CN106709169A - Property estimation method for crude oil processing process - Google Patents
Property estimation method for crude oil processing process Download PDFInfo
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- CN106709169A CN106709169A CN201611137789.0A CN201611137789A CN106709169A CN 106709169 A CN106709169 A CN 106709169A CN 201611137789 A CN201611137789 A CN 201611137789A CN 106709169 A CN106709169 A CN 106709169A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
Abstract
The invention provides a property estimation method for a crude oil processing process. The method comprises the steps of firstly performing sensitivity analysis on a light vacuum 1st side cut oil dry point by utilizing process simulation software, and selecting an auxiliary variable according to an analysis result; secondly performing principal component analysis on the auxiliary variable, and determining an input variable of a model; and finally performing multivariate regression analysis based on the input variable and an output variable, determining a soft measurement model, and performing prediction estimation on real-time properties according to the model. According to the method, a complex mechanism analysis process of a vacuum tower is effectively avoided; and meanwhile, the problem of excessively slow convergence of a vacuum tower model in an optimization process is solved, so that the running performance of an apparatus is improved. The method is convenient in solving, is proved to be effective through actual running, and has a high industrial application value.
Description
Technical field
The present invention relates to a kind of property online soft sensor method of Crude Oil Processing, more particularly to Atmospheric vacuum sideline product
The On-line Estimation method that shallow first vacuum side stream is done.
Background technology
Atmospheric and vacuum distillation is the faucet device of Crude Oil Processing, by the real-time optimization of device, can improve high value
Product yield, reduces plant energy consumption, and the lifting to refinery's economic benefit is significant.
In the production process of Petrochemical Enterprises, often through process simulation software atmospheric and vacuum distillation unit is carried out flowsheeting and
Operation parameter optimization.For vacuum tower, because its technique is increasingly complex compared with atmospheric tower, the mechanism mould of vacuum tower is hence set up
Type is particularly difficult.Found in actual production, it is inaccurate that inaccurate model will cause sideline product Key Quality to be analyzed, and then
Cause the optimum results of mistake.Meanwhile, complicated mechanism model also results in optimization and restrained slowly, or is difficult to restrain.
It is crucial quality index that side line is done.It is artificial collecting sample that traditional side line does measuring method, by chemically examining
Result, process units is fed back to after a few hours by room analytical test again, for instructing technologist to operate.The method is due to the time
On it is delayed, resulting sideline product is done can not well reflect operating mode at that time.Therefore, design a kind of effective
The method that vacuum tower side line is done is estimated in energy on-line measurement, with highly important industrial application value.
The content of the invention
The present invention proposes the method for estimation that a kind of shallow first vacuum side stream of Crude Oil Processing is done：It is soft first with flowsheeting
Part carries out the sensitivity analysis done on shallow first vacuum side stream, auxiliary variable is chosen according to analysis result, then to auxiliary variable
Principal component analysis is carried out, the input variable of model is determined, being finally based on input variable and output variable carries out multiple regression analysis,
Determine soft-sensing model.Specifically include following steps：
1) operating mode of atmospheric and vacuum distillation unit is simulated using process simulation software, the key operation to atmospheric and vacuum distillation unit becomes
Amount carries out the sensitivity analysis done on shallow first vacuum side stream, according to analysis result, chooses and shallow first vacuum side stream is done with aobvious
The performance variable for writing influence does the auxiliary variable of soft-sensing model as shallow first vacuum side stream；
2) to step 1) in auxiliary variable carry out principal component analysis, obtain the input variable of soft-sensing model；
3) the history operating mode value that collection auxiliary variable and shallow first vacuum side stream are done, sets up multiple regression soft-sensing model；
4) the actual condition value of auxiliary variable is gathered, according to step 4) in soft-sensing model shallow under current working is subtracted
One line oil does value and carries out real-time estimate estimation.
Preferably, key operating variable is carried out using the sensitivity analysis module in process simulation software subtracting one on shallow
The sensitivity analysis that line oil is done, key operating variable includes the just oily flow in top, often Atmospheric Tower temperature, the oily flow in top, Chang Yi
Linear flow rate, normal two linear flow rate, atmosphere 3rd side cut flow, Chang Ding walk around to flow, Chang Yizhong capacities of returns, shallow decompression heater outlet temperature, shallow subtract
Pressure tower top temperature, shallow vacuum 1st side cut flow, shallow second line of distillation flow, the shallow linear flow rate that subtracts three, it is shallow subtract one in capacity of returns, it is shallow subtract two in flow back
Amount, and it is shallow subtract three in capacity of returns.
Preferably, according to the result of sensitivity analysis, choose performance variable of the factor of influence not less than 0.4 and subtract one as shallow
Line oil does the auxiliary variable of soft-sensing model.
Preferably, the step of carrying out principal component analysis to auxiliary variable be：
1) the sample data x of each auxiliary variable is obtainedkmAnd it is standardized；
X=(x1,x2,…,xm,…,xp)
xm=[x1m,x2m,…,xkm,…,xnm]T
Wherein, the matrix that X is constituted for the sample data of auxiliary variable, m=1,2 ..., p, p are the number of auxiliary variable, k
=1,2 ..., n, n are the number of sample data, the sample data x of each auxiliary variablekmIt is standardized according to following formula：
2) the correlation matrix R of auxiliary variable X is set up：
R=(rij)p×p
3) characteristic root and corresponding characteristic vector of correlation matrix R are calculated：
Wherein, a1,a2,…,apRespectively characteristic root, λ1,λ2,…,λpCorresponding characteristic vector, and λ1≥λ2≥…≥λp
＞ 0；
4) contribution rate ξ is calculatedlWith contribution rate of accumulative total ξ*：
S=1,2 ..., p；
5) contribution rate of accumulative total ξ is chosen*Up to the principal component factor z corresponding to preceding s characteristic root during certain percentagei：
zi=a1i×x1+a2i×x2+…+asi×xs。
Preferably, the principal component corresponding to preceding s characteristic root of the contribution rate of accumulative total up to 85%~95% is chosen.
Preferably, the step of setting up multiple regression soft-sensing model be：
1) the history floor data of auxiliary variable and output variable is chosen as training sample；
2) multiple regression equation is set up：
Wherein,It is the predicted value of output variable, z1,z2,…,zsBe input variable, i.e. principal component factor zi；p0,p1,
p2,…,psIt is model undetermined coefficient；
3) above-mentioned model undetermined coefficient p is solved0,p1,p2,…,ps, according to comprehensive including relative error, the factor of coefficient correlation
Close and consider model, if model is unsatisfactory for the length of actual requirement, adjustment independent variable number or training sample, return to step 2)
In；Forecast model now is exported if model meets actual requirement.
Preferably, for given input variable and output variable, solving model undetermined coefficient so that object functionIt is minimum.
Preferably, return to calculate and calculated using Petro-SIM or MATLAB.
Beneficial effect：
The present invention proposes a kind of property method of estimation of Crude Oil Processing, online by set up that shallow first vacuum side stream does
Soft-sensing model, effectively prevent the complicated Analysis on Mechanism process of vacuum tower；Simultaneously vacuum tower model is also solved optimizing
Slow problem was restrained in journey.It is convenient that the method is solved, and it is effective that actual motion is proved, with good commercial Application valency
Value.
Brief description of the drawings
Fig. 1 is atmospheric and vacuum distillation unit schematic flow sheet
Fig. 2 does online soft sensor flow chart for shallow first vacuum side stream
Specific implementation case
Embodiments of the invention are elaborated below, the present embodiment is carried out under premised on technical solution of the present invention
Implement, give detailed implementation method and specific operating process, but protection scope of the present invention is not limited to following implementations
Example.
As seen from Figure 1, enterprise's atmospheric and vacuum distillation unit is mainly made up of primary distillation tower, atmospheric tower, primary vacuum column, deep vacuum column,
Constitute four sections of gasification flows of a fore-running-- two sections of normal pressure decompression.Crude oil is de- into electrical desalter desalination after being exchanged heat through tank field
Water, primary distillation tower is entered after exchanging heat again.The product of primary distillation tower is just top oil and just top fixed gas, and just base oil is after normal pressure stove heat
Into atmospheric tower.Atmospheric tower sets three side lines altogether, and sideline product is respectively aviation kerosine, light diesel fuel, heavy diesel fuel.Meanwhile, normal pressure
Tower sets a top backflow and two stage casing backflows altogether, and each circulating reflux takes after-heat away with reducing energy consumption.Vacuum tower is divided into shallow
Vacuum tower and deep vacuum column, the half of the number of plates everybody the original vacuum tower in two vacuum towers, so as to reduce tower internal pressure
Drop, improves the vacuum of gasification section, improves decompression extracting rate.
Below so that shallow first vacuum side stream is done as an example, the specific implementation step of its measurement method of estimation is provided, as shown in Figure 2.
A) using in the general process simulation software Petro-SIM (or other similar process simulation softwares) of Petrochemical Enterprises
Sensitivity analysis module, gathers the history floor data in enterprise half a year in past, carries out the spirit done on shallow first vacuum side stream
Basis of sensitivity analysis, wherein each performance variable is as shown in table 1 on factor of influence that shallow first vacuum side stream is done.
The sensitivity analysis result of table 1
| Performance variable | Factor of influence | Performance variable | Factor of influence |
| Just push up oily flow | 0.09 | Shallow decompression heater outlet temperature | 0.00 |
| Atmospheric Tower temperature | 0.71 | Primary vacuum column top temperature | 0.29 |
| Often push up oily flow | 0.22 | Shallow vacuum 1st side cut flow | 0.79 |
| A normal linear flow rate | 0.66 | Shallow second line of distillation flow | 0.05 |
| Normal two linear flow rate | 0.70 | The shallow linear flow rate that subtracts three | -0.01 |
| Atmosphere 3rd side cut flow | 0.73 | It is shallow subtract one in capacity of returns | -0.40 |
| Often top walks around to flow | 0.01 | It is shallow subtract two in capacity of returns | 0.00 |
| Chang Yizhong capacities of returns | 0.01 | It is shallow subtract three in capacity of returns | 0.00 |
According to table 1, performance variable of the selection factor of influence not less than 0.4 is used as auxiliary variable.Therefore, the auxiliary of selection becomes
It is Atmospheric Tower temperature, a normal linear flow rate, normal two linear flow rate, atmosphere 3rd side cut flow, shallow vacuum 1st side cut flow to measure, and it is shallow subtract one in return
Flow.
B) for ease of analysis, 15 groups of history floor datas the most typical are chosen, as shown in table 2.
The auxiliary variable history operating mode value of table 2
By the initial data x in table 2kmIt is standardized：
C) based on the data after the treatment of step b) Playsization, the correlation matrix R of auxiliary variable is set up：
The characteristic root and corresponding characteristic vector of correlation matrix are calculated, and calculates contribution rate ξl：
Be computed find, first principal component contribution rate be 78.2%, Second principal component, contribution rate be 12.3%, the 3rd it is main into
Contribution rate is divided to be 1.8%, first three principal component contribution rate of accumulative total can choose first three new factor up to 92.3%.Wherein,
The principal component coefficient matrix of first three factor is：
Principal component coefficient matrix according to first three factor can be obtained：
z1=0.4465x1+0.4433x2-0.4419x3-0.4394x4-0.1432x5+0.4419x6
z2=0.0339x1+0.0916x2+0.0225x3-0.1241x4+0.9830x5+0.0914x6
z3=0.2173x1-0.4422x2-0.5249x3+0.6040x4+0.0913x5+0.3294x6
Wherein, xi(i=1 ..., 6) it is respectively Atmospheric Tower temperature, a normal linear flow rate, normal two linear flow rate, atmosphere 3rd side cut stream
Amount, shallow vacuum 1st side cut flow, and it is shallow subtract one in capacity of returns.
D) the history operating mode value according to the auxiliary variable of table 2, the principal component factor, i.e. hard measurement are calculated according to formula in step c)
The argument value of model, and gather corresponding shallow first vacuum side stream under every group of working condition and do value, as shown in table 3.
The argument value and dependent variable value of the soft-sensing model of table 3
E) according to table 3, multiple regression soft-sensing model is set up, returning calculating can be soft using Petro-SIM or other instruments
Part, such as MATLAB, regression result are as shown in table 4.
The multivariate regression models result of calculation of table 4
Model can be obtained accordingly as follows：
F) actual value of auxiliary variable under multigroup operating mode is gathered, estimations is predicted according to the above results, and it is right with respective
The shallow first vacuum side stream answered is done actual value and is compared, and chooses 3 groups of nearest data as shown in table 5：
The multivariate regression models predicted value of table 5 compares with actual value
From table 5, the predicted value and the error of actual value that shallow first vacuum side stream is done under three groups of operating modes are respectively -0.27
DEG C, 1.41 DEG C, 0.81 DEG C, fully meet actual demands of engineering, the soft-sensing model set up accurately effectively can subtract one to shallow
Line oil does value and is predicted estimation.It is convenient that the method is solved, and can be prevented effectively from the complicated Analysis on Mechanism process of vacuum tower, from
And solve the problems, such as vacuum tower model and slow, the runnability of raising device was restrained in optimization process.
Claims (8)
1. the property method of estimation of a kind of Crude Oil Processing, it is characterised in that the method is shallow to the sideline product of atmospheric and vacuum distillation unit
First vacuum side stream is done carries out hard measurement, specifically includes following steps：
1) operating mode of atmospheric and vacuum distillation unit is simulated using process simulation software, the key operating variable to atmospheric and vacuum distillation unit enters
The sensitivity analysis that row is done on shallow first vacuum side stream, according to analysis result, selection is done with notable shadow to shallow first vacuum side stream
Loud performance variable does the auxiliary variable of soft-sensing model as shallow first vacuum side stream；
2) to step 1) in auxiliary variable carry out principal component analysis, obtain the input variable of soft-sensing model；
3) the history operating mode value that collection auxiliary variable and shallow first vacuum side stream are done, sets up multiple regression soft-sensing model；
4) gather auxiliary variable actual condition value, according to step 4) in soft-sensing model to the shallow vacuum 1st side cut under current working
Oil does value and carries out real-time estimate estimation.
2. the property method of estimation of a kind of Crude Oil Processing according to claim 1, it is characterised in that utilize flow mould
Intend the sensitivity analysis that the sensitivity analysis module in software to key operating variable done on shallow first vacuum side stream, it is crucial
Performance variable includes the just oily flow in top, often Atmospheric Tower temperature, the oily flow in top, a normal linear flow rate, normal two linear flow rate, atmosphere 3rd side cut stream
Amount, often top walk around to flow, Chang Yizhong capacities of returns, shallow decompression heater outlet temperature, primary vacuum column top temperature, shallow vacuum 1st side cut flow, shallow
Second line of distillation flow, the shallow linear flow rate that subtracts three, it is shallow subtract one in capacity of returns, it is shallow subtract two in capacity of returns, and it is shallow subtract three in capacity of returns.
3. the property method of estimation of a kind of Crude Oil Processing according to claim 1, it is characterised in that according to sensitivity
The result of analysis, chooses the auxiliary that performance variable of the factor of influence not less than 0.4 does soft-sensing model as shallow first vacuum side stream
Variable.
4. the property method of estimation of a kind of Crude Oil Processing according to claim 1, it is characterised in that to auxiliary variable
The step of carrying out principal component analysis be：
1) the sample data x of each auxiliary variable is obtainedkmAnd it is standardized；
X=(x1,x2,…,xm,…,xp)
xm=[x1m,x2m,…,xkm,…,xnm]T
Wherein, X for auxiliary variable sample data constitute matrix, m=1,2 ..., p, p for auxiliary variable number, k=1,
2 ..., n, n are the number of sample data, the sample data x of each auxiliary variablekmIt is standardized according to following formula：
2) the correlation matrix R of auxiliary variable X is set up：
R=(rij)p×p
3) characteristic root and corresponding characteristic vector of correlation matrix R are calculated：
Wherein, a1,a2,…,apRespectively characteristic root, λ1,λ2,…,λpCorresponding characteristic vector, and λ1≥λ2≥…≥λp＞ 0；
4) contribution rate ξ is calculatedlWith contribution rate of accumulative total ξ*：
S=1,2 ..., p；
5) contribution rate of accumulative total ξ is chosen*Up to the principal component factor z corresponding to preceding s characteristic root during certain percentagei：
zi=a1i×x1+a2i×x2+…+asi×xs。
5. the property method of estimation of a kind of Crude Oil Processing according to claim 4, it is characterised in that choose accumulative tribute
Offer the principal component corresponding to preceding s characteristic root of the rate up to 85%~95%.
6. the property method of estimation of a kind of Crude Oil Processing according to claim 4, it is characterised in that set up polynary time
The step of returning soft-sensing model be：
1) the history floor data of auxiliary variable and output variable is chosen as training sample；
2) multiple regression equation is set up：
Wherein,It is the predicted value of output variable, z1,z2,…,zsBe input variable, i.e. principal component factor zi；p0,p1,p2,…,ps
It is model undetermined coefficient；
3) above-mentioned model undetermined coefficient p is solved0,p1,p2,…,ps, examined according to including relative error, the combined factors of coefficient correlation
Model is considered, if model is unsatisfactory for the length of actual requirement, adjustment independent variable number or training sample, return to step 2) in；If
Model meets actual requirement and then exports forecast model now.
7. the property method of estimation of a kind of Crude Oil Processing according to claim 6, it is characterised in that for given
Input variable and output variable, solving model undetermined coefficient so that object functionIt is minimum.
8. the property method of estimation of a kind of Crude Oil Processing according to claim 6, it is characterised in that return calculating and adopt
Calculated with Petro-SIM or MATLAB.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111592907A (en) * | 2020-05-18 | 2020-08-28 | 南京富岛信息工程有限公司 | Online analysis and detection method for oil dry point of atmospheric tower top |
| CN113420499A (en) * | 2021-06-10 | 2021-09-21 | 北京宜能高科科技有限公司 | Physical logic reconstruction method for atmospheric and vacuum distillation unit |
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