Method for measuring and calculating product properties in atmospheric and vacuum conditions based on actual boiling point distillation product properties

Abstract

The invention relates to the technical field of petrochemical engineering, in particular to a method for measuring and calculating product properties in atmospheric and vacuum based on actual boiling point distillation product properties, which comprises the following steps: establishing an actual boiling point distillation range and Engler distillation range conversion model; the method comprises the following steps: establishing a naphtha yield YieldBase model and a physical property PropertyBase model; establishing a similar working condition query means, and screening out model data of a working condition similar to that of the to-be-tested naphtha; and carrying out cutting prediction on current evaluation analysis data through naphtha yield and physical property iterative checking, and carrying out regression calculation by utilizing a regression model formula after checking is completed. According to the method, by establishing the data association model and adopting a real-time checking mode, the predicted physical property data deviation is further reduced, a real-time conversion model of crude oil actual boiling point distillation and atmospheric and vacuum distillation in an enterprise is established, converted evaluation data is closer to the property of currently processed crude oil, and the method is rapid, efficient and capable of being directly applied to production.

Description

Technical Field

[0001] The invention relates to the technical field of petrochemical industry, and is a method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products. Background Art

[0002] Oil evaluation is mainly used in refinery design, crude oil processing flow formulation, etc. Its core part, the actual boiling point distillation data, is the main basis for designing distillation units. In the early stage of unit design and construction, the cut points and yields of each side line of crude oil atmospheric and vacuum distillation are generally determined based on the actual boiling point distillation data in crude oil evaluation. However, with the changes in the types and properties of processed crude oil, coupled with the structural differences between atmospheric and vacuum distillation units and actual boiling point distillation equipment, different separation principles, and different fraction overlap and separation accuracy (the control index of the side product of the atmospheric and vacuum distillation unit is the Engler distillation range), the yield of the actual boiling point distillation fraction is different from the yield of the atmospheric and vacuum distillation unit side product and the corresponding properties of the fraction, such as density, distillation range, sulfur content, etc.

[0003] At first, some foreign researchers compiled a set of conversion charts for petroleum distillation curves based on a large amount of experimental data. After introducing them to China, they found that these charts were not suitable for the conversion of distillation curves of some domestic oil products. Subsequently, some domestic researchers improved the charts based on the introduction of foreign charts after a large number of experiments, and basically applied them to the conversion of distillation curves of various oil products. In recent years, due to the rapid development of computer technology, various means have been used to establish model formulas, which have basically realized the conversion between real boiling point distillation and Engler distillation. For atmospheric and vacuum distillation and true boiling point distillation, there are certain problems in directly converting the distillation curve formula. First, the distillation range conversion formula is for the same oil product, and the distillation range conversion is studied under different distillation methods. For atmospheric and vacuum distillation and true boiling point distillation, the same crude oil is cut according to the two distillation methods, and the resulting gasoline (naphtha), kerosene, diesel, wax oil and residual oil have different physical properties. There will inevitably be certain deviations when converting the distillate oils obtained by different distillation methods using the conversion formula; secondly, true boiling point distillation is an intermittent and relatively static distillation method, which cuts the fixed distillation range according to the different terminal distillation points of different fractions, while atmospheric and vacuum distillation is a continuous and dynamic distillation cutting, and its distillation process has certain volatility, and each operating parameter will change within a certain range, resulting in non-constant product properties such as Englert distillation range dry point. The two distillation methods are converted according to the conversion relationship provided by the literature or software, and the deviation of the obtained product physical property data cannot meet the needs of the refinery.

[0004] The Chinese patent document with publication number CN105938092A discloses a method for correcting the actual boiling point distillation curve based on the real-time properties of crude oil. The invention uses near-infrared crude oil rapid evaluation technology to obtain the TBP distillation data of the current processed crude oil, and then uses process simulation software to perform simulation calculations to obtain the simulation values ​​of each side product. According to the actual measurement of the test values ​​of each side product of the current atmospheric and vacuum distillation device, and based on the simulation value and test value of each side product, the process simulation software is used to perform fitting calculations, and finally the corrected crude oil TBP curve is obtained. The invention combines process simulation means with crude oil rapid evaluation technology to better solve the problem of conversion between atmospheric and vacuum distillation and actual boiling point distillation curves. However, when using process simulation means, when the crude oil properties including the actual boiling point yield change, there will be a situation where the model does not converge, and it is necessary to continuously adjust and optimize the model, requiring technical personnel to have a certain degree of mastery of the simulation optimization software, and there is still a certain lag in the guiding role of actual production.

[0005] Therefore, how to develop a simpler and faster conversion method to directly serve production requires further research. Summary of the invention

[0006] The present invention provides a method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products, which overcomes the shortcomings of the above-mentioned prior art and can effectively solve the problems that the products of atmospheric and vacuum distillation devices are currently non-constant in properties due to fluctuations in production operations and are not easy to be directly applied to production.

[0007] The technical solution of the present invention is achieved by the following measures: A method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products, comprising the following steps:

[0008] The first step is to establish a conversion model between the actual boiling point distillation range and the Engler distillation range according to the conversion calculation of the distillation range of Engler distillation D86 and the actual boiling point distillation TBP;

[0009] The second step is to establish the naphtha yield Yield_Base model and the physical property Property_Base model by using the actual boiling point distillation range and Engel's distillation range conversion model and the physical property analysis data of the atmospheric and vacuum unit product yield corresponding to that date;

[0010] The third step is to establish similar operating condition query means in the naphtha yield Yield_Base model and the physical property Property_Base model to screen out model data with similar operating conditions to the naphtha to be tested;

[0011] The fourth step is to perform rolling iterative verification on the data with large deviations from the model data of the working conditions similar to the naphtha to be tested, collect the evaluation and analysis data of the latest batch, perform cutting prediction on the current evaluation and analysis data through iterative verification of naphtha yield and physical properties;

[0012] The fifth step is to use the regression model formula to perform regression calculations on the naphtha yield and physical properties after completing the verification.

[0013] The following are further optimizations and / or improvements to the above technical solutions:

[0014] In the first step above, in the distillation range conversion between Englert distillation D86 and true boiling point distillation TBP, the final distillation point ranges from 60°C to 400°C.

[0015] In the first step, the calculation method for converting the distillation range between Eng's distillation D86 and true boiling point distillation TBP is one or more of the least square method, stepwise regression, polynomial fitting, logarithmic fitting and gamma adjustment method.

[0016] In the first step above, the conversion model between boiling point distillation range and Engelhard distillation range is as follows:

[0017]

[0018] d k =Ga,...a=(a 0 , a 1 , ...an) T ; d=(d 0 , d 1 , …d n ) T

[0019]

[0020] Where a is the distillation range of the actual boiling point distillation, d is the Engler distillation range of the corresponding fraction, is the coefficient of a.

[0021] In the second step above, the Yield_Base model is obtained according to the following steps: taking the laboratory actual boiling point distillation cutting naphtha fraction yield and the major adjustment parameters of the device as independent variables, the atmospheric and vacuum distillation device product yield as the dependent variable, establishing a correlation model between the actual boiling point yield, key device operating parameters and device product yield, and deriving the initial cutting plan, wherein the Yield_Base model formula is as follows:

[0022] [[Y-TBP n ],[Operate n ]]×Yield_Base=[Yield n ]

[0023] In the formula, [Y-TBP n ] is the yield matrix of the actual boiling point distillation naphtha fraction (the specific cut-off distillation range is converted from the Engler distillation range); [Operate n] is the key adjustment parameter matrix of the device; Yield_Base is the Base matrix of yield; [Yield n ] is the actual yield matrix of naphtha fraction in the atmospheric and vacuum unit.

[0024] The above-mentioned physical property data of the actual boiling point cut fraction include one or more of naphtha density, Engler distillation range, PONA, sulfur content, arsenic content, and BMCI.

[0025] In the third step, the specific process of selecting the model data with similar working conditions to the naphtha to be tested includes: first, normalizing the physical property data of the actual boiling point cut fraction to obtain a normalized function; then, calculating the correlation coefficient r between the naphtha yield and the actual boiling point yield of the atmospheric and vacuum distillation unit and the adjustment parameters of the distillation unit according to the normalized function; then, calculating the similarity coefficient matrix d between the physical property data of the naphtha to be tested and the physical property data in the model library according to the physical property data of the actual boiling point cut fraction corresponding to the naphtha to be tested MIN ; Finally, according to the similarity coefficient matrix d MIN , from the Yield_Base model and the Property_Base model, sort and filter out the yield and physical property data sets that are close to the operating conditions of the naphtha fraction to be tested.

[0026] The above normalized function is as follows:

[0027]

[0028] Where, d H is the upper limit of the data before normalization; d L is the lower limit of the data before normalization; n H is the upper limit of the target planning interval; n L The lower limit of the target planning interval.

[0029] The calculation formula of the above correlation coefficient r is as follows:

[0030]

[0031] Wherein, X is the actual boiling point distillation yield and the distillation unit adjustment parameters; Y is the naphtha yield of the atmospheric and vacuum distillation unit;

[0032] Cov(X,Y) is the covariance of X and Y; Var[X] is the variance of X; Var[Y] is the variance of Y.

[0033] The above similarity coefficient matrix d MIN The calculation formula is as follows:

[0034] d MIN =[W-D86 i —W-D86 n ] 2 ×[rn ]

[0035] Where, W-D86 i is the actual boiling point distillation yield, density, and PONA physical property matrix corresponding to the naphtha fraction to be tested; [r n ] is the correlation coefficient matrix.

[0036] The method for sorting and screening the yield and property data sets close to the working conditions of the naphtha fraction to be tested from the Yield_Base model and the Property_Base model is one or more of the similarity query method and the cluster analysis method.

[0037] In the fourth step above, the time range of the cutting prediction is 1 day to 15 days from the current day.

[0038] In the fifth step above, the regression model formula is as follows:

[0039] Y=B 0 +B 1 X 1 +B 2 X 2 +B 3 X 3 …B n X n +ε

[0040] Where, Y is the yield or physical property value of the naphtha to be tested; X is the yield or physical property value of the screened naphtha; ε is the residual; B 0 is the intercept, B 1 , B 2 , …B n is the coefficient of X.

[0041] The present invention further reduces the deviation of predicted physical property data by establishing a data association model and adopting a real-time verification method, and establishes a real-time conversion model for actual boiling point distillation and atmospheric and vacuum distillation of crude oil within an enterprise. The converted evaluation data is closer to the properties of the current processed crude oil. The method is fast and efficient and can be directly applied to production. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Attached Figure 1 This is the actual boiling point distillation curve of the mixed crude oil in Example 15 of the present invention.

[0043] Attached Figure 2 This is a curve diagram showing the relationship between the final distillation points of naphtha using two distillation methods in Example 15 of the present invention.

[0044] Attached Figure 3 This is a comparison chart of the final distillation point of naphtha in Example 15 of the present invention.

[0045] Attached Figure 4This is a diagram of the deviation of naphtha yield in Example 15 of the present invention.

[0046] Attached Figure 5 This is a diagram of the density deviation of naphtha in Example 15 of the present invention.

[0047] Attached Figure 6 This is a deviation diagram of the 90% point of the Engler distillation range of naphtha in Example 15 of the present invention.

[0048] Attached Figure 7 This is a diagram showing the deviation of the naphtha paraffin content in Example 15 of the present invention.

[0049] Attached Figure 8 This is a flow chart of the method in Example 15 of the present invention.

[0050] Attached Fig. 9 This is a diagram of the rolling iterative verification process in Example 15 of the present invention. DETAILED DESCRIPTION

[0051] The present invention is not limited by the following embodiments, and specific implementation methods can be determined based on the technical solution of the present invention and actual conditions.

[0052] The present invention will be further described below in conjunction with embodiments:

[0053] Embodiment 1: The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of the actual boiling point distillation products comprises the following steps:

[0054] The first step is to establish a conversion model between the actual boiling point distillation range and the Engler distillation range according to the conversion calculation of the distillation range of Engler distillation D86 and the actual boiling point distillation TBP;

[0055] The second step is to establish the naphtha yield Yield_Base model and the physical property Property_Base model by using the actual boiling point distillation range and Engel's distillation range conversion model and the physical property analysis data of the atmospheric and vacuum unit product yield corresponding to that date;

[0056] The third step is to establish similar operating condition query means in the naphtha yield Yield_Base model and the physical property Property_Base model to screen out model data with similar operating conditions to the naphtha to be tested;

[0057] The fourth step is to perform rolling iterative verification on the data with large deviations from the model data of similar working conditions to the naphtha to be tested (to further improve the accuracy of model data prediction), collect the latest batch of evaluation and analysis data, perform cutting prediction on the current evaluation and analysis data through iterative verification of naphtha yield and physical properties;

[0058] The fifth step is to use the regression model formula to regress the naphtha yield and physical properties after the calibration is completed. The iterative calibration parameters of naphtha yield and physical properties are shown in Table 1.

[0059] The present invention first establishes a naphtha yield and physical property data model, then performs a similar working condition query method to screen model data close to the naphtha to be tested, and then uses a real-time rolling iterative verification method to reduce the deviation of the model prediction physical property data, and finally accurately associates the actual boiling point distillation naphtha physical property data with the atmospheric and vacuum distillation unit naphtha physical property data, thereby realizing the direct conversion of static actual boiling point distillation data into atmospheric and vacuum distillation unit production data, and further enhancing the role of crude oil evaluation data in guiding production.

[0060] Example 2: As an optimization of the above example, in the first step, in the distillation range conversion of Englert distillation D86 and true boiling point distillation TBP, the final distillation point range is 60°C to 400°C.

[0061] Example 3: As an optimization of the above example, in the first step, the calculation method for the distillation range conversion between Eng's distillation D86 and true boiling point distillation TBP is one or more of the least squares method, stepwise regression, polynomial fitting, logarithmic fitting and gamma adjustment method.

[0062] Example 4: As an optimization of the above example, in the first step, the boiling point distillation range and the Engler distillation range conversion model are as follows:

[0063]

[0064] d k =Ga,...a=(a 0 , a 1 , …a n ) T ; d=(d 0 , d 1 , …d n ) T

[0065]

[0066] Where a is the distillation range of the actual boiling point distillation, d is the Engler distillation range of the corresponding fraction, is the coefficient of a.

[0067] Example 5: As an optimization of the above example, in the second step, the Yield_Base model is obtained according to the following steps: the yield of the naphtha fraction cut by laboratory actual boiling point distillation (the specific cutting distillation range is converted from the Engler distillation range) and the major adjustment parameters of the device are used as independent variables, and the product yield of the atmospheric and vacuum distillation device is used as the dependent variable. A correlation model between the actual boiling point yield, the key device operating parameters and the device product yield is established, and the initial cutting plan is derived, wherein the Yield_Base model formula is as follows:

[0068] [[Y-TBP n ],[Operate n ]]×Yield_Base=[Yield n ]

[0069] In the formula, [Y-TBP n ] is the yield matrix of the actual boiling point distillation naphtha fraction (the specific cut-off distillation range is converted from the Engler distillation range); [Operate n ] is the key adjustment parameter matrix of the device; Yield_Base is the Base matrix of yield; [Yield n ] is the actual yield matrix of naphtha fraction in the atmospheric and vacuum unit.

[0070] Example 6: As an optimization of the above example, in the second step, the Property_Base model is obtained according to the following steps: Based on the Yield_Base model, a correlation model between the actual boiling point cut fraction physical property data (yield, density, PONA, etc.), the cut scheme and the device product analysis data is established to derive the initial product physical property data, wherein the Property_Base model formula is as follows:

[0071] [[W-TBP n ],[Yield_Base]]×Property_Base=[Property n ]

[0072] Where, [W-TBP n ] is the actual boiling point distillation yield, density, and PONA physical property matrix corresponding to the naphtha fraction after actual boiling point distillation and Engler distillation final boiling point conversion; Property_Base is the Base matrix of naphtha physical properties; [Property_Base ...; [Property_Base] is the actual boiling point distillation yield, density, and PONA physical property matrix corresponding to the naph n ] is the actual physical property matrix of the naphtha fraction from the atmospheric and vacuum unit.

[0073] Example 7: As an optimization of the above example, the physical property data of the actual boiling point cut fraction include one or more of naphtha density, Engler distillation range, PONA, sulfur content, arsenic content, and BMCI.

[0074] Embodiment 8: As an optimization of the above embodiment, in the third step, the specific process of screening out the model data of the working conditions similar to the naphtha to be tested includes: first, normalizing the physical property data of the actual boiling point cut fraction to obtain a normalized function formula; then, calculating the correlation coefficient r between the naphtha yield and the actual boiling point yield of the atmospheric and vacuum distillation unit and the adjustment parameters of the distillation unit according to the normalized function formula; then, calculating the similarity coefficient matrix d between the physical property data of the naphtha to be tested and the physical property data in the model library according to the physical property data of the actual boiling point cut fraction corresponding to the naphtha to be tested MIN ; Finally, according to the similarity coefficient matrix d MIN , from the Yield_Base model and the Property_Base model, sort and filter out the yield and physical property data sets that are close to the operating conditions of the naphtha fraction to be tested.

[0075] Embodiment 9: As an optimization of the above embodiment, the normalized function is as follows:

[0076]

[0077] Where, d H is the upper limit of the data before normalization; d L is the lower limit of the data before normalization; n H is the upper limit of the target planning interval; n L The lower limit of the target planning interval.

[0078] Embodiment 10: As an optimization of the above embodiment, the calculation formula of the correlation coefficient r is as follows:

[0079]

[0080] Wherein, X is the actual boiling point distillation yield and the distillation unit adjustment parameters; Y is the naphtha yield of the atmospheric and vacuum distillation unit;

[0081] Cov(X,Y) is the covariance of X and Y; Var[X] is the variance of X; Var[Y] is the variance of Y.

[0082] Embodiment 11: As an optimization of the above embodiment, the calculation formula of the similarity coefficient matrix d is as follows:

[0083] d MIN =[W-D86 i —W-D86 n ] 2 ×[r n ]

[0084] Where, W-D86 i is the actual boiling point distillation yield, density, and PONA physical property matrix corresponding to the naphtha fraction to be tested; [r n ] is the correlation coefficient matrix.

[0085] Example 12: As an optimization of the above example, the method for sorting and screening the yield and physical property data groups that are close to the operating conditions of the naphtha fraction to be tested from the Yield_Base model and the Property_Base model is one or more of the similarity query method (combining normalization, correlation coefficient, and sorting algorithm for comprehensive measurement) and cluster analysis method.

[0086] Embodiment 13: As an optimization of the above embodiment, in the fourth step, the time range of the cutting prediction is 1 day to 15 days from the current day.

[0087] Embodiment 14: As an optimization of the above embodiment, the regression model formula is as follows:

[0088] Y=B 0 +B 1 X 1 +B 2 X 2 +B 3 X 3 …B n X n +ε

[0089] Where, Y is the yield or physical property value of the naphtha to be tested; X is the yield or physical property value of the screened naphtha; ε is the residual; B 0 is the intercept, B 1 , B 2 , …B n is the coefficient of X.

[0090] Example 15: The method for calculating the properties of the products in the atmospheric and vacuum distillation process based on the properties of the actual boiling point distillation products (the specific process is as follows Figure 8 ), comprising the following steps:

[0091] The first step is to establish the conversion model of the actual boiling point distillation range and the Engler distillation range according to the conversion calculation of the distillation range of Engler distillation D86 and the actual boiling point distillation TBP:

[0092] Select the mixed crude oil (3 to 4 kinds of crude oil) processed by the refinery, with a water content of less than 0.3%, load it into a 10L to 20L distillation kettle, and distill and cut it in a real boiling point distillation 2892CC equipment according to the following steps:

[0093] (1) Cut out butane and other fractions before 15°C in mixed crude oil;

[0094] (2) Under normal pressure, the reflux ratio is controlled to be 5:1, and 20°C is cut as a narrow fraction to cut out the fraction before 200°C of the mixed crude oil;

[0095] (3) Control the reflux ratio to 5:1, the system residual pressure to 100 mmHg, and cut out the fraction before 300°C of the mixed crude oil; Step 4: Control the reflux ratio to 2:1, the system residual pressure to 10 mmHg, and cut out the fraction before 350°C of the mixed crude oil, as shown in Table 2 and Figure 1 shown.

[0096] The narrow fractions of the actual boiling point distillation of the mixed crude oil were blended into wide fractions with different distillation ranges, and then Engelhard distillation range analysis was performed. The results are shown in Table 3.

[0097] The end point of the actual boiling point distillation range is used as the X-axis, and the end point of the Engler distillation range of the corresponding fraction is used as the Y-axis to draw a graph, such as Figure 2 shown.

[0098] According to the least squares fitting formula, the linear fitting equation d=a is determined based on the graph. 0 +a 1 x, the final calculation result is:

[0099] d = 19.82 + 0.857x, its variance is R 2 =0.998.

[0100] Comparison of distillation ranges of naphtha and diesel fractions converted according to the fitting formula, such as Figure 3 shown.

[0101] The second step is to establish the naphtha yield Yield_Base model and the physical property Property_Base model using the actual boiling point distillation range and Engel's distillation range conversion model and the physical property analysis data of the atmospheric and vacuum unit product yield corresponding to that date:

[0102] The naphtha yield Yield_Base model was established according to the unit mixed crude oil naphtha yield matrix formula, and the relevant data are shown in Table 4.

[0103] The naphtha physical property Property_Base model is established according to the physical property matrix formula of the mixed crude oil naphtha in the device. The relevant data are shown in Table 5 below.

[0104] The third step is to establish similar operating condition query methods in the naphtha yield_Base model and the physical property_Base model to filter out model data with similar operating conditions to the naphtha to be tested:

[0105] The above yields and physical property models were normalized, similarities and rankings were calculated, and some data were listed as shown in Table 6.

[0106] The fourth step is to perform rolling iteration calibration on the data with large deviations from the model data of similar working conditions to the naphtha to be tested (the specific process is as follows Fig. 9As shown in the figure, collect the evaluation and analysis data of the latest batch, cut and predict the current evaluation and analysis data through iterative verification, and after the verification is completed, use the regression model formula to regress and calculate the naphtha yield and physical properties:

[0107] After sorting the physical property data of similar working conditions, the model data that is relatively close to the crude oil to be tested is screened out, and the evaluation and analysis data of the latest batch is collected to verify the accuracy of the model, and the screening model verification is completed. The specific process is as follows:

[0108] (1) Rapid evaluation of crude oil on date T, providing analysis data of mixed crude oil entering the plant T , call the most recent evaluation data DATA T-1 , dated T-1;

[0109] (2) Collect the yield of the device on date T-1 T-1 and device product analysisProperty T-1 ;

[0110] (3) Based on the Yield_Base model and the Property_Base model, DATA T-1 Predict the yield of the device on T-1 day T-1 ' and product property data T-1 ';

[0111] (4) Compare the differences between the predicted yield and properties of T-1 and the actual yield and properties;

[0112] (5) If the difference is large, the Base model needs to be recalibrated; if it is small, the Base model does not need to be recalibrated;

[0113] (6) Introduce the major operating parameters of the device Operate to further iterate and calibrate the Base model, and finally obtain the Base' model.

[0114] According to the above model, the naphtha fraction actual boiling point distillation physical properties are formed to predict the naphtha physical properties of the atmospheric and vacuum distillation unit. Among them, the allowable deviation value range between the predicted yield and properties and the actual yield and properties is compared with the analysis and production requirements, and the upper limit of the deviation is obtained based on previous statistical data. The iterative calibration standard refers to the analysis deviation requirements and the previous deviation data of production statistics to iteratively calibrate the target value.

[0115] After completing the accuracy verification of the screening model, regression calculation was performed to obtain the atmospheric and vacuum distillation naphtha properties associated with the actual boiling point properties of the crude oil to be tested. After regression analysis, the regression results of some physical properties are shown in Tables 7, 8 and 9, where Table 7 is a regression statistics table, Table 8 is a variance analysis table, and Table 9 is a regression parameter table.

[0116] After rolling iteration verification and regression calculation, the deviation diagram between the relevant predicted properties and the actual physical properties is as follows Figure 4 , Figure 5 , Figure 6 and Figure 7 As shown. Figure 4 , Figure 5 , Figure 6 and Figure 7 It can be seen that the average deviation of the calculated naphtha yield is 0.43%, and the average deviation of the density is 1.2 kg / m 3 The average deviation of 90% distillation temperature is 3.0℃, and the average deviation of paraffin content is 1.8%, which meets the production needs.

[0117] In summary, the present invention further reduces the deviation of predicted physical property data by establishing a data association model and adopting a real-time verification method, and establishes a real-time conversion model for actual boiling point distillation and atmospheric and vacuum distillation of crude oil within the enterprise. The converted evaluation data is closer to the properties of the current processed crude oil. The method is fast and efficient and can be directly applied to production.

[0118] The above technical features constitute the embodiments of the present invention, which have strong adaptability and implementation effect. Non-essential technical features can be added or reduced according to actual needs to meet the requirements of different situations.

[0119] Table 1

[0120] Verification Project Iterative calibration of initial deviation range Iteratively check target value Naphtha yield, % 1.3 to 5 1 <![CDATA[Naphtha density, kg / m 3 > 1.5 to 5 1.3 Naphtha normal alkanes, % 1 to 3 1 Naphtha isoparaffins, % 1.5 to 3 1 Naphtha cycloalkanes, % 1.5 to 3 1 Naphtha Energia distillation range 50%, ℃ 1.5 to 3 1

[0121] Table 2

[0122]

[0123] Table 3

[0124]

[0125] Table 4

[0126]

[0127] Table 5

[0128]

[0129]

[0130] Table 6

[0131]

[0132] Table 7

[0133]

[0134] Table 8

[0135] project Degrees of Freedom Error sum of squares Mean square error F-number Fa critical value Regression analysis 5 226.9258 45.38516 51.59456 2.17E-16 Residual 40 35.186 0.87965 —— —— total 45 262.1118 —— —— ——

[0136] Table 9

[0137]

Claims

1. A method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products. Features The steps include: The first step is to establish a conversion model between the actual boiling point distillation range and the Engler distillation range according to the conversion calculation of the distillation range of Engler distillation D86 and the actual boiling point distillation TBP; The second step is to establish the naphtha yield Yield_Base model and the physical property Property_Base model by using the actual boiling point distillation range and Engel's distillation range conversion model and the physical property analysis data of the atmospheric and vacuum unit product yield corresponding to that date; The third step is to establish similar operating condition query means in the naphtha yield Yield_Base model and the physical property Property_Base model to screen out model data with similar operating conditions to the naphtha to be tested; The fourth step is to perform rolling iterative verification on the data with large deviations from the model data of the working conditions similar to the naphtha to be tested, collect the evaluation and analysis data of the latest batch, perform cutting prediction on the current evaluation and analysis data through iterative verification of naphtha yield and physical properties; The fifth step is to use the regression model formula to perform regression calculations on the naphtha yield and physical properties after completing the verification.

2. The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products according to claim 1, Features In the first step, the final distillation point range is 60°C to 400°C in the distillation range conversion between Englert distillation D86 and true boiling point distillation TBP.

3. The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products according to claim 1 or 2, Features In the first step, the calculation method for the distillation range conversion between Eng's distillation D86 and true boiling point distillation TBP is one or more of the least square method, stepwise regression, polynomial fitting, logarithmic fitting and gamma adjustment method.

4. The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products according to claim 1, Features In the first step, the conversion model between boiling point distillation range and Engelhard distillation range is as follows: d k =Ga,…a=(a 0 ,a 1 ,…a n ) T ;d=(d 0 ,d 1 ,…d n ) T Where a is the distillation range of the actual boiling point distillation, d is the Engler distillation range of the corresponding fraction, is the coefficient of a.

5. The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of the actual boiling point distillation products according to claim 1, 2 or 4, Features In the second step, the Yield_Base model is obtained according to the following steps: taking the laboratory actual boiling point distillation cutting naphtha fraction yield and the major adjustment parameters of the device as independent variables, the atmospheric and vacuum distillation device product yield as the dependent variable, establishing a correlation model between the actual boiling point yield, key device operating parameters and device product yield, and deriving the initial cutting plan, where the Yield_Base model formula is as follows: [[Y-TBP n ],[Operate n ]]×Yield_Base=[Yield n ] In the formula, [Y-TBP n ] is the actual boiling point distillation naphtha fraction yield matrix; [Operate n ] is the key adjustment parameter matrix of the device; Yield_Base is the Base matrix of yield; [Yield n ] is the actual yield matrix of naphtha fraction in the atmospheric and vacuum unit.

6. The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products according to claim 5, Features In the second step, the Property_Base model is obtained according to the following steps: Based on the Yield_Base model, a correlation model between the actual boiling point cut fraction physical property data, the cut scheme and the device product analysis data is established to derive the initial product physical property data, wherein the Property_Base model formula is as follows: [[W-TBP n ],[Yield_Base]]×Property_Base=[Property n ] Where, [W-TBP n ] is the actual boiling point distillation yield, density, and PONA physical property matrix corresponding to the naphtha fraction after actual boiling point distillation and Engler distillation final boiling point conversion; Property_Base is the Base matrix of naphtha physical properties; [Property_Base ...; [Property_Base] is the actual boiling point distillation yield, density, and PONA physical property matrix corresponding to the naph n ] is the actual physical property matrix of the naphtha fraction from the atmospheric and vacuum unit.

7. The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products according to claim 6, Features The physical property data of the actual boiling point cut fraction include one or more of naphtha density, Englert distillation range, PONA, sulfur content, arsenic content, and BMCI.

8. The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of the actual boiling point distillation products according to claim 1, 2, 4, 6 or 7, Features In the third step, the specific process of selecting the model data with similar working conditions to the naphtha to be tested includes: first, normalizing the physical property data of the actual boiling point cut fraction to obtain a normalized function; then, calculating the correlation coefficient r between the naphtha yield and the actual boiling point yield of the atmospheric and vacuum distillation unit and the adjustment parameters of the distillation unit according to the normalized function; then, calculating the similarity coefficient matrix d between the measured physical property data of the naphtha to be tested and the physical property data in the model library according to the physical property data of the actual boiling point cut fraction corresponding to the naphtha to be tested MIN ; Finally, according to the similarity coefficient matrix d MIN , from the Yield_Base model and the Property_Base model, sort and filter out the yield and physical property data sets that are close to the operating conditions of the naphtha fraction to be tested.

9. The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products according to claim 8, Features The normalized function is as follows: Where, d H is the upper limit of the data before normalization; d L is the lower limit of the data before normalization; n H Set the upper limit of the target planning interval; n L The lower limit of the target planning interval.

10. The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products according to claim 8 or 9, Features The calculation formula of the correlation coefficient r is as follows: Wherein, X is the actual boiling point distillation yield and the distillation unit adjustment parameters; Y is the naphtha yield of the atmospheric and vacuum distillation unit; Cov(X,Y) is the covariance of X and Y; Var[X] is the variance of X; Var[Y] is the variance of Y.

11. The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products according to claim 10, Features Similarity coefficient matrix d MIN The calculation formula is as follows: d MIN =[W-D86 i —W-D86 n ] 2 ×[r n ] Where, W-D86 i is the actual boiling point distillation yield, density, and PONA physical property matrix corresponding to the naphtha fraction to be tested; [r n ] is the correlation coefficient matrix.

12. The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products according to claim 11, Features The method for sorting and screening the yield and property data sets close to the working conditions of the naphtha fraction to be tested from the Yield_Base model and the Property_Base model is one or more of the similarity query method and the cluster analysis method.

13. The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of the actual boiling point distillation products according to claim 1 or 2 or 4 or 6 or 7 or 9 or 11 or 12, Features In the fourth step, the time range of the cutting prediction is 1 day to 15 days from the current day.

14. The method for calculating the properties of products in atmospheric and vacuum distillation based on the properties of actual boiling point distillation products according to claim 13, Features In the fifth step, the regression model formula is as follows: Y=B 0 +B 1 X 1 +B 2 X 2 +B 3 X 3 …B n X n +ε Where, Y is the yield or physical property value of the naphtha to be tested; X is the yield or physical property value of the screened naphtha; ε is the residual; B 0 is the intercept, B 1 , B 2 ,…B n is the coefficient of X.

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