CN113139155A - Large-range crude oil blending selection optimization method - Google Patents
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Abstract
The invention discloses a large-range crude oil blending selection optimization method, which reduces the selection range of substitute crude oil by performing principal 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 the basic stability of the properties of the crude oil after blending before and after optimization, the crude oil blending formula cost is further optimized by a crude oil blending optimization method, so that a crude oil processing alternative scheme with close properties and higher cost is provided, an enterprise is guided to select to purchase and process opportunity crude oil 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 available in global markets.
Background
Crude oil blending is important content in the 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 around the world, such as different sulfur content, acid number, density, and the like. The design parameters of the production device of the refinery enterprise are corresponding to certain crude oil properties. The larger the change of the crude oil property is, the larger the influence on the stable operation of the device is, the more difficult the control on the product quality is, and further, the adverse effects on the utilization rate of crude oil resources, the energy consumption, the waste discharge and the like are generated. Blending is to mix the crude oil with different physical properties (called component crude oil) uniformly according to different formulas and proportions, so that the crude oil is suitable for processing and production of devices.
At present, crude oil is purchased by oil refining enterprises often based on experience, and even if lower-price opportunity crude oil is available in some cases under the condition of severe fluctuation of the global crude oil market, the crude oil cannot be purchased accurately in time due to inaccurate understanding of crude oil properties and the like. The types of crude oil which has been proved in the world are as many as thousands, even with the exploitation and consumption of resources, the number of the available crude oil which is produced in mass production currently is hundreds, and for refining enterprises, the number of the crude oil which is commonly used is two thirty more, less than ten, and the selection range is small overall. Therefore, if crude oil available in international markets 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 from hundreds of crude oils, the range is too large, which results in an excessive computational load. In recent years, with the rapid development of near infrared technology, domestic research institutes and enterprises have collected near infrared spectra of crude oil samples and corresponding property data, and how to guide the enterprises to scientifically purchase in the international market by means of the near infrared technology and related data analysis methods is a research hotspot of crude oil blending in recent years.
Disclosure of Invention
The invention provides a large-range crude oil blending selection optimization method aiming at the problems. The method aims at the characteristic that the crude oil near infrared spectrum can accurately reflect the crude oil property, reduces the calculation load through feature extraction and cluster analysis according to crude oil near infrared spectrum data collected in a large range, combines the crude oil price and property 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 samples of crude oil species currently available for sale in the world, and measuring each sample ciConstructing a crude oil property spectrum database by using the key properties of (i ═ 1-n) and the corresponding near infrared spectrum, wherein the key properties of the crude oil comprise crude oil density, sulfur content and acid value;
2) for each sample of crude oil ci(i ═ 1 to n) by conventional pre-processing, wherein conventional pre-processing comprises vector normalization, baseline correction;
3) performing principal component analysis on all the preprocessed spectral data, and calculating a principal component score matrix S;
4) select the first m principal component vectors y1,y2,…,ymMaking the percentage of the cumulative contribution of the principal component not less than p, wherein p is 85%;
5) taking the first m score vectors a in the principal component score matrix S1,a2,…,amForming a data matrix A to be clustered;
6) dividing the data of the A into k clusters by using a cluster analysis method, and recording a cluster index of each crude oil sample, wherein the total number of the clusters is more than or equal to 4 and less than or equal to 10;
7) aiming at the blending component oil c of the currently refined crude oil in a specific refining enterprise1,c2,…,cg(g is less than or equal to n), and the j (j is less than or equal to g) th crude oil c to be optimizedjCarrying out selection optimization to find cjZ (z is more than or equal to 3 and less than or equal to 5) crude oils which are most similar to the near infrared spectrum in the cluster form cjAlternative sources ofOil set { cj,1′,cj,2′,…,cj,z′}，cjWith any crude oil c in the clusterj,e' the degree of similarity of near infrared spectra is:
in the formula Qj,qDenotes cj(ii) absorbance of the near infrared spectrum at the Q-th wavelength, Qj′,qDenotes cj,e' Absorbance at the q-th wavelength of the near Infrared Spectrum, d (c)j′,cj) Denotes cjAnd cj,e' near infrared spectrum u wavelength absorbance Euclidean distance.
8) Repeating step 7) until c is completed1～cgAll crude oils to be optimized are selected and optimized to obtain c1～cgRespectively corresponding alternative crude oil sets { c1,1′,…,c1,z′},…,{cg,1′,…,cg,z' }, according to the same formula as c1～cgSimilarity degree pair c of near infrared spectrum1～cgThe crude oils in the corresponding set are sorted from small to large;
9) from { c1,1′,…,c1,z′},…,{cg,1′,…,cg,z' choose substitute c1～cgCrude oil c1′～cg' determining an objective function and constraint conditions for crude oil blending optimization, and performing optimization solution on the objective function and constraint conditions to obtain c1′～cg' corresponding blending ratio X1～XgAnd blending formula prices, wherein the objective function and constraints included in the crude oil blending optimization are:
an objective function:
in the formula [ theta ]cDenotes the price of component oil c, XcIn order to optimize the blending proportion of each component of crude oil in the blending formula of the crude oil, r kinds of original crude oil are consideredOil property, λjWeight, U, representing the jth property of the blended crudejShows the j property, U, of the blended crude oilj *Indicating the j th property expectation value of the blended crude oil.
Constraint conditions:
1) and (3) blending proportion constraint:
2) and (3) restricting the upper limit and the lower limit of the blending ratio:
in the above formula, beta represents the minimum blending ratio of the component oil c, SgRepresenting a set {1.. g } of component oils to be optimized;
3) and (3) constraining the property upper and lower limits of the blended crude oil:
in the above formula UjShows the j property, L, of the blended crude oillow,j、Lhigh,jRespectively represent the allowable lower limit and the allowable upper limit of the j-th property, SNARepresents a set of crude oil properties {1.. NA }.
10) Exhaustive blending recipe c) for step 9)1′～cg' all possible cases, comparison gives the optimum blending recipe c1′～cg' and the corresponding blending ratio X1～Xg。
Has the advantages 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 performing crude oil near infrared spectrum principal component analysis and cluster analysis. The system can provide timely and scientific guidance for purchasing and processing of enterprises in global markets, improve enterprise benefits and further improve the safety of crude oil energy supply in China.
Drawings
FIG. 1 is a flow diagram of a wide range crude oil blending selection optimization method of the present invention;
FIG. 2 shows the clustering results of the near-infrared spectra of the crude oil pool used in the examples.
Detailed Description
The invention is further described with reference to the accompanying drawings and specific examples, which illustrate the implementation of the method in a wide range of crude oil blending selection optimization by means of specific operation procedures. The present embodiment is implemented on the premise of the technical solution of the present invention, but the scope of the present invention is not limited to the following examples.
The invention relates to a large-range crude oil blending selection optimization method, which reduces the calculation load through feature extraction and clustering analysis according to the crude oil near infrared spectrum in a crude oil property spectrum database, performs blending selection optimization by combining the price and the property of the crude oil, and provides an optimal alternative component oil selection scheme for crude oil blending, wherein the flow is shown in figure 1. The following specific implementation steps are as follows, taking the large-scale crude oil blending selection optimization process of a certain oil refining enterprise as an example:
1) in this example, samples of 196 currently marketable crude oil species in the world were collected and measuredi(i is 1-196) and a near infrared spectrum corresponding to the key properties, and constructing a crude oil property spectrum database as shown in table 1, wherein the crude oil property spectrum database comprises c1,c2,…,c196The data of the 4000-4740 waveband near infrared spectrum and the data of the key properties of the crude oil, wherein the key properties of the crude oil comprise density, sulfur content and acid value.
TABLE 1 crude oil Properties spectral database data
2) Crude oil sample c using vector normalization, baseline correction1,c2,…,c196The obtained near infrared spectrum was subjected to conventional pretreatment to obtain a pretreated near infrared spectrum as shown in table 2:
TABLE 2 crude oil NIR spectra 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 is performed on all the preprocessed spectral data, and a principal component score matrix S is calculated and is shown in Table 3:
TABLE 3 principal component score matrix
4) As can be seen from the statistic results of the principal component variance contributions shown in Table 4, the first 3 principal component vectors y were selected1,y2,y3The accumulated contribution rate of the principal component can reach 90.21 percent, and exceeds the lower limit of 85 percent, thereby meeting the requirement of retaining most information.
TABLE 4 statistical results of principal component contribution
| Principal component | Percent by weight% | 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 S1,a2,a3Forming a data matrix A to be clustered:
6) clustering 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 results (k ═ 4)
7) The 3 CRUDEs currently processed by this enterprise in the examples were CRUDE010, CRUDE023, and CRUDE083, with an initial CRUDE blending ratio of 0.6:0.2: 0.2. The 1 st source to be optimized is used hereOil c1That is, the selection optimization of CRUDE010 is taken as an example, the cluster index of CRUDE010 is 2, and the similarity (ordered from small to large) of the near infrared spectrum between all CRUDE oils in the cluster and the CRUDE010 is calculated as shown in table 6:
near infrared spectrum similarity between CRUDE oil and CRUDE010 in cluster 2 of Table 6
| Crude oil numbering | Near infrared spectral 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 and CRUDE010 were spectrally very close together, i.e., meaning that these 5 CRUDE oils were similar in nature to CRUDE 010. It can be seen that the 1 st crude oil c to be optimized1Set of alternative crude oils { c }1,1′,c1,2′,c1,3′,c1,4′,c1,5' } CRUDE009, CRUDE046, CRUDE011, CRUDE037 and CRUDE 004.
8) Through the CRUDE oil c to be optimized which is numbered as CRUDE010, CRUDE023 and CRUDE0831,c2,c3The corresponding alternative crude oil sets are shown in table 7.
TABLE 7 alternative crude oil set for crude oil to be optimized
9) Select alternative c from Table 71～c3Crude oil c1′～c3' determining an objective function and constraint conditions for crude oil blending optimization, and performing optimization solution on the objective function and constraint conditions to obtain c1′～c3' corresponding blending ratio X1～X3And blending formula price, the objective function and constraint condition included in the crude oil blending optimization are as follows:
an objective function:
in the formula [ theta ]cDenotes the price of component oil c, XcIn order to optimize the blending proportion of each component of crude oil in the blending formula of the crude oil, the properties of r kinds of crude oil, lambda, are consideredjWeight, U, representing the jth property of the blended crudejShows the j property, U, of the blended crude oilj *Indicating the j th property expectation value of the blended crude oil.
Constraint conditions:
1) and (3) blending proportion constraint:
2) and (3) restricting the upper limit and the lower limit of the blending ratio:
in the above formula, beta represents the minimum blending ratio of the component oil c, SgA set of component oils to be optimized {1.. 3} is represented;
3) and (3) constraining the property upper and lower limits of the blended crude oil:
in the above formula UjShows the j property, L, of the blended crude oillow,j、Lhigh,jRespectively represent the allowable lower limit and the allowable upper limit of the j-th property, SNARepresenting a set of crude oil properties 1.
10) Exhaustive blending recipe c) for step 9)1′～c3' all possible cases, comparison to give the optimal blending recipe c1′～c3' and the corresponding blending ratio X1～X3On the premise of not affecting optimization, the prices in this example are relative prices, and the blending formula of the new crude oil and the old crude oil is shown in table 8:
TABLE 8 comparison of blending recipes for new and old crude oils
As can be seen from Table 5, the density, sulfur content and acid value of the blended crude oil obtained by solving the new crude oil blending formula according to the embodiment are all close to those of the original crude oil blending formula, so that the stability of the properties of the produced product during the replacement of the blending formula is ensured, but the comprehensive cost is lower.
In conclusion, the method fully utilizes the characteristic that the near infrared spectrum can accurately reflect the properties of the crude oil, and reduces the selection range of the alternative crude oil by performing principal component analysis and cluster analysis on the near infrared spectrum data of the alternative crude oil, thereby reducing the calculation load of large-range crude oil selection optimization. And then, on the premise of ensuring that the properties of the crude oil after blending before and after optimization are basically stable, the crude oil blending formula cost is further optimized by a crude oil blending optimization method, so that a crude oil processing alternative scheme with close properties and higher cost is provided, an enterprise is guided to select crude oil from a global market to purchase and process opportunity, the enterprise benefit is improved, and the safety of crude oil energy supply in China is further improved.
Claims (9)
1. A large-range crude oil blending selection optimization method is characterized in that the calculation load is reduced by carrying out feature extraction and clustering analysis on a crude oil near infrared spectrum, and crude oil blending selection optimization is carried out by combining the price and the property of crude oil, and the method specifically comprises the following steps:
1) collecting samples of crude oil species currently available for sale in the world, and measuring each sample ciConstructing a crude oil property spectrum database by using the key properties and the corresponding near infrared spectrum, wherein i is 1-n;
2) for each sample of crude oil ciPerforming conventional pretreatment on the near infrared spectrum;
3) performing principal component analysis on all the preprocessed spectral data, and calculating a principal component score matrix S;
4) select the first m principal component vectors y1,y2,…,ymMaking the percentage of the cumulative contribution of the principal components not less than p;
5) scoring principal component matricesThe first m score vectors a in S1,a2,…,amForming a data matrix A to be clustered;
6) dividing the data of the A into k clusters by using a cluster analysis method, and recording a cluster index of each crude oil sample;
7) aiming at the blending component oil c of the currently refined crude oil in a specific refining enterprise1,c2,…,cgG is less than or equal to n; for j crude oil c to be optimizedjCarrying out selection optimization, j is less than or equal to g, finding cjZ crude oils in the cluster most similar to its near infrared spectrum, composition cjAlternative crude oil set { c }j,1′,cj,2′,…,cj,z′}；
8) Repeating the step 7) until all the crude oils c to be optimized are finished1～cgIs optimized to obtain c1～cgRespectively corresponding alternative crude oil sets { c1,1′,…,c1,z′},…,{cg,1′,…,cg,z' }, according to the same formula as c1～cgSimilarity degree pair c of near infrared spectrum1～cgThe crude oils in the corresponding set are sorted from small to large;
9) from { c1,1′,…,c1,z′},…,{cg,1′,…,cg,z' choose substitute c1～cgCrude oil c1′～cg' determining an objective function and constraint conditions for crude oil blending optimization, and performing optimization solution on the objective function and constraint conditions to obtain c1′～cg' corresponding blending ratio X1～XgAnd blending formula price;
10) exhaustive blending recipe c) for step 9)1′～cg' all possible cases, comparison to give the optimal blending recipe c1′～cg' and the corresponding blending ratio X1～Xg。
2. The method of claim 1, wherein the key properties of the crude oil of step 1) comprise crude oil density, sulfur content, and acid number.
3. The method of claim 1, wherein the conventional preprocessing method of step 2) comprises vector normalization and baseline correction.
4. The method of claim 1, wherein in step 7), c is a step of optimizing crude oil blending selectionjWith any crude oil c in the clusterj,e' the degree of similarity of near infrared spectra is:
in the formula Qj,qDenotes cj(ii) absorbance of the near infrared spectrum at the Q-th wavelength, Qj′,qDenotes cj,e' Absorbance at the q-th wavelength of the near Infrared Spectrum, d (c)j′,cj) Denotes cjAnd cj,e' near infrared spectrum u wavelength absorbance Euclidean distance.
5. The method of claim 1, wherein the optimization of crude blending in step 9) comprises an objective function of:
in the formula [ theta ]cDenotes the price of component oil c, XcIn order to optimize the blending proportion of each component of crude oil in the blending formula of the crude oil, the properties of r kinds of crude oil, lambda, are consideredjWeight, U, representing the jth property of the blended crudejShows the j property, U, of the blended crude oilj *Representing the j property expectation value of the blended crude oil; g represents the number of component oils and r represents the number of crude oil properties.
6. The method of claim 5 for selecting and optimizing a wide range of crude oil blending, wherein the constraints of the crude oil blending optimization are as follows:
1) and (3) blending proportion constraint:
2) and (3) restricting the upper limit and the lower limit of the blending ratio:
in the above formula, beta represents the minimum blending ratio of the component oil c, SgRepresenting a set {1.. g } of component oils to be optimized;
3) and (3) constraining the property upper and lower limits of the blended crude oil:
in the above formula UjShows the j property, L, of the blended crude oillow,j、Lhigh,jRespectively represent the allowable lower limit and the allowable upper limit of the j-th property, SNARepresents a set of crude oil properties {1.. NA }.
7. The method of claim 1 wherein the cumulative principal component contribution p of step 4) is 85%.
8. The method of claim 1, wherein k is 4. ltoreq. k.ltoreq.10 for the cluster analysis in step 6).
9. The method of claim 1, wherein z is 3. ltoreq. z.ltoreq.5 for the most similar crude oil type in the cluster in step 7).
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001045007A1 (en) * | 1999-12-06 | 2001-06-21 | Bios Group Inc. | A method and system for discovery of trades between parties |
| CN1458220A (en) * | 2002-05-17 | 2003-11-26 | 中国石油天然气股份有限公司 | Low alkali or no-alkali composite oil displacement agent formula used in triple oil production |
| CN102643662A (en) * | 2012-04-25 | 2012-08-22 | 南京富岛信息工程有限公司 | Crude oil blending optimization method |
| CN105727777A (en) * | 2014-12-06 | 2016-07-06 | 中国石油化工股份有限公司 | Heavy bunker fuel oil optimization blending method |
| CN109724939A (en) * | 2017-10-30 | 2019-05-07 | 中国石油化工股份有限公司 | By the method for near infrared spectrum prediction hydrogenation tail oil property |
| CN109975238A (en) * | 2019-03-29 | 2019-07-05 | 浙江中烟工业有限责任公司 | A kind of alternative of tobacco leaf and cigarette tobacco leaf formulation based near infrared spectrum |
| CN110322933A (en) * | 2019-06-20 | 2019-10-11 | 浙江工业大学 | A kind of polypropylene melt index hybrid modeling method based on dynamic error compensation mechanism |
| CN110763649A (en) * | 2018-07-26 | 2020-02-07 | 中国石油化工股份有限公司 | Method for selecting target crude oil blending formula according to near infrared spectrum and properties |
| WO2020199666A1 (en) * | 2019-03-29 | 2020-10-08 | 华东理工大学 | Blending effect parameter calculation method for crude oil blending |
-
2021
- 2021-04-23 CN CN202110442225.2A patent/CN113139155B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001045007A1 (en) * | 1999-12-06 | 2001-06-21 | Bios Group Inc. | A method and system for discovery of trades between parties |
| CN1458220A (en) * | 2002-05-17 | 2003-11-26 | 中国石油天然气股份有限公司 | Low alkali or no-alkali composite oil displacement agent formula used in triple oil production |
| CN102643662A (en) * | 2012-04-25 | 2012-08-22 | 南京富岛信息工程有限公司 | Crude oil blending optimization method |
| CN105727777A (en) * | 2014-12-06 | 2016-07-06 | 中国石油化工股份有限公司 | Heavy bunker fuel oil optimization blending method |
| CN109724939A (en) * | 2017-10-30 | 2019-05-07 | 中国石油化工股份有限公司 | By the method for near infrared spectrum prediction hydrogenation tail oil property |
| CN110763649A (en) * | 2018-07-26 | 2020-02-07 | 中国石油化工股份有限公司 | Method for selecting target crude oil blending formula according to near infrared spectrum and properties |
| CN109975238A (en) * | 2019-03-29 | 2019-07-05 | 浙江中烟工业有限责任公司 | A kind of alternative of tobacco leaf and cigarette tobacco leaf formulation based near infrared spectrum |
| WO2020199666A1 (en) * | 2019-03-29 | 2020-10-08 | 华东理工大学 | Blending effect parameter calculation method for crude oil blending |
| CN110322933A (en) * | 2019-06-20 | 2019-10-11 | 浙江工业大学 | A kind of polypropylene melt index hybrid modeling method based on dynamic error compensation mechanism |
Non-Patent Citations (4)
| Title |
|---|
| ALBAR ABDULLAH 等: "Combining the power of IoT and big data to unleash the potential of digital oil field", 《INTERNATIONAL PETROLEUM TECHNOLOGY CONFERENCE》, pages 1 - 10 * |
| KELLY JEFFREY D. 等: "Distillation blending and cutpoint temperature optimization using monotonic interpolation", 《INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH》, vol. 53, no. 39, pages 15146 - 15156 * |
| 刘玲: "减压渣油分子组成及分布规律研究", 《中国博士学位论文全文数据库工程科技Ⅰ辑》, no. 09, pages 019 - 17 * |
| 蒋立沫: "炼化过程原油深度调合关键技术研究与开发", 《中国优秀硕士学位论文全文数据库工程科技Ⅰ辑》, no. 05, pages 017 - 16 * |
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