CN109255186A - A kind of industrial process flexible measurement method based on output constraint AP-XGBOOST model - Google Patents

A kind of industrial process flexible measurement method based on output constraint AP-XGBOOST model Download PDF

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CN109255186A
CN109255186A CN201811060031.0A CN201811060031A CN109255186A CN 109255186 A CN109255186 A CN 109255186A CN 201811060031 A CN201811060031 A CN 201811060031A CN 109255186 A CN109255186 A CN 109255186A
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葛志强
宋博
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Zhejiang University ZJU
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Abstract

The present invention discloses a kind of industrial process flexible measurement method based on output constraint AP-XGBOOST model, this method passes through partial least squares algorithm first, original variable space projection to latent variables space is subjected to measuring similarity, then classified using affine propagation clustering algorithm to multiple operating modes process, soft sensor modeling of the different limit grad enhancement tree-models for multi-state complex process is finally established on obtained data set.Compared to the soft-sensing model for combining traditional affine propagation clustering algorithm, affine propagation clustering based on output constraint-limit grad enhancement tree AP-XGBOOST model can effectively improve the accuracy to multiple operating modes process cluster, and it being capable of variable relation in accurate mapping complex production process, it is not limited by data distribution hypothesis, the size of data set, is provided simultaneously with good interpretation and robustness.

Description

A kind of industrial process flexible measurement method based on output constraint AP-XGBOOST model
Technical field
The invention belongs to industrial process control fields, and in particular to a kind of work based on output constraint AP-XGBOOST model Industry process flexible measurement method.
Background technique
With the increase of industrial process complexity, the appearance of the features such as multi-modal, multi-state would generally make hard measurement mould Type predictive ability is degenerated.For this problem, multi-model Modeling Method is suggested: passing through clustering algorithm, gauss hybrid models etc. Method classifies to multi-state data sample, then using machine learning such as statistical method, support vector regression, neural networks The sample set that method is directed under different operating conditions establishes hard measurement submodel respectively.However there are several places' deficiencies for such method: 1, gathering Class result is difficult to accurately reflect the multi-state classification of process.2, traditional clustering method such as K mean cluster method needs artificial in advance Determine class number, it is difficult to accurately divide data set according to different operating conditions；Though affine propagation clustering has certain advantage, Since influence degree of the different auxiliary variables for target variable is different, the selection of sample still has certain limitation.3, Hard measurement submodel is difficult to accurate characterization complex process, and the propositions such as LI Xiu-liang are using support vector regression as submodule Type, Jie Yu etc. propose Gauss-Markov dynamic fusion submodel approach, traditional statistics, probabilistic model and machine in normal service The contradiction that model is difficult to balance model accuracy and complexity during multi-model modeling is practised, the generalization ability of model is difficult to To guarantee.
Summary of the invention
It is an object of the invention to solve to be generally in different operation operating conditions due to complex process so as to cause soft The problems such as measurement model is degenerated, using a kind of limit grad enhancement tree (AP- based on output constraint affine propagation clustering algorithm XGBOOST) the industrial process flexible measurement method of model, compared to traditional clustering algorithm, the radiation based on output constraint is propagated Algorithm can more accurately distinguish the different operating stages of process, and effectively be promoted by establishing XGBOOST submodel To the prediction effect of complex nonlinear nongausian process.
The purpose of the present invention is achieved through the following technical solutions:
A kind of industrial process flexible measurement method based on output constraint AP-XGBOOST model, which is characterized in that including with Lower step:
(1) the input auxiliary variable matrix that given historical sample integrates is X ∈ RL×N, target variable matrix Y ∈ RL；Wherein L and N respectively represents sample number and input variable dimension, by Partial Least Squares to auxiliary variable matrix X and target variable matrix Y Supervision dimensionality reduction: X=TP is carried outT+ E, Y=TRT+F；Wherein, matrix T is that historical sample matrix is projected by Partial Least Squares Obtained new sample matrix, P are the loading matrix of historical sample matrix, and R is the loading matrix of target variable matrix Y, E and F Corresponding residual matrix when respectively with Partial Least Squares fitting X and Y；
(2) by sample matrix T calculate historical sample concentrate data point between similarity matrix S:s (i, j)=- | | ti-tj||2, wherein s (i, j) is the element in matrix S, the element t in representative sample matrix TiAnd tjBetween similarity it is big It is small；Carry out iteration according to similarity matrix S and updates Certainty Factor matrix and evidence availability matrix
Wherein, r (i, k), r (k, k), r (j, k) are the element of Certainty Factor matrix, and a (i, k), a (j, i) are respectively The element of evidence availability matrix；
(3) when Certainty Factor matrix and evidence availability matrix converges on fixed value or the number of iterations reaches maximum value When, calculate each cluster centre point I=1 ..., θ, and determine cluster centre number θ With corresponding Sub Data Set(m=1... θ)；
(4) XGBOOST modeling algorithm is called to construct multiple submodel H (m) on Sub Data Set, (m=1... θ)；When new Sample xqWhen arrival, according to formula tq=xqP calculates sample xqVariable t after being projected by Partial Least Squaresq；
(5) t is calculatedqFrom different cluster centre pointsThe distance between, and determine therefrom that the cluster nearest apart from the sample CenterAnd by tqThe XGBOOST submodel H (m) constructed in the step (4) corresponding with the cluster centre is inputted, is calculated final defeated It is worth out, as model is directed to original sample xqPredicted value.
Further, the step (1) specifically: the latent variables space dimension of the Partial Least Squares is K, is led to Least square method is crossed to be expressed as the input and output of history data set
Further, in the step (2) when calculating Certainty Factor matrix and availability matrix, Certainty Factor square The value of element a (i, k) initially selected in element r (i, k) and availability matrix initially selected is set as 0 in battle array.
Further, when updating element a (i, k) of availability matrix in the step (2), if i=k, a (i, k) Calculation method are as follows:
Further, t is measured in the step (5)qFrom different cluster centre pointsThe distance between be tqIn cluster The size of Euclidean distance between heart point.
Beneficial effects of the present invention are as follows:
Industrial process flexible measurement method of the invention is using the AP-XGBOOST algorithm based on output constraint in complicated multiplexing There is better predictive ability and lower prediction error in condition problem；Affine propagation clustering (AP) algorithm based on output constraint Sample Similarity is measured according to hidden variable, output information is effectively utilized, avoids influence of the information redundancy to clustering precision, it is more quasi- Really by the different producing condition classifications of process, more accurate submodel is established on this basis；Meanwhile based on integrated study XGBOOST model then improves model for the mapping ability of non-linear non-gaussian complex process.
Detailed description of the invention
Fig. 1 is the situation of change of CO residual concentration in process gas in historical sample under different operating conditions；Scheme (1a)-(1c) Respectively indicate operating condition 1,2,3；
Fig. 2 is that prediction of the AP-SVR method in test data set shows curve graph, and figure (2a)-(2c) respectively indicates operating condition 1,2,3；
Fig. 3 is that prediction of the AP-XGBOOST method in test data set shows curve graph；Figure (3a)-(3c) respectively indicates Operating condition 1,2,3；
Fig. 4 is that prediction of the AP-XGBOOST method of output constraint in test data set shows curve graph, is schemed (4a)- (4c) respectively indicates operating condition 1,2,3.
Specific embodiment
Below according to attached drawing and preferred embodiment the present invention is described in detail, the objects and effects of the present invention will become brighter White, below in conjunction with drawings and examples, the present invention will be described in further detail.It should be appreciated that described herein specific Embodiment is only used to explain the present invention, is not intended to limit the present invention.
The present invention is generally in different operation operating conditions for complex process so as to cause soft-sensing model degeneration etc. Problem measures Sample Similarity using affine propagation clustering (AP) algorithm based on output constraint, first by original variable space It projects to latent variables space and carries out measuring similarity, then classified using affine propagation clustering algorithm to multiple operating modes process, finally Soft survey of different limit grad enhancement tree (XGBOOST) models for multi-state complex process is established on obtained data set Amount modeling.
A kind of industrial process flexible measurement method based on output constraint AP-XGBOOST model, which is characterized in that including with Lower step:
(1) the input auxiliary variable matrix that given historical sample integrates is X ∈ RL×N, target variable matrix Y ∈ RL；Wherein L and N respectively represents sample number and input variable dimension, by Partial Least Squares to auxiliary variable matrix X and target variable matrix Y Supervision dimensionality reduction: X=TP is carried outT+ E, Y=TRT+F；Wherein, matrix T is that historical sample matrix is projected by Partial Least Squares Obtained new sample matrix, P are the loading matrix of historical sample matrix, and R is the loading matrix of target variable matrix Y, E and F Corresponding residual matrix when respectively with Partial Least Squares fitting X and Y；
(2) by sample matrix T calculate historical sample concentrate data point between similarity matrix S:s (i, j)=- | | ti-tj||2, wherein s (i, j) is the element in matrix S, the element t in representative sample matrix TiAnd tjBetween similarity it is big It is small；Carry out iteration according to similarity matrix S and updates Certainty Factor matrix and evidence availability matrix
Wherein, r (i, k), r (k, k), r (j, k) are the element of Certainty Factor matrix, and a (i, k), a (j, i) are respectively The element of evidence availability matrix；
(3) when Certainty Factor matrix and evidence availability matrix converges on fixed value or the number of iterations reaches maximum value When, calculate each cluster centre point I=1 ..., θ, and determine cluster centre number θ With corresponding Sub Data Set(m=1... θ)；
(4) XGBOOST modeling algorithm is called to construct multiple submodel H (m) on Sub Data Set, (m=1... θ)；When new Sample xqWhen arrival, according to formula tq=xqP calculates sample xqVariable t after being projected by Partial Least Squaresq；
(5) t is calculatedqFrom different cluster centre pointsThe distance between, and determine therefrom that the cluster nearest apart from the sample CenterAnd by tqThe XGBOOST submodel H (m) constructed in the step (4) corresponding with the cluster centre is inputted, is calculated final defeated It is worth out, as model is directed to original sample xqPredicted value.
Preferably, the step (1) specifically: the latent variables space dimension of the Partial Least Squares is K, is passed through The input and output of history data set are expressed as by least square method
Preferably, in the step (2) when calculating Certainty Factor matrix and availability matrix, Certainty Factor matrix In the value of element a (i, k) initially initially selected in selected element r (i, k) and availability matrix be set as 0.
Preferably, when updating element a (i, k) of availability matrix in the step (2), if the meter of i=k, a (i, k) Calculation method are as follows:
Preferably, t is measured in the step (5)qFrom different cluster centre pointsThe distance between be tqWith cluster centre The size of Euclidean distance between point.
Verified below by way of unit is just become for the conversion process of CO in synthesis ammonia platform proposed based on output Constrain the validity that AP-XGBOOST model is applied to multi-state complex process.It is the key that in synthetic ammonia process that height, which becomes unit, Production unit.During ammonia synthesis, a kind of important process materials are hydrogen, it is generated by methane decarbonization device.So And carbon is still with CO and CO2Form be present in process gas.The major function of height change unit is will be intractable Carbon monoxide (CO) is converted into carbon dioxide (CO2), in subsequent CO2Absorbing carbon dioxide in absorption tower.When process gas stream When crossing height converter unit, the overall chemical reaction of generation is described as follows:
Different reaction temperatures can generate different types of as a result, therefore respectively using difference in high temperature tower and cryogenic columns Catalyst realize, to meet a variety of production requirements.The final target of device is to reduce in process gas to the maximum extent CO content, and the technique is steadily run in a manner of energy-efficient.Therefore, it realizes and reduces this target of CO content in process gas First is that the content of CO is remained in measurement unit outer tube with most important step.Real process passes through offline laboratory point Analysis, with the content of extremely low sample rate measurement remnants CO.In order to predict the concentration of remnants CO in process gas, 26 are had chosen herein A performance variable and state variable establish soft-sensing model as auxiliary variable.
This method is using the 100000 variable sample datas collected in the platform history production process.When according to acquisition Between sequence, choose the first eight ten thousand data point for modeling, rear 20,000 data points are for test model for future time instance CO The estimated performance of concentration avoids modeling space from being overlapped with space is predicted with this.Fig. 1 shows under operating conditions different in historical sample The situation of change of CO residual concentration in process gas.
When establishing the AP-XGBOOST model based on output constraint, maximal tree depth and the number of iterations are respectively set to 3 Hes 200, XGBOOST submodule shape parameter is then obtained by cross validation.Fig. 2-Fig. 4 is AP-SVR model, AP-XGBOOST respectively Model, the AP-XGBOOST based on output constraint the prediction performance in test data set respectively.
AP algorithm it can be seen from Fig. 2-Fig. 4 based on output constraint measures Sample Similarity, effectively benefit according to hidden variable With output information, influence of the information redundancy to clustering precision is avoided, more accurately by the different producing condition classifications of process；Meanwhile XGBOOST algorithm improves model for the mapping ability of non-linear non-gaussian complex process.By to synthetic ammonia process height The practical study for becoming unit illustrates that method proposed by the present invention can efficiently solve the multi-state in complex process, non- Linearly, the characteristics such as non-gaussian have preferable accuracy when solving the problems, such as complex process soft sensor modeling.
It will appreciated by the skilled person that being not used to limit the foregoing is merely the preferred embodiment of invention System invention, although invention is described in detail referring to previous examples, for those skilled in the art, still It can modify to the technical solution of aforementioned each case history or equivalent replacement of some of the technical features.It is all Within the spirit and principle of invention, modification, equivalent replacement for being made etc. be should be included within the protection scope of invention.

Claims (5)

1. a kind of industrial process flexible measurement method based on output constraint AP-XGBOOST model, which is characterized in that including following Step:
(1) the input auxiliary variable matrix that given historical sample integrates is X ∈ RL×N, target variable matrix Y ∈ RL；Wherein L and N points Other representative sample number and input variable dimension carry out auxiliary variable matrix X and target variable matrix Y by Partial Least Squares There is supervision dimensionality reduction: X=TPT+ E, Y=TRT+F；Wherein, matrix T is that historical sample matrix projects to obtain by Partial Least Squares New sample matrix, P be historical sample matrix loading matrix, R be target variable matrix Y loading matrix, E and F difference Corresponding residual matrix when for Partial Least Squares fitting X and Y.
(2) by sample matrix T calculate historical sample concentrate data point between similarity matrix S:s (i, j)=- | | ti-tj ||2, wherein s (i, j) is the element in matrix S, the element t in representative sample matrix TiAnd tjBetween similarity size；According to Carry out iteration according to similarity matrix S and updates Certainty Factor matrix and evidence availability matrix
Wherein, r (i, k), r (k, k), r (j, k) are the element of Certainty Factor matrix, and a (i, k), a (j, i) are respectively evidence The element of availability matrix；
(3) when Certainty Factor matrix and evidence availability matrix converge on fixed value or the number of iterations reaches maximum value, Calculate each cluster centre point And determine cluster centre number θ and right The Sub Data Set answered
(4) XGBOOST modeling algorithm is called to construct multiple submodel H (m) on Sub Data Set, (m=1... θ)；As new samples xq When arrival, according to formula tq=xqP calculates sample xqVariable t after being projected by Partial Least Squaresq；
(5) t is calculatedqFrom different cluster centre pointsThe distance between, and determine therefrom that the cluster centre nearest apart from the sampleAnd by tqThe XGBOOST submodel H (m) constructed in the step (4) corresponding with the cluster centre is inputted, final output is calculated Value, as model are directed to original sample xqPredicted value.
2. the industrial process flexible measurement method according to claim 1 based on output constraint AP-XGBOOST model, special Sign is, the step (1) specifically: the latent variables space dimension of the Partial Least Squares is K, passes through least square The input and output of history data set are expressed as by method
3. the industrial process flexible measurement method according to claim 1 or 2 based on output constraint AP-XGBOOST model, It is characterized in that, it is initial in Certainty Factor matrix in the step (2) when calculating Certainty Factor matrix and availability matrix The value of element a (i, k) initially selected is set as 0 in selected element r (i, k) and availability matrix.
4. the industrial process flexible measurement method according to claim 3 based on output constraint AP-XGBOOST model, special Sign is, when updating element a (i, k) of availability matrix in the step (2), if the calculation method of i=k, a (i, k) are as follows:
5. the industrial process flexible measurement method according to claim 4 based on output constraint AP-XGBOOST model, special Sign is, t is measured in the step (5)qFrom different cluster centre pointsThe distance between be tqThe Europe between cluster centre point The size of family name's distance.
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CN111291020A (en) * 2019-11-11 2020-06-16 中国计量大学 Dynamic process soft measurement modeling method based on local weighted linear dynamic system
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