Method and model for rapidly predicting yield and property of atmospheric and vacuum side distillate

Abstract

The invention discloses a method for rapidly predicting yield and property of atmospheric and vacuum side-draw distillate, which is characterized in that an initial value of a model separation factor is estimated according to data of an atmospheric and vacuum unit, an optimization solution range is given, the model separation factor is calibrated by using actual production data, and the yield and property of the atmospheric and vacuum side-draw distillate are predicted. Based on the calibrated model, predicting the yield of the side distillate oil after atmospheric and vacuum processing of different crude oils. Compared with the high requirement of calculation by using a traditional mechanism model, the method establishes a simplified model and shortens the calculation time. Through combination of virtual component division and distillation separation factor application, simplified calculation and rapid evaluation of a complex distillation process are realized. According to the method, the accuracy and efficiency of evaluation are improved, and powerful support is provided for production optimization and cost control of refinery enterprises.

Description

Technical Field

[0001] The present invention relates to the field of petrochemical industry, and specifically to a method and model for quickly predicting the yields and properties of atmospheric and vacuum side stream distillates. Technical Background

[0002] In the petroleum refining industry, the atmospheric and vacuum unit is the leading unit for crude oil processing. The main function of this process is to effectively separate the components with different boiling points in the crude oil through the distillation process, and extract them from the side streams to provide basic raw materials for subsequent in-depth petroleum processing. Therefore, the yields of the atmospheric and vacuum side stream distillates affect the processing arrangements of the entire refinery. During the refinery planning and scheduling process, people often hope to quickly obtain the yields and properties of the atmospheric and vacuum side stream distillates after processing different crude oils in the refinery's atmospheric and vacuum unit, so as to guide crude oil procurement and arrange the processing plan in advance.

[0003] To obtain this information, a fractionation mechanism model is usually used for simulation and calculation, but there are the following problems in its application process. First, the calculation time of the mechanism model is generally long, which is difficult to meet the needs of rapid decision-making. Second, this technology usually requires relatively complex data input and has high requirements for its initial values. If the initial values are set improperly, it may lead to deviations in the calculation results or even calculation failures.

[0004] In addition, there are some models using simple data regression, which can reduce the calculation complexity, but the regression results often lack practical physical meaning and cannot correspond to the actual device, which makes it difficult to estimate the initial values and interpret the results of these models. In the case of large noise in the calibration data, the obtained models are also difficult to generalize.

[0005] Based on the above situation, the present invention proposes a simplified atmospheric and vacuum model with practical physical meaning for quickly predicting the side stream yields and properties, which can balance efficiency and accuracy and has important value for refining production. Summary of the Invention

[0006] In view of the above problems, the present invention discloses a method and model for quickly predicting the yields and properties of atmospheric and vacuum side stream distillates. By estimating the separation factors between each side stream of the atmospheric and vacuum actual device, and correcting the model coefficients using the characteristic relationship between the raw material and the side stream distillates, a side stream yield and property prediction model suitable for a specific atmospheric and vacuum device is obtained.

[0007] The present invention first discloses a method for quickly predicting the yields and properties of atmospheric and vacuum side stream distillates, and the method includes the following steps:

[0008] 1. Based on the actual number of trays between each side stream of the atmospheric and vacuum unit, multiply it by the tray conversion coefficient to obtain the initial value n of the separation factor n i of n i0, the plate conversion factors for different regions are shown in the following table:

[0009]

[0010]

[0011] Determine that the calibration solution range of the separation factor is ±50% of the initial value.

[0012] 2. Collect the yields and distillation ranges of the side-stream distillates from the atmospheric and vacuum distillation units.

[0013] 3. Use spline interpolation to complete the data of the distillation curves of the side streams, and uniformly convert the distillation curves of each side-stream distillate into TBP distillation curves;

[0014] 4. Based on the density and TBP distillation curve of the side-stream distillate, conduct virtual component division; divide the entire distillation range into j cutting distillation ranges and divide them into j virtual components; according to the temperature segments for dividing the virtual components, obtain the content of virtual component j of side-stream distillate i; use the temperature at the 50% point of the TBP distillation curve and the characterization factor of side-stream distillate i to estimate the density of the virtual component; conduct integral calculation based on the TBP distillation curve to obtain the boiling point T of virtual component j j .

[0015] 5. Based on the yields of each side-stream distillate, accumulate the virtual component contents of all side-stream distillates to obtain the raw material component composition, and then obtain the TBP distillation curve of the raw material.

[0016] 6. Based on the yields of the side-stream distillates and the TBP distillation curve of the raw material, calculate the cutting temperature T of side-stream distillate i c,i ; 7. Obtain the side-stream distillate yield prediction model using the following steps:

[0017] According to the top dry gas and i side-stream distillates in the atmospheric and vacuum distillation process, construct i sub-models. The separation calculation between adjacent side streams uses one sub-model, and calculate the i sub-models step by step from i to 1;

[0018] Based on the boiling point T of virtual component j j and the cutting temperature T of sub-model i c,i , calculate the average relative volatility α of virtual component j in this sub-model i,j :

[0019]

[0020] Based on the separation factor n i and α i,j , calculate the gas-phase component x D,i,j and liquid-phase component x B,i,j of this component in sub-model i:

[0021]

[0022] Calculate the upward gas flow rate D of sub-model i according to the material balance i and the withdrawn liquid flow rate B i The withdrawn liquid flow rate B i is the flow rate of the side-stream distillate oil i:

[0023]

[0024] In the formula, F i is the feed of sub-model i. For i = 1, F i = 0. For i > 1, F i = D i-1 Z i,j is the content of component j in the feed of sub-model i. For i > 1, Z i,j = x D,i-1,j .

[0025] The withdrawn liquid flow rate Bi is the flow rate of the side-stream distillate oil i. The flow rate of the side-stream distillate oil i divided by the total amount of raw materials is the yield of this side-stream.

[0026] 8. Calculate the TBP distillation curve of the side-stream distillate oil i based on the calculation rules of the properties of oil mixtures, where the 5% distillation temperature is denoted as T 5％,i and the 95% distillation temperature is denoted as T 95％,i .

[0027] 9. Use an optimization algorithm to perform optimization and solution with the goal of minimizing the residual err to obtain the separation factor n suitable for the unit i :

[0028]

[0029] where is the actual yield of side-stream i, and are the 5% and 95% distillation temperatures in the TBP distillation curve obtained by analyzing the side-stream distillate oil i respectively, and w F and w T are the weight coefficients of the yield and temperature respectively.

[0030] 10. For the new crude oil to be predicted, use the above model, based on the optimized separation factor n i and the cutting temperature T c,i , quickly predict the yield and properties of the side-stream distillate oil of the atmospheric and vacuum distillation unit

[0031] The present invention also discloses a model for quickly predicting the yields and properties of atmospheric and vacuum side-stream distillates. The model constructs i sub-models based on dry gas and i side-stream distillates during the atmospheric and vacuum distillation process. The separation calculation between adjacent side-streams uses one sub-model, and the i sub-models are calculated step by step from i to 1.

[0032] Construct the following sub-model. Based on the boiling point T j of component j and the cut temperature T c,i , calculate the average relative volatility α

[0033] of component j in this sub-model i,j :

[0034]

[0035] Construct the following sub-model. Based on the separation factor n i and α i,j , calculate the gas-phase component x D,i,j and liquid-phase component

[0036] x B,i,j of sub-model i:

[0037]

[0038] Adopt the following model to calculate the upward gas flow rate D i and the withdrawn liquid flow rate B i of sub-model i. The withdrawn liquid flow rate B i is the flow rate of side-stream distillate i. The flow rate of side-stream distillate i divided by the total raw material amount is the yield of this side-stream:

[0039]

[0040] In the formula, F i is the feed of sub-model i. For i = 1, F i = 0. For i > 1, F i = D i-1 , Z i,j is the content of component j in the feed of sub-model i. For i > 1, Z i,j = x D,i-1,j ;

[0041] Based on the liquid-phase component x B,i,j and the calculation rules of the properties of oil mixtures, calculate the properties after mixing the virtual components of each side-stream distillate.

[0042] Beneficial effects:

[0043] The present invention discloses a method and a model for quickly predicting the properties of side-stream distillate oils in an atmospheric and vacuum distillation unit. Compared with the high requirements of calculating using traditional mechanism models, this method establishes a simplified model, shortening the calculation time. By combining virtual component division and distillation separation factor application, simplified calculation and rapid evaluation of complex distillation processes are achieved. This method improves the accuracy and efficiency of evaluation, providing strong support for production optimization and cost control in refining and chemical enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Flow chart of a method and a model for quickly predicting the yields and properties of side-stream distillate oils in an atmospheric and vacuum distillation unit according to the present invention

[0045] Figure 2 Test results of side-stream yields in an embodiment of the present invention

[0046] Figure 3 Test results of naphtha side-stream in an embodiment of the present invention

[0047] Figure 4 Test results of kerosene side-stream in an embodiment of the present invention

[0048] Figure 5 Test results of heavy gas oil side-stream in an embodiment of the present invention DETAILED DESCRIPTION OF THE INVENTION

[0049] The present invention will be further described below in conjunction with the drawings and specific embodiments, and the implementation effects of this method in predicting the yields and properties of side-stream distillate oils in an atmospheric and vacuum distillation unit will be described by specific operation processes. This embodiment is implemented on the premise of the technical solution of the present invention, but the protection scope of the present invention is not limited to the following embodiments.

[0050] A large domestic refining and chemical enterprise has an atmospheric and vacuum processing unit with a capacity of 10 million tons / year. In order to quickly predict the yields and properties of side-stream distillate oils obtained after processing a new crude oil by this unit, a prediction model applicable to the yields and properties of side-stream distillate oils in this atmospheric and vacuum distillation unit is established using the technology of the present invention. The specific process is as Figure 1 shown.

[0051] This unit can separate dry gas and 7 side-stream distillate oils, namely naphtha, kerosene, light gas oil, heavy gas oil, vacuum light oil, vacuum heavy oil, and residue. The number of trays between dry gas, naphtha, kerosene, light gas oil, and heavy gas oil are 16, 12, 10, and 16 respectively. The sections between heavy gas oil, vacuum light oil, vacuum heavy oil, and residue are packed towers, and the estimated number of trays are 10, 8, and 8. Based on the tray reduction coefficient, the initial value n i of the separation factor n i0 is:

[0052] n i0=[8, 7, 4, 6.4, 3, 2.4, 2.4]

[0053] Determine the separation factor n i Calibrate the solution range:

[0054] [4, 3.5, 2, 3.2, 1.5, 1.2, 1.2] ≤ n i0 ≤ [12, 10.5, 6, 9.6, 4.5, 3.6, 3.6]

[0055] Track the atmospheric and vacuum distillation unit for a period of time while processing a certain crude oil, and collect the yield data of the side-stream distillate oil of the unit, as shown in Table 1.

[0056] Table 1 Yields of each side-stream of the atmospheric and vacuum distillation unit processing a certain crude oil

[0057] Side stream distillate Volume yield V% Dry gas 3.57 Naphtha 11.54 Kerosene 6.33 Light gas oil 9.75 Heavy gas oil 19.70 Vacuum light oil 18.63 Vacuum heavy oil 4.93 Residue 25.55

[0058] Collect the property data of the side-stream distillate oil of the unit, use cubic spline interpolation to complete the data of each distillation curve, and uniformly convert it into a TBP distillation curve, as shown in Table 2.

[0059] Table 2 TBP distillation curves (°C) of the side-stream distillate oil of each side of the atmospheric and vacuum distillation unit processing a certain crude oil

[0060]

[0061] Conduct virtual component division for each side-stream. Taking kerosene as an example, its virtual components are shown in Table 3.

[0062] Table 3 Virtual components of the kerosene side-stream

[0063]

[0064]

[0065] Accumulate the components of each side-stream to obtain the complete virtual component composition of the raw material, and calculate the cutting temperature T between each side-stream in combination with the yield c,i :

[0066] T c,i = [45, 140, 180, 240, 360, 500, 540]

[0067] Use the data in Table 1 and Table 2 to calibrate the separation factors of the sub-models corresponding to each side-stream one by one. After calculation, the separation factor n is obtained i :

[0068] n i = [8.1, 6.7, 5.1, 5.8, 5.1, 3.5, 3.1]

[0069] Using the crude oil assay data in Table 4 and based on the separation factor n obtained from calibration i and the cut temperature T c,i , predict the yields and distillation range data of each side stream from processing this crude oil.

[0070] Table 4 TBP distillation curve data of the crude oil to be processed

[0071] Volume yield / % Distillation temperature / °C 0 -6.55 5 58.73 10 98.39 30 218.09 50 349.17 70 495.29 90 671.65 95 766.09 100 870.87

[0072] To verify the model, collect the yield and property data of the side stream distillate oil within a period of time after the atmospheric and vacuum distillation unit processes the new crude oil, and compare them with the predicted data.

[0073] Figure 2 For the comparison between the predicted data of the yield model for each side stream and the actual data of the refinery, it can be seen from Figure 2 that the absolute value of the deviation of the predicted yield of each side stream by this model is within 2%, and it can better predict the yield of the side stream distillate oil in the atmospheric and vacuum distillation unit.

[0074] Figures 3 to 5 The comparison between the predicted distillation range data and the assay data of naphtha, kerosene, and heavy gas oil, which the refinery is most concerned about, is given. It can be seen that the average deviations of the distillation range prediction are 1.92°C, 0.75°C, and 3.37°C respectively, all within the assay repeatability range, meeting the requirements of refining and chemical production.

[0075] In addition, the time required for model calibration and prediction using this method is within 30 seconds, while under the same computer hardware conditions, using the mechanism model for model calibration and prediction takes more than 10 minutes.

[0076] In summary, this method and model can quickly and accurately predict the yield and properties of the side stream distillate oil in the atmospheric and vacuum distillation unit, improving the real-time performance while meeting the production accuracy requirements of the enterprise.

Claims

1. A method for rapidly predicting the yield and properties of atmospheric and vacuum side-line distillate oil, characterized in that The initial value of the model separation factor is estimated based on the atmospheric and vacuum unit data, and the optimization solution range is given. The model separation factor is calibrated using actual production data. The sideline distillate oil yield of different crude oils after atmospheric and vacuum processing is predicted based on the calibrated model. The following steps are included: 1) Calculate the separation factor n based on the number of plates between each side line of the atmospheric and vacuum unit i The initial value n i0 , and determine the separation factor calibration solution range to be ±50% of the initial value; 2) Collecting the yield and distillation range data of the side-line distillate oil of the atmospheric and vacuum unit; 3) using spline interpolation to complete the data of the distillation curve of the side line, and uniformly converting the distillation curve of each side line distillate oil into a TBP distillation curve; 4) Based on the density and TBP distillation curve of the side-line distillate oil, virtual component division is performed to obtain the content of virtual component j of side-line distillate oil i and the boiling point T of virtual component j j ; 5) Based on the yield of each side-line distillate oil, the virtual component contents of all side-line distillate oils are accumulated to obtain the raw material component composition, and then the TBP distillation curve of the raw material is obtained; 6) Based on the yield of the side-cut oil and the TBP distillation curve of the raw material, the cut temperature T of the side-cut oil i is calculated. c,i ; 7) Use the following steps to build a side cut oil yield prediction model: According to the dry gas and i side-line distillate oils in the atmospheric and vacuum distillation process, i sub-models are constructed, the separation calculation of adjacent side-lines uses one sub-model, and the i sub-models are calculated step by step from i to 1; The following sub-model is constructed, based on the boiling point T of component j j and cutting temperature T c,i , calculate the average relative volatility α of the j virtual component in this submodel i,j : Construct the following sub-model based on the separation factor n i and α i,j , calculate the gas phase component x of submodel i D,i,j and liquid component x B,i,j : The following model is used to calculate the rising gas phase flow rate D of sub-model i: i and produced liquid flow rate B i , produced liquid flow rate B i The flow rate of side-line distillate oil i is divided by the total amount of raw materials to obtain the side-line yield: Where F i is the feed of sub-model i, for i=1, F i =0, for i>1, F i =D i-1 , Z i,j is the content of component j in the feed of sub-model i. For i>1, Z i,j =x D,i-1,j ; 8) Based on liquid component x B,i,j According to the oil product mixing property calculation rules, the TBP distillation curve of each side-line distillate oil virtual component after mixing is calculated, where the 5% distillation temperature is recorded as T 5％,i , 95% distillation temperature is recorded as T 95％,i ; 9) Use the optimization algorithm to minimize the residual error err and obtain the separation factor n suitable for the device. i : in is the actual yield of side line i, and are the 5% and 95% distillation temperatures in the TBP distillation curve obtained from the analysis of the sideline distillate i, and w F and w T are the weight coefficients of yield and temperature, respectively; 10) For the new crude oil to be predicted, the above model is used to optimize the separation factor n i and cutting temperature T c,i , quickly predict the yield and properties of the side-line distillate oil from the atmospheric and vacuum unit.

2. A method for rapidly predicting the yield and properties of atmospheric and vacuum side-line distillate according to claim 1, characterized in that Using the plate reduction factor to estimate the separation factor n i Initial values, the plate conversion coefficients for different regions are shown in the following table:

3. A method for rapidly predicting the yield and properties of atmospheric and vacuum side-line distillate according to claim 1, characterized in that The following method is used to divide the side stream fraction i into virtual components: 1) The whole distillation range is cut into j cut distillation ranges, which are divided into j virtual components, and the content of virtual component j of side-line distillate i is obtained according to the temperature segment; 2) estimating the density of the virtual component based on the 50% point temperature of the TBP distillation curve and the characteristic factor of the side-cut oil i; 3) Based on the TBP distillation curve, the boiling point T of the virtual component j is calculated by integration j .

4. A model for rapidly predicting the yield and properties of atmospheric and vacuum side-stream distillate oil, characterized in that The model constructs i sub-models according to the dry gas and i side-line distillate oils in the atmospheric and vacuum distillation process, and the separation calculation of adjacent side lines uses one sub-model, and the i sub-models are calculated step by step from i to 1; The following sub-model is constructed, based on the boiling point T of component j j and cutting temperature T c,i , calculate the average relative volatility α of the j virtual component in this submodel i,j : Construct the following sub-model based on the separation factor n i and α i,j , calculate the gas phase component x of submodel i D,i,j and liquid component x B,i,j : The following model is used to calculate the rising gas phase flow rate D of sub-model i: i and produced liquid flow rate B i , produced liquid flow rate B i The flow rate of side-line distillate oil i is divided by the total amount of raw materials to obtain the side-line yield: Where F i is the feed of sub-model i, for i=1, F i =0, for i>1, F i =D i-1 , Z i,j is the content of component j in the feed of sub-model i. For i>1, Z i,j =x D,i-1,j ; Based on liquid component x B,i,j The properties of each side-line distillate virtual component after mixing are calculated according to the calculation rules of oil mixed properties.

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