CN113139155B - Large-scale crude oil blending selection optimization method - Google Patents
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- 239000010779 crude oil Substances 0.000 title claims abstract description 163
- 238000002156 mixing Methods 0.000 title claims abstract description 88
- 238000005457 optimization Methods 0.000 title claims abstract description 33
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 31
- 238000001228 spectrum Methods 0.000 claims abstract description 10
- 238000007621 cluster analysis Methods 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims abstract description 6
- 239000003921 oil Substances 0.000 claims description 15
- 239000011159 matrix material Substances 0.000 claims description 10
- 239000013598 vector Substances 0.000 claims description 9
- 239000000203 mixture Substances 0.000 claims description 8
- 229910052717 sulfur Inorganic materials 0.000 claims description 8
- 238000007670 refining Methods 0.000 claims description 7
- 238000002835 absorbance Methods 0.000 claims description 6
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 5
- 239000002253 acid Substances 0.000 claims description 5
- 239000011593 sulfur Substances 0.000 claims description 5
- 230000001186 cumulative effect Effects 0.000 claims description 4
- 238000000513 principal component analysis Methods 0.000 claims description 4
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- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
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- 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
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Abstract
The invention discloses a large-range crude oil blending selection optimization method, which reduces the selection range of alternative crude oil by carrying out main component analysis and cluster analysis on near infrared spectrum data of a crude oil property spectrum database, thereby reducing the calculation load of large-range crude oil selection optimization. On the premise of ensuring that the properties of the crude oil are basically stable after blending before and after optimization, the cost of the crude oil blending formula is further optimized by a crude oil blending optimization method, so that a crude oil processing alternative scheme with properties close to those of the crude oil and better cost is provided, enterprises are guided to select crude oil with purchasing and processing opportunities from the global market, the enterprise benefit is improved, and the safety of crude oil energy supply in China is further improved.
Description
Technical Field
The invention relates to the field of crude oil blending in the field of petrochemical industry, in particular to a method for carrying out crude oil blending selection optimization on crude oil sold in the global market.
Background
Crude oil blending is an important content in production of refining enterprises, and has great influence on intensive use of crude oil resources, energy conservation and emission reduction. Crude oil varies in physical properties throughout the world, such as sulfur content, acid value, and density. Design parameters of production devices of refining enterprises correspond to certain crude oil properties. The larger the change of the crude oil property, the larger the influence on the stable operation of the device, the more difficult the control on the product quality, and further the adverse effects on the utilization rate of crude oil resources, energy consumption, waste emission and the like are generated. The blending is to uniformly mix crude oil (called component crude oil) with different physical properties according to different formulas and proportions, so that the blending is suitable for processing and production by a device.
At present, oil refining enterprises often purchase crude oil based on existing experience, and even if lower-price opportunity crude oil appears under the condition of severe fluctuation of the current global crude oil market, the crude oil cannot be purchased timely and accurately due to inaccurate grasp of crude oil properties and the like. The types of crude oil which are ascertained in the world are thousands of, even with the exploitation and consumption of resources, the available crude oil produced in mass currently is hundreds of, while for refining enterprises, the number of crude oil which is commonly used is twenty-thirty, the number of crude oil is less than ten, and the selection range is small as a whole. Therefore, if crude oil available in the international market can be provided for enterprises and the optimal selection scheme is recommended, the method has important value for guiding the enterprises to scientifically purchase the crude oil.
However, if one chooses among hundreds of crude oils, its range is too large, which would result in excessive computational load. In recent years, with the rapid development of near infrared technology, domestic research institutions and enterprises have collected near infrared spectrums of a plurality of crude oil samples and corresponding property data thereof, and how to guide enterprises to scientifically purchase on the international market by means of the near infrared technology and related data analysis methods is always a research hotspot of crude oil blending in recent years.
Disclosure of Invention
Aiming at the problems, the invention provides a large-scale crude oil blending selection optimization method. Aiming at the characteristic that the crude oil near infrared spectrum can accurately reflect the property of crude oil, the method reduces the calculation load through feature extraction and cluster analysis according to the crude oil near infrared spectrum data collected in a large range, combines the price and the property of the crude oil to carry out blending selection optimization, and provides an optimal selection scheme for crude oil blending on the premise of ensuring the stability of the product property, and specifically comprises the following steps:
1) Collecting crude oil sample available for sale in the world, and measuring each sample c i (i=1 to n) and the corresponding near infrared spectrum thereof, constructing a crude oil property spectrum database, wherein the crude oil key properties comprise crude oil density, sulfur content and acid value;
2) For each sample c of crude oil i (i=1 to n), wherein the conventional pretreatment comprises vector normalization, baseline correction;
3) Performing principal component analysis on all the preprocessed spectrum data, and calculating a principal component score matrix S;
4) Selecting the first m principal component vectors y 1 ,y 2 ,…,y m Making the cumulative contribution percentage of the principal component not smaller than p, where p=85%;
5) Taking the first m score vectors a in the principal component score matrix S 1 ,a 2 ,…,a m Forming a data matrix A to be clustered;
6) Dividing the data of A into k clusters by using a cluster analysis method, and recording cluster indexes of each crude oil sample, wherein k is more than or equal to 4 and less than or equal to 10 in total clusters;
7) Aiming at the blending component oil c of the crude oil which is currently and commonly refined in specific refining enterprises 1 ,c 2 ,…,c g (g.ltoreq.n), for the j (j.ltoreq.g) th crude oil c to be optimized j Performing selection optimization to find c j Z (3.ltoreq.z.ltoreq.5) crude oils in the cluster most similar to their near infrared spectrum, composition c j Alternative crude oil set { c } j,1 ′,c j,2 ′,…,c j,z ′}，c j With any crude oil c in the cluster j,e ' near infrared spectrum similarity degreeThe method comprises the following steps:
q in j,q Representation c j Absorbance at the Q-th wavelength of the near infrared spectrum of (2), Q j′,q Representation c j,e Absorbance at the q-th wavelength of the' near infrared spectrum, d (c) j,e ′,c j ) Representation c j And c j,e The euclidean distance of absorbance at u wavelengths of the' near infrared spectrum.
8) Repeating step 7) until c is completed 1 ～c g Optimizing the selection of all crude oil to be optimized to obtain c 1 ～c g Respectively corresponding alternative crude oil sets { c } 1,1 ′,…,c 1,z ′},…,{c g,1 ′,…,c g,z ' according to c }, with 1 ～c g Similarity of near infrared spectrum to c 1 ～c g Sorting crude oil in the corresponding set from small to large;
9) From { c 1,1 ′,…,c 1,z ′},…,{c g,1 ′,…,c g,z Alternative c is selected from' } 1 ～c g Crude oil c of (2) 1 ′～c g ' determining objective function and constraint condition of crude oil blending optimization, and carrying out optimization solution to obtain c 1 ′～c g ' corresponding blending proportion X 1 ～X g And blending formula price, wherein the crude blending optimization comprises the objective functions and constraints of:
(1) objective function:
in theta c Indicating the price of component oil c, X c In order to optimize the blending proportion of each component crude oil in the crude oil blending formula, r crude oil properties, lambda are considered j Weight indicating j-th property of crude oil after blending, U j Represents the j-th property of crude oil after blending, U j * Representation ofThe j-th property of the crude oil after blending is expected.
(2) Constraint conditions:
1) Blending proportion constraint:
2) Upper and lower limit constraint of blending proportion:
wherein beta represents the lowest blending proportion of component oil c, S g Represents the set { 1..g } of component oils to be optimized;
3) Upper and lower limit constraints on crude oil properties after blending:
u in the above j Represents the j-th property, L, of crude oil after blending low,j 、L high,j Respectively represent the allowable lower limit and upper limit of the jth property, S NA Represents the set { 1..na } of crude oil properties.
10 For step 9) an exhaustive blending formula c) 1 ′～c g ' all possible cases, comparison yields the optimal blend formula c 1 ′～c g ' and its corresponding blending proportion X 1 ～X g 。
The beneficial effects are that:
the invention discloses a large-range crude oil blending selection optimization method, which can greatly reduce the calculation load of large-range crude oil selection optimization by establishing a crude oil property spectrum database and carrying out crude oil near infrared spectrum principal component analysis and cluster analysis. Timely and scientific guidance is performed for the selection purchase and processing of enterprises on the global market, so that the enterprise benefit is improved, and the safety of crude oil energy supply in China is further improved.
Drawings
FIG. 1 is a flow chart of a wide range crude blending selection optimization method of the present invention;
FIG. 2 shows the clustering results of the near infrared spectrum of the crude oil reservoir used in the examples.
Detailed Description
The invention will be further described with reference to the accompanying drawings and specific examples, which illustrate the effect of the process in a wide range of crude blending selection optimizations by specific operational procedures. The present embodiment is implemented on the premise of the technical scheme of the present invention, but the protection scope of the present invention is not limited to the following examples.
According to the method for selecting and optimizing the crude oil blending in a large range, according to the near infrared spectrum of the crude oil in the crude oil property spectrum database, the calculation load is reduced through feature extraction and cluster analysis, the blending selection optimization is carried out by combining the price and the property of the crude oil, and the optimal alternative component oil selection scheme is provided for the crude oil blending, and the flow is shown in figure 1. Taking a large-scale crude oil blending selection optimization process of a certain oil refining enterprise as an example, the specific implementation steps are as follows:
1) In this example, samples of 196 crude oil types currently available for sale in the world were collected and each sample c was measured i The key properties of (i=1 to 196) and the corresponding near infrared spectrum thereof, and a crude oil property spectrum database is constructed as shown in table 1, and comprises c 1 ,c 2 ,…,c 196 4000-4740 band near infrared spectral data and crude oil key properties data, wherein the crude oil key properties comprise density, sulfur content, and acid number.
Table 1 crude oil property spectrum database data
2) Crude oil sample c using vector normalization, baseline correction 1 ,c 2 ,…,c 196 Conventional pretreatment was performed on the near infrared spectrum of (c) to obtain a pretreated near infrared spectrum as shown in table 2:
TABLE 2 near infrared spectra of crude oil after pretreatment
| Crude oil numbering | 4740 | 4739 | 4730 | … | 4005 | 4001 | 4000 |
| CRUDE001 | 0.354864 | 0.356730 | 0.285442 | … | -0.373490 | -0.174191 | -0.142660 |
| CRUDE002 | 0.854533 | 0.842617 | 0.758876 | … | 0.183372 | 0.147940 | 0.143013 |
| CRUDE003 | -0.114910 | -0.122843 | -0.147299 | … | 0.201460 | 0.169466 | 0.164033 |
| CRUDE004 | 0.916946 | 0.906215 | 0.882235 | … | 0.169950 | 0.105762 | 0.093731 |
| … | … | … | … | … | … | … | … |
| CRUDE193 | 0.814713 | 0.814416 | 0.825078 | … | -0.176491 | -0.203892 | -0.209012 |
| CRUDE194 | -1.655759 | -1.669833 | -1.639157 | … | -1.071437 | -0.920078 | -0.885387 |
| CRUDE195 | -0.759938 | -0.761478 | -0.686346 | … | -1.457064 | -1.263046 | -1.224736 |
| CRUDE196 | -0.417931 | -0.416227 | -0.403145 | … | -0.135508 | 0.036528 | 0.070521 |
3) Principal component analysis was performed on all the spectral data after pretreatment, and a principal component score matrix S was calculated as shown in table 3:
TABLE 3 principal component score matrix
4) As can be seen from the principal component variance contribution statistics shown in Table 4, the first 3 principal component vectors y are selected 1 ,y 2 ,y 3 The accumulated contribution rate of the main components can reach 90.21 percent and exceed the lower limit of 85 percent, thereby meeting the requirement of keeping most of information.
TABLE 4 principal component contribution statistics
| Main component | Percent% | Cumulative contribution% |
| 1 | 62.3873 | 62.3873 |
| 2 | 17.7822 | 80.1696 |
| 3 | 10.0427 | 90.2123 |
| 4 | 5.0236 | 95.2359 |
| 5 | 2.3648 | 97.6007 |
| 6 | 0.8886 | 98.4892 |
| 7 | 0.5463 | 99.0355 |
| 8 | 0.3326 | 99.3681 |
5) Taking the first 3 score vectors a in the principal component score matrix S 1 ,a 2 ,a 3 Forming a data matrix A to be clustered:
6) Cluster analysis was performed on a and fig. 2 shows the spatial distribution of samples when the data was divided into 4 clusters. The specific clustering results are shown in table 5:
table 5 clustering analysis results (k=4)
7) In the examples, 3 CRUDE oils currently processed by the enterprise are CRUDE010, CRUDE023 and CRUDE083, and the initial CRUDE oil blending ratio is 0.6:0.2:0.2. Crude oil c to be optimized in this case 1 st 1 For example, the optimization of the selection of CRUDE010, the index of the CRUDE010 cluster is 2, and the similarity of near infrared spectrum with the CRUDE010 is calculated for all CRUDE oil in the cluster (from smallTo large order) is shown in table 6:
table 6 near infrared spectral similarity between CRUDE oil and CRUDE010 in Cluster 2
| Crude oil numbering | Near infrared spectrum similarity |
| CRUDE009 | 0.0933 |
| CRUDE046 | 9.217 |
| CRUDE011 | 9.8790 |
| CRUDE037 | 14.9123 |
| CRUDE004 | 23.3514 |
| … | … |
| CRUDE014 | 1171.6434 |
| CRUDE066 | 1457.2493 |
As can be seen from Table 6, in cluster 2, CRUDE009, CRUDE046, CRUDE011, CRUDE037, CRUDE004 is very close to the CRUDE010 spectrum, meaning that these 5 CRUDEs are similar in nature to CRUDE 010. It can be seen that crude oil c to be optimized 1 1 Alternative crude oil set { c } 1,1 ′,c 1,2 ′,c 1,3 ′,c 1,4 ′,c 1,5 ' CRUDE009, CRUDE046, CRUDE011, CRUDE037, CRUDE004.
8) Through the CRUDE oil c to be optimized with the serial numbers of CRUDE010, CRUDE023 and CRUDE083 1 ,c 2 ,c 3 The respective corresponding alternative crude oil sets are shown in table 7.
TABLE 7 alternative crude oil collections of crude oils to be optimized
9) Alternative c from Table 7 1 ～c 3 Crude oil c of (2) 1 ′～c 3 ' determining objective function and constraint condition of crude oil blending optimization, and carrying out optimization solution to obtain c 1 ′～c 3 ' corresponding blending proportion X 1 ～X 3 And the price of the blending formula, the crude oil blending optimization comprises the following objective functions and constraint conditions:
(1) objective function:
in theta c Indicating the price of component oil c, X c In order to optimize the blending proportion of each component crude oil in the crude oil blending formula, r crude oil properties, lambda are considered j Weight indicating j-th property of crude oil after blending, U j Represents the j-th property of crude oil after blending, U j * Indicating the expected value of the j-th property of the crude oil after blending.
(2) Constraint conditions:
1) Blending proportion constraint:
2) Upper and lower limit constraint of blending proportion:
wherein beta represents the lowest blending proportion of component oil c, S g Represents the set { 1..3 } of component oils to be optimized;
3) Upper and lower limit constraints on crude oil properties after blending:
u in the above j Represents the j-th property, L, of crude oil after blending low,j 、L high,j Respectively represent the allowable lower limit and upper limit of the jth property, S NA Represents the set of crude oil properties { 1..3 }.
10 For step 9) an exhaustive blending formula c) 1 ′～c 3 ' all possible cases, comparison yields the optimal blend formula c 1 ′～c 3 ' and its corresponding blending proportion X 1 ～X 3 On the premise of not affecting optimization, the prices in the embodiment are relative prices, and the new and old crude oil blending formulas are shown in table 8:
table 8 comparison of New and old crude oil blending formulas
As can be seen from Table 5, the new crude oil blending formulation obtained according to the solution of the present case has a density, sulfur content and acid value similar to those of the old crude oil blending formulation, and ensures the stability of the properties of the produced product when the blending formulation is replaced, but has lower comprehensive cost.
In summary, the method fully utilizes the characteristic that the near infrared spectrum can accurately reflect the property of the crude oil, and reduces the selection range of the alternative crude oil by carrying out main component analysis and cluster analysis on the near infrared spectrum data of the alternative crude oil, thereby reducing the calculation load of the selection optimization of the crude oil in a large range. And then, on the premise of ensuring that the properties of the crude oil are basically stable after blending before and after optimization, the cost of the crude oil blending formula is further optimized by a crude oil blending optimization method, so that a crude oil processing alternative scheme with the properties close to those of the crude oil and better cost is provided, enterprises are guided to select the crude oil with the opportunity of purchasing and processing from the global market, the enterprise benefit is improved, and the safety of crude oil energy supply in China is further improved.
Claims (6)
1. A large-scale crude oil blending selection optimization method is characterized by reducing the calculation load by carrying out feature extraction and cluster analysis on a crude oil near infrared spectrum, and carrying out crude oil blending selection optimization by combining the price and the property of crude oil, and specifically comprises the following steps:
1) Collecting crude oil sample for sale, measuring each sample c i The key properties of the (1) and the corresponding near infrared spectrum thereof, and constructing a crude oil property spectrum database, wherein i=1 to n;
2) For each sample c of crude oil i Conventional pretreatment is carried out on the near infrared spectrum of the (E) glass;
3) Performing principal component analysis on all the preprocessed spectrum data, and calculating a principal component score matrix S;
4) Selecting the first m principal component vectors y 1 ,y 2 ,…,y m The cumulative contribution rate percentage of the main component is not smaller than p;
5) Taking the first m score vectors a in the principal component score matrix S 1 ,a 2 ,…,a m Forming a data matrix A to be clustered;
6) Dividing the data of A into k clusters by using a cluster analysis method, and recording the cluster index of each crude oil sample;
7) Aiming at the blending component oil c of the crude oil which is currently and commonly refined in specific refining enterprises 1 ,c 2 ,…,c g G is less than or equal to n; for the j-th crude oil c to be optimized j Selecting and optimizing, j is less than or equal to g, finding c j Z crude oils in the cluster most similar to their near infrared spectrum, composition c j Alternative crude oil set { c } j,1 ′,c j,2 ′,…,c j,z ′}；c j With any crude oil c in the cluster j,e The' near infrared spectrum similarity is:
q in j,q Representation c j Absorbance at the Q-th wavelength of the near infrared spectrum of (2), Q j′,q Representation c j,e Absorbance at the q-th wavelength of the' near infrared spectrum, d (c) j,e ′,c j ) Representation c j And c j,e The Euclidean distance of absorbance at u wavelengths of the near infrared spectrum;
8) Repeating the step 7) until all the crude oil c to be optimized is completed 1 ～c g Is optimized by selection to obtain c 1 ～c g Respectively corresponding alternative crude oil sets { c } 1,1 ′,…,c 1,z ′},…,{c g,1 ′,…,c g,z ' according to c }, with 1 ～c g Similarity of near infrared spectrum to c 1 ～c g Sorting crude oil in the corresponding set from small to large;
9) From { c 1,1 ′,…,c 1,z ′},…,{c g,1 ′,…,c g,z Alternative c is selected from' } 1 ～c g Crude oil c of (2) 1 ′～c g ' determining objective function and constraint condition of crude oil blending optimization, and carrying out optimization solution to obtain c 1 ′～c g ' corresponding blending proportion X 1 ～X g And the price of the blending formula;
the crude oil blending optimization comprises the following objective functions:
in theta c Indicating the price of component oil c, X c In order to optimize the blending proportion of each component crude oil in the crude oil blending formula, r crude oil properties, lambda are considered j Weight indicating j-th property of crude oil after blending, U j Represents the j-th property of crude oil after blending, U j * Indicating the j-th property expectation of the crude oil after blending; g represents the amount of component oil, r represents the amount of crude oil property;
the constraint conditions for crude oil blending optimization are as follows:
9-1) blending ratio constraint:
9-2) upper and lower limit constraints on blending proportions:
wherein beta represents the lowest blending proportion of component oil c, S g Represents the set { 1..g } of component oils to be optimized;
9-3) upper and lower limit constraints on crude oil properties after blending:
u in the above j Represents the j-th property, L, of crude oil after blending low,j 、L high,j Respectively represent the allowable lower limit and upper limit of the jth property, S NA A set { 1..na } representing crude oil properties;
10 For step 9) an exhaustive blending formula c) 1 ′～c g ' all possible cases, comparison yields the bestOptimal blending formula c 1 ′～c g ' and its corresponding blending proportion X 1 ～X g 。
2. The method of claim 1, wherein the key properties of the crude oil in step 1) include crude oil density, sulfur content and acid number.
3. The method of claim 1, wherein the conventional pretreatment method of step 2) comprises vector normalization and baseline correction.
4. A method of optimizing a wide range crude blending selection as set forth in claim 1 wherein the principal component cumulative contribution p = 85% of step 4).
5. The method for optimizing large-scale crude oil blending selection as claimed in claim 1, wherein the total number of clusters in the step 6) is 4.ltoreq.k.ltoreq.10.
6. The method for optimizing blending selection of a large range crude oil according to claim 1, wherein z is 3.ltoreq.5 for the most similar crude oil category in the cluster in step 7).
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