CN107220705B - Atmospheric tower top dry point prediction method for atmospheric and vacuum device - Google Patents
Atmospheric tower top dry point prediction method for atmospheric and vacuum device Download PDFInfo
- Publication number
- CN107220705B CN107220705B CN201610163932.7A CN201610163932A CN107220705B CN 107220705 B CN107220705 B CN 107220705B CN 201610163932 A CN201610163932 A CN 201610163932A CN 107220705 B CN107220705 B CN 107220705B
- Authority
- CN
- China
- Prior art keywords
- data
- tower top
- variable
- atmospheric
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000000691 measurement method Methods 0.000 claims abstract description 7
- 238000004989 laser desorption mass spectroscopy Methods 0.000 claims abstract 3
- 230000002159 abnormal effect Effects 0.000 claims description 18
- 238000005259 measurement Methods 0.000 claims description 18
- 210000002569 neuron Anatomy 0.000 claims description 15
- 230000002068 genetic effect Effects 0.000 claims description 12
- 238000012360 testing method Methods 0.000 claims description 11
- 238000012549 training Methods 0.000 claims description 10
- 238000000354 decomposition reaction Methods 0.000 claims description 9
- 238000012546 transfer Methods 0.000 claims description 9
- 238000002790 cross-validation Methods 0.000 claims description 6
- 238000004519 manufacturing process Methods 0.000 claims description 5
- 238000012847 principal component analysis method Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 229910002056 binary alloy Inorganic materials 0.000 claims description 3
- 230000001186 cumulative effect Effects 0.000 claims description 3
- 238000011161 development Methods 0.000 claims description 3
- 238000007670 refining Methods 0.000 claims description 3
- 238000010992 reflux Methods 0.000 claims description 3
- 230000009897 systematic effect Effects 0.000 claims description 2
- 239000003921 oil Substances 0.000 description 5
- 230000008030 elimination Effects 0.000 description 4
- 238000003379 elimination reaction Methods 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000000513 principal component analysis Methods 0.000 description 3
- 238000003556 assay Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012369 In process control Methods 0.000 description 1
- 239000010779 crude oil Substances 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000010965 in-process control Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000003359 percent control normalization Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000005292 vacuum distillation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/088—Non-supervised learning, e.g. competitive learning
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Testing And Monitoring For Control Systems (AREA)
- Feedback Control In General (AREA)
Abstract
The invention relates to a method for predicting a dry point at the top of an atmospheric tower of an atmospheric and vacuum device, which mainly solves the problem that a soft measurement method for the dry point at the top of the atmospheric tower is not available in the prior art. The invention adopts a method for predicting the dry point of the atmospheric tower top of the atmospheric and vacuum device, and is used for predicting the dry point of the atmospheric tower top by logging in a prediction system; the prediction system is arranged on a server, the server is respectively connected with the real-time database system and the LIMS system through network cables, and the client is a computer with authority and a mobile terminal.
Description
Technical Field
The invention relates to a method for predicting a dry point of an atmospheric tower top of an atmospheric and vacuum device.
Background
In the normal pressure reduction device, the top oil dry point of the normal pressure tower is a main quality control index of a normal pressure top product, and the top oil dry point mainly reflects the light weight of an oil product to be extracted, so the control quality of the top oil dry point is related to the extraction rate of crude oil in the normal pressure tower, and the subsequent processing process is influenced. Currently, for dry spots, there is no suitable meter that can give measurements in real time, and most refineries rely on laboratory manual analysis of the values. For manual analysis, the results are obtained from field sampling to laboratory testing and then recorded into the LIMS system for a long time of about 1-2 h; furthermore, the period of manual analysis is typically every 4 hours or 8 hours. Therefore, the time lag is very serious, and the quality of the tower top product under the previous operating condition can only be approximately known through the test process, so that the real-time direct control of the product quality cannot be realized at all.
In order to solve the above problems, studies have been made in some aspects in academic, but in practical applications, there are problems of low prediction accuracy and poor robustness. In process control, a number of soft-measurement methods have been developed, such as an atmospheric overhead oil dry point on-line soft-measurement method (application No. 201110198455.5), a soft-measurement method for on-line determination of atmospheric overhead naphtha quality index (application No. 200710171116.1), using various mathematical models to estimate dry point values. However, no patent is available for the application of soft measurement of the dry point of the tower top of the atmospheric pressure tower.
The basic idea of soft measurement is to combine the theory of automatic control with knowledge of the production process, apply computer technology, select some other variables (or called auxiliary variables) that are easy to measure for important variables (or called active variables) that are difficult to measure or temporarily impossible to measure, infer and estimate by forming some mathematical relationship, and replace the hardware (sensor) function with software. The method has the advantages of quick response, capability of continuously giving active variable information, low investment, simple maintenance and the like.
Disclosure of Invention
The invention aims to solve the technical problem of a soft measurement method for the dry point of the atmospheric tower top in the prior art, and provides a new prediction method for the dry point of the atmospheric tower top of an atmospheric and vacuum device. The method is used for the atmospheric and vacuum distillation device, and has the advantages of accurate test data, closer measurement result to reality and the like.
In order to solve the problems, the technical scheme adopted by the invention is as follows: a method for predicting the dry point of the atmospheric tower top of an atmospheric and vacuum device is characterized in that the dry point of the atmospheric tower top is predicted by logging in a prediction system; the prediction system is arranged on a server, the server is respectively connected with a real-time database system and a server of an LIMS system through network cables, and the client side is a computer and a mobile terminal with authority; the working steps of the prediction system are as follows:
1) selected auxiliary variables
According to the control experience of field operators, considering relevant auxiliary variables which have large influence on the dry point of the tower top actually, wherein the relevant auxiliary variables comprise the feeding temperature, the normal top return flow, the pressure of the tower top, the temperature of the tower top, the steam amount at the tower bottom and the reflux ratio of the tower top;
2) outlier culling of auxiliary variable raw data
The method of using the median filter in the moving window identifies the abnormal points of the single process variable on line and eliminates the abnormal values, and the formula is as follows:
where mean is a function of the median, X*The median of the data is obtained, 1.4826 is a coefficient, the threshold t is 3, 11 points are taken according to the size of the moving window, and the removed median is filled by using the calculated median;
after the wild value of the original data is removed, the data which obviously deviate from the measured value of the attachment moment is removed;
3) auxiliary variable noise rejection
(1) Wavelet method preliminary denoising
The wavelet decomposition of the measuring signal decomposes the original data into a high frequency part and a low frequency part, wherein the high frequency part reflects noise interference, and the low frequency part reflects the true value of the signal;
haar wavelets are selected, and the original univariate signals are decomposed into high-frequency parts and low-frequency parts by using the following formula:
wherein d is a scale coefficient, β is a wavelet coefficient, G and H are high-pass and low-pass decomposition filters, respectively, and l is a time parameter;
the decomposition scale n is 3, the high frequency part is filtered out completely and reconstructed by the following formula:
in the formula, G*And H*High-pass and low-pass reconstruction filters;
the reconstructed data does not contain the high-frequency part of the original data, namely the noise of the high-frequency part is removed, so that the data used for the soft instrument more accurately reflects the true value of the instrument;
(2) principal component analysis method deep denoising
Identifying abnormal working conditions of the auxiliary variable data subjected to preliminary denoising by using a principal component analysis method so as to eliminate the influence of the abnormal working conditions on modeling and realize deep denoising;
the data was first normalized as follows:
the normalized data is decomposed as follows:
in the formula, the number k of the principal components is 5, and the square error of the principal component model at the time i is as follows:
in the formula, XijFor the measured value of the j-th input variable at time i,
the principal component model prediction value, T, for the j-th data variable at time i2The control limit of the statistic is calculated using the F distribution as follows:
wherein, Ff,m-1,aIs an F distribution critical value corresponding to the test level a, with the degree of freedom F and under the condition of m < -1 >;
the test level a is 0.05, the degree of freedom f is 5, m is the width of the moving window, and half an hour of data is taken: 1/15 s, m 120, and SPE and T2Draw the control limit for cumulative distribution of 95%, when SPE or T2When the working condition exceeds 95% of the control limit, the working condition is identified as an abnormal working condition, and the data of the abnormal working condition cannot be used for establishing a soft measurement model;
4) determination of the lag time of an active variable relative to a secondary variable
Determining the lag time by using a genetic algorithm, wherein the method comprises the following steps:
the genetic algorithm input variables are as follows:
N＝[N1,N2,…,Nj]j＝1,2,…,v
wherein N isjIs the lag time of the jth input variable, and v is the number of auxiliary variables;
the genetic algorithm objective function is as follows:
wherein, yiIs the value of the dominant variable off-line assay,
the predicted value is a 5-fold cross validation predicted value of the GRNN model, and u is the number of training samples;
in the establishment of the soft measurement model, v is 6, and the lag time range is Nj0-60min, NjThe value of (1) is a positive integer, and is converted into a binary system with the length of 6 for calculation; the population size of the genetic algorithm is 200, the population is initialized randomly, the iteration times are 500, the cross probability is 0.4, and the variation probability is 0.2;
5) a soft measurement method;
modeling soft measurement of a dry point of the atmospheric tower top of the atmospheric tower by using a Generalized Regression Neural Network (GRNN), wherein the GRNN network structure comprises four layers which are an input layer, a mode layer, a summation layer and an output layer respectively; the number of nodes of an input layer is 6, the number of neurons of a mode layer is the number of training samples, and the number of neurons of an output layer is equal to 1; the mode layer neuron transfer function is:
the transfer function of the summation layer neurons is:
the transfer function of the output layer neurons is:
determining the Spread speed Spread of the radial basis function in the GRNN model training process as 0.2 by a 5-fold cross validation method;
6) systematic algorithmic technical route
7) Data interface development
In order to obtain the production data of the actual device, various data acquisition interfaces are developed, the data of auxiliary variables are acquired from various main stream real-time databases of the refining enterprises, and the requirements of various field implementation environments can be met; meanwhile, an ODBC interface is developed to connect with an LIMS (Laboratory Information Management System) of an enterprise, so that data of active variables are acquired online, and the algorithm of the atmospheric tower top dry point prediction System is checked and corrected.
According to the method, wild value elimination and noise elimination of the auxiliary variable original data are added, unnecessary interference is reduced and even avoided, and sample data is more accurate; the lag time of the active variable relative to the auxiliary variable is considered, the actual operation of the industry is met, and the prediction result of the active variable is closer to the actual operation; the developed on-line monitoring system has various on-site data acquisition interfaces, has strong adaptability and obtains better technical effect.
Drawings
FIG. 1 is a logic diagram of an algorithm of an atmospheric tower top dry point prediction system of an atmospheric and vacuum device.
FIG. 2 is a diagram of a hardware distribution.
In fig. 2, 1 is a real-time database; 2 is an auxiliary variable; 3 is LIMS database; 4 is an active variable; 5 is a firewall; 6 is a prediction system of the dry point of the atmospheric tower top of the atmospheric and vacuum device; 7 is a wireless router; 8 is a tablet computer; 9 is an office computer.
The present invention will be further illustrated by the following examples, but is not limited to these examples.
Detailed Description
[ example 1 ]
By adopting the method, the dry point of the tower top at normal pressure is predicted by logging in a prediction system; the prediction system is arranged on a server, the server is respectively connected with a real-time database system and a server of an LIMS system through network cables, and the client side is a computer and a mobile terminal with authority; the working steps of the prediction system are as follows:
1) selected auxiliary variables
According to the control experience of field operators, relevant auxiliary variables which have large influence on the dry point of the tower top actually are considered, wherein the relevant auxiliary variables comprise the feeding temperature, the normal top return flow, the pressure of the tower top, the temperature of the tower top, the steam quantity at the tower bottom and the reflux ratio of the tower top.
2) Outlier culling of auxiliary variable raw data
Outliers are measurements of process variables where the value at one time deviates significantly from the value at other adjacent times. The outliers are due to measurement equipment errors or noise and cannot reflect real operating conditions. If not culled, the accuracy of the soft gauge model will be reduced. The method of the median filter in the mobile window is used for identifying the abnormal points of the single process variable on line and eliminating abnormal values. The formula is as follows:
where mean is a function of the median, X*Is the median value of the data, 1.4826 is the coefficient, and the threshold t is 3. The size of the moving window takes 11 points and the culled median is padded with the calculated median.
After the raw data are subjected to outlier rejection, data which obviously deviate from the measured value of the attachment moment can be rejected.
3) Auxiliary variable noise rejection (wavelet, PCA)
Noise is a random error that is prevalent in the measurement data, whose value follows a normal distribution. Noise has a significant effect on the measured data, causing the measured values to deviate from the true values.
(2) Wavelet method preliminary denoising
Wavelet decomposition of the measurement signal can decompose the raw data into a high frequency part reflecting the noise interference and a low frequency part reflecting the true value of the signal.
Haar wavelets are selected and the original univariate signal is decomposed into a high-frequency part and a low-frequency part by using the following formula.
Where d is the scale coefficient, β is the wavelet coefficient, G and H are the high-pass and low-pass decomposition filters, respectively, and l is the time parameter.
The decomposition scale n is 3, and the high frequency part is completely filtered out and reconstructed by the following formula.
In the formula, G*And H*High-pass and low-pass reconstruction filters.
The reconstructed data does not contain the high-frequency part of the original data, namely the noise of the high-frequency part is removed, so that the data used for the soft instrument more accurately reflects the actual value of the instrument.
(2) Principal component analysis method deep denoising
Abnormal working conditions related to a plurality of auxiliary variables also belong to noise, and the auxiliary variable data after preliminary denoising is subjected to abnormal working condition identification by using a Principal Component Analysis (PCA) method so as to eliminate the influence of the abnormal working conditions on modeling and realize deep denoising. The data is first normalized as follows,
Decomposing the normalized data according to the following formula
In the formula, the number k of the principal component is 5, and the square error (SPE) of the principal component model at time i is as follows
In the formula, XijFor the measured value of the j-th input variable at time i,
and the predicted value is the pivot model predicted value of the j-th data variable at the moment i. T is2The control limit of the statistic can be calculated using the F-distribution as follows
Wherein, Ff,m-1,aIs the F distribution threshold corresponding to the test level a, with the degree of freedom F, m-1.
Here, the test level a is 0.05, the degree of freedom f is 5, m is the width of the moving window, and half-hour data (1/15 s) are taken, and m is 120. And for SPE and T2Draw the control limit for cumulative distribution of 95%, when SPE or T2The condition at this time when the 95% control limit is exceeded will be identified as an abnormal condition, and its data will not be used to build a soft measurement model.
4) Determination of the lag time of an active variable relative to a secondary variable
The operation has time delay due to the long flow of the atmospheric and vacuum device, and the auxiliary variable is at the time t1Until time t2(t2>t1) Can be reflected on the dominant variable and, therefore,the lag time of the dominant variable relative to the auxiliary variable needs to be determined.
The patent determines the lag time by using a genetic algorithm, and the method specifically comprises the following steps:
genetic algorithm input variables are as follows
N＝[N1,N2,…,Nj]j＝1,2,...,v
Wherein N isjThe lag time of the jth input variable is shown, and v is the number of auxiliary variables.
The genetic algorithm objective function is as follows
Wherein, yiIs the value of the dominant variable off-line assay,
and u is a predicted value of 5-fold cross validation of the GRNN model, and u is the number of training samples.
In the establishment of the soft measurement model, v is 6, and the lag time range is Nj0-60 min. Due to NjThe value of (1) is a positive integer, and is converted into a binary system with the length of 6 (which can represent the lag time of 0-63 min) for calculation. The population size of the genetic algorithm is 200, the population is initialized randomly, the iteration times are 500, the cross probability is 0.4, and the variation probability is 0.2.
5) Soft measuring method (GRNN)
The method uses a Generalized Regression Neural Network (GRNN) to model the soft measurement of the atmospheric tower top dry point. The GRNN network structure is composed of four layers, an input layer, a mode layer, a summation layer, and an output layer. The number of nodes of the input layer is 6, the number of neurons of the mode layer is the number of training samples, and the number of neurons of the output layer is equal to 1. The mode layer neuron transfer function is
The transfer function of the summation layer neurons is
The transfer function of the output layer neurons is
The expansion speed Spread of the radial basis function in the GRNN model training process is determined to be 0.2 by a 5-fold cross validation method.
6) The system algorithm technology route is shown in figure 1.
7) Data interface development
In order to obtain the production data of the actual device, various data acquisition interfaces are developed, such as API, ODBC, WebService, OPC and the like, the data of auxiliary variables can be acquired from main real-time databases of refining enterprises such as InfoPlus.21, Plant Information System, Process HistoryDatabase and the like, and the requirements of various field implementation environments can be met.
Meanwhile, an LIMS system of an enterprise connected with an ODBC interface is developed, active variable data are acquired on line, and the algorithm of the atmospheric tower top dry point prediction system is checked and corrected.
8) Hardware environment
The hardware architecture is shown in fig. 2. And a server is configured in the central control room, is respectively connected with the servers of the real-time database system and the LIMS system through network cables, and is installed and operated with a server version of an atmospheric and vacuum device atmospheric tower top dry point prediction system.
The client can be any computer and mobile terminal with authority in an enterprise office network, such as a smart phone, a tablet computer and the like.
9) Server-side system application
9.1 starting System
Connecting hardware, starting each subsystem, and opening a server-side program.
9.2 Authority control
Different identities are detected or selected to enter the system based on the user input.
9.3 configuration modeling:
the part mainly completes the modeling of the reasoning algorithm. And collecting historical values of the auxiliary variable and the active variable as samples for algorithm training.
9.4 real-time monitoring
And connecting the real-time database of the enterprise, and starting real-time monitoring by the system.
1) And acquiring auxiliary variable real-time data from a production field, and performing real-time reasoning to calculate a real-time value of the current dry point.
2) And comparing the value obtained by the soft measurement with the test value of the LIMS system every 4-8 hours according to the data output time of the LIMS system, and correcting in real time.
10) Client system application
The developed client is of a B/S (browser/Server) framework, is convenient for a user to use any computer on an office network in an enterprise, and is suitable for device managers; and the system has a C/S structure, so that a user can check detailed calculation details, and the system is suitable for a tablet computer in front of an operator and a PC computer in an office of a technician.
According to the method, wild value elimination and noise elimination of the auxiliary variable original data are added, unnecessary interference is reduced and even avoided, and sample data is more accurate; the lag time of the active variable relative to the auxiliary variable is considered, the actual operation of the industry is met, and the prediction result of the active variable is closer to the actual operation; the developed on-line monitoring system has various on-site data acquisition interfaces, has strong adaptability and obtains better technical effect.
Claims (1)
1. A method for predicting the dry point of the atmospheric tower top of an atmospheric and vacuum device is characterized in that the dry point of the atmospheric tower top is predicted by logging in a prediction system; the prediction system is arranged on a server, the server is respectively connected with a real-time database system and a server of an LIMS system through network cables, and the client side is a computer and a mobile terminal with authority; the working steps of the prediction system are as follows:
1) selected auxiliary variables
According to the control experience of field operators, considering relevant auxiliary variables which have large influence on the dry point of the tower top actually, wherein the relevant auxiliary variables comprise the feeding temperature, the normal top return flow, the pressure of the tower top, the temperature of the tower top, the steam amount at the tower bottom and the reflux ratio of the tower top;
2) outlier culling of auxiliary variable raw data
The method of using the median filter in the moving window identifies the abnormal points of the single process variable on line and eliminates the abnormal values, and the formula is as follows:
where mean is a function of the median, X*The median of the data is obtained, 1.4826 is a coefficient, the threshold t is 3, 11 points are taken according to the size of the moving window, and the removed median is filled by using the calculated median;
after the wild value of the original data is removed, the data which obviously deviate from the measured value of the attachment moment is removed;
3) auxiliary variable noise rejection
(1) Wavelet method preliminary denoising
The wavelet decomposition of the measuring signal decomposes the original data into a high frequency part and a low frequency part, wherein the high frequency part reflects noise interference, and the low frequency part reflects the true value of the signal;
haar wavelets are selected, and the original univariate signals are decomposed into high-frequency parts and low-frequency parts by using the following formula:
wherein d is a scale coefficient, β is a wavelet coefficient, G and H are high-pass and low-pass decomposition filters, respectively, and l is a time parameter;
the decomposition scale n is 3, the high frequency part is filtered out completely and reconstructed by the following formula:
in the formula, G*And H*High-pass and low-pass reconstruction filters;
the reconstructed data does not contain the high-frequency part of the original data, namely the noise of the high-frequency part is removed, so that the data used for the soft instrument more accurately reflects the true value of the instrument;
(2) principal component analysis method deep denoising
Identifying abnormal working conditions of the auxiliary variable data subjected to preliminary denoising by using a principal component analysis method so as to eliminate the influence of the abnormal working conditions on modeling and realize deep denoising;
the data was first normalized as follows:
wherein,
for normalized data, xOAs the original data, it is the original data,
is the average of the raw data, s is the standard deviation;
the normalized data is decomposed as follows:
in the formula, the number k of the principal components is 5, and the square error of the principal component model at the time i is as follows:
in the formula, XijFor the measured value of the j-th input variable at time i,
the principal component model prediction value, T, for the j-th data variable at time i2The control limit of the statistic is calculated using the F distribution as follows:
wherein, Ff,m-1,aIs an F distribution critical value corresponding to the test level a, with the degree of freedom F and under the condition of m < -1 >;
the test level a is 0.05, the degree of freedom f is 5, m is the width of the moving window, and half an hour of data is taken: 1/15 s, m 120, and SPE and T2Draw the control limit for cumulative distribution of 95%, when SPE or T2When the working condition exceeds 95% of the control limit, the working condition is identified as an abnormal working condition, and the data of the abnormal working condition cannot be used for establishing a soft measurement model;
4) determination of the lag time of an active variable relative to a secondary variable
Determining the lag time by using a genetic algorithm, wherein the method comprises the following steps:
the genetic algorithm input variables are as follows:
N＝[N1,N2,…,Nj]j＝1,2,…,v
wherein N isjIs the lag time of the jth input variable, and v is the number of auxiliary variables;
the genetic algorithm objective function is as follows:
wherein, yiIs offline of the dominant variableThe value of the test is tested,
the predicted value is a 5-fold cross validation predicted value of the GRNN model, and u is the number of training samples;
in the establishment of the soft measurement model, v is 6, and the lag time range is Nj0-60min, due to NjThe value of (1) is a positive integer, and is converted into a binary system with the length of 6 for calculation; the population size of the genetic algorithm is 200, the population is initialized randomly, the iteration times are 500, the cross probability is 0.4, and the variation probability is 0.2;
5) a soft measurement method;
the method comprises the following steps of using a generalized regression neural network to model soft measurement of a dry point of the atmospheric tower top of an atmospheric tower, wherein a GRNN network structure comprises four layers which are an input layer, a mode layer, a summation layer and an output layer respectively; the number of nodes of an input layer is 6, the number of neurons of a mode layer is the number of training samples, and the number of neurons of an output layer is equal to 1; the mode layer neuron transfer function is:
the transfer function of the summation layer neurons is:
the transfer function of the output layer neurons is:
determining the Spread speed Spread of the radial basis function in the GRNN model training process as 0.2 by a 5-fold cross validation method;
6) systematic algorithmic technical route
7) Data interface development
In order to obtain the production data of the actual device, various data acquisition interfaces are developed, the data of auxiliary variables are acquired from various main stream real-time databases of the refining enterprises, and the requirements of various field implementation environments can be met; meanwhile, an LIMS system of an enterprise connected with an ODBC interface is developed, active variable data are acquired on line, and the algorithm of the atmospheric tower top dry point prediction system is checked and corrected.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610163932.7A CN107220705B (en) | 2016-03-22 | 2016-03-22 | Atmospheric tower top dry point prediction method for atmospheric and vacuum device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610163932.7A CN107220705B (en) | 2016-03-22 | 2016-03-22 | Atmospheric tower top dry point prediction method for atmospheric and vacuum device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107220705A CN107220705A (en) | 2017-09-29 |
| CN107220705B true CN107220705B (en) | 2020-10-09 |
Family
ID=59927211
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610163932.7A Active CN107220705B (en) | 2016-03-22 | 2016-03-22 | Atmospheric tower top dry point prediction method for atmospheric and vacuum device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107220705B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108152791A (en) * | 2017-12-06 | 2018-06-12 | 南京航空航天大学 | Radio cheating signal framing system based on GRNN neural networks |
| CN109658988A (en) * | 2018-12-20 | 2019-04-19 | 中海石油炼化有限责任公司 | A kind of Hydrobon catalyst performance prediction method |
| CN111592907B (en) * | 2020-05-18 | 2021-11-09 | 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 |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2281269A1 (en) * | 1998-09-01 | 2000-03-01 | A.C. Analytical Controls Holding B.V. | A method and apparatus for predicting a distillation temperature range of a hydrocarbon-containing compound |
| CN101169387A (en) * | 2007-11-28 | 2008-04-30 | 华东理工大学 | A Soft Sensing Method for Online Determination of Quality Index of Atmospheric Tower Top Naphtha |
| CN101201331A (en) * | 2007-11-28 | 2008-06-18 | 华东理工大学 | A Soft Sensing Method for Online Determining the Quality Index of Naphtha at the Top of Primary Distillation Tower |
| EP2416153A1 (en) * | 2010-08-02 | 2012-02-08 | ENI S.p.A. | Method for predicting the quality and yields of a crude oil |
| CN102880905A (en) * | 2011-07-15 | 2013-01-16 | 中国石油天然气股份有限公司 | Online soft measurement method for normal oil dry point |
| CN104965967A (en) * | 2015-03-26 | 2015-10-07 | 华东理工大学 | Yield real-time prediction method for atmospheric and vacuum distillation unit |
-
2016
- 2016-03-22 CN CN201610163932.7A patent/CN107220705B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2281269A1 (en) * | 1998-09-01 | 2000-03-01 | A.C. Analytical Controls Holding B.V. | A method and apparatus for predicting a distillation temperature range of a hydrocarbon-containing compound |
| CN101169387A (en) * | 2007-11-28 | 2008-04-30 | 华东理工大学 | A Soft Sensing Method for Online Determination of Quality Index of Atmospheric Tower Top Naphtha |
| CN101201331A (en) * | 2007-11-28 | 2008-06-18 | 华东理工大学 | A Soft Sensing Method for Online Determining the Quality Index of Naphtha at the Top of Primary Distillation Tower |
| EP2416153A1 (en) * | 2010-08-02 | 2012-02-08 | ENI S.p.A. | Method for predicting the quality and yields of a crude oil |
| CN102880905A (en) * | 2011-07-15 | 2013-01-16 | 中国石油天然气股份有限公司 | Online soft measurement method for normal oil dry point |
| CN104965967A (en) * | 2015-03-26 | 2015-10-07 | 华东理工大学 | Yield real-time prediction method for atmospheric and vacuum distillation unit |
Non-Patent Citations (3)
| Title |
|---|
| Soft Sensing Based on LS-SVM and Its Application to a Distillation Column;Yafen li 等;《Sixth International Conference on Intelligent Systems Design and Applications》;20061211;第2006年卷;1-6 * |
| 基于多神经网络模型的石脑油干点软测量;张笑天 等;《控制工程》;20040731;第11卷;52-54 * |
| 常减压装置常压塔塔顶汽油干点的软测量;金思毅 等;《化工进展》;20060901;第25卷;74-76 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107220705A (en) | 2017-09-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP7461440B2 (en) | COMPUTER SYSTEM AND METHOD FOR PERFORMING ROOT CAUSE ANALYSIS AND BUILDING PREDICTION MODELS FOR THE OCCURRENCE OF RARE EVENTS IN PLANT-WIDE OPERATIONS - Patent application | |
| US7136716B2 (en) | Method for providing control to an industrial process using one or more multidimensional variables | |
| CN112232447A (en) | Construction method of complete sample set of power equipment state monitoring data | |
| CN108549908B (en) | Chemical process fault detection method based on multi-sampling probability kernel principal component model | |
| CN112414694B (en) | Equipment multistage abnormal state identification method and device based on multivariate state estimation technology | |
| CN105868164B (en) | A kind of soft-measuring modeling method based on the linear dynamic system model for having supervision | |
| CN107220705B (en) | Atmospheric tower top dry point prediction method for atmospheric and vacuum device | |
| CN116415126A (en) | Method, device and computing device for anomaly detection of doctor blade of paper machine | |
| CN115856204A (en) | Method for predicting gas concentration of tunneling working face based on three-dimensional echo state network | |
| CN116985183A (en) | Quality monitoring and management method and system for near infrared spectrum analyzer | |
| CN112949816A (en) | ANN-based method and system for identifying abnormal data of hydrostatic level | |
| CN107220252B (en) | Atmospheric tower normal line flash point prediction method for atmospheric and vacuum device | |
| CN107220393B (en) | Atmospheric tower common line dry point prediction method for atmospheric and vacuum device | |
| CN112069621B (en) | Method for predicting residual service life of rolling bearing based on linear reliability index | |
| CN106647274B (en) | Operating condition stable state method of discrimination in a kind of continuous flow procedure | |
| CN107220392B (en) | Normal pressure tower and normal line 10% point prediction method for atmospheric and vacuum device | |
| Wang et al. | Failure rate prediction based on AR model and residual correction | |
| CN105117606A (en) | Method for determining element fault probability change tendency | |
| Tsang | Sensor data validation using gray models | |
| Slišković et al. | Data preprocessing in data based process modeling | |
| CN117705178A (en) | Wind power bolt information detection method and device, electronic equipment and storage medium | |
| CN112486096A (en) | Machine tool operation state monitoring method | |
| CN105259135B (en) | Suitable for real-time online without measuring point temperature-compensating near-infrared measuring method | |
| CN117147807B (en) | Oil quality monitoring system and method for petroleum exploration | |
| CN114563016B (en) | Redundant inertial measurement unit fault detection method based on Granges causal analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |