CN111592907B - Online analysis and detection method for oil dry point of atmospheric tower top - Google Patents
Online analysis and detection method for oil dry point of atmospheric tower top Download PDFInfo
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Abstract
The invention discloses an online analysis and detection method for oil dry point at top of atmospheric tower, which firstly judges whether an online analyzer can work normally or not, and if not, judges whether the online analyzer can not work normallyThe working principle selects related variables according to the process mechanism and then passes through a soft measurement model M1Calculating the measured value y of the oil dry point at the top of the tower1(ii) a And performing principal component analysis on the historical data of the variable to be measured and the related variable to obtain an estimation model M2Calculating the estimated value y of the oil dry point at the tower top2(ii) a Finally fusing y through Kalman filtering1And y2And obtaining the optimal estimated value y of the oil dry point at the tower top. The invention provides a tower top oil dry point estimation algorithm based on principal component analysis, and combines an estimated value with a measured value based on a soft measurement model, so that the accuracy of on-line component analysis and detection can be effectively improved, and the method plays an important role in implementing advanced control in the crude oil distillation process.
Description
Technical Field
The invention relates to industrial process state online detection, in particular to an online analysis and detection method for an oil dry point at the top of an atmospheric tower in a crude oil distillation process.
Background
In the advanced control of the crude oil distillation process, the multivariable control using quality indexes as controlled parameters is often used, so that the purposes of highest product yield, minimum processing energy consumption of a device and product quality edge clamping guarantee are achieved, and the economic benefit is maximized. Wherein, the common topping oil dry point is the main quality control index of the common topping product.
The common oil dry point is difficult to be directly measured by an instrument, most of the common oil dry points depend on off-line analysis and test, the real-time performance is poor, and the quality closed-loop control cannot be realized. An on-line analysis technology based on near infrared spectrum is developed later, the common top oil is sampled and heat exchanged to 40 ℃, and then spectrum data are analyzed so as to calculate dry points, but the near infrared spectrometer is high in cost and sometimes fails or is in a maintenance state. The dry point calculation based on soft measurement techniques may be based on a mechanistic model or a statistical model. The mechanism model is based on scientific principles such as physics and chemistry, and utilizes technologies such as material balance and energy balance to obtain a mathematical model of a common-overhead oil dry point and related process variables, but the establishment of the mechanism model requires deep knowledge of the crude oil distillation process and is accompanied with certain assumed conditions, so that the model precision is not high enough. The statistical model is obtained by collecting a great deal of process historical data and modeling by using a statistical analysis data processing method, but the actual calculation precision of the established model is reduced because the crude oil property and the operation condition are frequently changed.
Disclosure of Invention
Aiming at the problems, the invention discloses an online analysis and detection method for oil dry points at the top of an atmospheric tower, which is characterized in that an estimation model is established based on principal component analysis by selecting relevant variables, and the estimated value of the oil dry points at the top of the atmospheric tower is fused with the measured value obtained by a soft measurement model to obtain the optimal estimated value, so that the online detection of the oil dry points at the top of the atmospheric tower is realized.
The method comprises the following steps:
(1) judging whether the normal oil dry point online analyzer works normally, if so, directly measuring the dry point value, and turning to the step (9), and if not, turning to the step (2);
(2) selecting variables related to the variable to be detected to form a vector x;
(3) obtaining a state space model M of the system through soft measurement calculation1Calculating the measured value y of the variable to be measured1；
(4) Taking normal historical data of the variable to be measured and the related variable as a training set to form a sample matrix X;
(5) performing principal component analysis on the sample matrix X to obtain a load matrix P;
(6) make the direction vector xi of the variable to be measuredi＝[1 0 … 0]TAnd calculating xiiProjection into residual subspace
(7) Computing estimation model M2：
y2＝Cx (2)
Wherein, y2As an estimate of the variable to be measured, I1Is a matrix of the units,
after being unitized
I2Is a ratio of I1A one-dimensional less identity matrix;
(8) fusing measurement value y by Kalman filtering1And the estimated value y2Obtaining the optimal estimated value y of the variable;
(9) and (6) ending.
In the present method, selecting variables related to the overhead oil dry point includes: the top temperature of the atmospheric tower, the top pressure of the atmospheric tower, the cold reflux temperature at the top of the atmospheric tower, the gas phase temperature of the middle layer of the atmospheric tower, the feeding temperature of the atmospheric tower, the top circulating reflux heat extraction ratio and the first medium reflux heat extraction ratio are 7 variables in total.
In the method, the heat extraction ratio P of top circulation reflux1Can be circulated from the top of the atmospheric tower1Constant-top circulating heat exchange temperature difference delta T of constant-pressure tower1And calculating the feeding amount F to obtain:
in the method, the heat extraction ratio P is obtained by refluxing2The flow F can be returned from the normal2Constant-pressure tower heat exchange temperature difference deltaT2And calculating the feeding amount F to obtain:
in the method, model M1Can be obtained by combining a mechanism analysis method and least square regression.
Has the advantages that:
the online analysis and detection method for the atmospheric tower top oil dry point disclosed by the invention can estimate the tower top oil dry point by using an estimation model based on principal component analysis through the correlation among variables according to other measured correlation variables when an online analyzer cannot work, and comprehensively calculates the estimated value with a measured value obtained by soft measurement calculation to obtain an optimal estimated value, so that the result is more accurate, the advanced control engineering of the industrial process is effectively implemented, and the economic benefit of an enterprise is improved.
Drawings
FIG. 1 is a flow chart of an implementation of an online analysis and detection method for oil dry point at the top of an atmospheric tower
FIG. 2 is a schematic diagram of a process of atmospheric tower in a refinery
FIG. 3 is a comparison graph of online analysis and detection results of oil dry point at the top of atmospheric tower of a certain oil refining enterprise
Detailed description of the preferred embodiment
The following detailed computing process and specific operation flow are given in conjunction with the accompanying drawings and specific examples to further explain the present invention. 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 embodiment.
In the case, a certain oil refining enterprise is taken as an example, in order to ensure the stable operation of the production process and reduce the cost while ensuring the product quality, the enterprise designs a plurality of advanced controllers for the crude oil atmospheric and vacuum distillation device, wherein the advanced controllers comprise a furnace efficiency controller, an atmospheric furnace branch balance controller, an atmospheric tower controller and the like. The constant-top oil dry point is a main quality control index of a constant-top product and is an important controlled variable in an atmospheric tower controller.
The effectiveness and the implementation process of the method are illustrated by the online analysis and detection of the oil dry point at the top of the atmospheric tower in the atmospheric and vacuum device of the enterprise. 500 sets of data were selected, with the first 400 as the training set and the last 100 as the test set to validate the method. The implementation flow of this case is shown in fig. 1, and the specific implementation steps are as follows:
firstly, from the process mechanism, the factors related to the common oil dry point are qualitatively analyzed, and the process schematic diagram of the atmospheric tower of the enterprise is shown in fig. 2. According to the specific process analysis, the dry point of the atmospheric top oil is related to 7 variables of the atmospheric tower top temperature, the atmospheric tower top pressure, the atmospheric tower top cold reflux temperature, the atmospheric tower middle layer gas phase temperature (in the case of 35 layers of the atmospheric tower), the atmospheric tower feeding temperature, the medium reflux heat extraction ratio and the top circulation reflux heat extraction ratio, as shown in table 1:
TABLE 1 Dry Point related variables for the Top product of atmospheric tower
| Serial number | DCS position number | Description of variables |
| 1 | TI1103A.PV | Top temperature of atmospheric tower |
| 2 | PI1112.PV | Top pressure of atmospheric tower |
| 3 | TI1135.PV | Cold reflux temperature at top of atmospheric tower |
| 4 | TI1129.PV | 35-layer gas phase temperature of atmospheric tower |
| 5 | TI1139.PV | Atmospheric tower feed temperature |
| 6 | T2LOOP1.PV | Top circulation reflux heat extraction ratio |
| 7 | T2LOOP2.PV | Heat extraction ratio of one medium reflux |
One of the medium reflux heat extraction ratio and the top circulation reflux heat extraction ratio can be obtained by calculating the heat exchange temperature difference, the flow and the feeding amount, and the formula is shown in table 2:
TABLE 2 formula for intermediate variable calculation
Establishment of common-roof oil dry point soft measurement model M by combining mechanism analysis with partial least square method1Written as a spatial state system model in the form:
x(t)＝Ax(t-1) (1)
y1(t)＝Bx(t) (2)
wherein x (t) is a state vector at time t, is a 7 × 1 vector, and is composed of the 7 relevant variables; state transition matrix
y1(t) is the common-ceiling oil dry point measurement at time t; state observation matrix B ═ 1.01290.51340.5967 … 0.9265]。
All 100 groups of data in the test set are substituted into a soft measurement model M1The measurement result of the constant oil dry point is calculated and obtained as shown in FIG. 3a, and the mean square error MSE13.5360, the maximum error is 6.0544.
Then, 400 groups of samples in the training set form a training matrix X, and principal component analysis is performed to obtain 6 number of principal components and 8 × 6P dimension of the load matrix, as shown in formula (3):
forming 100 groups of test set data into a test matrix, and enabling a direction vector xi of a variable to be testedi＝[1 0 0 … 0]TSubstituting the load matrix P into the calculated xiiProjection into residual subspace
As shown in formula (4):
calculation model M2As shown in formulas (5) and (6):
y2＝Cx (5)
according to model M2Calculating to obtain the variable value y to be measured2Mean square error, as shown in FIG. 3bPoor MSE22.6731, the maximum error is 4.8317.
Will y1(t) and y2(t) performing iterative computation according to Kalman filtering formulas shown in formulas (7) to (12):
x(t|t-1)＝Ax(t-1) (7)
P(t|t-1)＝AP(t-1)AT (8)
K(t)＝P(t|t-1)CT[CP(t|t-1)CT]-1 (9)
x(t)＝x(t|t-1)+K(t)(y(t)-Cx(t|t-1)) (10)
y(t)＝Cx(t) (11)
P(t)＝(I-K(t)C)P(t|t-1) (12)
wherein the matrices A, C are respectively model M1、M2The parameter matrix of (a), calculated in the above step; let the initial value y (0) be 170, P (0) be a 7 × 7-dimensional matrix with all elements being 0.1; x (t | t-1) is based on the state vector x (t-1) at time t-1 and is based on the model M1Recursion is carried out to obtain a t-moment state vector value; p (t | t-1) is a state vector covariance matrix corresponding to x (t | t-1); p (t) is the corresponding covariance matrix of x (t); k (t) is a Kalman gain matrix at time t; x (t) is the optimal estimation of the state vector at time t; and y (t) is the optimal estimation of the constant top oil dry point at the time t.
The optimal estimation value of the common-top oil dry point is finally obtained after Kalman filtering, as shown in FIG. 3c, the mean square error MSE31.0526, the maximum error is 3.3645.
The error comparison of the above three results is shown in Table 3, the mean square error and maximum error ratio model M of the method1And M2The result is small, so the method can more accurately obtain the variable value to be measured and meet the process control requirement.
TABLE 3 error comparison
| Model (model) | Mean square error | Maximum error |
| M1 | 3.5360 | 6.0544 |
| M2 | 2.6731 | 4.8317 |
| Fusion value | 1.0526 | 3.3645 |
Claims (5)
1. An online analysis and detection method for oil dry points at the top of an atmospheric tower is characterized in that an estimation model is established based on principal component analysis, and an estimated value of the oil dry points at the top of the atmospheric tower is fused with a measured value obtained through a soft measurement model to obtain an optimal estimated value, so that the online detection of the oil dry points at the top of the atmospheric tower is realized, and the online analysis and detection method comprises the following steps:
(1) judging whether the normal oil dry point online analyzer works normally, if so, directly measuring the dry point value, and turning to the step (9), and if not, turning to the step (2);
(2) selecting variables related to the variable y to be measured to form a vector x;
(3) obtaining a state space model M of the system through soft measurement calculation1Calculating the measured value y of the variable y to be measured1；
(4) Taking normal historical data of the variable y to be measured and the related variable as a training set to form a sample matrix X;
(5) performing principal component analysis on the sample matrix X to obtain a load matrix P;
(6) make the direction vector xi of the variable to be measuredi＝[1 0…0]TAnd calculating xiiProjection into residual subspace
(7) Computing estimation model M2：
y2＝Cx (2)
Wherein, y2As an estimate of the variable to be measured, I1Is a matrix of the units,
after being unitized
I2Is a ratio of I1A one-dimensional less identity matrix;
(8) fusing measurement value y by Kalman filtering1And the estimated value y2Obtaining the optimal estimated value y of the variable;
(9) and (6) ending.
2. The method according to claim 1, wherein the variables related to the dry point of the atmospheric tower top oil are selected from the group consisting of atmospheric tower top temperature, atmospheric tower top pressure, atmospheric tower top cold reflux temperature, atmospheric tower middle layer gas phase temperature, atmospheric tower feeding temperature, top circulation reflux heat extraction ratio, and medium reflux heat extraction ratio, which are 7 variables.
3. The on-line analysis and detection method for the dry point of the atmospheric tower top oil according to claim 2, characterized in that the heat extraction ratio P of the top circulation reflux is P1Can be circulated from the top of the atmospheric tower1(T/h) atmospheric tower normal top heat-circulating temperature difference delta T1Calculated in degrees centigrade and the amount of feed F (t/h) are:
wherein: the feeding amount is the total feeding amount of the atmospheric furnace plus the primary side oil flow.
4. The on-line analysis and detection method for the dry point of the atmospheric tower top oil according to claim 2, characterized in that the heat extraction ratio P of the first intermediate reflux is P2The flow F can be returned from the normal2(T/h) normal-medium heat exchange temperature difference delta T of atmospheric tower2Calculated in degrees centigrade and the amount of feed F (t/h) are:
wherein: the feeding amount is the total feeding amount of the atmospheric furnace plus the primary side oil flow.
5. The on-line analysis and detection method for the oil dry point of the atmospheric tower top according to claim 1, characterized in that the model M is1Can be obtained by combining a mechanism analysis method and least square regression.
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