CN111595811A - Crude oil blending method - Google Patents
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- 229910052717 sulfur Inorganic materials 0.000 claims description 14
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
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- C—CHEMISTRY; METALLURGY
- C10—PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS; LUBRICANTS; PEAT
- C10G—CRACKING HYDROCARBON OILS; PRODUCTION OF LIQUID HYDROCARBON MIXTURES, e.g. BY DESTRUCTIVE HYDROGENATION, OLIGOMERISATION, POLYMERISATION; RECOVERY OF HYDROCARBON OILS FROM OIL-SHALE, OIL-SAND, OR GASES; REFINING MIXTURES MAINLY CONSISTING OF HYDROCARBONS; REFORMING OF NAPHTHA; MINERAL WAXES
- C10G35/00—Reforming naphtha
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
-
- C—CHEMISTRY; METALLURGY
- C10—PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS; LUBRICANTS; PEAT
- C10G—CRACKING HYDROCARBON OILS; PRODUCTION OF LIQUID HYDROCARBON MIXTURES, e.g. BY DESTRUCTIVE HYDROGENATION, OLIGOMERISATION, POLYMERISATION; RECOVERY OF HYDROCARBON OILS FROM OIL-SHALE, OIL-SAND, OR GASES; REFINING MIXTURES MAINLY CONSISTING OF HYDROCARBONS; REFORMING OF NAPHTHA; MINERAL WAXES
- C10G2300/00—Aspects relating to hydrocarbon processing covered by groups C10G1/00 - C10G99/00
- C10G2300/20—Characteristics of the feedstock or the products
- C10G2300/201—Impurities
- C10G2300/202—Heteroatoms content, i.e. S, N, O, P
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- C—CHEMISTRY; METALLURGY
- C10—PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS; LUBRICANTS; PEAT
- C10G—CRACKING HYDROCARBON OILS; PRODUCTION OF LIQUID HYDROCARBON MIXTURES, e.g. BY DESTRUCTIVE HYDROGENATION, OLIGOMERISATION, POLYMERISATION; RECOVERY OF HYDROCARBON OILS FROM OIL-SHALE, OIL-SAND, OR GASES; REFINING MIXTURES MAINLY CONSISTING OF HYDROCARBONS; REFORMING OF NAPHTHA; MINERAL WAXES
- C10G2300/00—Aspects relating to hydrocarbon processing covered by groups C10G1/00 - C10G99/00
- C10G2300/20—Characteristics of the feedstock or the products
- C10G2300/30—Physical properties of feedstocks or products
-
- C—CHEMISTRY; METALLURGY
- C10—PETROLEUM, GAS OR COKE INDUSTRIES; TECHNICAL GASES CONTAINING CARBON MONOXIDE; FUELS; LUBRICANTS; PEAT
- C10G—CRACKING HYDROCARBON OILS; PRODUCTION OF LIQUID HYDROCARBON MIXTURES, e.g. BY DESTRUCTIVE HYDROGENATION, OLIGOMERISATION, POLYMERISATION; RECOVERY OF HYDROCARBON OILS FROM OIL-SHALE, OIL-SAND, OR GASES; REFINING MIXTURES MAINLY CONSISTING OF HYDROCARBONS; REFORMING OF NAPHTHA; MINERAL WAXES
- C10G2300/00—Aspects relating to hydrocarbon processing covered by groups C10G1/00 - C10G99/00
- C10G2300/20—Characteristics of the feedstock or the products
- C10G2300/30—Physical properties of feedstocks or products
- C10G2300/308—Gravity, density, e.g. API
Abstract
The invention provides a crude oil blending method. The blending method of the crude oil is described in the specification. The method can quickly and accurately generate a crude oil blending formula suitable for producing ethylene or reforming naphtha based on the near infrared spectrum, the crude oil property and the naphtha property and composition of crude oil, and the crude oil depot of the formula components is open and is simple to maintain.
Description
Technical Field
The invention relates to the field of crude oil processing, in particular to a crude oil blending method.
Background
With the development of national economy, the requirements of finished oil and chemical products in China are increasingly raised, and the crude oil serving as the raw material of the finished oil and the chemical product is greatly limited by global energy supply. According to the development report of oil and gas industry at home and abroad in 2017 issued by the economic and technical institute of the China Petroleum group, the apparent consumption of petroleum in 2017 in China reaches 5.9 hundred million tons, the net import quantity of petroleum reaches 3.96 hundred million tons, and the external dependence reaches 67.4 percent. The increase in the degree of external dependence not only means an increase in the number of crude oil processed but also reflects the diversification of the crude oil varieties, and increases the degree of freedom in the selection of refinery crude oils and also brings uncertainty in the operation of refineries.
Naphtha is a light component in crude oil before 220 ℃, and refineries mainly use the naphtha as reforming and chemical raw materials and have various distillation ranges according to different purposes. Naphtha for various uses has its own characteristics, such as naphtha for ethylene cracking feedstock requires high paraffin content, naphtha for reforming feedstock requires high naphthenes and aromatics content. The different requirements dictate that the crude oil from which the naphtha is obtained cannot be purchased and processed at will. For the processing route of naphtha as ethylene cracking material, it is preferable to select crude oil having a higher paraffin content of paraffin base or intermediate partial paraffin base, and for the processing route of naphtha as reforming material, it is preferable to select crude oil of naphthene base or intermediate partial naphthene base. This particular demand for crude oil makes the procurement and processing of multiple crude oils difficult. If the mixed crude oil with the property close to that of the target crude oil and the naphtha composition can be obtained by blending a plurality of different crude oils, the crude oil selection scope of a refinery can be increased, not only the enterprise benefit can be improved from the crude oil purchasing level, but also the safe production period can be improved from the raw material stabilizing level, and the unplanned shutdown of the enterprise is reduced.
There are several references disclosing methods for obtaining a blended crude oil with properties similar to the properties of the target crude oil by blending several different crude oils. Ma Wei et al have established a multi-objective crude oil selection and mixed optimization model, can turn into crude oil selection and mixed optimization problem with seeking the substitute crude oil. On the basis of an original multi-target cuckoo search (MOCS) algorithm, a coding and Levy flight algorithm are improved, and an improved multi-target cuckoo search (IMOCS) algorithm is provided by combining a non-dominant sorting method. The selection and mixing proportion of the crude oil can be determined simultaneously by solving the model by using an IMOCS algorithm. CN102663221A discloses a multi-crude oil multi-property blending optimization method, which is to calculate the blending mass ratio of crude oils with various components to make various properties of a target crude oil reach optimal values, and the method comprises the following specific steps: establishing a crude oil property blending rule base, carrying out optimization pretreatment, setting equipment constraint size according to the actual working capacity of crude oil blending equipment, establishing a multi-crude oil multi-property optimization model, carrying out optimization calculation based on a constrained adaptive particle swarm optimization algorithm, and obtaining the optimal proportion of crude oil with various components. The method has the advantages of poor calculation consistency, low calculation efficiency and frequent occurrence of the condition that the obtained mixture ratio is not the optimal ratio. CN102643662A discloses a crude oil blending optimization method, which is oriented to crude oil processing enterprises and supports minimum deviation of property content and target of blended crude oil, namely, under the condition of meeting a certain constraint condition, the proportion of various blended crude oils is obtained by solving the deviation function of the related property content and the set target of each blended crude oil in a minimum way. For safety and economic considerations, the method also supports three optimization modes of the crude oil property content, namely, in-range value determination, lower limit optimization and upper limit optimization. The method also supports an economic optimal optimization target, namely the minimum deviation of the property content of the blended crude oil from the target is considered, and meanwhile, the production cost is minimized.
Some of the methods have complex calculation process, large calculation amount and long time consumption. Some multi-crude oil multi-property optimization models are established based on blending rule base and equipment constraint, and the range of selectable crude oil is small. And there is no report on blending of crude oils suitable for producing a suitable naphtha feedstock.
Disclosure of Invention
The invention provides a crude oil blending method.
The crude oil blending method comprises the following steps:
s1, collecting crude oil samples with known properties (preferably, the properties of the crude oil are measured by a standard method), forming a crude oil library with formula components, measuring the near infrared spectrum of each crude oil sample, performing real boiling point distillation on the crude oil samples to obtain naphtha, and measuring the properties and the composition of the naphtha by the standard method;
s2, after first order differentiation and vector normalization are carried out on the near infrared spectrum of the crude oil sample, a characteristic spectrum region (preferably 4000 cm) is taken-1～4700cm-1And 5300cm-1～6000cm-1Characteristic spectrum region) to form a near infrared spectrum matrix X of the formula component crude oil library;
s3, performing principal component analysis on the near infrared spectrum matrix X of the formula component crude oil library to obtain a score matrix and a load matrix of the crude oil sample, and establishing a score-physical matrix X' of the formula component crude oil library by using the score matrix and corresponding crude oil properties and naphtha properties;
s4, measuring the near infrared spectrum of the target crude oil according to the same method as the step S2, performing first order differentiation and vector normalization, and taking a characteristic spectrum area (preferably 4000 cm)-1～4700cm-1And 5300cm-1～6000cm-1Characteristic spectral region) to form near infrared spectrum data of the target crude oil, calculating the score of the target crude oil according to a load matrix of the crude oil sample, and forming a score-property vector T of the target crude oil according to crude oil properties, naphtha properties and composition of the target crude oil;
s5, listing all combinations C of n crude oil combinations from the crude oil depot of the formula components in a permutation and combination modem nWherein m is the number of samples in the crude oil reservoir of the recipe components, fitting the score-property matrix XX' of the n crude oil samples in each combination and the score-property vector T of the target crude oil according to equation ① subject to the score-property vector T of the target crude oil:
in formula ①, T is the score-property vector, XX'iIs the score-physical matrix X' of the ith crude oil of the formula component crude oil library-the score-property vector, k is the number of the spectrum of the formula component crude oil library, aiFitting coefficients corresponding to the ith crude oil in the crude oil reservoir of the formula components;
wherein the fitting coefficient aiIs obtained by non-negative-constrained least squares method, i.e. satisfies the following objective function：
Fitting coefficient a obtained by the equation ②iAll non-zero fitting coefficients in the data are extracted and normalized according to the formula ③ to obtain a normalized fitting coefficient bi：
In the formula ③, g is the number of non-zero fitting coefficients, biThe corresponding proportion of each component crude oil participating in blending the target crude oil in the formula component crude oil depot is determined;
s6, calculating the property y of the formula crude oil according to the formula,
in said formula ④, biAs fitting coefficient, qiThe property values corresponding to crude oil samples in the crude oil depot of the formula components;
calculating the membership degree of each property of the blended crude oil according to the formula,
in said formula ⑤,. mu.jFor the jth property of the blended crude oil to correspond to the degree of membership, y, of the jth property of the target crude oiljα for property j of blended crude oiljFor the j property, σ, of the target crude oiljA membership standard variance for the jth property;
s7, calculating the similarity S of each combined blended crude oil and the target crude oil,
in the formula, cjIs the influence factor of the j-th property, mujTo blend the jth of the crude oilThe membership degree of the property, and q is the number of the crude oil properties used for calculating the similarity S;
and S8, taking the combination with the similarity larger than a preset value, and taking the combination as a final crude oil blending formula according to the mixing proportion of the n crude oils.
According to an embodiment of the present invention, the number of the crude oil samples in the step S1 is 100 or more.
According to another embodiment of the present invention, the resolution of the near infrared spectrum of the crude oil sample measured in the step S1 is 4cm-1Or 8cm-1The number of scans was 16 or 32.
According to another embodiment of the present invention, the crude oil property in the step S1 is selected from one or more of crude oil density, acid number, sulfur content, carbon residue, wax content, gum content, and asphaltene content; the naphtha properties and composition are selected from one or more of naphtha yield, density, sulfur content, acid value, normal paraffins, isoparaffins, naphthenes, and aromatics.
According to another embodiment of the present invention, the score matrix and the load matrix of the crude oil sample are calculated in the step S3 in the formula @,
X＝Tx×P ⑥
in the formula ⑥, X is a near infrared spectrum matrix of the crude oil depot of the formula components, namely an m × n matrix and TxThe method comprises the steps of obtaining a crude oil sample, obtaining a crude oil sample score matrix, obtaining a crude oil sample load matrix, and obtaining a crude oil sample load matrix, wherein the crude oil sample score matrix is an m × c matrix, the crude oil sample load matrix is a c × n matrix, m is the number of crude oil samples in a formula component crude oil bank, n is the.
According to another embodiment of the present invention, the amount of the main component in the step S3 is 10 to 30.
According to another embodiment of the present invention, the score of the target crude oil is calculated according to formula (c) in the step of S4,
tu＝xu×P′ ⑦
in said formula ⑦, tuIs the score, x, of the target crude oiluIs near red of the target crude oilAnd (3) external spectrum data, wherein P 'is a transposed matrix of a load matrix of the crude oil sample, and P' is a matrix of n × c, wherein n is the number of wave number sampling points of the near infrared spectrum of the crude oil sample, and c is the number of principal components.
According to another embodiment of the present invention, n in the step of S5 is an integer of 2 to 10, preferably an integer of 2 to 5.
According to another embodiment of the present invention, the similarity S in S7 is calculated as follows,
S＝(0.8μdensity of crude oil+0.6μSulfur content of crude oil+0.4μAcid value of crude oil+0.4μMetal content of crude oil+0.4μNaphtha yield+0.4μDensity of naphtha+0.4μNaphtha normal alkane+0.4μNaphtha cycloalkanes+0.4μNaphtha aromatics)/4.2。
According to another embodiment of the present invention, the predetermined value in the step of S8 is greater than 0.9.
The method can quickly and accurately generate a crude oil blending formula suitable for producing ethylene or reforming naphtha based on the near infrared spectrum, the crude oil property and the naphtha property and composition of crude oil, and the crude oil depot of the formula components is open and is simple to maintain.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In the context of the present specification, anything or things which are not mentioned, except where explicitly stated, are directly applicable to those known in the art without any changes. Moreover, any embodiment described herein may be freely combined with one or more other embodiments described herein, and the technical solutions or ideas thus formed are considered part of the original disclosure or original disclosure of the present invention, and should not be considered as new matters not disclosed or contemplated herein, unless such combination is clearly unreasonable.
Unless otherwise expressly indicated, all percentages, parts, ratios, etc. referred to in this specification are by mass unless otherwise not conventionally recognized by those of skill in the art.
The crude oil blending method is a crude oil blending method which is suitable for producing ethylene or reforming naphtha and is screened based on the near infrared spectrum, the crude oil property, the naphtha property and the composition of crude oil, and comprises the steps of obtaining or measuring the near infrared spectrum and the physical property data of various crude oil samples, and the naphtha property and the composition data. The method comprises the steps of carrying out first-order differential and vector normalization processing on the near infrared spectrum of crude oil, selecting absorbance of a characteristic wave band to establish a near infrared spectrum matrix of a formula component crude oil library, carrying out principal component analysis on the matrix to obtain a score matrix and a load matrix, and forming the score matrix of the formula component crude oil library, the properties of the selected crude oil, the properties of naphtha and the composition into a score-physical property matrix of the formula component crude oil library. And measuring the near infrared spectrum of the target crude oil, performing first-order differentiation and vector normalization treatment, selecting a characteristic waveband, calculating a score matrix of the target crude oil according to a load matrix of a formula component crude oil reservoir, and forming a score-property vector of the target crude oil together with corresponding key physical properties. And (3) calculating a fitting coefficient by using the score-property vector of the target crude oil as a dependent variable and the score-physical property matrix of the formula component crude oil reservoir as an independent variable through a non-negative least square method, and normalizing all non-zero fitting coefficients to obtain the blending proportion of each component crude oil participating in blending the target crude oil. The names and respective blending ratios of the crude oils of the components are the blending formula of the target crude oil.
Specifically, in step S1, crude oil samples with known properties are collected to form a crude oil pool of formulation components, the near infrared spectrum of each crude oil sample is measured, the crude oil properties are measured by a standard method, the crude oil is subjected to real boiling point distillation to obtain naphtha, and the properties and the composition of the naphtha are measured by a standard method. The number of crude oil samples in the formula component crude oil depot is preferably more than 200. The resolution for measuring the near infrared spectrum of the crude oil sample is 4cm-1Or 8cm-1The number of scans was 16 or 32. The crude oil property may be selected from one or more of crude oil density, acid number, sulfur content, carbon residue, wax content, gum content, and asphaltene content. Naphtha Properties and groupsThe composition may be selected from one or more of naphtha yield, density, sulfur content, acid value, normal paraffins, isoparaffins, naphthenes, and aromatics. The crude oil properties and naphtha properties and compositions are not limited to those listed above, but may be any other suitable properties, which can be determined by those skilled in the art according to actual needs.
Then, in step S2, the first order differentiation and vector normalization are performed on the near infrared spectrum of the crude oil sample, and 4000cm is taken-1～4700cm-1And 5300cm-1～6000cm-1And the absorbance of the characteristic spectrum region forms a near infrared spectrum matrix X of the crude oil library of the formula components. The vector normalization method preferably employs the following method:
first, the average value of the absorbance of the spectrum to be processed is calculated, and then this average value is subtracted from the absorbance of the spectrum to obtain the absorbance difference, and then the sum of squares of the absorbance differences is calculated, and finally the square of this sum of squares is divided by the absorbance difference. As shown in formula (viii):
in formula ⑧, AiIs the spectral data (absorbance), A 'at a certain sampling point of any one sample to be processed'iThe spectrum data at the sampling point after vector normalization processing is carried out, and n is the number of sampling points of the wave number in the near infrared spectrum characteristic region of the crude oil sample.
Then, in step S3, the near infrared spectrum matrix X of the crude oil library of the formulation components is subjected to principal component analysis to obtain a score matrix and a load matrix of the crude oil sample, and the score-physical property matrix X' of the crude oil library of the formulation components is established by the score matrix and corresponding crude oil properties, naphtha properties and compositions.
In the step S3, the scoring matrix and the loading matrix of the crude oil sample can be calculated according to the formula [ ],
X＝Tx×P ⑥
in the formula ⑥, X is a near infrared spectrum matrix of the crude oil depot of the formula components, namely an m × n matrix, and T isxScoring matrix for crude oil samplesThe method comprises the steps of obtaining a crude oil sample load matrix, wherein the crude oil sample load matrix is an m × c matrix, P is a crude oil sample load matrix and is a c × n matrix, m is the number of crude oil samples in a formula component crude oil library, n is the number of wave number sampling points of a crude oil sample near infrared spectrum, and c is the number of main components, wherein the number of the main components can be selected to be proper according to requirements, and is 10-30 for example.
Thereafter, in step S4, the near infrared spectrum of the target crude oil was measured in the same manner as in step S2, and after first order differentiation and vector normalization, 4000cm was taken-1～4700cm-1And 5300cm-1～6000cm-1And the absorbance of the characteristic spectrum region forms near infrared spectrum data of the target crude oil, the score of the target crude oil is calculated by the load matrix of the crude oil sample, and the score-property vector T of the target crude oil is formed by the crude oil property, the naphtha property and the composition of the target crude oil.
The score of the target crude oil may be calculated according to formula (c) in step S4,
tu＝xu×P′ ⑦
in formula ⑦, tuIs the score, x, of the target crude oiluThe method comprises the steps of taking near infrared spectrum data of target crude oil, wherein P 'is a transposed matrix of a load matrix of a crude oil sample, and P' is a matrix of n × c, wherein n is the number of wave number sampling points of the near infrared spectrum of the crude oil sample, and c is the number of principal components.
Thereafter, in step S5, all combinations C of the n crude oil groups are listed in the arrangement combination from the recipe component crude oil poolm nWhere m is the number of samples in the recipe component crude oil library, fitting the score-property matrix XX' for n crude oils in each combination and the score-property vector T for the target crude oil according to equation ① with the score-property vector T for the target crude oil as the target:
in formula ①, T is the score-property vector, XX 'of the target crude oil'iIs a score-physical matrix X' of the ith crude oil of a formula component crude oil reservoir and a score-property vector k is of a formula component crude oil reservoir spectrumNumber, aiThe fitting coefficient corresponding to the ith crude oil in the crude oil library of the formula components.
The method for establishing the score-property matrix XX' of the n crude oils comprises the following steps: and extracting data corresponding to the crude oil numbers forming the combination from a score-property matrix X ' of a formula component crude oil library, wherein if the combination selects three crude oils with the numbers of i, j and k, the first column of the XX ' matrix is the ith column of the X ', the second column of the XX ' matrix is the jth column of the X ', and the third column of the XX ' matrix is the kth column of the X '.
In formula ①, the fitting coefficient aiThe method is solved by adopting a non-negative constraint least square method, namely the following objective functions are satisfied:
specific algorithms for non-negative constrained least squares are described in the literature: L.Lawson and R.J.Hanson, solvent Least Squares reports, Prentice-Hall, Englewood Cliffs, NJ (1974); 160-165.
Fitting coefficient a obtained by equation ②iAll non-zero fitting coefficients in the data are extracted and normalized according to the formula ③ to obtain a normalized fitting coefficient bi：
In formula ③, g is the number of non-zero fitting coefficients, biIs the corresponding proportion of each component crude oil participating in the blending of the target crude oil in the formula component crude oil depot.
The value of n can be 2-10, preferably 2-5, for example 2, 3 or 4. Taking 3 as an example, all possible combinations of the three crude oils are listed from the recipe component crude oil library in a permutation and combination manner, and then the score-property matrix of the three crude oils in each combination and the score-property vector T of the target crude oil are sequentially used for fitting according to the method in the step S5. The crude oils involved in the fitting were 2 and 4 cases, and the procedure was the same.
Then, the property y of the formulated crude oil is calculated in the step S6 according to the formula,
in formula ④, biAs fitting coefficient, qiCalculating the membership degree of each property of the blended crude oil according to a formula ⑤,
in formula ⑤,. mu.jFor the jth property of the blended crude oil to correspond to the degree of membership, y, of the jth property of the target crude oiljFor the jth property of the blended crude, αjFor the j property, σ, of the target crude oiljAnd the standard variance of the membership grade of the jth property is an adjustable parameter. For fixed yjAnd aj，σjThe value of the function of the membership degree is determined and defined as the variance of the membership degree, and the value is given according to experience and can be the data shown in the table 1.
The similarity of the formula crude oil and the target crude oil is obtained by multiplying membership function of each property of the formula crude oil and the target crude oil by respective influence factors, and the result can comprehensively describe the similarity of the formula crude oil and the target crude oil. Namely, in the step S7, the similarity S between each combined blended crude oil and the target crude oil is calculated,
in the formula, cjIs the influence factor of the j-th property, mujTo reconcile the membership of the jth property of the crude, q is the number of crude properties used to calculate the similarity S. Taking crude oil properties such as crude oil density, crude oil sulfur content, crude oil acid value, crude oil metal content, naphtha yield, naphtha density, naphtha normal paraffin, naphtha naphthene, and naphtha aromatics as examples, the similarity S can be calculated by the following formula, where S ═ 0.8 μmDensity of crude oil+0.6μSulfur content of crude oil+0.4μAcid value of crude oil+0.4μMetal content of crude oil+0.4μNaphtha yield+0.4μDensity of naphtha+0.4μNaphtha normal alkane+0.4μNaphtha cycloalkanes+0.4μNaphtha aromatics)/4.2. Those skilled in the art will appreciate that the above formula is merely exemplary and that the appropriate crude oil properties and their corresponding degrees of membership may be selected on a case-by-case basis.
Finally, in step S8, the combination with similarity greater than the predetermined value is taken and the blending ratio of the n crude oils is used as the final crude oil blending formula. Preferably, the predetermined value is greater than 0.9.
TABLE 1 statistics of variance of properties at membership calculation
Example 1
1. Constructing a spectral library of crude oil samples
500 representative crude oil samples were collected, the crude oil varieties essentially covering the major crude oil producing regions of the world. The near infrared spectrum of the crude oil sample is measured and subjected to first order differentiation and vector normalization. Measuring properties such as crude oil density, acid value, sulfur content, carbon residue, wax content, colloid content, asphaltene content and the like by using the conventional method; the crude oil is subjected to real boiling point distillation to obtain naphtha fraction, and the properties of the naphtha such as yield, density, sulfur content, acid value, normal paraffin, isoparaffin, cycloparaffin, aromatic hydrocarbon and the like are measured. The spectrum of crude oil and its corresponding 14 property data arrays (Y)14×500) And forming a crude oil spectrum database.
2. Establishing a score-physical matrix for a crude oil sample
Performing principal component analysis on a near infrared spectrum matrix of a formula component crude oil library, taking the first 10 principal components to obtain a score matrix and a load matrix of a library sample, and establishing a score-physical property matrix X of the formula component crude oil library by using the score matrix of the library sample and corresponding key physical properties24×500’。
3. Obtaining a score-property vector for a target crude oil
The near infrared spectrum of the target crude oil was measured in the same mannerAfter the first order differential sum vector normalization, 4000cm is taken-1～4700cm-1And 5300cm-1～6000cm-1And the absorbance of the characteristic spectrum region forms the near infrared spectrum data of the target crude oil. Calculating the score of the target crude oil by using the load matrix of the library sample, and forming a score-property vector T of the target crude oil with corresponding key physical properties24×1。
4. Fitting score-property vectors for target crude oils
Score-property vector T with target crude oil24×1Based on a fitted library matrix P24×500And fitting the crude oil to be tested. Calculating the properties of the formulated crude oil based on the fitting coefficients and the corresponding properties of the library spectral samples involved in the fitting, which may be calculated from the formula
And b represents a normalized fitting coefficient, and q represents a corresponding property value of the library spectrum sample. The properties of the formulated crude oil and the target crude oil are compared in table 2.
TABLE 2 comparison of Properties of formulated and target crudes
5. Mixing ratio of formula crude oil
Table 3 shows the blending ratio of the formulated crude oils obtained with the number of the crude oils involved in the fitting set to 3, and table 4 shows the blending ratio of the formulated crude oils obtained with the number of the crude oils involved in the fitting set to 4.
TABLE 3 blending ratio of formulated crude oils obtained with 3 crude oils participating in fitting
| Serial number | Participate in mixing the crude oil to compileNumber (C) | Mixing ratio |
| 1 | 023 | 0.107 |
| 2 | 168 | 0.513 |
| 3 | 256 | 0.380 |
TABLE 4 blend ratio of formulated crude oils obtained with 4 crude oils participating in the fitting
| Serial number | Participating in blending crude oil numbering | Mixing ratio |
| 1 | 086 | 0.346 |
| 2 | 155 | 0.287 |
| 3 | 239 | 0.128 |
| 4 | 358 | 0.239 |
Example 2
1. Constructing a spectral library of crude oil samples frequently processed in a certain refinery
27 crude oil samples frequently processed by the refinery are collected, and the near infrared spectrum of the crude oil samples is measured and subjected to first order differentiation and vector normalization. Measuring properties such as crude oil density, acid value, sulfur content, carbon residue, wax content, colloid content, asphaltene content and the like by using the conventional method; the crude oil is subjected to real boiling point distillation to obtain naphtha fraction, and the properties of the naphtha such as yield, density, sulfur content, acid value, normal paraffin, isoparaffin, cycloparaffin, aromatic hydrocarbon and the like are measured. The spectrum of crude oil and its corresponding 14 property data arrays (Y)14×500) And forming a crude oil spectrum database.
2. Establishing a score-physical matrix for a crude oil sample
Performing principal component analysis on a near infrared spectrum matrix of a formula component crude oil library, taking the first 10 principal components to obtain a score matrix and a load matrix of a library sample, and establishing a score-physical property matrix X of the formula component crude oil library by using the score matrix of the library sample and corresponding key physical properties24×27’。
3. Obtaining a score-property vector for a target crude oil
Measuring the near infrared spectrum of the target crude oil according to the same method, and taking 4000cm after first order differentiation and vector normalization-1～4700cm-1And 5300cm-1～6000cm-1And the absorbance of the characteristic spectrum region forms the near infrared spectrum data of the target crude oil. Calculating the score of the target crude oil by using the load matrix of the library sample, and forming a score-property vector T of the target crude oil with corresponding key physical properties24×1。
4. Fitting score-property vectors for target crude oils
Score-property vector with target crude oilT24×1Based on a fitted library matrix P24×27And fitting the crude oil to be tested. Calculating the properties of the formulated crude oil based on the fitting coefficients and the corresponding properties of the library spectral samples involved in the fitting, which may be calculated from the formula
And b represents a normalized fitting coefficient, and q represents a corresponding property value of the library spectrum sample. In view of the possibility of industrial operation, a two-component and three-component solution was chosen and the properties of the formulated and target crudes were compared in table 5.
TABLE 5 comparison of Properties of formulated and target crudes
5. Mixing ratio of formula crude oil
Table 6 shows the blending ratio of the formulated crude oils obtained with the number of the crude oils involved in the fitting set to 2, and table 7 shows the blending ratio of the formulated crude oils obtained with the number of the crude oils involved in the fitting set to 3.
TABLE 6 blending ratio of formulated crude oils obtained with 2 crude oils participating in fitting
| Serial number | Participating in mixing crude oil | Mixing ratio |
| 1 | Crude oil A | 0.30 |
| 2 | Crude oil B | 0.70 |
TABLE 7 blend ratio of formulated crude oils obtained with 3 crude oils participating in the fitting
| Serial number | Participating in mixing crude oil | Mixing ratio |
| 1 | Crude oil A | 0.24 |
| 2 | Crude oil C | 0.56 |
| 3 | Crude oil D | 0.20 |
6. Actual blending of crude oil processing conditions
Actual processing tests were conducted according to the crude oil blending scheme of the two components in Table 5, the test results are shown in Table 8, and the predicted values and the target crude oil property values are also shown in Table 8. From Table 8, it can be seen that the properties of the actual processed formulated crude and the calculated formulated crude are very similar, which is also the result obtained in the presence of adverse disturbance factors of the tank bottoms, and that the method of the present invention is very effective. (Note: in actual production practice, crude oil storage tanks cannot store each crude oil in only one tank, so that the properties of the crude oil in each tank are influenced by the original stored oil.)
TABLE 8 comparison of Properties of formulated crude and actual processed crude
The present invention is capable of other embodiments, and various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention.
Claims (10)
1. A crude oil blending method comprises the following steps:
s1, collecting crude oil samples with known properties (preferably, the properties of the crude oil are measured by a standard method), forming a crude oil library with formula components, measuring the near infrared spectrum of each crude oil sample, performing real boiling point distillation on the crude oil samples to obtain naphtha, and measuring the properties and the composition of the naphtha by the standard method;
s2, after first order differentiation and vector normalization are carried out on the near infrared spectrum of the crude oil sample, a characteristic spectrum region (preferably 4000 cm) is taken-1～4700cm-1And 5300cm-1～6000cm-1Characteristic spectrum region) to form a near infrared spectrum matrix X of the formula component crude oil library;
s3, performing principal component analysis on the near infrared spectrum matrix X of the formula component crude oil library to obtain a score matrix and a load matrix of the crude oil sample, and establishing a score-physical matrix X' of the formula component crude oil library by using the score matrix and corresponding crude oil properties and naphtha properties;
s4, measuring the near infrared spectrum of the target crude oil according to the same method as the step S2, performing first order differentiation and vector normalization, and taking a characteristic spectrum area (preferably 4000 cm)-1～4700cm-1And 5300cm-1～6000cm-1Characteristic spectral region) constitutes the near infrared spectral data of the target crude oil,calculating the score of the target crude oil according to the load matrix of the crude oil sample, and forming a score-property vector T of the target crude oil according to the crude oil property, naphtha property and composition of the target crude oil;
s5, listing all combinations C of n crude oil combinations from the crude oil depot of the formula components in a permutation and combination modem nWherein m is the number of samples in the crude oil reservoir of the recipe components, fitting the score-property matrix XX' of the n crude oil samples in each combination and the score-property vector T of the target crude oil according to equation ① subject to the score-property vector T of the target crude oil:
in formula ①, T is the score-property vector, XX'iIs the score-physical matrix X' of the ith crude oil of the formula component crude oil library-the score-property vector, k is the number of the spectrum of the formula component crude oil library, aiFitting coefficients corresponding to the ith crude oil in the crude oil reservoir of the formula components;
wherein the fitting coefficient aiThe method is solved by adopting a non-negative constraint least square method, namely the following objective functions are satisfied:
fitting coefficient a obtained by the equation ②iAll non-zero fitting coefficients in the data are extracted and normalized according to the formula ③ to obtain a normalized fitting coefficient bi：
In the formula ③, g is the number of non-zero fitting coefficients, biThe corresponding proportion of each component crude oil participating in blending the target crude oil in the formula component crude oil depot is determined;
s6, calculating the property y of the formula crude oil according to the formula,
in said formula ④, biAs fitting coefficient, qiThe property values corresponding to crude oil samples in the crude oil depot of the formula components;
calculating the membership degree of each property of the blended crude oil according to the formula,
in said formula ⑤,. mu.jFor the jth property of the blended crude oil to correspond to the degree of membership, y, of the jth property of the target crude oiljα for property j of blended crude oiljFor the j property, σ, of the target crude oiljA membership standard variance for the jth property;
s7, calculating the similarity S of each combined blended crude oil and the target crude oil,
in the formula, cjIs the influence factor of the j-th property, mujThe membership degree of j-th property of the blended crude oil is obtained, and q is the number of the crude oil properties used for calculating the similarity S;
and S8, taking the combination with the similarity larger than a preset value, and taking the combination as a final crude oil blending formula according to the mixing proportion of the n crude oils.
2. The method as claimed in claim 1, wherein the number of the crude oil samples is 100 or more in the step of S1.
3. The method as claimed in claim 1, wherein the resolution of the near infrared spectrum of the crude oil sample measured in the step of S1 is 4cm-1Or 8cm-1The number of scans was 16 or 32.
4. The method of claim 1, wherein the crude oil property in step S1 is selected from one or more of crude oil density, acid number, sulfur content, carbon residue, wax content, gum content, and asphaltene content; the naphtha properties and composition are selected from one or more of naphtha yield, density, sulfur content, acid value, normal paraffins, isoparaffins, naphthenes, and aromatics.
5. The method as set forth in claim 1, wherein the score matrix and the load matrix of the crude oil samples are calculated in S3 in accordance with the formula,
X＝Tx×P ⑥
in the formula ⑥, X is a near infrared spectrum matrix of the crude oil depot of the formula components, namely an m × n matrix and TxThe method comprises the steps of obtaining a crude oil sample, obtaining a crude oil sample score matrix, obtaining a crude oil sample load matrix, and obtaining a crude oil sample load matrix, wherein the crude oil sample score matrix is an m × c matrix, the crude oil sample load matrix is a c × n matrix, m is the number of crude oil samples in a formula component crude oil bank, n is the.
6. The method according to claim 1, wherein the amount of the main component in the step of S3 is 10 to 30.
7. The method as claimed in claim 1, wherein the step S4 calculates the score of the target crude oil according to formula (c),
tu＝xu×P′ ⑦
in said formula ⑦, tuIs the score, x, of the target crude oiluAnd (3) obtaining the near infrared spectrum data of the target crude oil, wherein P 'is a transposed matrix of a load matrix of the crude oil sample, and P' is a matrix of n × c, wherein n is the number of wave number sampling points of the near infrared spectrum of the crude oil sample, and c is the number of principal components.
8. The method according to claim 1, wherein n in the step of S5 is an integer of 2 to 10.
9. The method according to claim 1, wherein said similarity S in said S7 is calculated as follows,
S＝(0.8μdensity of crude oil+0.6μSulfur content of crude oil+0.4μAcid value of crude oil+0.4μMetal content of crude oil+0.4μNaphtha yield+0.4μDensity of naphtha+0.4μNaphtha normal alkane+0.4μNaphtha cycloalkanes+0.4μNaphtha aromatics)/4.2。
10. The method of claim 1, wherein the predetermined value is greater than 0.9 in step S8.
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