CN103902825A - Independent component regression analysis model promotion based melt index soft measurement method - Google Patents
Independent component regression analysis model promotion based melt index soft measurement method Download PDFInfo
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- 238000000691 measurement method Methods 0.000 title claims abstract description 14
- 238000000611 regression analysis Methods 0.000 title claims abstract description 14
- 239000004743 Polypropylene Substances 0.000 claims abstract description 21
- -1 polypropylene Polymers 0.000 claims abstract description 21
- 229920001155 polypropylene Polymers 0.000 claims abstract description 21
- 238000004519 manufacturing process Methods 0.000 claims abstract description 18
- 238000005259 measurement Methods 0.000 claims abstract description 16
- 239000000155 melt Substances 0.000 claims abstract description 13
- 238000012952 Resampling Methods 0.000 claims abstract description 6
- 238000000034 method Methods 0.000 claims description 37
- 238000002844 melting Methods 0.000 claims description 35
- 230000008018 melting Effects 0.000 claims description 35
- 239000011159 matrix material Substances 0.000 claims description 18
- 238000010606 normalization Methods 0.000 claims description 16
- 238000012880 independent component analysis Methods 0.000 claims description 11
- 230000001737 promoting effect Effects 0.000 claims description 9
- 238000012360 testing method Methods 0.000 claims description 6
- 238000002474 experimental method Methods 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 7
- 230000000694 effects Effects 0.000 description 4
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 2
- 238000013480 data collection Methods 0.000 description 2
- 229910052739 hydrogen Inorganic materials 0.000 description 2
- 239000001257 hydrogen Substances 0.000 description 2
- QQONPFPTGQHPMA-UHFFFAOYSA-N propylene Natural products CC=C QQONPFPTGQHPMA-UHFFFAOYSA-N 0.000 description 2
- 125000004805 propylene group Chemical group [H]C([H])([H])C([H])([*:1])C([H])([H])[*:2] 0.000 description 2
- 238000003908 quality control method Methods 0.000 description 2
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 1
- 229910052782 aluminium Inorganic materials 0.000 description 1
- 239000004411 aluminium Substances 0.000 description 1
- 239000002216 antistatic agent Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001311 chemical methods and process Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- AHUXYBVKTIBBJW-UHFFFAOYSA-N dimethoxy(diphenyl)silane Chemical compound C=1C=CC=CC=1[Si](OC)(OC)C1=CC=CC=C1 AHUXYBVKTIBBJW-UHFFFAOYSA-N 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
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Abstract
The invention discloses an independent component regression analysis model promotion based melt index soft measurement method. The soft measurement method includes: selecting key variables affecting changes of a melt index as input variables and taking a melt index value acquired from laboratory analysis as output variables; subjecting a modeling data set to multiple resampling to structure multiple subdata sets by adopting a promotion learning algorithm, and establishing an independent component regression analysis model for each subdata set; integrating and compositing information of different submodels to realize online soft measurement for the melt index of the polypropylene production process. By the soft measurement method, soft measurement estimation precision of the melt index of the polypropylene production process can be improved, and robustness of the soft measurement model is improved.
Description
Technical field
The invention belongs to chemical process soft sensor modeling and application, particularly a kind of polypropylene melt index soft sensor modeling and online test method based on promoting independent component regression analysis model.
Background technology
Polypropylene, as a kind of important material, all has a very wide range of applications in a lot of industry, and in this production run, a very important index is melting index.In real process, the measurement of this index and difficulty thereof, conventional method is that chamber off-line measurement obtains by experiment at present.Compare online method for real-time measurement, the off-line measurement of melting index often needs the time of 1-2 hour, and this is very disadvantageous for the closed loop quality control of polypropylene process.In order to improve automaticity and the product quality of polypropylene production process, conventionally need to carry out on-line measurement to melting index.Flexible measurement method, by the relation between variable and the melting index easily measured in process is carried out to modeling, utilizes this model online melting index to be estimated, obtains in real time the online value of melting index, can effectively avoid the shortcoming of off-line analysis method large dead time.But due to the complicacy of polypropylene production process, single soft-sensing model is often difficult to the full spectrum information of complete capture-process, therefore, is conventionally difficult to obtain satisfied effect.Promoting learning method is the study hotspot of robotization in recent years and computer realm, by constructing multiple data models, and their result is carried out integrated and comprehensive, often can obtain than unit model and better classify and regression effect.
Summary of the invention
The object of the invention is to the difficult point for the prediction of multi-state polypropylene production process melting index, a kind of melt index flexible measurement method based on promoting independent component regression analysis model is provided.
The object of the invention is to be achieved through the following technical solutions: a kind of melt index flexible measurement method based on promoting independent component regression analysis model, its feature comprises the following steps:
(1) data of collecting polypropylene production process key variables by Distributed Control System (DCS) and real-time dataBase system: X={x
i∈ R
m}
i=1,2 ..., n.Wherein, n is number of samples, and m is key variables number, and R is set of real numbers.Deposit these data in historical data base respectively, and selected part data are as modeling sample.
(2) analyze and obtain the corresponding melt index values of sample for modeling in historical data base by test experiment chamber, as the output y ∈ R of soft-sensing model
n.
(3) respectively key variables and output variable are carried out to pre-service and normalization, the average that makes each process key variables and melting index is zero, and variance is 1, obtains new data set.
(4) for the data set after normalization, utilize lifting learning algorithm to carry out resampling to data set, obtain multiple subdata collection { X
c, Y
c}
c=1,2 ..., C, wherein C is subdata collection number.Then, the input using the key variables of process as soft-sensing model, melting index data matrix, as the output of soft-sensing model, is set up independent component analysis soft-sensing model, and this model parameter is deposited in database for subsequent use.
(5) collect new process data, and it is carried out to pre-service and normalization.
(6) new data after normalization is input to respectively in each independent component analysis model, calculates melt index values corresponding to this real time data.
(7) undertaken integratedly and comprehensive by the result that each submodel is obtained, obtain final melt index flexible measurement result.
Beneficial effect of the present invention: the present invention is towards polypropylene industrial production run, by Ensemble Learning Algorithms to the resampling of modeling data collection, obtain multiple new subdata collection, in subrange, set up multiple independent component regression analysis models, realize the On-line Estimation of polypropylene production process melting index.Then, the data message of different models is carried out integrated and comprehensive, obtain last soft measurement result.Compare other current flexible measurement method, the present invention not only can improve the melt index flexible measuring accuracy of polypropylene production process, and has strengthened the robustness of soft-sensing model.
Accompanying drawing explanation
Fig. 1 the inventive method melting index online soft sensor result;
The melting index online soft sensor result of Fig. 2 based on single independent component regression model.
Embodiment
The present invention is directed to the melting index forecasting problem of polypropylene production process, by the key variables of easily measuring in process, set up sub-independent component regretional analysis and integrated model, for the online soft sensor of this process melting index.
The present invention is based on the melt index flexible measurement method that promotes independent component regression analysis model, comprise the following steps:
The first step: under each operation operating mode, the data of collecting polypropylene production process key variables by Distributed Control System (DCS) and real-time dataBase system: X={x
i∈ R
m}
i=1,2 ..., n.Wherein, n is number of samples, and m is key variables number, and R is set of real numbers.Deposit these data in historical data base respectively, and selected part data are as modeling sample.
Second step: analyze and obtain the corresponding melt index values of sample for modeling in historical data base by test experiment chamber, as the output y ∈ R of soft-sensing model
n.
This step is in order to obtain the output variable in soft sensor modeling, i.e. melt index values.Generally, obtaining melt index values by off-line analysis often needs several hours, and this is also the reason that why needs to carry out soft measurement in polypropylene production process.By the variable of easily measuring in process, the melt index values that is difficult to measure is predicted, greatly improved the prediction real-time of melting index, the production quality control tool of process is very helpful.
The 3rd step: respectively key variables and output variable are carried out to pre-service and normalization, the average that makes each process key variables and melting index is zero, and variance is 1, obtains new data matrix collection.
In historical data base, the process data collecting is carried out to pre-service, reject outlier and obvious coarse error information, in order to make the yardstick of process data can not have influence on the result of monitoring, data to different variablees are normalized respectively, the average that is each variable is zero, and variance is 1.Like this, the data of various process variable are just under identical yardstick, can not have influence on afterwards follow-up modeling effect.
The 4th step: for the data set after normalization, utilize lifting learning algorithm to carry out resampling to data set, obtain multiple subdata collection { X
c, y}
c=1,2 ..., C, wherein C is subdata collection number.Then, the input using the key variables of process as soft-sensing model, melting index data matrix, as the output of soft-sensing model, is set up independent component analysis soft-sensing model, and this model parameter is deposited in database for subsequent use.
By antithetical phrase data set { X
c, y}
c=1,2 ..., Ccarry out independent component analysis, can obtain:
X
c=A
cS
c+E
c
Wherein, S
cfor the independent component matrix extracting, A
cfor hybrid matrix, E
cfor residual matrix.Independent component S
cand regression relation between melting index y is as follows
And then obtain process key variables X
cwith regression relation between melting index y is
Wherein, W
cfor the split-matrix of independent component model, R
cfor the regression matrix of soft-sensing model.
The 5th step: collect new process data, and it is carried out to pre-service and normalization.
For the data sample of newly collecting in process, except it is carried out pre-service, the model parameter while adopting modeling is in addition normalized this data point, deducts modeling average and divided by modeling standard deviation.
The 6th step: the new data after normalization is input to respectively in each independent component analysis model, extracts independent component information, and obtain partial melting exponential quantity y corresponding to real time data
new, c, be calculated as follows:
s
new,c=W
cx
new
e
new,c=x
new-A
cs
new,c
y
new,c=R
cx
new
Wherein s
new, cand e
new, cfor corresponding independent component and residual information.
The 7th step: undertaken integratedly and comprehensive by the soft measurement result that each sub-independent component regression model obtained, obtain final polypropylene production process melting index y
newonline soft sensor result, is calculated as follows
Embodiment:
Below in conjunction with a concrete polypropylene production process example, validity of the present invention is described.The data of this process are from domestic certain large-scale chemical plant, have gathered altogether 500 data and have been used for modeling, independently in addition gather 400 data and are used for checking, and the melt index values that has obtained these 900 data by off-line analysis is used for modeling and test.In this process, we have chosen altogether 14 process key variables melting index have been carried out to soft measurement, as shown in table 1.Next in conjunction with this detailed process, implementation step of the present invention is at length set forth:
1. respectively the key variables in 500 modeling samples and output variable are carried out to pre-service and normalization, the average that makes each process key variables and melting index is zero, and variance is 1, obtains new modeling data matrix.
2. the melting index soft sensor modeling based on promoting independent component regretional analysis
Utilize and promote learning algorithm, modeling data collection is carried out to resampling, the number of samples that makes each subdata collection is 250, obtains altogether 20 sub-data sets.Then, input using the data matrix of 14 process key variables compositions choosing as soft-sensing model, melting index data matrix, as the output of soft-sensing model, is set up 20 independent component analysis soft-sensing models, and the parameter of each model is deposited in model database for subsequent use.
3. obtain real-time measuring data information in polypropylene production process, and it is carried out to pre-service and normalization
In order to test the validity of new method, we test 400 checking samples, and normalized parameter while utilizing modeling is processed it.
4. the online soft sensor of melting index
400 checking samples are carried out to online soft sensor, obtain corresponding melting index predicted value.Fig. 1 and Fig. 2 have provided respectively the inventive method and the On-line Estimation result of traditional single independent component regression analysis to 400 checking samples, wherein " * " be the On-line Estimation value of soft-sensing model, " o " represents the off-line analysis value of each sample.As can be seen from the figure, compare traditional single independent component regression modeling method, the soft measurement effect of melting index is significantly improved.
Table 1: polypropylene process key variables
| Sequence number | Variable | Sequence number | Variable |
| 1 | The density of hydrogen of the first reactor | 8 | The first reactor propylene feed |
| 2 | The density of hydrogen of the second reactor | 9 | The second reactor propylene feed |
| 3 | The density of the first reactor | 10 | The first reactor power |
| 4 | The density of the second reactor | 11 | The second reactor power |
| 5 | Aluminium triethyl flow | 12 | The second reactor liquid level |
| 6 | Dimethoxydiphenylsilane flow | 13 | The first temperature of reactor |
| 7 | Antistatic agent flow | 14 | The second temperature of reactor |
Above-described embodiment is used for the present invention that explains, rather than limits the invention, and in the protection domain of spirit of the present invention and claim, any modification and change that the present invention is made, all fall into protection scope of the present invention.
Claims (4)
1. the melt index flexible measurement method based on promoting independent component regression analysis model, is characterized in that, comprises the following steps:
(1) data of collecting polypropylene production process key variables by Distributed Control System (DCS) and real-time dataBase system: X={x
i∈ R
m}
i=1,2 ..., n, wherein, n is number of samples, m is key variables number; Deposit these data in historical data base respectively, and selected part data are as modeling sample;
(2) analyze and obtain the corresponding melt index values of sample for modeling in historical data base by test experiment chamber, as the output y ∈ R of soft-sensing model
n;
(3) key variables and the output variable that respectively step 1 and step 2 are obtained are carried out pre-service and normalization, and the average that makes each process key variables and melting index is zero, and variance is 1, obtains new data set;
(4) for the data set after normalization, utilize lifting learning algorithm to carry out resampling to data set, obtain multiple subdata collection { X
c, Y
c}
c=1,2 ..., C, wherein C is subdata collection number, then, the input using the key variables of process as soft-sensing model, melting index data matrix, as the output of soft-sensing model, is set up independent component analysis soft-sensing model, and this model parameter is deposited in database for subsequent use;
(5) collect new process data, and it is carried out to pre-service and normalization;
(6) new data after normalization is input to respectively in each independent component analysis model, calculates melt index values corresponding to this real time data;
(7) undertaken integratedly and comprehensive by the result that each submodel is obtained, obtain final melt index flexible measurement result.
2. the melt index flexible measurement method based on promoting independent component regression analysis model according to claim 1, it is characterized in that, described step 4 is specially: for each subdata collection after normalization, input using the key variables of process as soft-sensing model, melting index data matrix, as the output of soft-sensing model, is set up independent component analysis soft-sensing model; By antithetical phrase data set { X
c, y}
c=1,2 ..., Ccarry out independent component analysis, can obtain:
X
c=A
cS
c+E
c
Wherein, S
cfor the independent component matrix extracting, A
cfor hybrid matrix, E
cfor residual matrix.Independent component S
cand regression relation between melting index y is as follows:
And then obtain process key variables X
cwith regression relation between melting index y is:
Wherein, W
cfor the split-matrix of independent component model, R
cfor the regression matrix of soft-sensing model.
3. the melt index flexible measurement method based on promoting independent component regression analysis model according to claim 1, it is characterized in that, described step 6 is specially: the new data after normalization is input to respectively in each independent component analysis model, extract independent component information, and obtain partial melting exponential quantity y corresponding to real time data
new, c, be calculated as follows:
s
new,c=W
cx
new
e
new,c=x
new-A
cs
new,c
y
new,c=R
cx
new
Wherein, s
new, cand e
new, cfor the independent component and the residual information that extract.
4. the melt index flexible measurement method based on promoting independent component regression analysis model according to claim 1, it is characterized in that, described step 7 is specially: undertaken integratedly and comprehensive by the soft measurement result that each sub-independent component regression model obtained, obtain final polypropylene production process melting index y
newonline soft sensor result, is calculated as follows:
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113030156A (en) * | 2021-03-13 | 2021-06-25 | 宁波大学科学技术学院 | Polypropylene melt index soft measurement method based on nonlinear slow characteristic regression model |
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|---|---|---|---|---|
| CN113030156A (en) * | 2021-03-13 | 2021-06-25 | 宁波大学科学技术学院 | Polypropylene melt index soft measurement method based on nonlinear slow characteristic regression model |
| CN113030156B (en) * | 2021-03-13 | 2023-02-24 | 宁波大学科学技术学院 | Polypropylene melt index soft measurement method based on nonlinear slow characteristic model |
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Application publication date: 20140702 |