corresponding property(ies) or yield(s). The output of the computer 5 may be in the form of spectral absorbancies or a property or yield of the product in line 1 and may be printed in hard copy. Preferably however, the output as a signal is used to control the process involved with the product in line 1. i.e. for which line 1 is a feed or a product line; in this case the computer 9 is linked to and instructs the controller 9 which, via line 10, controls that process by acting on operating conditions e.g. via valves/temperature and/or pressure controls in line 1 or in relation to line 1. By this means the property or yield of product in line 1 can be optimized.
In FIG. 2, the initial operation 11 is to measure the absorption of the unknown, after which in the second step 12, the absorptions are compared to absorptions in spectra of standards, and in the third step 13, the spectra of the standards Sm are chosen according to criteria described above, and then in step 14, the property(ies) of the standard(s) Sm chosen is used to obtain the desired property or yield. If the spectrum of only 1 standard Sm is chosen, then the value P.sub.x of the unknown is the same as that of that standard Pm. If more than 1 spectrum is chosen, the value P.sub.x of the unknown is the average of the values Pm of the standards. If desired in an optional step 15, the value P.sub.x is compared to the desired value for the unknown and in step 16 the process involving the unknown is adjusted to make the value P.sub.x the same as the desired value.
DETAILED DESCRIPTION OF THE INVENTION
Thus for the performance of the method of the invention, a bank is prepared in which the NIR spectra are recorded at many wavelengths for a large number of standard materials, together with their properties (or those of products obtained by processes therefrom) determined by alternative techniques e.g. gaschromatography for chemical compositions and yields determined by known methods. The standards are chosen to cover the area in which the method is to be used, so for octane number determination, a range of gasolines is chosen of widely varying octane numbers, with different contents of lead, or other additives such as alkyl ethers and aromatics. The number of wavelengths chosen may be 2-1000 e.g. 5-200 or 10-20 such as 40-80 while the number of standards can be at least 100 or 1000, or 100,000 up to 5 million depending on property(ies) chosen.
The wavelengths chosen may be at regular intervals such as each 1-50 or 15-35 nm (or each 1-5 nm or each nanometer) or may be at irregular intervals e.g. with intervals of 1-200 nm e.g. 1-100 or 1-50 such as 4-50 or 10-60 nm, which may be random or chosen because of a change in the shape of the spectral curve at that wavelength e.g. a peak, trough or shoulder. The wavelengths may be in the region 600-2600 nm, such as 800-2600 nm, in particular 1500-2600 or 2000-2550 nm, or 800-2000 especially 1000-1800 nm for diene containing gasolines such as ones produced by cracking e.g. steam cracking. The wavenumbers may be in the region 16,600-3840 cm.sup.-1, e.g. 12,500 to 3840 cm.sup.-1 in particular 6660-3840 or 5000-3900 cm.sup.-1, or 12500-5000 especially 10000-5500 cm.sup.-1 ; corresponding frequencies in Hertz can be obtained by multiplying this wavelength by 3.times.10.sup.10 cm/sec.
The absorptions for the unknown sample are compared with the absorptions at the same wavelength of the standards, and those standards chosen having the smallest differences. The properties of those chosen standards are then averaged to determine the property of the unknown sample. The absorptions at more than one wavelength may be chosen, e.g. 2-1000 such as 5-100 or 10-20.
In the method of the invention the standards chosen are those with the smallest average values of the absolute difference at each wavelength i between the absorption/optical density (or a derivative thereof) D.sub.ix for the unknown material and the corresponding absorption/optical density (or derivative thereof) D.sub.im for the standard. The averages may be in respect of the mean value of D.sub.ix -D.sub.im (whatever its sign i.e. absolute diference), or (D.sub.ix -D.sub.im).sup.2 and may be the simple mean value or the differences may be weighted to take account of the different sensitivity of the absorption to the property at that wavelength or the different sensitivity of the spectrometer at that wavelength. For each standard in the bank of standards for the type of material in question, the average difference is found as described and the standard or standards with the smallest average differences chosen, e.g. at least 1 but preferably at least 2 such as upto 1000 smallest such as 1 (or 2)-100 or 1 (or 2)-20 but is particular 1 (or 2)-10 and especially 2-6 smallest. Advantageously the average differences chosen and hence the standard (or standards) S.sub.m chosen for the property or yield wanted are such that in relation to the unknown material X and each chosen standard S.sub.m the following functions is met ##EQU1## wherein i.sub.xm is the proximity index and is defined by i.sup.2 (xm)=.SIGMA.(D.sub.ix -D.sub.im).sup.2 and the experimental error is in determining said property or yield in the standard. The value P.sub.x of the property or yield is the same as property or yield P.sub.m or the average P.sub.m if more than one standard S.sub.m is chosen.
In order to aid the choice of the appropriate standards, especially in relation to a large number of wavelengths for a complex unknown mixture, it is preferred to limit the choice to those defined by means of a minimal index. For the chosen standard the minimal index is at least the same as the differences between the absorptions of the unknown and the standards. Mathematically, this may be expressed as i.sup.2 ab.ltoreq.i.sup.2 M where iM is the minimal index for the property, and iab is a measure of the deviation (called the proximity index) at all the chosen wavelengths between absorption of the unknown and a chosen standard b. That measure is defined by
i(ab).sup.2 =.SIGMA..sub.i (D.sub.ia -D.sub.ib).sup.2 (I)
where D.sub.ia is the optical density (or absorbence) of unknown a at wavelength i (or a derivative thereof e.g. a first, second or third derivative of that density), and D.sub.ib is the optical density (or absorbence) of standard b at that wavelength i (or a derivative thereof e.g. a first, second or third derivative of that density). The value of D.sub.1 is the optical density or the optical density difference with respect to the baseline of the spectrum at that wavelength, or the baseline interpolated between 2 wavelengths on either side thereof.
If desired instead of the optical density D.sub.i a normalized density W.sub.i may be used where W.sub.i =D.sub.i /.SIGMA.D.sub.i. This normalization avoids errors due to small electronic fluctuations in the apparatus and compensates for small differences in the optical path between the optical cells. In this case the proximity index is defined by
I(ab).sup.2 =.SIGMA..sub.i (W.sub.ia -W.sub.ib).sup.2 (2)
The indices can be weighted as desired for increasing resolution. One approach is to define the indices as follows.
I(ab).sup.m =.SIGMA.Abs value (X.sub.ia -X.sub.ib).sup.m /.sigma..sub.i.sup.n (3)
where X.sub.i is D.sub.i or W.sub.i or a mathematical combination thereof, .sigma..sub.i is the standard deviation of X for the set of samples considered (at that wavelength) and each of m and n which are the same or different is weighting factor which is positive but can be a whole number or a fraction. Other variants can be used with other weighting factors such as those involving the spectral experimental error e.sub.i, where e.sub.i is the reproducibility of the spectral measurement at wavelength i. The choice between the different options for the weighted indices may be dictated by numerical efficiency.
The reproducibility of the experimental measurements in the standards may be at least 90% or 94% or 95%. The minimal index may be obtained from a reference standard samples set according to the following procedure, hereafter called the Minimal Index Procedure. The NIR spectra for 2 standard samples A and B and their property P e.g. Octane Number are determined. By means of equation (1), (2) or (3), the value of the proximity index i.sub.ab is determined via the absorptions at a series of wavelengths; this index is applicable to the difference in properties P.sub.a -P.sub.b called EP.sub.ab. This process is repeated with other pairs of standards c and d, e and f etc to obtain a series of Proximity Indices i.sub.cd etc with corresponding property differences EP.sub.cd etc. For different values of a parameter L which is greater than the indices i.sub.ab etc, the corresponding values of EP.sub.ab etc are averaged to give an average EP.sub.ij for that value of L; the different values of EP.sub.ij +t.sigma./.sqroot.K are then plotted on a graph against L .sigma. is the accuracy of the property determination and K is the number of pairs of samples for which i.sub.ab is inferior to a given L. t is the Student factor at a given level of confidence. The intercept is then measured between the curve obtained and a line usually horizontal which is the reproducibility of the property level at an appropriate confidence interval e.g. 90% or more usually 95%; the abcissa portion of the intercept gives the minimal index i.sub.min, which is the minimum value of i.sub.ab for which P.sub.a =Pb within the frame of experimental error.
From this minimal index by Procedure 1, the standards can be chosen which have values of i.sup.2.sub.ab .ltoreq.i.sup.2.sub.min where in this case a is the unknown and b is a standard, as in this case the difference between Property a and Property b is less than or equal to .sigma..sqroot.2, where .sigma. is the experimental error in measuring the property. Then from the property P value or values of the chosen standard, the property of the unknown is obtained directly or by averaging those values, usually the arithmetic mean, but optionally with weighting.
The method of the invention may be used to determine more than one Property P at once, e.g. at least 2, such as 1-30 e.g. 2-10 properties at once. Each property of the standards has a particular unweighted, minimal index, which may lie in the region 0-10.sup.-10 e.g. 10.sup.-2 to 10.sup.-8, in particular 5.times.10.sup.-7 to 5.times.10.sup.-4. If the Minimal Index chosen is the smallest for all the properties desired, then the same one may be used for all the properties and the standards chosen will be suitable for all the properties. The Minimal Index for each property may be used separately, with different numbers of standards chosen for each property (assuming different Minimal Indices). If desired the same Minimal Index may be used, which is not the smallest, resulting in some of the chosen standards (with a higher Minimal Index) giving some properties of high accuracy and some (with a lower Minimal Index) giving some properties of less high accuracy.
The property to be determined may be of the sample being analyzed or a product obtained from that sample e.g. a product of blending or cracking the sample, as the property value obtained is derived from the standards, and they will have been determined as needed for the eventual use. Our U.S. Pat. Nos. 5,452,232 and 5,475,612 referred to above describes such techniques when applied to use of NIR with correlation to blending or cracking operation; the same principles apply in the present method.
If the density of the standards in the data bank is sufficient to have i.sup.2 ab.ltoreq.i.sup.2 min as is usually the case, the above procedure is very satisfactory. But there are occasions when the bank is incomplete, because of shortage of data of properties in a particular area i.e. a low density of standards or the sensitivity of the property to changes in absorption is so small, that a very small Minimal Index is required and there may be few standards with proximity indices meeting it. It is possible simply choose a larger Minimal Index with e.g. 1-5 times such as 1.5-2 times the Minimal Index; the results may be less accurate than those from a smaller minimal index.
However, a more accurate approach with a low density of standards involves a special densification process of Procedure 2, in which random or semi random densification of the neighbourhood of the unknown is achieved by generation of synthetic standards, based on standards already in the bank. Each new synthetic standard may be obtained from combinations of standards taken at random from the bank but preferably it is obtained from the other standards by the constraint of choosing only a mixture of N standards for which
(Min)C.sub.j -u.sub.j .ltoreq.C.sub.j .ltoreq.(Max)C.sub.j +u.sub.j(4)
and
.SIGMA.C.sub.j =1 (5)
where C.sub.j is the fraction of component j in the sample i.
Min C.sub.j is the minimum amount of j in the initial industrial calibration mixture or in the samples for which the method is to be used.
Max C.sub.j is the maximum amount of j in the initial industrial calibration mixture or in the samples for which the method is to be used.
uj is usually between 1 and 0.01 preferably between 0.5 and 0.1 and can be fixed for each property.
The constraints over the choice of such mixtures of N standards can also be equally fixed in the spectral area from which the samples will be drawn in order to remain in the areas of similar chemical nature.
The number of samples effectively drawn into the bank in this densification can be of several thousand generally 1000-2000. The calculation time is extended without significant deterioration in the results. If no further neighbours are found, the trawl of new samples drawn in is enlarged.
The spectrum of each mixture is calculated by the combination of the spectra of the standards used according to the formula
S.sub.Mi =.SIGMA.C.sub.ij XS.sub.j (6)
where S.sub.j is the spectrum in the mixture of component j in the calibration matrix.
The properties of each mixture PMi can be calculated by a generally linear combination of the properties of the standards according to the formula
P.sub.Mi =.SIGMA.C.sub.ij XP.sub.j (7)
where P.sub.j is the property of component j
In the case of non linear additive properties, appropriate mixing factors can be applied e.g. by blending factors or similar for density and viscosity.
Having obtained the spectrum and the properties of the synthetic mixtures, these can be used as "standards" to help determine the properties of an unknown sample in the same way as a conventional standard.
Instead of using either of the two above approaches, 1-7, a third type Procedure 3 may be used as follows. The Q nearest samples to unknown X can be found from a selection from the bank samples for which the proximity index to the unknown sample is (V) X i.sub.min) where v is 0.1<v<10, (8) preferably 0.5<v<2 or 1.ltoreq.v.ltoreq.5. Then by the method of least squares is found a generally linear combination of the standard products, which are the Q nearest samples to reproduce the spectrum of X according to the equation.
S.sub.x =.SIGMA.C.sub.R XS.sub.r (9)
where C.sub.r is the coefficient for sample R in the total Q and S.sub.R is the spectrum of sample R. The coefficient C.sub.R which can be normalized to C.sub.R =1 or not and/or optimized by the least squares route, allows an estimation of the property P.sub.x according to the equation.
P.sub.x =.SIGMA.C.sub.R XP.sub.R (10)
where P.sub.R is the property of sample R.
The eventual size of the estimation error can be derived by application of Gaussian theory, also called the propagation error (see Eq. 10).
The above third approach can only be applied if the product X is situated inside the maximum extension of the standard products defined by equation (8). If this is not the case, X is outside the field of the actual bank of products and escapes from the area of knowledge of the method into the area of learning.
The densification process described in relation to equations 4-7, or 9 or 10 is usually applied to the method of the invention involving no correlation or regression techniques. However, if desired the densification process may be applied to increase the number of "standards" for consideration in an NIR analytical technique involving the correlation on regression techniques as described above e.g. MLR. The present invention also provides a method for adding an extra synthetic standard to a bank of known standards, each of which relates at least one absorption in the 600-2600 nm region (or derivative thereof) of a known material to a known property related to that material, which method comprises choosing from the bank at least 2 standards for which equations 4 and 5 above are met, considering mixing the chosen standards in at least one proportion to produce at least one mixture for use as a synthetic standard, and estimating the spectrum and property of said mixture according to equation 6 and 7 respectively.
The spectrum and property of each "mixture" can then be added to the bank and used to develop models through the known correlation/regression approach, e.g. as described in the above mentioned patents.
The method of the invention may be applied from the spectrum of a material to determine at least one physical, chemical, physicochemical and/or rheological property of that material, which may be a product of a chemical or physical or separation process, or which may be a feed to such a process, or the method can be used to determine at least one of said properties of a product of that process from the spectrum of at least one feed to that process, or to determine the yield of at least one product of that process. Each of the feed (or feeds) or products to the process may be a solid liquid or gas preferably at least one feed or product is a liquid.
Thus the method may be used for the physicochemical determination or prediction in relation to at least one feed or product used in or obtained by an industrial process of the refining of oil and/or in petrochemical operations. The process maybe a hydrocarbon conversion or separation process, preferably a reforming or catalytic cracking or hydrotreatment process or distillation or blending. In particular it may be used for determination of at least one property of a feed and/or the prediction and/or determination of at least one property and/or yield of product from a number of different processes such as processes for separating petroleum products such as atmospheric distillation vacuum distillation or separation by distillation, under pressure greater than atmospheric, as well as thermal or catalytic conversion, with or without partial or total hydrogenation, of a petroleum product, such as catalytic cracking e.g. fluid catalytic cracking (FCC), hydrocracking, reforming, isomerization, selective hydrogenation, viscoreduction or alkylation.
Of particular value is the use of the method in blending operations involving the prediction and/or determination of at least one property of a blend of liquid hydrocarbons (optionally with other additives such as alkyl ethers), this method including or not the determination for each constituent of the blend of a blend index for the property considered. In this method as applied to blending, the blend indices can be obtained simply by calculation and without the need for preparation of standard physical mixtures other than those contained in the databank. The blend indices can be combined linearly or non linearly within the fields of stability to determine from the value of this combination a value for at least one property of the blend obtained. The blend may be made by mixing at least 2 of butane, hydrogenated steamcracked gasoline, isomerate, reformate, MTBE or TAME, FCC derived gasoline. This process may be repeated with numerical addition of other constituents separately to the liquid hydrocarbon base to determine a series of blending indices and then determination from these indices of the properties of the multi constituent blend (see e.g. Ex. 2 hereafter).
Examples of properties that can be determined and/or predicted include the following: for automobile fuels/gasolines, at least one of the Research Octane Number (RON), Motor Octane Number (MON) and/or their arithmetic mean, with or without lead additive and/or the methyl tert, butyl ether or methyl isoamyl ether and/or benzene content:
For automobile fuels/gasolines, at least one of the vapour pressure, density, volatility, distillation curve, e.g. percentage distilled at 70.degree. C. and/or 100.degree. C., oxygen content or benzene or sulphur content, chemical composition and/or gum content e.g. expressed in mg/100 ml, and/or susceptibility to lead (these properties are particularly determined for use in blending operations):
For diesel fuels or gas oils, at least one of the cetane number (e.g. motor measured), cetane index, cloud point, "discharge point", filterability, distillation curve, density e.g. at 15.degree. C., flash point, viscosity e.g. at 40.degree. C., chemical composition, sensitivity to additives and percentage of sulphur;
For distillation products from crude oil e.g. under atmospheric pressure at least one of the density, percentage of sulphur, viscosity at 100.degree. C., distillation curve, paraffin content, residual carbon content or Conradson carbon content, naphtha content, flash point for petrol, cloud point for gas oil e.g. light gas oil and/or viscosity at 100.degree. C. and/or sulphur content for atmospheric residues, and yield for at least one of the cuts, gasoline (bp 38.degree.-95.degree. C.), benzine (bp 95.degree.-149.degree. C.) naphtha bp 149.degree.-175.degree. C., jet fuel bp 175.degree.-232.degree. C., light gas oil bp 232.degree.-342.degree. C., heavy gas oil bp 342.degree.-369.degree. C., and atmospheric residue greater than 369.degree. C.
For at least one of a feed or a product of a process of a catalytic cracking e.g. FCC process, at least one of the density, percentage of sulphur, aniline point, gas oil index, gasoline index, viscosity at 100.degree. C., refractive index at 20.degree. C. and/or 60.degree. C., molecular weight, distillation temperature e.g. 50% distillation temperature, percentage of aromatic carbon, content of total nitrogen and factors characterizing the suitability of the feed for the cracking e.g. KUOP, crackability factor, cokability factor, and yield e.g. of gas, gasoline, gas oil or residue. Thus there may be determined the yields and/or properties of the different products obtained by distillation of the cracked products, such as RON and/or MON, clear or leaded for the gasoline cut and the viscosity at 100.degree. C. for the distillation residue.
For at least one of a product or a feed of a catalytic reforming process, at least one of the density, distillation temperature and/or chemical composition (expressed as a percentage) of saturated linear hydrocarbon, isoparaffins, naphthenes, aromatics and olefins.
For at least one of a product or a feed of a process of hydrogenating gasoline at least one of the density, distillation temperature, RON and/or MON, clear or leaded vapour pressure, volatility, chemical composition (expressed as a percentage) of saturated linear hydrocarbons, isoparaffins, naphthenes, aromatics e.g. benzene, and mono/di substituted benzenes, olefins e.g. cyclic and non cyclic olefins, diolefins, the maleic anhydride index, and yield e.g. of at least one of the products obtained.
The method of the invention may also be used with chemical reactions in which at least one product is a hydrocarbon, and none of the feeds or products contains an element other than carbon or hydrogen. The hydrocarbon which may be gaseous or liquid at 25.degree. C. Such reactions may involve as feed or product at least one olefin or acetylene e.g. linear or branched, aliphatic or cycloaliphatic olefin with an internal or external ethylenic unsaturation, preferably of 2-20 carbons especially 2-8 carbons for alkenes or alkynes (such as ethylene, propylene, butene 1 or 2, isobutene, isopentene) or acetylene, and 5-8 carbons for cycloalkenes e.g. cyclohexene. The feed or product may also be an aromatic hydrocarbon e.g. benzene or naphthalene, optionally substituted by at least one (e.g. 1-3) alkyl or alkenyl group e.g. of 1-20 carbons, such as 1-6 carbons, especially methyl, ethyl or isopropyl; examples are benzene, toluene xylene, cumene and styrene. The feed or product may also be a non aromatic hydrocarbon, e.g. linear or branched aliphatic or cycloaliphatic with e.g. 1-20 or 5-8 carbons respectively, preferably 1-6 carbons and 6 or 7 carbons respectively, examples are methane, ethane, propane, n-butane, isobutane, and cyclohexane. The feed or product may also be a diene, conjugated or unconjugated, aliphatic or cycloaliphatic with e.g. 4-20 carbons or 6-20 carbons respectively; examples are butadiene and isoprene and cyclohexadiene. Examples of the reactions are hydrogenation (e.g. butadiene to butene-1 or 2 or cyclohexene to cyclohexane) dehydrogenation (e.g. ethane to ethylene or ethyl benzene to styrene), isomerisation (e.g. butene-1 or -2 to isobutene, or pentene-1 to isopentene) alkylation (e.g. benzene with ethylene to form ethylbenzene and/or styrene, or isobutene with butane to form iso octane), and cracking.
In addition to the use in petrochemical operations, the method is of wider application and may be applied in the pharmaceutical industry such as the production of pharmaceutically active compounds for use as medicines e.g. by fermentation, and in the perfumery industry for making perfumes and fragances, especially in their blending and control thereof. The method may also be used in the food industry e.g. in brewing to control fermentaion processes, in fermentation to make wine and quality control thereof, and control of food production e.g. sugar and water content in fruit juice and in control of maturing processes for fruits and vegetables. In each case the method may be applied to determine a property of the sample tested or product from that sample e.g. a fermentation or blended product preferably on line and especially with continuous feed back from the results to control the production process.
In each of the above processes the property or yield of a product determined or predicted by the method of the invention can be compared to the desired figure and notice taken of any deviations by adjusting the parameters of the process e.g. proportion or nature of feed(s) and/or temperature/pressure etc to bring the property back to the desired figure. This control of the process, which may be a blending, separation or chemical process, is usually performed with a micro computer which is linked to the spectrometer and also performs the search for the standards Sm. The inline control of the process is very efficient and very fast.
The present invention also provides an apparatus suitable for carrying out the method of the invention comprising an infra red spectrometer and a computer wherein the infra red spectrometer is linked to the computer programmed in such manner that the property or yield may be determined continuously and in real time. The spectrometer is suitable for measuring spectra in the 600-2600 nm wavelength range and can be linked to a signal processing device to allow numerical treatment of the spectrum, preferably by Fourier Transformation. The spectrometer receives at least one signal from a vessel containing product or from a feed or product line. The information obtained can be used as an information vector for the computer which is programmed to determine the property or yield e.g. via calculations on the proximity indices in relation to standards. Conveniently in relation to a process, the computer may be used in a closed loop feedback control system for controlling processing equipment e.g. changing the process parameters in response to variations in the property and/or yield of product from the desired value, from measurement of more than one absorptions in the NIR spectrum of the product and/or feed.
The benefits of invention allow improvements in modelling with the following areas, identification and classification of novel products, simultaneous estimation of all of P properties on a sample without the need for generating P different models, and with the option of automatic upgrading of the model, the method being self learning or adjusting. The method of the invention overcomes the difficulties with the classical regressional approach, in particular avoiding all difficulties with numerical stability of the models, allowing easy and rapid identification and classification of a sample of a product analyzed by spectral recognition and then instant conclusions as to whether the sample is known or unknown, allowing simultaneous determination of many properties and whether the property is simply additive or synergetic in relation to a blend composition; the latter is particularly useful for different blend indices and the indices considered.
The method also allows an extension of the field of application of the method without the need to rewrite the model, apart from the need to integrate the new samples which are inside or outside the previous field of validity of the method. This possibility of automatic learning, which is not possessed by traditional regression techniques, is a decisive advantage in the framework of continuous inline industrial control processes, because it allows the return of the industrial plant operations to the model in a certain and rapid manner in a minimum time and with all the properties considered in the model. In contrast classical regression methods would necessitate the redevelopment of all the models, which is long and laborious without being able to guarantee the result of the new model obtained, because a new validation period is necessary; in addition during the redevelopment of the model any commercial refinery use of the model is very limited. Furthermore, the method of invention allows equally the easy extension to a number of properties, which are simply incorporated into the known bank.
This remarkable possibility is true not only for conventional properties such as physical chemical and/or rheological properties, but also for complex ones (such as octane number). Also it is possible to quantify by the process the response or susceptibility to lead of automobile fuels as well as the response to additives such as nitrates, of fuels used in diesel engines. The methods of the invention equally allow application of the models from one apparatus to another and from one spectral region to another, where conventional regressive method cannot give satisfactory solutions. This apparatus portability is made possible by the fact that the differences between different spectra are the same in one apparatus as another, for the same type of spectrometer being considered (e.g. network scatter, Fourier transform, accousto optical system AOTS, diode array etc). This portability between spectral regions depends on the fact that as the spectral regions are intercorrelated, the relations between the spectra are maintained between one another.
The invention is illustrated in the following Examples in which the Minimal Index is calculated according to the Minimal Index Procedure described above. Mathematically the steps concerned are as follows.
For each couple of standard samples i, j, the Proximity Index i.sub.ij is determined from the NIR spectra by use of equation 1, 2, or 3 and the properties are measured. For each Proximity Index is calculated the absolute difference EP.sub.ij between the properties of the samples. The Minimal Index for property P is obtained from the average (EM.sub.p L) of EP.sub.ij for different values of L when L.gtoreq.ij. Thus the EM.sub.p L=1/K.SIGMA..SIGMA.EP.sub.ij for each of K samples for which ij.gtoreq.L.
EMp(L)+t.sigma.(M) is plotted against the proximity index and in addition there is plotted the reproducibility of the standard method at a given level of confidence, as defined in the Minimal Index Procedure above. The intercept of the curve from EMpL and the reproducibility give the upper limit i.e. the Minimal Index.
For the Examples the data is expressed in Tables in a form as shown below in which the data is as follows.
______________________________________ Absorption Un- Esti- Standard Standard Weighting known mated A B______________________________________Proximity IndexWavelength .lambda.cm.sup.-1 nmProperty lProperty jProperty m______________________________________
The wavelengths chosen are shown in columns 1 and 2.
Column 3 gives the weight loading associated with each wavelength for the proximity index for the standards; 1 denotes no loading.
Column 4 shows for the unknown sample the absorption at the various wavelengths and at the bottom the properties of that sample determined by standard methods.
Column 5 shows for the unknown sample the estimated values of the properties and the absorptions using the method of the invention based on the properties and absorptions of the chosen standards.
Columns 6, 7 etc show the values of the absorptions and properties for the standards chosen from the bank. Line 2 give the value of the proximity index between the unknown sample and each of the chosen standards.
EXAMPLE 1
Determination of Octane Number and other Properties of a Motor Fuel
The NIR spectra between 4800 and 4000 cm.sup.-1 of a superfuel 1D and a number of standard superfuels of known properties were measured. The base line was taken at 4780 cm.sup.-1 though similar results would be obtained with baseline drawn between 2 or more points. The absorbances were normalized.
By the Minimal Index Procedure described above, with use of equation 2 and non weighting of the absorbences the Minimal Index (MI) was calculated to be 1.times.10.sup.-4. Following reference to the bank of data on superfuels and use of Procedure 1, 3 standard samples were found with a proximity index with respect to the superfuel of less than M1. The properties of these standards are shown in Table 1. From the properties of the standard samples, octane numbers (RON and MON), vapour pressure (hpa) volatility, percentage distilled at 70.degree. C. and at 100.degree. C., gum content (in mg/ml), and content of sulphur, benzene (vol %) and MTBE were calculated for the superfuel by taking the arithmetic mean of the values for the 3 chosen standards. The estimated results are compared with the measured results.
All the properties were obtained from the single NIR measurement on the unknown superfuel and without any regression calculations, and with an accuracy in agreement with the reproducibilities of the reference methods. Other properties can be determined in a similar way.
EXAMPLE 2
(a) Production of an Unleaded Mixed Fuel from 6 Components
A target SUPER98 superfuel of the properties given in column 3 of Table 2a1, was to be obtained by mixing the remains of a tank of finished gasoline with 5 components, butane, hydrogenated steamcracked gasoline HEN, isomerate ISOM, reformate (REF) and MTBE. NIR absorptions at 4800-4000 cm.sup.-1 measured with a Fourier Transform spectrometer were measured, with a base line taken at 4780 cm.sup.-1 and absorbances normalized. Results are in Table 2a1.
Mathematic calculations were done with a computer to mix the spectra and properties of the 6 components to reproduce a finished product.
5% MTBE (on target fuel) (i.e. 4-76% in the final mixture) was "added" mathematically to a spectrum of the target fuel to give a mixture whose NIR spectrum was noted. The Minimal Index was 1.times.10.sup.-4 determined as described above from the finished gasoline. 3 standards 2A, 2B and 2C were found with proximity indices with respect to the mixture, without weighting, and hence by averaging the properties of the standards the properties of the mixture were obtained. Table 2a.2 shows the spectrum of the mixture, the 3 standards and the estimation for the mixture as well as the properties of the standards and the estimated figures). The process was repeated with addition of each of the other 4 components to the spectrum of gasoline target.
On the basis of the figures obtained, the blending index for each property was found according to the linear formula
I.sup.P (mix)=�(1+.alpha.).times.P(mix)-P(ref)!/.alpha.
where
I.sup.P (mix) is the blending index for the ingredient in the mixture in relation to property P
.alpha. is the percentage of ingredient in the mixture
P (mix) is the property of the mixture (ingredient+gasoline) added) estimated by the process.
P (ref) is the property of the reference target gasoline.
The blending index for addition of MTBE is shown in Table 2a3.
In order to obey the linearity law here, it is necessary to limit the additions to not more than a quarter of the minimum to maximum range of the constituent studied in the industrial mixtures. However for concentration less than 20% such as for these oxygenated compounds, addition of 5% is acceptable.
The process with MTBE added to the gasoline was repeated with the other 4 components (and on the basis of linearity in the blending as with MTBE) to obtain blending indices for them as well (see Table 2a3). Then with the blending indices for each property for each ingredient, one can calculate the relative volume fractions needed to give the desired properties for the Superfuel 98 and hence the blending order. The 6 components were then mixed in the desired proportions and then properties of the mixture tested and compared to those estimated by the method of the invention from the components present (see Results in Table 2a4). In the estimation of the products and the comparison with the bank of standards, the Minimum Index was 1.times.10.sup.-4. 3 standards 2D, 2E, 2F were found with suitable proximity indices from which the properties of the superfuel were estimated by averaging as described in Procedure 1. There was good agreement between the properties obtained via the blending order, these measured on the fuel made and those estimated by the method of the invention. The differences are very small and in the area of reproducibility of the standard methods.
EXAMPLE 2b
Production of a 5 component leaded Superfuel mixture
A target superfuel of the SUPER 97 type had with 0.15 g/l of lead tetraethyl and having as specification an RON of 97, an NIR spectrum as in Col 3 of Table 2b1 below and other properties as given in col 3 of Table 2b1 below. There were available 4 components (HEN, 150M, REF and an FCC cat cracker gasoline) and the remains of a tank of finished refined gasoline for making the target fuel. The NIR spectra of these 5 components were measured as in Ex 2. The results are in Table 2b1.
As in Ex. 2a, mathematical calculations were done with a computer to obtain the spectra and properties of 5 components to reproduce a finished product. Proximity indices with respect to standard samples were calculated based on normalized absorbencies which were not weighted. The method of the invention was used to find appropriate standards, using the procedure of artificial mixtures as described in Procedure 3 and equation 8 above in which v was 1 and with a Min. Index of 2.times.10.sup.-4, the latter having been calculated for standard fuel mixtures as described above. Table 2b2 describes the results of addition of 5% of the FCC gasoline to a reference Super 97 gasoline target as well as the 3 standards 2G, 2H, 2J found by the method of this invention, from which the estimated properties were found. The same procedure was performed with the other components.
The blending indices were found in the same way as for Ex 2a, with the results for FCC gasoline in Table 2b3 and for the other components in the same way. The spectral blending index (for the linear area) is obtained for each property as shown in Table 2b3. A blending order was also calculated, as in Ex 2a, the results being in Table 2b3.
The process of Ex 2a was repeated but with the above components and a Minimal Index of 2.times.10.sup.-4. The results are in Table 2b4. 3 standards 2K, 2L, 2M were found with appropriate proximity indices, which allowed the properties of the product to be estimated by averaging. Again good agreements is seen between the properties estimated from the blending order and those measured on the product made, and also between the same properties measured and those measured by the process. The differences seen are very small and in the area of reproducibility of the standard methods. Other properties can be obtained in a similar way.
EXAMPLE 3
Determination of cetane index and other properties of a gas oil
The properties of an unknown gas oil 3A were desired. The method of this invention was applied with respect to a bank of known standard gas oils with known NIR spectra. The NIR spectra were obtained by F T spectrometer in the 4800-4000 cm.sup.-1 region �with 4780 cm.sup.-1 baseline and were normalized! The proximity indices were calculated on the basis of Equation 2, and the Minimal Index was 2.5.times.10.sup.-6 (estimated from standard gas oil data as described above). The bank of standards was sufficiently dense for there to be found 2 standards 3B and 3C inside the sphere with proximity index less than 2.5.times.10.sup.-6. Table 3.1 gives the details of the spectra and properties of the unknown oil A, and the standards and the estimated spectrum and properties, obtained by averaging. All the properties were obtained with an accuracy in agreement with the limits of reproducibility of the reference methods. Other properties can be obtained in a similar way.
EXAMPLE 4
On line prediction, based on NIR spectra on a mixture of crude oils fed to an atmosphere distillation unit, of yields and properties of the different distillation cuts such as gasoline (38.degree.-95.degree. C.) benzine (95.degree.-149.degree. C.) naphtha (149.degree.-175.degree. C., jet fuel (175.degree.-232.degree. C.) light gas oil (232.degree.-242.degree. C.) heavy gas oil (342.degree.-369.degree. C.) and atmospheric residue (bp). 369.degree. C.).
An atmospheric distillation unit in a refinery was fed with a charge 4C which was a mixture in wt % of the following crudes, RUMASHKINO 81%, Iranian Heavy 18%, Iranian light 1%.
Yields of various distillation cuts were desired, the boiling ranges being given above, as well as key properties of each cut as described in Table 4.1, NIR spectra were measured as in Ex 1 on the crude oil. Min. Index was determined from NIR spectra on standard crude oil (as described above) and was 2.6.times.10.sup.-6. The method of the invention was applied using Procedure 3 and equation 8, in which v was 1, to the bank which was sufficiently dense for 2 standards 4A and 4B to be found with small enough proximity indices. These standards contained (wt %) (for 4A) Romashkino 52% Iranian Heavy 29%. Arabian Heavy 11%, Kuwait 4%, Arabian light 2% and Iranian light 2%) and (for 4B) Iranian Heavy 78%, Romashkino 21% and Arabian Heavy 1%. The data in Table 4.1 shows the observed properties as well as the yields of the cuts and their properties. The results obtained by this procedure were extremely satisfactory, the differences observed being in accordance with standard methods of measurement. Other properties can be obtained in a similar way.
The yields and properties of the distillation cuts remarkably were obtained directly on the basis of the NIR spectra of the feed and in line without regressional type calculations.
EXAMPLE 5
Determination in line of the properties of a mixture of crude oils
Other properties of the charge mixture of crude oils of Ex 4 were sought, based on the NIR spectra determined as in Ex 4. The method of the invention was applied as in Ex 1 with the Minimal Index in all cases being 2.6.times.10.sup.-6. Two standard crude petrols 4A and 4B were found in the bank by using Equation 2. The results are shown in Table 5.1. Other properties can be obtained in a similar way.
Here too the method demonstrates its capacity to predict all types of properties without any regression type of calculation requiring fastidious calculations. The results generally, as in the other Example, were in accordance with the results obtained by the reference methods, the deviations being found in the limits of reproducibility of the same methods.
EXAMPLE 6
Determination of the Properties of a feed to a reformer
A feed 6D to a reformer unit was analysed by the method of the invention as described in Example 1 with the NIR spectra recorded at 2000-2500 nm, the absorbancies normalised and not weighted. The NIR spectrum was compared by the method of Procedure 3 and equation 8 (wherein v is 1) with a Minimum Index of 2.times.10.sup.-4, which had been previously calculated as described above from NIR spectra on standard reformer feeds. Three standards 6A, 6B and 6C from the reference feed bank were found with small enough proximity indices; details of the spectra of the feed and the standards are given in Table 6.1, together with 5 properties estimated for the feed by averaging the corresponding values of those standards. The actual properties of the feed were measured for comparison; the measurements were by traditional methods (gas chromatography and density), the former necessitating laboratory determination for several hours, compared to the present NIR process which gave the same results in a few minutes and on line (real time in the unit) and with better reproducibility.
The process allows the obtaining of a result with remarkable economy while avoiding having to produce 5 regressive models. The differences between the 5 properties as estimated and as measured experimentally are in agreement with the reproducibility of the known reference methods, namely 1.5% for gas chromatography for chemical compositions and 2%o for density. The method can be equally applied for other properties such as ASTM distillation temperature curve for the feed.
EXAMPLE 7
Determination of the properties of a feed to an FCC unit, as well as the yield and properties of the products obtained
The NIR spectrum of the above feed 7D was measured at 4800-4000 m.sup.-1, with base line at 4780 cm.sup.-1, normalisation of the spectrum and no weighting. The procedure 3 was used with equation 8, with v=1, and the Min. Index of 2.5.times.10.sup.-6 the latter having been previously calculated as described above from NIR spectra on standard FCC feeds of known properties.
The properties of the feed charge 7D sought were listed in Table 7.1 and included factors characterising the charge to the FCC unit, such as KUOP, crackability and cokability. The KUOP or Watson factor is defined as
KUOP=.sup.3 .sqroot..theta./density 60/60
where .theta. is boiling point on a Rankin scale (Absolute Fahrenheit scale) and density 60/60 is the density of the feed at 60.degree. F. compared to that of water at 60.degree. F.
The cracking unit operated under the following conditions: riser inlet temperature 250.degree. C., riser outlet temperature 525.degree. C., MHSV (Mass Hourly Space Velocity) 78 kg/h per kg, C/O ratio 6.6, activity of catalyst 65 (in Microactivity Test).
The cracking gave a gasoline cut defined by ASTM distillation with initial point of 38.degree. C. and 90% distilled at 190.degree. C. and a residue defined by ASTM distillation with 10% distilling at 385.degree. C.
By application of Procedure 3 to the bank of samples of FCC feeds 2 standards were found namely 7A, 7B and the properties and yields estimated as shown in Table 7.1. The results were all in line with the accuracy based on the reference methods, as well as in line with the properties and yields actually meansured. Other properties of the charge or products can be estimated in a similar way.
EXAMPLE 8
On line determination of properties of the feed to a gasoline Hydrogenation unit
The gasolines obtained from steam cracking units have the inconvenience of containing non negligible amounts of unsaturated dienic compounds, which have the effect of inducing and encouraging formation of gums which are undesirable in motor fuel. These gasolines are therefore selectively hydrogenated to eliminate the dienes without at the same time hydrogenating other unsaturated compounds present in the gasoline such as monoolefins and aromatics. The control over these dienes is therefore essential not only for the final quality of the fuel (principly RON and MON) but also for the hydrogen consumption of the hydrogenation unit.
Units for Hydrogenating gasolines from steamcrackers are generally coupled to a downstream distillation unit to separate a gasoline from a light cut (95% distillation by about 75.degree. C.) and one from a heavy cut (initial point about 95.degree. C.), before extraction of the benzene in the core cut and recycle of the extraction residue from that cut called raffinate.
It was desired to determine by the process of the invention the properties of the gasoline from the steam cracker, which was a feed to a gasoline hydrogenation unit. NIR spectra were obtained on the feed on line at 1000-1600 nm using a scatter dispersion spectrometer. The absorbences were normallised, but the data was not weighted for use in Equation 8, in which v was 1 and .sup.i min was 2.5.times.10.sup.-5 (the latter having been determined from NIR spectra on similar feeds of known properties). 5 standards 8A-8E were found in the search using Procedure 3, and the properties of the feed calculated therefrom by averaging were all in agreement with the measured properties of the feed. The results are shown in Table 8.1.
In addition the chemical composition of the feed was obtained with great particularity allowing a distinction to be made for example between cyclic and non cyclic olefins as well as benzene and mono and di substituted aromatics. Equally by the process potential yields were obtained of the distillation cuts after the selective hydrogenation of the gasoline. All the properties were obtained with great accuracy within the limits of the experimental reproducibility for that kind of property.
Other properties can be determined such as Octane Indices for the different cuts or temperatures of ASTM distillation curves for the gasoline.
EXAMPLE 9
Method for use when the density of standards in the bank is insufficient
The MON level for a reformate 9A was sought. The NIR spectrum was measured at 4800-4000 cm.sup.-1 with a base line at 4780 cm.sup.-1 ; the spectra were normalised. With reference to NIR spectra on reformates of known properties the Minimal Index was found by calculation as described above to be 2.times.10.sup.-5. The proximity indices of reformate 9A and known standards were determined by Procedure 1. The results were as given in Table 9.1. 5 standards 9B-F were found from the reformate bank with proximity indices low in relation to the reformate 9A, but insufficiently low to be less than Minimal Index, as the density of the bank was too small. It was thus not possible to calculate the properties with the accuracy desired. Procedure 1 using Equation 1 was replaced by Procedure 2 using Equations 4-7, with in Equation 4 values of Cj between -0.3 and +1.3, in order to increase the density of "standards" in the bank by providing new synthetic mixtures.
Tables 9.2 and 9.3 show the results obtained, showing in Column 3 the absorbancies and properties for the "standards" (MC1, MC2) obtained by this densification, and with small enough proximity indices. Col. 4 and subsequent columns give the absorbances properties of the standards 9B, 9D and 9G in the reformate bank used to generate the new "standards". Line 2 in these Tables show for each standard the fraction retained in the mixture to generate the new "standards" This fraction can be negative, but comprises between -0.3 (or -0.4) and +1.3 (in Eq. 4).
Using the data on MC1 and MC2 as "standards", the properties of the reformate 9A were calculated by averaging (as shown in Table 9.4). The calculated MON of reformate 9A accords well with the experimentally measured figure, and is inside the limits of reproducibility of the standard method. The process can be used in a similar way for other properties.
The method of the invention equally allows immediate automatic upgrading of the bank by automatic integration of the new samples. The process with the proximity indices allows consideration as a standard of all the novel "standards" introduced into the bank. This property is remarkable because it allows very rapid determination of properties in the case of novel products not recognised in the bank and then the gaining of precious time in the adjustment of operating conditions for the manufacturing unit.
Table 9.5 shows that a novel "sample" measured immediately after incorporation of the above unrecognised sample 9A in the databank, now used as a standard, is recognised and is perfectly calculated for the totality of its properties and without any modification nor intervention on the used models. It is important to note the superiority of the procedure over classical regressional models. The latter are incapable of predicting properties of samples not included within their application range or predict them with a non acceptable error, and therefore would need to be reactivated by the necessity to remake the model (one for each property) and this without guarantee of success, and with the commercial plant functioning blind during the recalibration period.
TABLE 1.1__________________________________________________________________________Determination of Octane Indices and other Properties in an automobilefuelProximity 1DIndex Estimated 1A 1B 1CWavelength 1D 0,0000275 0,00006 0,00007 0,00009.lambda. (cm - 1) .lambda. (nm) Weighting Measured 26 7452 2577 6807__________________________________________________________________________4720 2119 1 0,002103 0,0021115 0,00219 0,00206 0,00206 1 85 78 834670 2141 1 0,01696 0,016887 0,01702 0,01683 0,01680 9 1 14640 2155 1 0,016172 0,016464 0,01717 0,01569 0,01652 1 5 74615 2167 1 0,023426 0,022955 0,02267 0,02276 0,02342 1 5 94585 2181 1 0,014407 0,014379 0,01424 0,01486 0,01403 1 3 44485 2230 1 0,011377 0,011472 0,01151 0,01178 0,01111 6 8 24460 2242 1 0,015794 0,015825 0,01571 0,01533 0,01642 8 1 84385 2281 1 0,092392 0,090762 0,09071 0,09287 0,08870 4 14332 2308 1 0,127 0,12402 0,12292 0,1241 0,125054305 2323 1 0,10482 0,10678 0,1021 0,10946 0,108794260 2347 1 0,10001 0,099412 0,09862 0,09552 0,10409 1 44210 2375 1 0,065489 0,06726 0,06746 0,06666 0,06765 3 44170 2398 1 0,063954 0,06449 0,06643 0,06449 0,06254 4 1 64135 2418 1 0,066992 0,067348 0,06552 0,06707 0,06944 3 5 54105 2436 1 0,066911 0,066291 0,06655 0,06498 0,06733 1 7 64060 2463 1 0,10946 0,11196 0,11349 0,11337 0,109034040 2475 1 0,10273 0,10157 0,10564 0,10211 0,09695 9RON clear 99,4 99,2 99 99,3 99,4MON clear 88,4 88,2 88 88,1 88,4TV hpa 700 705,0 710 715 690Volatility 980 975,0 983 967 975% Dist 58 54,7 54 58 52100.degree. C.% Dist 70.degree. C. 36,8 37 39 37 35Resin 1.4 1,6 1,2 1,7 1,8% Sulphur 0.038 0,043 0,035 0,045. 0,048Benzene 0.7 0,8 0,6 0,85 0,9% VolMTBE 5.6 5,8 4,7 6,3 6,5__________________________________________________________________________
TABLE 2a.1__________________________________________________________________________NIR Spectra of Unleaded mixed fuel and base fuel and additives SUPER BASE.lambda. (cm - 1) .lambda. (nm) FUEL FUEL BUTANE HEN ISOM MTBE REF__________________________________________________________________________4720 2119 0,00138 0,00132 0,00036 0,00487 0,00045 0,00039 0,00178 33 86 614 46 176 505 994670 2141 0,01540 0,01569 0,00059 0,03592 0,00181 0,00079 0,02742 1 8 139 9 07 6854640 2155 0,01445 0,01478 0,00154 0,03355 0,00208 0,00199 0,02658 8 6 83 54 07 14615 2167 0,02162 0,02193 0,00243 0,04847 0,00337 0,00336 0,03561 9 22 67 45 34585 2181 0,01317 0,01355 0,00390 0,02682 0,00324 0,00433 0,02632 3 6 46 2 92 56 74485 2230 0,01069 0,01070 0,01376 0,01651 0,00575 0,01324 0,01271 9 5 6 73 1 24460 2242 0,01531 0,01564 0,01671 0,01885 0,01010 0,02791 0,0181 8 6 7 8 8 14385 2281 0,09402 0,09463 0,10437 0,08112 0,09525 0,13276 0,08467 3 8 5 5 64332 2308 0,12974 0,13083 0,14701 0,09487 0,1474 0,18122 0,11291 64305 2323 0,10626 0,10476 0,12279 0,09342 0,11981 0,06388 0,10927 5 54260 2347 0,10094 0,09888 0,11439 0,08813 0,11705 0,07465 0,09048 1 3 7 74210 2375 0,06567 0,06590 0,07431 0,05429 0,07231 0,09115 0,05800 2 2 3 5 6 2 74170 2398 0,06528 0,06506 0,05780 0,04981 0,07479 0,09572 0,05451 9 3 5 1 7 54135 2418 0,06914 0,06866 0,07986 0,04623 0,08584 0,08344 0,04925 7 4 2 5 7 8 64105 2436 0,06864 0,06770 0,08969 0,05082 0,08208 0,06768 0,05322 1 2 7 6 2 94060 2463 0,10677 0,10794 0,0875 0,12437 0,09998 0,07699 0,129894040 2475 0,10145 0,10197 0,08367 0,13189 0,07952 0,08123 0,10917 4 4 5RON clear 99,1MON clear 88,2Vapour 731,74PressureVolatilite 985% Dist 49,93100.degree. C.% Dist 70.degree. C. 34,4__________________________________________________________________________
TABLE 2a.2__________________________________________________________________________Effect of addition of MTBE on the Super Fuel Mixture +Proximity 5% Estimated 2A 2B 2CIndex MTBE 0,0000190 0,00004 0,00006 0,0000680 (cm - 1) .lambda. (nm) Weighting Exp. 69 957 0618 8613__________________________________________________________________________4720 2119 1 0,00133 0,0012761 0,00127 0,00126 0,00128 62 48 91 454670 2141 1 0,01470 0,014562 0,01438 0,01408 0,01521 5 4 3 84640 2155 1 0,01386 0,013804 0,01452 0,01356 0,01332 4 3 7 34615 2167 1 0,02075 0,02143 0,02148 0,02110 0,02169 9 9 4 84585 2151 1 0,01275 0,01255 0,01220 0,01312 0,01232 2 4 74485 2230 1 0,01082 0,010514 0,01041 0,01042 0,01071 24460 2242 1 0,01591 0,015584 0,01615 0,01522 0,01536 7 8 6 84385 2281 1 0,09586 0,096666 0,09610 0,09918 0,09471 8 7 14332 2308 1 0,13219 0,13256 0,1298 0,12971 0,138174305 2323 1 0,10425 0,10443 0,10689 0,10497 0,101424260 2347 1 0,09969 0,10039 0,10436 0,09832 0,09849 1 4 54210 2375 1 0,06688 0,066455 0,06654 0,06578 0,06703 5 4 2 94170 2398 1 0,06673 0,067485 0,06509 0,06906 0,06830 8 1 1 34135 2418 1 0,06982 0,071186 0,06945 0,07235 0,07175 8 74105 2436 1 0,06859 0,066773 0,06593 0,06768 0,0667 6 74060 2463 1 0,10535 0,10236 0,10385 0,101 0,102224040 2475 1 0,10049 0,10197 0,10152 0,10314 0,10126 Reference SUPER98__________________________________________________________________________RON clear 99,1 99,6 99,7 99,5 99,5MON clear 88,2 88,7 88,9 88,5 88,7Vapour 731,74 718,8 711,2 720,0 725,2PressureVolatility 985 972,3 970,0 979,2 967,6% Dist 49,93 52,3 52,0 54,0 50,8100.degree. C.% Dist 70.degree. C. 34,4 35,8 36,3 35,4 35,8__________________________________________________________________________
TABLE 2a.3__________________________________________________________________________Blending Indices and Blending Order (blending Base order) Fuel Butane HEN ISOM MTBE REF__________________________________________________________________________Volume 19,30% 4,10% 31,70% 32,10% 5,60% 7,2%FractionRON clear 99,4 100,2 97,5 103,6 93,0 109,6 100,7MON clear 88,2 88,0 88,5 88,0 86,7 98,7 88,42Vapour 709,1 767,0 4700,0 98,7 923,0 460 208,2PressureVolatility 972,4 975,0 5000,0 212,0 1430,0 718,3 177,3% Dist 54,9 50,2 200,0 -5,0 97,5 99,7 24100.degree. C.% Dist 70.degree. C. 37,8 31,3 142,8 -12,9 84,2 63,8 -8,3__________________________________________________________________________
TABLE 2a.4__________________________________________________________________________Comparison of the result obtained via the blending order and those oftheproduct obtained__________________________________________________________________________ ProductProximity Estimated 2D 2E 2FIndex 0,0000275 0,00006 0,00007 0,00009.lambda. (cm - 1) .lambda. (nm) Weight Made 26 7452 2577 6807__________________________________________________________________________4720 2119 1 0,0021031 0,0021115 0,00219 0,00206 0,00206 85 78 834670 2141 1 0,01696 0,016887 0,01702 9,01683 0,01680 9 1 14640 2155 1 0,016172 0,016464 0,01717 0,01569 0,01652 1 5 74615 2167 1 0,023426 0,022955 0,02267 0,02276 0,02342 1 5 94585 2181 1 0,014407 0,014379 0,01424 0,01486 0,01403 1 3 44485 2230 1 0,011377 0,011472 0,01151 0,01178 0,01111 6 8 24460 2242 1 0,015794 0,015825 0,01571 0,01533 0,01642 8 1 84385 2281 1 0,092392 0,090762 0,09071 0,09287 0,08870 4 14332 2308 1 0,127 0,12402 0,12292 0,1241 0,125054305 2323 1 0,10482 0,10678 0,1021 0,10946 0,108794260 2347 1 0,10001 0,099412 0,09862 0,09552 0,10409 1 44210 2375 1 0,065489 0,06726 0,06746 0,06666 0,06765 3 4 34170 2398 1 0,063954 0,06449 0,06643 0,06449 0,06254 4 1 64135 2418 1 0,066992 0,067348 0,06552 0,06707 0,069444105 2436 1 0,066911 0,066291 0,06655 0,06498 0,06733 1 7 64060 2463 1 0,10946 0,11196 0,11349 0,11337 0,109034040 2475 1 0,10273 0,10157 0,10564 0,10211 0,09695 Measured (blending order) standards__________________________________________________________________________RON clear 99,4 99,4 99,2 99 99,3 99,4MON clear 88,2 88,4 88,2 88 88,1 88,4Vapour 709,1 700 705,0 710 715 690PressureVolatility 972,4 980 975,0 983 967 975% Dist 100.degree. C. 54,9 58 54,7 54 58 52% Dist 70.degree. C. 37,8 36,8 37 39 37 35__________________________________________________________________________
TABLE 2b.1__________________________________________________________________________NMR spectrum of Target Super Fuel, and base stocks available.lambda. (cm - 1) .lambda. (nm) Target Gasoline FCC HEN ISOM REF__________________________________________________________________________4720 2119 0,00142 0,001269 0,00292 0,00458 0,00040 0,00165 34 5 38 38 93 74670 2141 0,00928 0,009059 0,00596 0,03701 0,00204 0,02444 28 4 67 1 27 94640 2155 0,00925 0,00908 0,00682 0,03386 0,00219 0,02384 99 02 8 45 84615 2167 0,01323 0,012989 0,00923 0,04636 0,00359 0,03223 6 42 1 67 14585 2181 0,00982 0,009666 0,00778 0,02885 0,00333 0,02375 47 2 79 7 95 44485 2230 0,01077 0,010379 0,01451 0,01548 0,00584 0,01223 7 2 6 11 34460 2242 0,01440 0,014075 0,01682 0,01811 0,0102 0,01751 9 8 9 54385 2281 0,09332 0,093268 0,09549 0,07888 0,09520 0,08559 9 8 8 1 54332 2308 0,14045 0,1408 0,1543 0,09269 0,14681 0,118524305 2323 0,12096 0,12085 0,13213 0,09168 0,11947 0,110784260 2347 0,11073 0,11062 0,11559 0,08642 0,11763 0,09356 8 24210 2375 0,06891 0,069012 0,07340 0,05286 0,07213 0,06046 3 6 1 6 14170 2398 0,06968 0,069948 0,07379 0,04902 0,07519 0,05775 3 8 2 14135 2418 0,07122 0,071736 0,06714 0,04547 0,08547 0,05259 7 3 7 14105 2436 0,07000 0,070819 0,06500 0,05096 0,08220 0,05617 3 3 3 3 64060 2463 0,10201 0,10186 0,08935 0,13909 0,09970 0,1243 7 54040 2475 0,08448 0,08458 0,0697 0,1286 0,07938 0,10458 9 1RON clear 97,9MON clear 86,2Vapour 596PressureVolatility 905,4% Dist 62,54100.degree. C.% Dist 70.degree. C. 42,24__________________________________________________________________________
TABLE 2b.2__________________________________________________________________________Effect of addition of 5% FCC gasoline on Super 97 ProductProximityIndex MixtureWavelength Estimated 2G 2H 2J.lambda. (cm - 1) .lambda. (nm) Weight Actual 3.41E-05 5.77E-05 6.53E-05 7.88E-05__________________________________________________________________________4720 2119 1 0.001495 0.001416 0.00141 0.00141 0.00142 1 4 14670 2141 1 0.009125 0.008837 0.00865 0.00876 0.00908 8 8 54640 2155 1 0.009144 0.008948 0.00901 0.00895 0.00887 6 5 44615 2167 1 0.013045 0.012691 0.01263 0.01303 0.01240 1 5 74585 2181 1 0.009728 0.009605 0.00945 0.00968 0.00967 4 2 94485 2230 1 0.010955 0.010851 0.01063 0.01090 0.01100 9 9 54460 2242 1 0.014524 0.014847 0.01487 0.01491 0.01475 7 44385 2281 1 0.093432 0.094816 0.09425 0.09672 0.09346 9 2 64332 2308 1 0.14111 0.14368 0.14703 0.13759 0.14644305 2323 1 0.12149 0.12506 0.12261 0.12658 0.125984260 2347 1 0.11096 0.11021 0.11119 0.11141 0.108054210 2375 1 0.069127 0.06648 0.06667 0.06549 0.06726 6 9 64170 2398 1 0.069879 0.068946 0.06788 0.06949 0.06946 1 3 44135 2418 1 0.071032 0.070704 0.06910 0.07203 0.07097 2 2 94105 2436 1 0.069765 0.069612 0.07161 0.06915 0.06807 1 3 44060 2463 1 0.1014 0.1015 0.10061 0.101 0.102894040 2475 1 0.083785 0.081801 0.08234 0.08285 0.08020 3 4 7 Reference SUPER97RON clear 97,9 97.6 97.7 97.5 97.6MON clear 86,2 85.9 86.1 85.7 85.9Vapour 596 586.1 590.0 584.2 584.2PressureVolatility 905,4 892.2 901.0 887.5 888.0% Dist 62,54 62.3 62.8 62.6 61.4100.degree. C.% Dist 70.degree. C. 42,24 41.7 42.5 41.7 40.9__________________________________________________________________________ In this Table 3.41E 05 means 3.41 .times. 10.sup.-5
TABLE 2b.3______________________________________Blending Indices and Blending orderVolume (blending Gasoline FCC HEN ISOM REFFraction order) 12,93% 31,85% 8,46% 37,55% 9,20%______________________________________RON clear 97.7 97.9 91.6 101.1 101.5 100MON 86.3 86.4 79.9 83.5 92 87.9clearVapour 589.9 648.0 388.1 137.7 930.0 235.0PressureVolatility 952.7 968.5 628.2 274.2 1559.0 204.7% Dist 65.5 63.2 57.5 6.3 96.0 26.4100.degree. C.% Dist 45.4 43.1 30.9 -10.7 83.3 -4.370.degree. C.______________________________________
TABLE 2b.4__________________________________________________________________________Comparison between the results from the blending order and the__________________________________________________________________________productProximity Product 2L 2MIndex Estimated 2K 0.00015 0.0019.lambda. (cm - 1) .lambda. (nm) Weight Measured 8.41E-05 5.36E-05 9 9__________________________________________________________________________4720 2119 1 0.001742 0.001809 0.001775 0.00185 0.00180 1 24670 2141 1 0.009508 0.009166 0.009131 0.00926 0.00910 2 44640 2155 1 0.008698 0.00926 0.009326 0.00969 0.00875 5 94615 2167 1 0.012758 0.013206 0.012772 0.01357 0.01327 44585 2181 1 0.009725 0.009549 0.009242 0.00992 0.00948 1 44485 2230 1 0.010459 0.010438 0.010486 0.01033 0.01049 6 34460 2242 1 0.014142 0.014252 0.014878 0.01389 0.01398 6 24385 2281 1 0.090899 0.093317 0.092897 0.09338 0.09367 64332 2308 1 0.13685 0.13974 0.13652 0.13589 0.146834305 2323 1 0.11596 0.12103 0.1195 0.1248 0.118794260 2347 1 0.11499 0.1128 0.11271 0.11216 0.113524210 2375 1 0.071524 0.068713 0.071255 0.06831 0.06656 8 64170 2398 1 0.070662 0.070304 0.069855 0.06938 0.07166 8 94135 2418 1 0.072077 0.071069 0.070001 0.07464 0.06856 64105 2436 1 0.069448 0.071756 0.073371 0.07107 0.07082 6 24060 2463 1 0.10444 0.10095 0.10163 0.09851 0.10269 54040 2475 1 0.086116 0.08264 0.084647 0.08329 0.07997 7 5__________________________________________________________________________ Measured (blending order) standards__________________________________________________________________________RON clear 97.7 97.5 97.7 97.8 97.5 97.7MON clear 86.3 86.4 86.2 86 86.1 86.5Vapour 589.9 595 598.0 596 600 598PressureVolatility 952.7 949 956.7 955 960 955% Dist 100.degree. C. 65.5 62 63.0 66 63 60% Dist 70.degree. C. 45.4 47 44.2 42 46.5 44__________________________________________________________________________
TABLE 3.1__________________________________________________________________________Determination of cetane index and other properties of a gas oilProximity Gas Oil A Index Estimated 3B 3C.lambda. (cm - 1) .lambda. (nm) Weight Measured 1,71E-06 1,39E-06 2,23E-06__________________________________________________________________________4720 2118,6 1 0,000120 0,0001266 0,00013 0,00011 383 18 9825 34114672 2140,4 1 0,001962 0,0020139 0,00201 0,00201 853 13 5876 19494640 2155,2 1 0,003434 0,0034151 0,00343 0,00339 747 09 8675 15434616 2166,4 1 0,004544 0,0044907 0,00447 0,00450 314 99 6561 50374584 2181,5 1 0,004729 0,0046754 0,00463 0,00471 896 465 61494484 2230,2 1 0,007119 0,0069323 0,00690 0,00695 883 37 8771 59034460 2242,2 1 0,010349 0,0101333 0,01006 0,01020 409 88 4653 21224384 2281 1 0,074606 0,0749252 0,07493 0,07492 084 07 0117 02984332 2308,4 1 0,158677 0,1577450 0,15799 0,15749 852 31 051 95514304 2323,4 1 0,101824 0,1022666 0,10221 0,10231 835 97 7602 57934260 2347,4 1 0,131871 0,1313314 0,13138 0,13128 507 53 0548 23574208 2376,4 1 0,088627 0,0886376 0,08855 0,08871 865 84 913 62374168 2399,2 1 0,092899 0,0930121 0,09310 0109291 205 26 5408 88444132 2420,1 1 0,084503 0,0847591 0,08482 0,08469 812 1 7844 03764104 2436,6 1 0,081106 0,0812978 0,08136 0,08123 377 51 1676 40274060 2463,1 1 0,086428 0,0864872 0,08648 0,08648 37 85 7285 72854040 2475,2 1 0,067192 0,0674037 0,06741 0,06738 608 2 8449 8991Cetane Index 52 52,5 53,3 51,7Cetane Number 55,3 52,75 52,1 53,4Density 15.degree. C. 0,8434 0,84085 0,8385 0,8432Flash Point 62 57,5 60 55% Sulphur 0,29 0,25 0,23 0,27Cloud Point 5,1 5,5 5 6Filterability 1 0,5 1 0Viscosity 40.degree. C. 3,1 3,7 3,7 3,7__________________________________________________________________________
TABLE 4.1__________________________________________________________________________Determination of yields and properties of cuts from distillation ofmixture ofcrude feed oilsProximity Charse 4C 4A 4B.lambda. (cm- Index Estimated 1,21E- 1,33E-1) .lambda. (nm) Weight Measured 9,98E-07 06 06__________________________________________________________________________4672 2140,4 1 0,001777 0,001748 0,00177 0,00172 942 627 1733 5524640 2155,2 1 0,003139 0,003211 0,00325 0,00316 917 964 6211 77174616 2166,4 1 0,003779 0,00327 0,00383 0,00381 11 795 5639 99524584 2181,5 1 0,003794 0,003797 0,00382 0,00376 844 791 9737 58454484 2230,2 1 0,006094 0,006144 0,00627 0,00601 959 54 2386 66944460 2242,2 1 0,009258 0,009155 0,00927 0,00903 476 818 6757 48794384 2281 1 0,078089 0,077898 0,07766 0,07813 814 738 7019 04574332 2308,4 1 0,157733 0,157794 0,15779 0,15779 36 497 3411 55844304 2323,4 1 0,104631 0,104524 0,10417 0,10486 107 1 9066 91354260 2347,4 1 0,130690 0,130249 0,13044 0,13005 546 322 5176 34684208 2376,4 1 0,087815 0,087751 0,08783 0,08766 393 054 8988 3124172 2396,9 1 0,091208 0,090879 0,09087 0,09088 037 399 8774 00254132 2420,1 1 0.084648 0,084706 0,08465 0,08476 925 329 164 10194104 2436,6 1 0,082485 0,082364 0,08238 0,08234 5 989 9016 09624060 2463,1 1 0,087068 0,087578 0,08747 0,08768 028 898 5 27954040 2475,2 1 0.067784 0,068366 0,06843 0,06829 043 138 9449 2827Density 15.degree. C. 0,8663 0,86555 0,8646 0,8665% Gasoline 7,4 7,4 7,4 7,4% Benzine 7,6 7,2 7,3 7,2% Naphta 4,3 4,5 4,5 4,5% Petrol 8,5 8,5 8,6 8,4% light gas oil 18,9 18,8 19,2 18,5LGO% Heavy gas oil 4,5 4,5 4,6 4,4% Residue RAT 49 49,2 48,6 49,8% Paraffines 52,2 52,1 53,1 51,2NaphtaFlash Point 59,2 59,5 59,8 59,3PetrolCloud point LGO -8,1 -8,1 -8,8 -7,5% Sulphur RAT 2,8 2,8 2,8 2,9Viscosity 100.degree. C. 53,16 52,72 48,53 56,91RAT__________________________________________________________________________
TABLE 5.1__________________________________________________________________________On line determination of properties of a mixture of crude oilsProximity Mixture 4C 4A 4B.lambda. (cm- Index Estimated 1,27E- 1,35E-1) .lambda. (nm) Weight Measured 1,04E-06 06 06__________________________________________________________________________4672 2140,4 1 0,001777 0,001748 0,00177 0,00172 942 49 1058 4844640 2155,2 1 0,003139 0,003211 0,00325 0,00316 917 2 4971 64684616 2166,4 1 0,003779 0,00327 0,00383 0,00381 11 12 4179 84454584 2181,5 1 0,003794 0,003797 0,00382 0,00376 844 19 8279 4364484 2230,2 1 0,006094 0,006144 0,00627 0,00601 959 61 9999 43224460 2242,2 1 0,009258 0,009155 0,00927 0,00903 476 72 3227 13174384 2281 1 0,078089 0,077898 0,07766 0,07813 814 54 7461 96474332 2308,4 1 0,157733 0,157794 0,15779 0,15779 36 6 336 3364304 2323,4 1 0,104631 0,104524 0,10417 0,10486 107 9419 77824260 2347,4 1 0,130690 0,130249 0,13044 0,13005 546 58 5533 21834208 2376,4 1 0,087815 0,087751 0,08783 0,08766 393 55 5559 85524172 2396,9 1 0,091208 0,090879 0,09087 0,09088 037 88 4188 41884132 2420,1 1 0.084648 0,084706 0,08465 0,08476 925 1 9424 75954104 2436,6 1 0,082485 0,082364 0,08238 0,08234 5 77 7661 84934060 2463,1 1 0,087068 0,087578 0,08747 0,08768 028 65 171 82194040 2475,2 1 0.067784 0,068366 0,06843 0,06829 043 5 3403 5897Density 0,8663 0,86555 0,8646 0,8665% Sulphur 1,6 1,65 1,6 1,7Viscosity 100.degree. C. 2,27 2,265 2,36 2,17% Conradson 4,8 5 4, 5,1Carbon% Paraffin 5 4,95 4,9 5content__________________________________________________________________________
TABLE 6.1__________________________________________________________________________Determination of the Properties of a reformer feedProximity Index Feed 6D 6A 6B 6C.lambda. (nm) Weight Measured Estimated 5,7E-A 8,2E-5 9,7E-5__________________________________________________________________________2210 1 0,04624 0,04659897 0,04648 0,04671 0,046592260 1 0,18118 0,18154437 0,18233 0,18132 0,180852266 1 0,25391 0,25482278 0,25605 0,25439 0,253862276 1 0,33866 0,33942652 0,3412 0,33844 0,338572286 1 0,33776 0,33747772 0,3395 0,33634 0,336522307 1 0,54602 0,54558172 0,54286 0,54922 0,543752328 1 0,38819 0,3877O261 0,38812 0,38791 0,386852344 1 0,4557 0,4561672 0,45568 0,45592 0,457172376 1 0,31751 0,31727184 0,31483 0,32067 0,315432397 1 0,33674 0,33644352 0,33466 0,33858 0,335612408 1 0,31787 0,31746329 0,31737 0,31904 0,315252418 1 0,32524 0,32334235 0,3244 0,32341 0,321862437 1 0,34758 0,34790932 0,34915 0,34543 0,349962457 1 0,38142 0,38057046 0,3793 0,38076 0,38195% Linear Saturated 33,0 32,6 32,5 32,4 32,9% Isoparaffins 30,1 30,8 31,4 31,1 29,9% Naphthenes 29,3 29,2 29,2 28,5 30% Aromatics 7,6 7,4 6,9 8 7,2Density 0,7151 0,7158 0,7152 0,7167 0,7155__________________________________________________________________________
TABLE 7.1__________________________________________________________________________Determination of Properties of feed to FCC reactor and yields propertiesofproductsProximity Feed 7D 7A 7B.lambda. (cm- Index Estimated 1,28E- 1,30E-1) .lambda. (nm) Weight Measured 1,10E-06 06 06__________________________________________________________________________4720 2118,6 1 0,000240 0,0002830 0,00023 0,00032 17 04 8346 76624672 2140,4 1 0,002238 0,0020103 0,00189 0,00212 801 64 0879 9854640 2155,2 1 0,004237 0,0039032 0,00387 0,00393 234 27 4117 23364612 2168,3 1 0,005237 0,0049726 0,00486 0,00507 444 67 6233 91024584 2181,5 1 0,005332 0,0050550 0,00503 0,00507 797 95 1089 91024484 2230,2 1 0,007970 0,0077563 0,00774 0,00776 887 54 4263 84464460 2242,2 1 0,011303 0,0112109 0,01121 0,01120 264 67 2199 97364384 2281 1 0,072994 0,0727349 0,07292 0,07254 455 1 398 58394332 2308,4 1 0,152067 011521593 0,15194 0,15237 643 48 5649 30474304 2323,4 1 0,100517 0,1003975 0,10060 0,10019 606 69 1923 32144260 2347,4 1 0,131209 0,1315142 0,13148 0,13154 247 01 7607 07944212 2374,2 1 0,091618 0,0916231 0,09156 0,09168 024 92 4633 17514168 2399,2 1 0,094011 0,0943229 0,09427 0,09437 773 62 582 01044132 2420,1 1 0,086184 0,0866753 9,08667 0,08667 908 14 8538 2094104 2436,6 1 0,081457 0,0819160 0,08198 0,08185 005 22 1133 09124060 2463,1 1 0,084267 0,0843180 0,08444 0,08419 922 52 4043 2064040 2475,2 1 0,069110 0,0691467 0,06923 0,06905 82 52 9547 3957Density 0,926 0,9225 0,922 0,923% Sulphur 1,97 1,85 1,83 1,87Aniline Point 83,5 83,2 78,2 88,2Viscosity 100.degree. C. 8,8 9,1 8,7 9,5Temp. 50% 461 464 457 471distilledKUOP 11,8 11,85 11,85 11,9Mol. Weight 450,6 449,95 434,5 465,4% Aromatic Carbon 21,8 21,2 21,6 20,9CRACKABILITY 2,47 2,57 2,55 2,59COKABILITY 1,01 1,00 0,99 1,01GASOIL INDEX 1,55 1,515 1,54 1,49GASOLINE INDEX 0,99 0,985 0,99 0,98Gasoline Yield (%) 45 44,5 43 46Residue Yield (%) 12 11,25 13 9,5RON Clear Gasoline 92,7 92,4 92,4 92,4__________________________________________________________________________
TABLE 8.1__________________________________________________________________________On line Determination of properties of feed to hydrogenation unit forgasoline.lambda. Prox. Feed 8F 8A 8B 8C 8D 8E(cm- Index Estimated 0,00000 0,00000 0,00001 0,00001 0,000021) .lambda. (nm) Weight Measured 0,0000042 34 47 14 30 22__________________________________________________________________________8949 1117,5 1 0,006443 0,0064568 0,00608 0,00695 0,00613 0,00650 0,00660 2 7 8 78795 1137 1 0,036107 0,035343 0,03528 0,03595 0,03542 0,03534 0,03470 2 4 8 3 68780 1139 1 0,039287 0,0385 0,03859 0,03909 0,03851 0,03849 0,03781 1 2 6 1 28764 1141 1 0,040899 0,040123 0,04034 0,04071 0,04001 0,04007 0,03946 1 9 9 78737 1144,5 1 0,039495 0,0389040 0,3915 0,03943 0,03864 0,03892 0,03836 2 5 1 3 98688 1151 1 0,027962 0,027745 0,02798 0,02810 0,02718 0,02777 0,02767 3 6 7 4 78673 1153 1 0,024452 0,024293 0,02450 0,02456 0,02374 0,02432 0,02433 1 2 5 1 58651 1156 1 0,020612 0,020536 0,02067 0,02069 0,02008 0,02060 0,02063 1 4 3 38621 1160 1 0,018274 0,018286 0,01833 0,01830 0,01804 0,01838 0,01835 9 6 9 6 28576 1166 1 0,01793 0,018054 0,01810 0,01785 0,01814 0,01814 0,01802 8 8 28565 1167,5 1 0,018035 0,018177 0,01825 0,01792 0,01832 0,01824 0,01813 7 7 1 8 48525 1173 1 0,018845 0,01903 0,01916 0,01862 0,01925 0,01909 0,01901 4 2 6 1 58496 1177 1 0,020612 0,020832 0,02093 0,02037 0,02105 0,02994 0,02085 7 1 8 48446 1184 1 0,0274 0,027769 0,02768 0,02713 0,02817 0,02807 0,02778 2 6 2 2 18418 1188 1 0,031615 0,032149 0,03190 0,03148 0,03262 0,03257 0,03215 6 1 6 1 98389 1192 1 0,033492 0,034192 0,03384 0,03356 0,03445 0,03444 0,03420 5 3 7 2 38347 1198 1 0,031083 0,031498 0,03140 0,03127 0,03152 0,03148 0,03178 9 9 9 7 68326 1201 1 0,028905 0,029171 0,02908 0,02908 0,02913 0,02899 0,02956 5 8 38313 1203 1 0,027531 0,027733 0,02758 0,02772 0,02770 0,02749 0,02816 3 4 8 28285 1207 1 0,024969 0,0251 0,02483 0,02517 0,02513 0,02482 0,02553 2 2 9 78264 1210 1 0,022844 0,022933 0,02267 0,02297 0,02300 0,02266 0,02334 7 7 9 38203 1219 1 0,015306 0,015327 0,01537 0,01522 0,01554 0,01500 0,01548 4 3 2 9 88140 1228,5 1 0,00894 0,0089636 0,00910 0,00886 0,00907 0,00875 0,00901 1 8 8 7 48065 1240 1 0,004327 0,0043406 0,90446 0,00427 0,00439 0,00421 0,00436 7 3 37758 1289 1 0,000897 0,0009398 0,00089 0,00094 0,00091 0,00102 0,00092 5 8 5 18117 1232 1 0,005349 0,0053616 0,00530 0,00559 0,00520 0,00549 0,00521 4 1 9 1 37424 1347 1 0,00869 0,008592 0,00855 0,00879 0,00844 0,00870 0,00846 4 8 7 17396 1352 1 0,012209 0,012095 0,01205 0,01211 0,01192 0,01228 0,01209 6 9 9 37380 1355 1 0,015806 0,015784 0,01567 0,01561 0,01568 0,01613 0,01581 4 4 8 67356 1359,5 1 0,022613 0,022831 0,02263 0,02249 0,02291 0,02345 0,02265 3 9 5 2 67348 1361 1 0,024681 0,024929 0,02475 0,02462 0,02503 0,02553 0,02469 3 3 3 8 87339 1362,5 1 0,026435 0,026707 0,02655 0,02642 0,02681 0,02730 0,02643 2 8 8 1 87321 1366 1 0,029615 0,029773 0,02975 0,02954 0,02984 0,03021 0,02950 9 7 97273 1375 1 0,038104 0,038252 0,03821 0,03774 0,03873 0,03878 0,03777 5 7 8 1 77254 1378,5 1 0,042097 0,042361 0,04233 0,04178 0,04290 0,04293 0,04184 3 9 4 77241 1381 1 0,044261 0,044511 0,04452 0,04395 0,04495 0,04503 0,04408 1 1 5 9 77231 1383 1 0,04541 0,045556 0,04562 0,04501 0,04588 0,04599 0,04526 5 4 2 2 97199 1389 1 0,04833 0,048276 0,04833 0,04766 0,04868 0,04862 0,04808 3 4 47184 1392 1 0,049461 0,049347 0,04944 0,04881 0,04981 0,04959 0,04907 2 2 6 77161 1396,5 1 0,049514 0,049304 0,04952 0,04914 0,04952 0,04921 0,04910 6 9 2 6 97151 1398,5 1 0,04893 0,048706 0,04893 0,04870 0,04873 0,04845 0,04869 7 8 5 4 77117 1405 1 0,0471 0,04703 0,04708 0,04696 0,04664 0,04645 0,04800 3 5 6 5 37105 1407,5 1 0,046793 0,46839 0,04678 0,04657 0,04648 0,04624 0,0481 9 9 4 27087 1411 1 0,045855 0,046047 0,04589 0,04558 0,04582 0,04545 0,04747 4 6 9 4 47070 1414,5 1 0,043481 0,043682 0,04357 0,04322 0,04347 0,04300 0,04513 3 6 2 87018 1425 1 0,036493 0,036481 0,03625 0,03639 0,03626 0,03593 0,03756 2 6 1 3 46991 1430,5 1 0,037461 0,037372 0,03702 0,0375 0,03744 0,03717 0,03772 4 1 46974 1434 1 0,037514 0,037391 0,03716 0,03760 0,03762 0,03726 0,03729 2 5 9 86971 1434,5 1 0,037387 0,037261 0,03706 0,03748 0,03749 0,03713 0,03712 6 6 5 76930 1443 1 0,032307 0,032112 0,03221 0,03245 0,03221 0,03188 0,03179 8 1 7 46849 1460 1 0,022126 0,021897 0,02217 0,02229 0,02180 0,02164 0,02156 7 5 6 2 56824 1465,5 1 0,021591 0,021377 0,02168 0,02174 0,02138 0,02115 0,02092 2 5 4 56752 1481 1 0,018013 0,01796 0,01820 0,01841 0,01773 0,01777 0,01767 3 1 5 4 86720 1488 1 0,016098 0,016058 0,01625 0,01660 0,01575 0,01585 0,01581 7 7 9 5 16693 1494 1 0,014581 0,014542 0,01474 0,01504 0,01436 0,01435 0,0141 1 2 56614 1512 1 0,010568 0,01045 0,01054 0,01107 0,01032 0,01019 0,01011 6 5 1 3 46566 1523 1 0,007881 0,007794 0,00782 0,00843 0,00753 0,00756 0,00761 7 2 3 2 66536 1530 1 0,007116 0,007114 0,00711 0,00772 0,00678 0,00699 0,0069S 7 3 3 76481 1543 1 0,005233 0,0052664 0,00529 0,00589 0,00482 0,00515 0,00516 9 5 1 4 3 GA 612 Estime GA 616 GA 621 GA 406 GA 420 GM 452% Linear Saturated 8,73 8,96 7,73 7,98 10,65 l8,86 9,59% Isoparaffins 6,83 7,35 6,51 6,62 8,8 8,26 6,56% Naphthenes 5,83 5,73 6,38 6,93 5,56 4,87 4,95% Linear Olefins 11,33 11,92 12,41 12,09 10,49 11,33 13,28% Cyclic Olefins 12,79 12,77 12,99 13,68 12,94 11,81 12,44% Benzene 22,46 21,74 21,35 21,38 23,34 21,53 21,1% Toluene 13,72 13,63 13,71 13,51 13,55 13,18 14,18% Xylene 5,08 6,74 5,97 5,57 4,89 11,36 5,9% Alkyl benzene 5,96 5,94 5,98 5,6 5,11 6,84 6,16% Dienes 16,44 15,90 17,67 17,94 14,83 13,57 15,47DENSITY 0,8124 0,8066 0,8133 0,8097 0,8012 0,803 0,8058Yield light cut 25,7 26,7 24,6 25,1 28,5 27,5 27,8Yield heavy cut 40,3 39,4 41,7 41,2 35,7 39,7 38,7Tieid raffinate 13,7 13.3 13,5 13,4 13,7 12,4 13,5Yield benzene 20,3 20,6 20,2 20,3 22,1 20,4 20,0__________________________________________________________________________
TABLE 9.1__________________________________________________________________________Determination of MON of a reformate Reformate 9AProximity Estimated 9B 9C 9D 9E 9FIndex 0,0000581 0,00006 0,00006 0,00018 0,00018 0,00019.lambda. (cm - 1) (nm).sup.2 Weight Measured 24 398 638 149 529 385__________________________________________________________________________4720 2119 1 0,001098 ?? 0,00120 0,00123 0,00076 0,00108 0,00091 1 43 59 051 01 5534670 2141 1 0,017744 ?? 0,01897 0,01653 0,01473 0,01558 0,01558 1 7 14640 2155 1 0,038144 ?? 0,01907 0,01635 0,01428 0,0159 0,01534 6 8 1 44615 2167 1 0,024297 ?? 0,02532 0,02190 0,01943 0,02341 0,02100 4 3 5 4 64585 2181 1 0,020515 ?? 0,02061 0,01786 0,01602 0,01611 0,01642 2 9 1 4 94485 2230 1 0,012619 ?? 0,01188 0,01141 0,01024 0,01136 0,01052 5 5 2 94460 2242 1 0,018197 ?? 0,01700 0,01622 0,01536 0,01671 0,01534 6 1 2 9 44385 2281 1 0,092064 ?? 0,08981 0,09008 0,09165 0,09445 0,08858 3 2 7 64332 2308 1 0,12886 ?? 0,12812 0,13172 0,12815 0,1269 0,134634305 2323 1 0,11882 ?? 1,11606 0,11689 0,1205 0,12271 0,114484260 2347 1 0,098322 ?? 0,09716 0,10023 0,09901 0,9446 0,10223 84210 2375 1 0,064577 ?? 0,06328 0,06512 0,06665 0,06591 0,06736 4 4 4 54170 2398 1 0,061405 ?? 0,06043 0,06342 0,06702 0,06513 0,06598 9 5 6 2 64135 2418 1 0,059296 ?? 0,05848 0,05179 0,06518 0,06324 0,06268 5 4 4 44105 2436 1 0,06198 ?? 0,06197 0,06443 0,06633 0,00207 0,06608 5 8 3 1 84060 2463 1 0,11037 ?? 0,11022 0,1131 0,11289 0,10222 0,11224040 2475 1 0,091698 ?? 0,09428 0,09166 0,09175 0,09691 0,09060 1 1 1 4 4MON 0 00 ?? 88,3 86,2 87,2 89,2 82,4__________________________________________________________________________
TABLE 9.2______________________________________"Sample" MC1 obtained by densificationFractioninMixture.lambda. 9B 9D 9G(cm - 1) .lambda. (nm) MC1 0,889 -0,276 0,387______________________________________4720 2119 0,001175932 0,0012043 0,00076051 0,000812534670 2141 0,017365599 0,018971 0,014737 0,0117654640 2155 0,017767782 0,019076 0,014281 0,0122394615 2167 0,024118931 0,025324 0,019435 0,017964585 2181 0,019041012 0,020612 0,016021 0,0132374485 2230 0,011987639 0,011885 0,010242 0,0109524460 2242 0,017122848 0,017086 0,015362 0,0159124385 2281 0,091829933 0,089813 0,09165 0,0960984332 2308 0,1300549 0,12812 0,12815 0,132814305 2323 0,11911918 0,11606 0,1205 0,126824260 2347 0,09859607 0,09716 0,099018 0,101944210 2375 0,064520009 0,063284 0,066654 0,0687094170 2398 0,061986802 0,060439 0,067026 0,0689634135 2418 0,060186195 0,058485 0,065184 0,067494105 2436 0,062468569 0,061975 0,066333 0,0661874060 2463 0,110237982 0,11622 0,11289 0,0980964040 2475 0,093415832 0,094281 0,091751 0,090004MON00 88,4 88,3 87,2 87,6______________________________________
TABLE 9.3______________________________________"Sample" MC1 obtained by densificationFractioninMixture.lambda. 9B 9C 9D(cm - 1) .lambda. (nm) MC2 1,162 0,24 -0,402______________________________________4720 2119 0,00117871 0,0012043 0,00035436 0,00076051 84670 2141 0,01727589 0,018971 0,0048161 0,014737 24640 2155 0,01792139 0,019076 0,0062335 0,0142814615 2167 0,02374750 0,025324 0,0088912 0,019435 64585 2181 0,01954407 0,020612 0,0084724 0,016021 84485 2230 0,01194680 0,011885 0,0093905 0,010242 64460 2242 0,01715360 0,017086 0,01448 0,015362 84385 2281 0,09142892 0,089813 0,099623 0,09165 64332 2308 0,13153274 0,12812 0,14239 0,128154305 2323 0,11829752 0,11606 0,13282 0,12054260 2347 0,09879868 0,09716 0,1071 0,099018 44210 2375 0,06434942 0,063284 0,073368 0,0666544170 2398 0,06142750 0,060439 0,075591 0,067026 64135 2418 0,05990896 0,058485 0,075639 0,065184 24105 2436 0,06281052 0,061975 0,072756 0,066333 44060 2463 0,11139522 0,11622 0,090539 0,112894040 2475 0,09128022 0,094281 0,07754 0,091751MON00 88,2 88,3 86 87,2______________________________________
TABLE 9.4______________________________________Determination of MON of reformate based on "Samples" generated Reformate 9AProximity Estimated MC1 MC2Index 0,0000092 0,00001 0,00001.lambda. (cm - 1) .lambda. (nm) Weight Measured 71 0043 3457______________________________________4720 2119 1 0,001098 0,0011773 0,00117 0,00117 1 59 874670 2141 1 0,017744 0,017321 0,01736 0,01727 6 64640 2155 1 0,018144 0,017845 0,01776 0,01792 8 24615 2167 1 0,024297 0,0239335 0,02411 0,02374 9 84585 2181 1 0,020515 0,0192925 0,01904 0,01954 1 44485 2230 1 0,012619 0,011968 0,01198 0,01194 8 84460 2242 1 0,018197 0,0171385 0,01712 0,01715 3 44385 2281 1 0,092064 0,0916295 0,09183 0,09142 94332 2308 1 0,12886 0,130795 0,13006 0,131534305 2323 1 0,11882 0,11871 0,11912 0,11834260 2347 1 0,098322 0,098697 0,09859 0,09879 6 84210 2375 1 0,064577 0,064435 0,06452 0,064354170 2398 1 0,061405 0,0617075 0,06198 0,06142 7 84135 2418 1 0,059296 0,0600475 0,06018 0,05990 6 94105 2436 1 0,06198 0,06264 0,06246 0,06281 9 14060 2463 1 0,11037 0,110815 0,11024 0,111394040 2475 1 0,091698 0,092348 0,09341 0,09128 6MON00 0 88 88,3 88,4 88,2______________________________________
TABLE 9.5______________________________________Proximity Estimated 9AIndex 0,00001223 0,00001223.lambda. (cm - 1) .lambda. (nm) Weight Measured 5 5______________________________________4720 2119 1 0,0010702 0,0010981 0,00109814670 2141 1 0,0171 0,017744 0,0177444640 2155 1 0,017768 0,018144 0,0181444615 2167 1 0,024103 0,024297 0,0242974585 2181 1 0,020269 0,020515 0,0205154485 2230 1 0,012224 0,012619 0,0126194460 2242 1 0,018338 0,018197 0,0181974385 2281 1 0,091998 0,092064 0,0920614332 2308 1 0,1306 0,12886 0,128864365 2323 1 0,11841 0,11882 0,118824260 2347 1 0,098802 0,098322 0,0983224210 2375 1 0,06262 0,064577 0,0645774170 2398 1 0,060234 0,061405 0,0614054135 2418 1 0,059762 0,059296 0,0592964105 2436 1 0,062527 0,06198 0,061984060 2463 1 0,11151 0,11037 0,110374040 2475 1 0,092677 0,091698 0,091698MON00 87,9 88 88______________________________________

Claims

1. A method of determining or predicting a value P.sub.x which is a value of a property of a material X or a property of a product of a process from said material or yield of said process, which method comprises measuring the absorption D.sub.ix of said material at more than one wavelength in the region 600-2600 nm, comparing the said absorptions or a derivative thereof with absorptions D.sub.im or derivatives thereof at the same wavelength for a number of standards S in a bank for which the said property or yield P is known, and choosing from the bank at least one standard S.sub.m with property P.sub.m said standard having the smallest average value of the absolute difference at each wavelength I between the absorption D.sub.i x (or derivative thereof) for the material and the absorption D.sub.i m (or derivative thereof) for the standard S.sub.m to obtain P.sub.x, with averaging of said properties or yields P.sub.m when more than one standard S.sub.m is chosen and wherein the standard S.sub.m chosen for the property or yield wanted is such that in relation to the unknown material X and each chosen standard S.sub.m a function i.sub.xm, which is a proximity index defined by i.sup.2 (xm)=.SIGMA.(D.sub.ix D.sub.im).sup.2, is less than a minimal index i.sub.m which has been determined from preselected standards by (a) calculating for each pair of the standards a value of a corresponding proximity index to obtain a series of proximity indices with corresponding property differences, (b) relating values of the proximity indices to corresponding property differences, (c) calculating an average of the corresponding property differences for predetermined values L which are greater than a corresponding proximity index, and (d) calculating the minimal index based on the average property differences and a reproducibility standard for the property.
2. A method according to claim 1, wherein the properties of synthetic standards, which are mixtures, and their spectra for consideration for possible choice for S.sub.m are estimated from existing standards in the bank for which, in respect of each existing standard for use in said mixture equation (4) and (5) are met,
(Min)Cj-uj.ltoreq.Cj.ltoreq.(Max)Cj+uj (4)
and
.SIGMA.Cj=1 (5)
wherein C.sub.j is fraction of component j in the sample i, Min C.sub.j is the minimum of j in the samples for which the method is to be used, Max C.sub.j is the maximum of j in the samples for which the method is to be used, and uj is between 1.0 and 0.05.
3. A method according to claim 2 wherein at least one of (i) the estimated Standards and the corresponding spectra, and (ii) the property P.sub.x of the unknown material and its spectrum, are added to the bank.
4. A method according to claim 1 wherein properties of standards and spectra for consideration for possible choice are estimated by interpolation from measured properties of standards and spectra for which the proximity index with respect to the unknown X is not more than 10 times the minimal Index.
5. A method according to claim 1 wherein the property is a physicochemical property of material X.
6. A method according to claim 1 wherein the property is a physicochemical property or yield of a product of a process to which at least one material X is a feed.
7. A method according to claim 1 wherein said process is a hydrocarbon conversion or separation process.
8. A method according to claim 7 wherein said process is a distillation to give at least 1 distillation product and a residue and the properties/yields are obtained in respect of said product and/or residue.
9. A method according to claim 7 wherein said process is a reforming or catalytic cracking or hydrotreatment, or distillation or blending.
10. A method according to claim 1 wherein said property is in respect of a motor fuel and is at least one of an Octane Number, vapour pressure, volatility percentage distilled at 70.degree. and at 100.degree. C., gum content in mg/100 ml and content of sulphur, benezene or methyl tert. butyl ether.
11. A method according to claim 10 wherein the property is in respect of a blend comprising gasoline, the spectra are measured on feeds to said blending, and by calculation the blend index obtained as a linear or non linear function.
12. A method according to claim 1 wherein said property is in respect of gas oil and is at least one of cetane index, cetane number, percentage of sulphur, density at 15.degree. C., clear point, cloud point, filtrability and viscosity at 40.degree. C.
13. A method according to claim 1 wherein said property is in respect of a crude oil and is at least one of density, percentage of sulphur, viscosity at 100.degree. C., content of paraffin and residual carbon percentage (Conradson Carbon).
14. A method according to claim 1 wherein said property is in respect of a feed to a reforming process and is at least one of percentages of saturated linear, isoparaffins, napthenes, and aromatics and density.
15. A method according to claim 1 wherein said property is in respect of a feed to a fluid catalytic cracking unit and is at least one of the density, the weight percentage of sulphur, the aniline point, viscosity at 100.degree. C., refractive index at 20.degree. C. or 60.degree. C., 50% distillation point, molecular weight, percentage of aromatic carbon and the KUOP, crackability or cokability of the feed or yield of gas, gasoline, gas oil or residue.
16. A method according to claim 1 wherein said percentage is in respect of the feed to a hydrogenation unit and is at least one of percentages of linear saturation, isoparaffins, naphthenes, linear olefins, cylic olefins, benzene, toluene xylene, alkylbenzene, density, or yield of light cut, heavy cut, or raffinate or benzene.
17. A method according to claim 1 wherein the said property or yield of said process is used to control and optimise said process.
18. A method according to claim 1 wherein selected steps are computer implemented.
19. A method according to claim 18 wherein the selected steps include assessing, comparing and choosing.
20. Apparatus suitable for use in the method of claim 1 which comprises an NIR spectrometer receiving at least one signal from a feed or product line in said process and being coupled to a computer to effect continuous measurement of the spectra of the feed and/or product and provide feed back control of the process.
21. A method according to claim 1 wherein the property is a physicochemcial property of material X and wherein said property is in respect of a motor fuel and is at least one of an Octane Number, vapour pressure, volatility percentage distilled at 70.degree. and at 100.degree. C., gum content in mg/100 ml and content of sulphur, benzene or methyl tert. butyl ether.
22. A method according to claim 21 wherein said property or yield of said process is used to control and optimise said process.
23. A method according to claim 1 wherein the property is a physicochemcial property of material X and wherein said property is in respect of gas oil and is at least one of cetane index, cetane number, percentage of sulphur, density at 15.degree. C., clear point, cloud point, filterability and viscosity at 40.degree. C.
24. A method according to claim 23 wherein said property or yield of said process is used to control and optimise said process.
25. A method according to claim 1 wherein the property is a physicochemcial property or yield of a product of a process to which at least one material X is a feed and wherein said property is in respect of a motor fuel and is at least one of an Octane Number, vapour pressure, volatility percentage distilled at 70.degree. and at 100.degree. C., gum content in mg/100 ml and content of sulphur, benzene or methyl tert. butyl ether.
26. A method according to claim 25 wherein said property or yield of said process is used to control and optimise said process.
27. A method according to claim 1 wherein the property is a physicochemcial property or yield of a product of a process to which at least one material X is a feed and wherein said property is in respect of gas oil and is at least one of cetane index, cetane number, percentage of sulphur, density at 15.degree. C., clear point, cloud point, filterability and viscosity at 40.degree. C.
28. A method according to claim 27 wherein said property or yield of said process is used to control and optimise said process.
29. A method for adding an extra synthetic standard to a bank of known standards, each of which relates at least one absorption in the 600-2600 mm region (or derivative thereof) of a known material to a known property related to that material, which property is of said material, or is of a product of a process from said material or yield of said process, which method comprises choosing from the bank at least 2 standards for which equations 4 and 5 are met,
(Min)Cj-uj.ltoreq.Cj.ltoreq.(Max)Cj+uj (4)
and
.SIGMA.Cj=1 (5)
wherein C.sub.j is fraction of component j in a sample i, Min C.sub.j is the minimum of j in samples for which the method is to be used, Max C.sub.j is the maximum of j in the samples for which the method is to be used, and uj is between 1.0 and 0.05,
considering mixing the chosen standards in at least one proportion to produce at least one mixture for use in a synthetic standard, and estimating the spectrum of said mixture according to equation 6
S.sub.mi =.SIGMA.C.sub.ij XS.sub.j
where Sj is the spectrum in the mixture of component j in the calibration matrix, and estimating a property of said mixture according to equation 7
P.sub.mi =.SIGMA.C.sub.ij XP.sub.j
where P.sub.j is the property of component j, and then adding the spectrum and property of each "mixture" to the bank, and using them in at least one model involving a correlation/regression approach to relate NIR spectra to at least one property.
30. A computer programmed to perform the method of determining or predicting a value P.sub.x which is a value of a property of a material X or a property of a product of a process from said material or yield of said process, which method comprises measuring the absorption D.sub.ix of said material at more than one wavelength in the region 600-2600 nm comparing the said absorptions or a derivative thereof with absorptions D.sub.im or derivatives thereof at the same wavelength for a number of standards S in a bank for which the said property or yield P is known, and choosing from the bank at least one standard S.sub.m with property P.sub.m said standard having the smallest average value of the absolute difference at each wavelength I between the absorption D.sub.i x (or derivative thereof) for the material and the absorption D.sub.i m (or derivative thereof) for the standard S.sub.m to obtain P.sub.x, with averaging of said properties or yields P.sub.m when more than one standard S.sub.m is chosen and wherein the standard S.sub.m chosen for the property or yield wanted is such that in relation to the unknown material X and each chosen standard S.sub.m a function i.sub.xm, which is a proximity index defined by i.sup.2 (xm)=.SIGMA.(D.sub.ix D.sub.im).sup.2, is less than a minimal index i.sub.m which has been determined from preselected standards by (a) calculating for each pair of the standards a value of a corresponding proximity index to obtain a series of proximity indices with corresponding property differences, (b) relating values of the proximity indices to corresponding property differences, (c) calculating an average of the corresponding property differences for predetermined values L which are greater than a corresponding proximity index, and (d) calculating the minimal index based on the average property differences and a reproducibility standard for the property.
31. A computer programmed to perform the method of adding an extra synthetic standard to a bank of known standards, each of which relates at least one absorption in the 600-2600 nm region (or derivative thereof) of a known material to a known property related to that material, which property is of said material or is of a product of a process from said material or yield of said process, which method comprises choosing from the bank at least 2 standards for which equations 4 and 5 are met,
(Min)Cj-uj.ltoreq.Cj.ltoreq.(Max)Cj+uj (4)
and
.SIGMA.Cj=1 (5)
wherein C.sub.j is fraction of component j in a sample i, Min C.sub.j is the minimum of j in samples for which the method is to be used, Max C.sub.j is the maximum of j in the samples for which the method is to be used, and uj is between 1.0 and 0.05,
considering mixing the chosen standards in at least one proportion to produce at least one mixture for use in a synthetic standard, and estimating the spectrum of said mixture according to equation 6
S.sub.mi =.SIGMA.C.sub.ij XS.sub.j
where S.sub.j is the spectrum in the mixture of component j in the calibration matrix, and estimating a property of said mixture according to equation 7
P.sub.mi =.SIGMA.C.sub.ij XP.sub.j
where P.sub.j is the property of component j, and then adding the spectrum and property of each "mixture" to the bank, and using them in at least one model involving a correlation/regression approach to relate NIR spectra to at least one property.
32. A computer implemented method for a system including a spectrometer linked to a process line containing a material X, which is a product of a process or feed to a process, a computer linked to the spectrometer, and a controller linked to the computer and the process line, the computer including databanks having stored therein absorptions of standard materials and corresponding properties of said materials, or of products of a process from said materials or yield of said process, the method comprises steps of:
measuring absorption at more than one wavelength in the region 600-2600 nm at the process line and producing absorption signals by the spectrometer in accordance therewith:
assessing the databanks of the computer in accordance with the absorption signals:
comparing, by the computer, the absorption signals to the absorptions of the standard materials stored in the databanks;
choosing at least one standard based on the comparing, said standard having the smallest average value of the absolute difference at each wavelength i between the absorption (or derivative thereof) for the material and the absorption (or derivative thereof) for the standards, with averaging of said properties or yields when more than one standard is chosen, and
controlling said process line in accordance with the outputted property/yield,
wherein the standard chosen for the property or yield wanted is such that in relation to the unknown material X and each chosen standard a function i.sub.xm, which is a proximity index defined by i.sup.2 (xm)=.SIGMA.(D.sub.ix D.sub.im).sup.2, is less than a minimal index i.sub.m which has been determined from preselected standards by (a) calculating for each pair of the standards a value of a corresponding proximity index to obtain a series of proximity indices with corresponding property differences, (b) relating values of the proximity indices to corresponding property differences, (c) calculating an average of the corresponding property differences for predetermined values L which are greater than a corresponding proximity index, and (d) calculating the minimal index based on the average property differences and a reproducibility standard for the property.

Priority Claims (1)

Number	Date	Country	Kind
94430009	Oct 1994	GBX

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