CN113191615B - Polypropylene production process anomaly detection method based on analysis of multiple related components - Google Patents
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- -1 Polypropylene Polymers 0.000 title claims abstract description 44
- 239000004743 Polypropylene Substances 0.000 title claims abstract description 44
- 229920001155 polypropylene Polymers 0.000 title claims abstract description 42
- 238000001514 detection method Methods 0.000 title claims abstract description 38
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- QQONPFPTGQHPMA-UHFFFAOYSA-N propylene Natural products CC=C QQONPFPTGQHPMA-UHFFFAOYSA-N 0.000 claims description 9
- 125000004805 propylene group Chemical group [H]C([H])([H])C([H])([*:1])C([H])([H])[*:2] 0.000 claims description 9
- 229910052739 hydrogen Inorganic materials 0.000 claims description 7
- 238000000354 decomposition reaction Methods 0.000 claims description 6
- 239000001257 hydrogen Substances 0.000 claims description 6
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims description 5
- 239000003054 catalyst Substances 0.000 claims description 5
- 238000010992 reflux Methods 0.000 claims description 3
- 230000001960 triggered effect Effects 0.000 claims description 3
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- 238000000034 method Methods 0.000 abstract description 21
- 238000005516 engineering process Methods 0.000 abstract description 5
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- 239000000284 extract Substances 0.000 abstract 1
- 239000000155 melt Substances 0.000 description 8
- 238000009776 industrial production Methods 0.000 description 2
- 238000006116 polymerization reaction Methods 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 150000002431 hydrogen Chemical class 0.000 description 1
- 238000005984 hydrogenation reaction Methods 0.000 description 1
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- 239000012071 phase Substances 0.000 description 1
- 239000004033 plastic Substances 0.000 description 1
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Abstract
The invention discloses a polypropylene production process anomaly detection method based on multiple pieces of related component analysis. The method analyzes the correlation among the measured variables of a plurality of sub-production units by a multi-block correlation component analysis technology, thereby distinguishing the correlation and the non-correlation characteristics among blocks, and implementing distributed abnormality detection on the polypropylene production process on the basis. In other words, the method does not simply implement data-driven anomaly detection separately according to four reaction kettles, but extracts inter-block related characteristic components from each sub-block by a multi-block related analysis technology, so that the correlation among the sub-blocks is considered during distributed modeling. Therefore, the method not only can carry out distributed anomaly detection on the polypropylene production process, but also realizes the purpose of correlation analysis among the subblocks.
Description
Technical Field
The invention relates to an anomaly detection method, in particular to a polypropylene production process anomaly detection method based on analysis of multiple blocks of related components.
Background
As one of five common plastics, polypropylene has wide application in military and aerospace, industrial production and daily life, and is the most important downstream product of propylene. The data show that 50% of the propylene in the world and 65% of the propylene in our country are used to produce polypropylene. Among them, the melt index is the most important quality index of polypropylene, and is the key to guide and control the whole process in industrial production. In the production process of polypropylene, the main raw materials of polypropylene are propylene and hydrogen, so the hydrogen flow and the propylene flow entering a reaction kettle can certainly influence the measurement of the melt index; at the same time, the catalyst influences the speed of the polymerization reaction, so the melt index is also influenced by the flow rate of the catalyst in the reaction vessel.
In addition to the hydrogen and propylene flow rates affecting the quality of the final polypropylene product, conventional measurement variables such as temperature, pressure, level, etc. of the reactor in the polypropylene production process also affect the final polypropylene product. Therefore, to ensure the continuous stability of the polypropylene production process, it is necessary to detect abnormal conditions in real time. In recent years, due to the wide application of advanced measuring instruments and computing technologies, a polypropylene production process can acquire a large amount of information such as temperature, pressure, flow and the like in real time at high frequency, and the massive sampling data lays a full data foundation for implementing data-driven anomaly detection.
At present, most of domestic polypropylene production processes adopt a Hypol process with four reaction kettles connected in series, each reaction kettle can carry out a hydrogenation polymerization reaction, and a reflux device is arranged at the top of each reaction kettle. Thus, polypropylene production is actually a complex process flow resulting from the combination of multiple production units. Generally speaking, for this type of production process, it is usually more convenient to implement distributed anomaly detection, which not only can reduce the complexity of modeling analysis, but also can perform anomaly detection in blocks and units. However, although the polypropylene production process comprises 4 production units, the mutual influence of the production units is mutually restricted, and the influence of the connection among the units is ignored by directly decomposing the production units into four independent subunits and respectively carrying out abnormality detection. Therefore, the technology for distributed detection of abnormal conditions in the polypropylene production process needs to be improved and improved.
Disclosure of Invention
The invention aims to solve the main technical problems that: how to implement distributed anomaly detection on the polypropylene production process by utilizing a plurality of blocks of related component analysis on the premise of correlation among the sub-production units. Specifically, the method analyzes the correlation among the measurement variables of a plurality of sub-production units by a multi-block correlation component analysis technology so as to distinguish correlation and non-correlation characteristics among blocks, and performs distributed abnormality detection on the polypropylene production process on the basis of the correlation and non-correlation characteristics.
The technical scheme adopted by the method for solving the problems is as follows: a polypropylene production process abnormity detection method based on multi-block related component analysis comprises the following steps:
step (1): determining the measurement variables of the polypropylene production process, specifically comprising 28 measurement variables of four reaction kettles; wherein, every reation kettle all relates to 7 measured variables, is in proper order: reactor temperature, reactor pressure, reactor liquid level, hydrogen feed flow, propylene feed flow, catalyst feed flow, and reflux flow.
Step (2): continuously collecting sample data of N sampling moments according to the determined measurement variables, and storing the sample data corresponding to the measurement variables into an Nx 28-dimensional data matrix X e RN×28Then, the column vectors z in X are respectively aligned according to the following formula1,z2,...,z28Carrying out standardization processing to obtain data matrix
Wherein R isN×28Representing a matrix of real numbers of dimension Nx 28, R representing a set of real numbers, mukAnd deltakRespectively represent a column vector zk∈RN×1The mean and standard deviation of all elements in (k) e {1, 2., 28},representing a matrix of dataIn the column direction of the k-th columnAmount of the compound (A).
And (3): according to the measured variable related to each reaction kettle, correspondingly combining the data matrixDivided into four sub-block matrices X1,X2,X3,X4(ii) a Wherein, Xb∈RN×7Is a subblock matrix R corresponding to the b-th reaction kettle in the production process of polypropyleneN×7Representing a matrix of real numbers of dimension N x 7, b ∈ {1, 2, 3, 4 }.
And (4): four subblock matrices X according to steps (4.1) to (4.6) as shown below1,X2,X3,X4Carrying out multi-block correlation component analysis to obtain a correlation transformation matrix W corresponding to each sub-block matrixbThe load matrix PbCorrelation component matrix SbAnd residual matrix Eb。
Step (4.1): the initialization d is 1.
Step (4.2): randomly initializing four transformation vectors w1∈R7×1,w2∈R7×1,w3∈R7×1And w4∈R7×1After that, b is set to 1.
Step (4.3): solving generalized eigenvalue problemMedium maximum eigenvalue lambdabCorresponding feature vector vbThen according to the formulaUpdating a transformation vector wb(ii) a Wherein the upper symbol T represents the transpose of a matrix or vector, and the matrix thetabThe calculation of (c) is as follows:
in the above formula, c is formed by {1, 2, 3, 4}, and when b is not equal to c, H is formedb,cWhen b is c, Hb,c=0。
Step (4.4): judging whether b is less than 4; if yes, after b is set to b +1, returning to the step (4.3); if not, obtaining the updated transformation vector w1,w2,w3,w4And then step (4.5) is executed.
Step (4.5): judging four transformation vectors w1,w2,w3,w4Whether both converge; if not, setting b to be 1 and then returning to the step (4.3); if yes, the correlation coefficient J ═ λ is calculated1+λ2+λ3+λ4) After/4, step (4.6) is performed.
Step (4.6): judging whether the correlation coefficient J is larger than a threshold value sigma; if yes, the formula s is firstly and respectively determined according tob=XbwbAndcalculating a correlation component vector sbAnd a load vector pbThen according to the formulaUpdating subblock matrix XbAnd transforming the correlation matrix W1,W2,W3,W4The column vectors of the d-th column in the column are respectively set to w1,w2,w3,w4Will load the matrix P1,P2,P3,P4The column vectors of the d-th column are respectively set to p1,p2,p3,p4A matrix S of correlation components1,S2,S3,S4The column vectors of the d-th column are respectively set to s1,s2,s3,s4Then, after d is set to d +1, the step (4.2) is returned; if not, obtaining a related transformation matrix W corresponding to each subblock matrixbThe load matrix PbAnd a correlation component matrix SbAnd by setting Eb=XbA residual matrix E can be obtained1,E2,E3,E4。
It is to be noted that, in the above step (4.6), the matrix W is transformedbThe column vector of the d-th column in (d) is equal to wbThe load matrix PbThe column vector of the d-th column in (d) is equal to pbThe correlation component matrix SbThe column vector of the d-th column in (d) is equal to sb,b∈{1,2,3,4}。
And (5): according toRespectively for residual error matrix E1,E2,E3,E4Performing singular value decomposition with retention of decomposition matrixWhere b is ∈ {1, 2, 3, 4}, diagonal matrix ΛbThe elements on the diagonal being composed of non-zero singular values, UbAnd VbAre two unitary matrices of a singular value decomposition,is represented bybThe inverse matrix of (c).
And (6): are respectively according to the formulaAndcalculating an anomaly detection index vector psibAnd QbThen respectively transferring psibAnd QbThe maximum value of the element in (1) is recorded asAndwhere diag () denotes an operation of converting the elements of the matrix diagonal in brackets into column vectors.
And (7): after the abnormal comprehensive detection index vector D is calculated according to the formula shown below, the abnormal comprehensive detection index vector D is addedThe maximum value of (A) is recorded as Dlim:
And (8): at the latest sampling time t, sample data x corresponding to the measured variable is collectedt(1),xt(2),...,xt(28) And respectively normalizing them according to the following formula to obtain data vectors
And (9): according to the measured variable related to each reaction kettle, correspondingly calculating the data vectorDivision into four sub-block vectorsThen, again according toAndrespectively calculating related feature vectorsAnd residual vector eb。
Step (10): according toAndrespectively calculating abnormality detection indexesAnd q isbThen, the abnormal comprehensive detection index D corresponding to the latest sampling time t is calculated according to the formula shown in the specificationt:
Step (11): judging whether the conditions are met: dt≤Dlim(ii) a If yes, the polypropylene production process at the current sampling moment is not abnormal, and the step (8) is returned to continue to carry out the abnormal detection of the latest sampling moment; if not, executing step (12) to decide whether to trigger an abnormal alarm.
Step (12): returning to the step (8) to continue to carry out the abnormal detection of the latest sampling time until the abnormal comprehensive detection indexes of the continuous 6 latest sampling times are obtained, and then judging whether the 6 abnormal comprehensive detection indexes are all larger than Dlim(ii) a If yes, triggering an abnormal alarm; if not, the abnormal alarm is not triggered, and the step (8) is returned to continue to detect the abnormality of the latest sampling time.
The implementation step (4) of the method of the invention utilizes a multi-block correlation component analysis algorithm to implement the correlation analysis among four sub-block matrixes, and the algorithm aims at respectively finding out corresponding transformation vectors w for each sub-block matrix1,w2,w3,w4So as to satisfy the objective function shown below:
the above-described constrained objective function aims to maximize the sum of squares of correlation coefficients between feature components. In other words, the objective function is intended to pass through the transform vector w1,w2,w3,w4And extracting the characteristic component with the maximum correlation.
The lagrange function L is defined by the lagrange multiplier method as follows:
by setting L relative to wbThe partial differential equation of (a) is equal to 0, the following reasoning process can be obtained:
on both sides of the upper equal sign, the left multiplication is carried out simultaneouslyThen, can obtainThus, the maximized objective function is equivalent to maximizing the sum of the eigenvalues, i.e., λ1+λ2+λ3+λ4。
In order to avoid using different technical terms for the same symbol, the problem of the generalized eigenvalues in step (4.3) of the method of the invention is actually to fit w in the above equationbIs replaced by a feature vector vb. And further by formulaSatisfy the constraint condition
By carrying out the steps described above, the advantages of the method of the invention are presented below.
When the method is used for detecting the quality abnormity of the polypropylene product, the technical means of directly measuring the melt index of the polypropylene, namely long and serious hysteresis, is not relied on, and the characteristic variable or component which is most relevant to the melt index of the polypropylene and is obtained by double-layer relevant characteristic analysis of a neighbor component analysis algorithm and a typical relevant analysis algorithm is used for indirectly detecting the abnormity. The method can detect whether the quality of the polypropylene product is abnormal in real time according to the sampling frequency of the measured variable, and overcomes the problem of hysteresis of the traditional method.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a schematic flow diagram of a polypropylene process
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1, the present invention discloses a method for detecting quality abnormality of polypropylene product based on double-layer correlation characteristic analysis, and the following describes a specific embodiment of the method according to the present invention with reference to a specific application example.
As shown in fig. 2, the production flow of a polypropylene process object comprises four reactors, respectively referred to as a first reactor, a second reactor, a third reactor, and a fourth reactor; wherein the first reactor and the second reactor are liquid phase continuous stirred reactors and the third reactor and the fourth reactor are gas phase fluidized bed reactors. Each reactor had a feed of propylene and hydrogen and a feed of catalyst. In addition, the product at the outlet of the fourth reactor is a polypropylene product.
Step (1): the measured variables of the polypropylene production process are determined, and specifically comprise 28 measured variables of four reactors.
Step (2): continuously collecting sample data of N sampling moments by using a measuring instrument installed in the production process of polypropylene; meanwhile, the melt index of the polypropylene product in the fourth reactor is obtained by sampling and analyzing every 2 hours; and then storing the sample data corresponding to the measurement variable into an Nx 28-dimensional data matrix X, and storing N data corresponding to the melt index into an N X1-dimensional data vector y.
And (3): element filling is carried out on the data vector Y according to the formula (i), and thus a column vector Y epsilon R is obtainedN×1. In the present embodiment, the sampling period of the measured variable is 6 minutes, i.e. the sampling frequency is equal to 1/(6 × 60), while the sampling frequency of the melt index is equal to 1/(120 × 60). Therefore, f in formula (i) is (120 × 60)/(6 × 60) is 20.
And (4): according to the formula 2, respectively aligning the column vectors z in X1,z2,...,z28And standardizing the column vector Y to obtain an input matrixAnd outputting the vector
And (5): obtaining the weight vector w according to the optimization of the steps (5.1) to (5.6)0∈R28×1Thereby obtaining a direct correlation feature matrix X1∈RN×mAnd uncorrelated feature matrix X2∈RN×(28-m)。
And (6): solving generalized eigenvalue problemThe feature vector beta corresponding to the middle maximum feature value eta is calculated according toCalculating a correlation projection vector q ∈ R(28-m)×1Then according to the formulaComputing indirect correlation feature vectors
And (7): mixing X1Andcombined into an input correlation feature matrixThen, the above steps (7.1) to (7.4) are executed to determine the upper limit D of the abnormality detection indexlim。
And (8): at the latest sampling time t, sample data x corresponding to the measured variable is collectedt(1),xt(2),...,xt(28) And normalizing them according to the formula (R) to obtain input vectors
And (9): according to the weight vector w0∈R1×28The column where the maximum m elements are located corresponds to the input vectorElements in the same column constitute directly related feature vectorsThen will beThe remaining 28-m elements in the set constitute uncorrelated feature vectors
Step (10): according to the formulaCalculating an indirect correlation feature stThen, the mixture is mixed withAnd stCombined into an input-dependent feature vector
Step (11): calculating an abnormality detection index D according to the aforementioned steps (11.1) to (11.2)t。
Step (12): judging whether the conditions are met: dt≤Dlim(ii) a If yes, the quality of the polypropylene product at the current sampling moment is not abnormal, and the step (8) is returned; if not, executing step (13) to decide whether to trigger an abnormal alarm.
Step (13): returning to the step (8) to continue to carry out the quality abnormity detection of the polypropylene product at the latest sampling time, if the abnormity detection indexes of the continuous 6 latest sampling times are all larger than DlimTriggering an abnormal alarm of the quality of the polypropylene product; otherwise, no exception alarm is triggered.
Claims (1)
1. A polypropylene production process abnormity detection method based on multi-block related component analysis is characterized by comprising the following steps:
step (1): determining measurement variables of the polypropylene production process, specifically comprising 28 measurement variables of four reaction kettles; wherein, every reation kettle all relates to 7 measured variables, is in proper order: reactor temperature, reactor pressure, reactor liquid level, hydrogen feed flow, propylene feed flow, catalyst feed flow, and reflux flow;
step (2): continuously collecting sample data of N sampling moments according to the determined measurement variables, and storing the sample data corresponding to the measurement variables into an Nx 28-dimensional data matrix X e RN×28Then, the column vectors z in X are respectively aligned according to the following formula1,z2,…,z28Carrying out standardization processing to obtain data matrix
Wherein R isN×28Representing a matrix of real numbers of dimension Nx 28, R representing a set of real numbers, mukAnd deltakRespectively representing column vectors zk∈RN×1The mean and standard deviation of all elements in (k) e {1, 2, …, 28},representing a matrix of dataA column vector of the kth column;
and (3): according to the measured variable related to each reaction kettle, correspondingly combining the data matrixDivided into four sub-block matrices X1,X2,X3,X4(ii) a Wherein, Xb∈RN×7Is a subblock matrix R corresponding to the b-th reaction kettle in the production process of polypropyleneN×7Representing a real number matrix of dimension Nx 7, b ∈ {1, 2, 3, 4 };
and (4): four subblock matrices X according to steps (4.1) to (4.6) as shown below1,X2,X3,X4Carrying out multi-block correlation component analysis to obtain a correlation transformation matrix W corresponding to each sub-block matrixbThe load matrix PbCorrelation component matrix SbAnd residual matrix Eb;
Step (4.1): after the threshold value sigma is set, initializing d to 1;
step (4.2): randomly initializing four transformation vectors w1∈R7×1,w2∈R7×1,w3∈R7×1And w4∈R7×1Then, setting b to be 1;
step (4.3): solving generalized eigenvalue problemMedium maximum eigenvalue lambdabCorresponding feature vector vbThen according to the formulaUpdating a transformation vector wb(ii) a Wherein the upper symbol T represents the transpose of a matrix or vector, and the matrix thetabThe calculation of (c) is as follows:
in the above formula, c is ∈ {1, 2, 3, 4}, and when b ≠ c, Hb,cWhen b is c, Hb,c=0;
Step (4.4): judging whether b is less than 4; if yes, after b is set to b +1, returning to the step (4.3); if not, obtaining the updated transformation vector w1,w2,w3,w4And then executing the step (4.5);
step (4.5): judging four transformation vectors w1,w2,w3,w4Whether both converge; if not, setting b to be 1 and then returning to the step (4.3); if yes, the correlation coefficient J ═ λ is calculated1+λ2+λ3+λ4) After/4, performing step (4.6);
step (4.6): judging whether the correlation coefficient J is larger than a threshold value sigma; if yes, the formula s is firstly and respectively determined according tob=XbwbAndcalculating a correlation component vector sbAnd a load vector pbThen according to the formulaUpdating subblock matrix X1,X2,X3,X4And set up a phaseThe correlation matrix W1,W2,W3,W4The column vectors of the d-th column in the column are respectively equal to w1,w2,w3,w4Setting a load matrix P1,P2,P3,P4The column vectors of the d-th column in (A) are respectively equal to p1,p2,p3,p4Setting a correlation component matrix S1,S2,S3,S4The column vectors of the d-th column in (A) are respectively equal to s1,s2,s3,s4Then, after d is set to d +1, the step (4.2) is returned; if not, obtaining a related transformation matrix W corresponding to the four subblock matrixes1,W2,W3,W4The load matrix P1,P2,P3,P4And a correlation component matrix S1,S2,S3,S4And by setting Eb=XbA residual matrix E can be obtained1,E2,E3,E4;
And (5): according toRespectively for residual error matrix E1,E2,E3,E4Performing singular value decomposition with retention of decomposition matrixWhere b is ∈ {1, 2, 3, 4}, diagonal matrix ΛbThe elements on the diagonal being composed of non-zero singular values, UbAnd VbAre two unitary matrices of a singular value decomposition,is represented bybThe inverse matrix of (d);
and (6): according to the formula respectivelyAndcalculating an anomaly detection index vector psibAnd QbThen respectively transferring psibAnd QbThe maximum value of the element in (1) is recorded asAndwherein diag () represents an operation of converting elements of matrix diagonal in parentheses into vectors;
and (7): calculating an abnormal comprehensive detection index vector D according to a formula shown in the specification, and recording the maximum value in the D as the Dlim:
And (8): at the latest sampling time t, sample data x corresponding to the measurement variable is collectedt(1),xt(2),…,xt(28) And respectively normalizing them according to the following formula to obtain data vectors
and (9): according to the measured variable related to each reaction kettle, correspondingly calculating the data vectorDivision into four sub-block vectorsThen, again according toAndrespectively calculating related feature vectorsAnd residual vector eb;
Step (10): according toAndrespectively calculating abnormality detection indexesAnd q isbThen, the abnormal comprehensive detection index D corresponding to the latest sampling time t is calculated according to the formula shown in the specificationt:
Step (11): judging whether the conditions are met: dt≤Dlim(ii) a If yes, the polypropylene production process at the current sampling moment is not abnormal, and the step (8) is returned to continue to carry out the abnormal detection of the latest sampling moment; if not, then executeStep (12) deciding whether to trigger an abnormal alarm;
step (12): returning to the step (8) to continue to carry out the abnormal detection of the latest sampling time until the abnormal comprehensive detection indexes of the continuous 6 latest sampling times are obtained, and then judging whether the 6 abnormal comprehensive detection indexes are all larger than Dlim(ii) a If yes, triggering an abnormal alarm; if not, the abnormal alarm is not triggered, and the step (8) is returned to continue to detect the abnormality of the latest sampling time.
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