CN110763830A - Method for predicting content of free calcium oxide in cement clinker - Google Patents

Method for predicting content of free calcium oxide in cement clinker Download PDF

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CN110763830A
CN110763830A CN201911232888.0A CN201911232888A CN110763830A CN 110763830 A CN110763830 A CN 110763830A CN 201911232888 A CN201911232888 A CN 201911232888A CN 110763830 A CN110763830 A CN 110763830A
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李凡军
李颖
王孝红
路士增
蒋萍
李实�
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Abstract

The invention discloses a method for predicting the content of free calcium oxide in cement clinker, which comprises the following steps: s1: collecting a cement clinker sample; s2: constructing a time sequence of free calcium oxide content of cement clinker; s3: performing time sequence decomposition on the time sequence of the content of free calcium oxide in the mud clinker based on an empirical mode decomposition method; s4: constructing input and output sample pairs required by a training feature extraction module; s5: training the feature extraction submodule, namely a modularized echo state neural network, and learning the multi-time scale features of the time sequence: s6: training the prediction module-echo state neural network: s7: and predicting the content of free calcium oxide in the clinker. The method predicts the content of free calcium oxide at the next moment based on off-line experimental data, and solves the problem of lag of laboratory measurement results; compared with the measurement method of an on-line analyzer, the method has low cost, and the measurement accuracy is less influenced by on-site smoke dust and actual working conditions.

Description

Method for predicting content of free calcium oxide in cement clinker
Technical Field
The invention relates to a method for predicting the content of free calcium oxide in cement clinker, and belongs to the technical field of cement production control.
Background
The content of clinker free calcium oxide in the cement calcination process is an important standard for measuring the cement quality, and the content of clinker free calcium oxide represents the residual degree after calcium oxide is combined with silicon oxide, aluminum oxide and iron oxide in the calcination of raw materials, and the strength of the cement clinker is directly influenced by the content of the calcium oxide. Free calcium oxide of cement clinker is one of important indexes for reflecting the quality of the cement clinker, and the content of the free calcium oxide in the clinker during the calcination process of the cement clinker needs to be controlled within a reasonable range. The real-time prediction of the content of free calcium oxide in clinker is crucial to the guarantee of the calcination quality of cement clinker and the optimization of the calcination process.
At present, most laboratories of cement factories manually measure the content of free calcium oxide in cement clinker, adopt a glycerol-mango alcohol system as an extraction solvent, generate alkaline glycerol with the free calcium, and titrate with a hexanol benzoate standard solution. The on-line analyzer measuring method utilizes the fact that after ethylene glycol reacts with free calcium oxide in cement clinker, the conductivity of a solution and the content of the free calcium oxide form a certain proportional relation, and the content of the free calcium oxide is indirectly obtained through conductivity measurement.
The chemical test method has large delay and cannot truly reflect the production condition in the rotary kiln, so that the quality of the clinker is difficult to be effectively controlled. The online analyzer measurement method has the advantages of high equipment cost and high maintenance cost, and the measurement accuracy is easily influenced by site smoke dust and actual working conditions.
Disclosure of Invention
Aiming at the defects of the method, the invention provides a method for predicting the content of free calcium oxide in cement clinker, which can accurately predict the free calcium oxide in cement clinker in real time.
The technical scheme adopted for solving the technical problems is as follows:
the embodiment of the invention provides a method for predicting the content of free calcium oxide in cement clinker, which comprises the following steps:
s1: collecting a cement clinker sample;
s2: constructing a time sequence of free calcium oxide content of cement clinker;
s3: performing time sequence decomposition on the time sequence of the content of free calcium oxide in the mud clinker based on an empirical mode decomposition method:
s4: constructing input and output sample pairs required by a training feature extraction module;
s5: training the feature extraction submodule, namely a modularized echo state neural network, and learning the multi-time scale features of the time sequence:
s6: training the prediction module-echo state neural network:
s7: and predicting the content of free calcium oxide in the clinker.
As a possible implementation manner of this embodiment, in step S1, the sampling period is 1 hour, n times of continuous sampling are performed to obtain cement clinker samples online, and laboratory offline detection is performed manually, and the content u of free calcium oxide in cement clinker is recorded1,u2,……,un。
As a possible implementation manner of this embodiment, in step S3, the process of performing time-series decomposition on the time series of the free calcium oxide content of the clinker includes the following steps:
s3.1: setting an initial characteristic sequence number p to be 0; let h0(t) U (t), U (t) is cement clinker free calcium oxide content time sequence, U (t) ut，t＝1,2,……,n；
S3.2: setting the initial cycle number q to be 0;
s3.3: updating the cycle number q to q + 1;
s3.4: finding out all extreme points of the time sequence U (t) of the content of free calcium oxide of the cement clinker;
s3.5: forming a lower envelope e for the minimum points by cubic spline interpolationmin(t) forming an upper envelope e for the maximamax(t)；
S3.6: calculating the mean of the extreme points:
Figure BDA0002302318500000021
s3.7: details of extraction:
dq(t)＝hp(t)-m(t)； (2)
s3.8: for detail signal dq(t) repeating steps S3.3 to S3.7 until a detail signal dq(t) has a mean value of 0;
s3.9: updating the characteristic sequence number p to p + 1;
s3.10: recording a feature sequence IMFp(t)；
S3.11: calculating a residual sequence:
hp(t)＝hp-1(t)-IMFp(t)； (3)
s3.12: to hp(t) repeating S3.2 to S3.11 until hp(t) is a monotonic function;
s3.13: let IMFp(t)＝hp(t)。
As a possible implementation manner of this embodiment, in step S4, the input/output sample pairs required for constructing the training feature extraction module are:
Ωi＝(U(t),IMFi(t)) (4)
wherein i is 1,2, …, p + 1; t is 1,2, …, n.
As a possible implementation manner of this embodiment, the step S5 specifically includes the following steps:
s5.1: setting an initial module number k to be 0;
s5.2: updating the number k of the modules to k + 1;
s5.3: setting module parameters: number of input neurons 1, number of output neurons 1, number of hidden layer neurons nkSpectral radius ρkDegree of sparsity spk；
S5.4: randomly generating networks with uniform distribution over the interval (-0.1,0.1)Input weight matrix
Figure BDA0002302318500000031
S5.5: randomly generating sparsity sp with uniform distribution over the interval (-1,1)kOf (2) matrix
Figure BDA0002302318500000032
S5.6: computing matrices
Figure BDA0002302318500000033
Maximum eigenvalue of
Figure BDA0002302318500000034
S5.7: calculating hidden layer weight Wk：
Figure BDA0002302318500000035
S5.8: computing hidden layer neuron states:
Figure BDA0002302318500000041
where t is 1,2, L L, n, sk(0)＝0；
S5.9: constructing a hidden layer state matrix:
Figure BDA0002302318500000042
s5.10: calculating a network output weight matrix:
Figure BDA0002302318500000043
wherein
Figure BDA0002302318500000044
For the hidden layer state matrix HkThe transposed matrix of (2);
s5.11: and (3) calculating network output:
Figure BDA0002302318500000045
s5.12: repeating steps S5.2 to S5.11 until k ═ p + 1;
s5.13: collecting submodule output:
Figure BDA0002302318500000046
as a possible implementation manner of this embodiment, the step S6 includes the following steps:
s6.1: constructing network input I and reference output
Figure BDA0002302318500000047
Figure BDA0002302318500000049
S6.2: setting module parameters: number of input neurons p +1, number of output neurons 1, number of hidden layer neurons nRSpectral radius ρRDegree of sparsity spR；
S6.3: randomly generating a network input weight matrix with uniform distribution over an interval (-0.1,0.1)
S6.4: randomly generating sparsity sp with uniform distribution over the interval (-1,1)ROf (2) matrix
Figure BDA00023023185000000411
S6.5: computing matrices
Figure BDA00023023185000000412
Maximum eigenvalue of
Figure BDA00023023185000000413
S6.6: calculating hidden layer weight WR
Figure BDA0002302318500000051
S6.7: computing hidden layer neuron states sR(t)：
Figure BDA0002302318500000052
Wherein s isR(0)＝0；
S6.8: constructing a hidden layer state matrix HR：
Figure BDA0002302318500000053
S6.9: computing network output weight matrix
Figure BDA0002302318500000055
Wherein
Figure BDA0002302318500000056
For the hidden layer state matrix HRThe transposed matrix of (2).
As a possible implementation manner of this embodiment, the step S7 specifically includes: inputting the test sample into the trained feature extraction module, and outputting by the prediction module to obtain the free calcium oxide content of the cement clinker at the next moment.
The technical scheme of the embodiment of the invention has the following beneficial effects:
aiming at the problem of online prediction of the free calcium oxide content of the cement clinker, the invention designs an intelligent prediction method of the free calcium oxide content of the cement clinker based on time sequence decomposition and a neural network, and realizes accurate prediction of the free calcium oxide content of the cement clinker; the empirical mode decomposition and the modularized echo state are combined, a time series multi-time scale feature learning method is provided, the automatic extraction of the time series multi-time scale features is realized, and the free calcium oxide of the cement clinker can be accurately predicted in real time.
The method predicts the content of free calcium oxide at the next moment based on off-line experimental data, and solves the problem of lag of laboratory measurement results; compared with the measurement method of an on-line analyzer, the method has low cost, and the measurement accuracy is less influenced by on-site smoke dust and actual working conditions.
Description of the drawings:
FIG. 1 is a flow chart illustrating a method for predicting free calcium oxide content of cement clinker in accordance with an exemplary embodiment;
FIG. 2 is a diagram of a predictive model topology;
FIG. 3 is a comparison graph of predicted values and measured values;
fig. 4 is a prediction error map.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
in order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
FIG. 1 is a flow chart illustrating a method for predicting free calcium oxide content of cement clinker according to an exemplary embodiment. As shown in fig. 1, a method for predicting the content of free calcium oxide in cement clinker provided by the embodiment of the present invention includes the following steps:
s1: collecting a cement clinker sample;
s2: constructing a time sequence of free calcium oxide content of cement clinker;
s3: performing time sequence decomposition on the time sequence of the content of free calcium oxide in the mud clinker based on an empirical mode decomposition method:
s4: constructing input and output sample pairs required by a training feature extraction module;
s5: training the feature extraction submodule, namely a modularized echo state neural network, and learning the multi-time scale features of the time sequence:
s6: training the prediction module-echo state neural network:
s7: and predicting the content of free calcium oxide in the clinker.
As a possible implementation manner of this embodiment, in step S1, the sampling period is 1 hour, n times of continuous sampling are performed to obtain cement clinker samples online, and laboratory offline detection is performed manually, and the content u of free calcium oxide in cement clinker is recorded1,u2,……,un。
As a possible implementation manner of this embodiment, in step S3, the process of performing time-series decomposition on the time series of the free calcium oxide content of the clinker includes the following steps:
s3.1: setting an initial characteristic sequence number p to be 0; let h0(t) U (t), U (t) is cement clinker free calcium oxide content time sequence, U (t) ut，t＝1,2,……,n；
S3.2: setting the initial cycle number q to be 0;
s3.3: updating the cycle number q to q + 1;
s3.4: finding out all extreme points of the time sequence U (t) of the content of free calcium oxide of the cement clinker;
s3.5: forming a lower envelope e for the minimum points by cubic spline interpolationmin(t) forming an upper envelope e for the maximamax(t)；
S3.6: calculating the mean of the extreme points:
Figure BDA0002302318500000071
s3.7: details of extraction:
dq(t)＝hp(t)-m(t)； (2)
s3.8: for detail signal dq(t) repeating steps S3.3 to S3.7 until a detail signal dq(t) has a mean value of 0;
s3.9: updating the characteristic sequence number p to p + 1;
s3.10: recording a feature sequence IMFp(t)；
S3.11: calculating a residual sequence:
hp(t)＝hp-1(t)-IMFp(t)； (3)
s3.12: to hp(t) repeating S3.2 to S3.11 until hp(t) is a monotonic function;
s3.13: let IMFp+1(t)＝hp(t)。
As a possible implementation manner of this embodiment, in step S4, the input/output sample pairs required for constructing the training feature extraction module are:
Ωi＝(U(t),IMFi(t)) (4)
wherein i is 1,2, …, p + 1; t is 1,2, …, n.
As a possible implementation manner of this embodiment, the step S5 specifically includes the following steps:
s5.1: setting an initial module number k to be 0;
s5.2: updating the number k of the modules to k + 1;
s5.3: setting module parameters: number of input neurons 1, number of output neurons 1, number of hidden layer neurons nkSpectral radius ρkDegree of sparsity spk；
S5.4: randomly generating network input weight moments in uniform distribution over an interval (-0.1,0.1)Matrix of
Figure BDA0002302318500000081
S5.5: randomly generating sparsity sp with uniform distribution over the interval (-1,1)kOf (2) matrix
Figure BDA0002302318500000082
S5.6: computing matrices
Figure BDA0002302318500000083
Maximum eigenvalue of
Figure BDA0002302318500000084
S5.7: calculating hidden layer weight Wk：
Figure BDA0002302318500000085
S5.8: computing hidden layer neuron states:
Figure BDA0002302318500000086
wherein t is 1,2, … …, n; s, sk(0)＝0；
S5.9: constructing a hidden layer state matrix:
Figure BDA0002302318500000087
s5.10: calculating a network output weight matrix:
Figure BDA0002302318500000088
wherein
Figure BDA0002302318500000089
For the hidden layer state matrix HkThe transposed matrix of (2);
s5.11: and (3) calculating network output:
Figure BDA00023023185000000810
s5.12: repeating steps S5.2 to S5.11 until k ═ p + 1;
s5.13: collecting submodule output:
as a possible implementation manner of this embodiment, the step S6 includes the following steps:
s6.1: constructing network input I and reference output
Figure BDA0002302318500000092
Figure BDA0002302318500000093
Figure BDA0002302318500000094
S6.2: setting module parameters: number of input neurons p +1, number of output neurons 1, number of hidden layer neurons nRSpectral radius ρRDegree of sparsity spR；
S6.3: randomly generating a network input weight matrix with uniform distribution over an interval (-0.1,0.1)
Figure BDA0002302318500000095
S6.4: randomly generating sparsity sp with uniform distribution over the interval (-1,1)ROf (2) matrix
Figure BDA0002302318500000096
S6.5: computing matrices
Figure BDA0002302318500000097
Maximum eigenvalue of
Figure BDA0002302318500000098
S6.6: calculating hidden layer weight WR
Figure BDA0002302318500000099
S6.7: computing hidden layer neuron states sR(t)：
Figure BDA00023023185000000910
Wherein s isR(0)＝0；
S6.8: constructing a hidden layer state matrix HR：
Figure BDA00023023185000000911
S6.9: computing network output weight matrix
Figure BDA00023023185000000912
Figure BDA0002302318500000101
Wherein
Figure BDA0002302318500000102
For the hidden layer state matrix HRThe transposed matrix of (2).
As a possible implementation manner of this embodiment, the step S7 specifically includes: as shown in fig. 2, the test sample is input into the trained feature extraction module, and the output of the prediction module is the free calcium oxide content of the cement clinker at the next moment.
The test result is shown in figure 3, the X axis is sample (number/hour), the Y axis is free calcium oxide content (%), ○ is prediction result, is actually measured free calcium oxide content, the error between the model prediction result and the actually measured free calcium oxide content is shown in figure 4, the X axis is sample (number/hour), the Y axis is the difference between the model prediction result and the actually measured free calcium oxide content, and the result proves the effectiveness of the method.
The foregoing is only a preferred embodiment of the present invention, and it will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements are also considered to be within the scope of the present invention.

Claims (7)

1. A method for predicting the content of free calcium oxide in cement clinker is characterized by comprising the following steps:
s1: collecting a cement clinker sample;
s2: constructing a time sequence of free calcium oxide content of cement clinker;
s3: performing time sequence decomposition on the time sequence of the content of free calcium oxide in the mud clinker based on an empirical mode decomposition method:
s4: constructing input and output sample pairs required by a training feature extraction module;
s5: training the feature extraction submodule, namely a modularized echo state neural network, and learning the multi-time scale features of the time sequence:
s6: training the prediction module-echo state neural network:
s7: and predicting the content of free calcium oxide in the clinker.
2. The method as claimed in claim 1, wherein in step S1, the sampling period is 1 hour, the cement clinker sample is obtained on-line n times by continuous sampling, the laboratory off-line detection is performed manually, and the cement clinker free calcium oxide content u is recorded1,u2,……,un。
3. The method as claimed in claim 2, wherein the step of performing time-series decomposition of the free calcium oxide content of the clinker in step S3 comprises the steps of:
s3.1: setting an initial characteristic sequence number p to be 0; let h0(t) U (t), U (t) is cement clinker free calcium oxide content time sequence, U (t) ut，t＝1,2,……,n；
S3.2: setting the initial cycle number q to be 0;
s3.3: updating the cycle number q to q + 1;
s3.4: finding out all extreme points of the time sequence U (t) of the content of free calcium oxide in the cement clinker;
s3.5: forming a lower envelope e for the minimum points by cubic spline interpolationmin(t) forming an upper envelope e for the maximamax(t)；
S3.6: calculating the mean of the extreme points:
s3.7: details of extraction:
dq(t)＝hp(t)-m(t)； (2)
s3.8: for detail signal dq(t) repeating steps S3.3 to S3.7 until a detail signal dq(t) has a mean value of 0;
s3.9: updating the characteristic sequence number p to p + 1;
s3.10: recording a feature sequence IMFp(t)；
S3.11: calculating a residual sequence:
hp(t)＝hp-1(t)-IMFp(t)； (3)
s3.12: to hp(t) repeating S3.2 to S3.11 until hp(t) is a monotonic function;
s3.13: let IMFp(t)＝hp(t)。
4. The method as claimed in claim 3, wherein in step S4, the input and output sample pairs required for constructing the training feature extraction module are:
Ωi＝(U(t),IMFi(t)) (4)
wherein i is 1,2, …, p + 1; t is 1,2, …, n.
5. The method as claimed in claim 4, wherein said step S5 includes the following steps:
s5.1: setting an initial module number k to be 0;
s5.2: updating the number k of the modules to k + 1;
s5.3: setting module parameters: number of input neurons 1, number of output neurons 1, number of hidden layer neurons nkSpectral radius ρkDegree of sparsity spk；
S5.4: randomly generating a network input weight matrix with uniform distribution over an interval (-0.1,0.1)
Figure FDA0002302318490000022
S5.5: randomly generating sparsity sp with uniform distribution over the interval (-1,1)kOf (2) matrix
Figure FDA0002302318490000023
S5.6: computing matrices
Figure FDA0002302318490000024
Maximum eigenvalue of
Figure FDA0002302318490000025
S5.7: calculating hidden layer weight Wk：
Figure FDA0002302318490000031
S5.8: computing hidden layer neuron states:
Figure FDA0002302318490000032
where t is 1,2, … …, n, sk(0)＝0；
S5.9: constructing a hidden layer state matrix:
Figure FDA0002302318490000033
s5.10: calculating a network output weight matrix:
Figure FDA0002302318490000034
wherein
Figure FDA0002302318490000035
For the hidden layer state matrix HkThe transposed matrix of (2);
s5.11: and (3) calculating network output:
Figure FDA0002302318490000036
s5.12: repeating steps S5.2 to S5.11 until k ═ p + 1;
s5.13: collecting submodule output:
Figure FDA0002302318490000037
6. the method as claimed in claim 5, wherein said step S6 includes the steps of:
s6.1: constructing network input I and reference output
Figure FDA0002302318490000038
Figure FDA0002302318490000039
Figure FDA00023023184900000310
S6.2: setting module parameters: number of input neurons p +1, number of output neurons 1, number of hidden layer neurons nRSpectral radius ρRDegree of sparsity spR；
S6.3: randomly generating a network input weight matrix with uniform distribution over an interval (-0.1,0.1)
Figure FDA0002302318490000041
S6.4: randomly generating sparsity sp with uniform distribution over the interval (-1,1)ROf (2) matrix
Figure FDA0002302318490000042
S6.5: computing matrices
Figure FDA0002302318490000043
Maximum eigenvalue of
Figure FDA0002302318490000044
S6.6: calculating hidden layer weight WR
Figure FDA0002302318490000045
S6.7: computing hidden layer neuron states sR(t)：
Figure FDA0002302318490000046
Wherein s isR(0)＝0；
S6.8: constructing a hidden layer state matrix HR：
Figure FDA0002302318490000047
S6.9: computing network output weight matrix
Figure FDA0002302318490000048
Figure FDA0002302318490000049
Wherein
Figure FDA00023023184900000410
For the hidden layer state matrix HRThe transposed matrix of (2).
7. The method for predicting the content of free calcium oxide in cement clinker according to any one of claims 1 to 6, wherein the step S7 is specifically as follows: inputting the test sample into the trained feature extraction module, and outputting by the prediction module to obtain the free calcium oxide content of the cement clinker at the next moment.
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