CN113759834A - Chaos multi-scale intelligent optimal propylene polymerization process measuring instrument - Google Patents
Chaos multi-scale intelligent optimal propylene polymerization process measuring instrument Download PDFInfo
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- QQONPFPTGQHPMA-UHFFFAOYSA-N propylene Natural products CC=C QQONPFPTGQHPMA-UHFFFAOYSA-N 0.000 title claims abstract description 58
- 125000004805 propylene group Chemical group [H]C([H])([H])C([H])([*:1])C([H])([H])[*:2] 0.000 title claims abstract description 58
- 238000006116 polymerization reaction Methods 0.000 title claims abstract description 43
- 230000000739 chaotic effect Effects 0.000 claims abstract description 59
- -1 polypropylene Polymers 0.000 claims abstract description 45
- 239000004743 Polypropylene Substances 0.000 claims abstract description 44
- 229920001155 polypropylene Polymers 0.000 claims abstract description 44
- 238000005259 measurement Methods 0.000 claims abstract description 35
- 230000006870 function Effects 0.000 claims description 53
- 235000012907 honey Nutrition 0.000 claims description 40
- 238000001514 detection method Methods 0.000 claims description 22
- 238000013507 mapping Methods 0.000 claims description 16
- 238000005070 sampling Methods 0.000 claims description 15
- 241000257303 Hymenoptera Species 0.000 claims description 14
- 239000000155 melt Substances 0.000 claims description 14
- 238000004458 analytical method Methods 0.000 claims description 12
- 239000003054 catalyst Substances 0.000 claims description 11
- 238000005457 optimization Methods 0.000 claims description 9
- 238000010187 selection method Methods 0.000 claims description 8
- 238000007689 inspection Methods 0.000 claims description 7
- 230000004044 response Effects 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 6
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims description 5
- 229910052739 hydrogen Inorganic materials 0.000 claims description 5
- 239000001257 hydrogen Substances 0.000 claims description 5
- 239000007788 liquid Substances 0.000 claims description 5
- 230000010355 oscillation Effects 0.000 claims description 4
- 238000004519 manufacturing process Methods 0.000 abstract description 16
- 238000000034 method Methods 0.000 description 18
- 230000008569 process Effects 0.000 description 6
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- 238000012360 testing method Methods 0.000 description 3
- FFBHFFJDDLITSX-UHFFFAOYSA-N benzyl N-[2-hydroxy-4-(3-oxomorpholin-4-yl)phenyl]carbamate Chemical compound OC1=C(NC(=O)OCC2=CC=CC=C2)C=CC(=C1)N1CCOCC1=O FFBHFFJDDLITSX-UHFFFAOYSA-N 0.000 description 2
- 238000007334 copolymerization reaction Methods 0.000 description 2
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- 238000001228 spectrum Methods 0.000 description 2
- 229920005992 thermoplastic resin Polymers 0.000 description 2
- 239000004698 Polyethylene Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000011127 biaxially oriented polypropylene Substances 0.000 description 1
- 229920006378 biaxially oriented polypropylene Polymers 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
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- 239000004745 nonwoven fabric Substances 0.000 description 1
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- 229920003023 plastic Polymers 0.000 description 1
- 229920000573 polyethylene Polymers 0.000 description 1
- 230000000379 polymerizing effect Effects 0.000 description 1
- 229920000915 polyvinyl chloride Polymers 0.000 description 1
- 239000004800 polyvinyl chloride Substances 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000003756 stirring Methods 0.000 description 1
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Abstract
The invention discloses a chaotic multi-scale intelligent optimal propylene polymerization process measuring instrument which is used for detecting the quality of a polypropylene production product. The chaos multi-scale intelligent optimal propylene polymerization process measuring instrument realizes the on-line measurement of important quality indexes, gives consideration to the multi-scale characteristic and the chaos characteristic of the polymerization process, and has strong application and popularization capability and high precision.
Description
Technical Field
The invention relates to the field of polymerization process measuring instruments, machine learning and intelligent optimization, in particular to a chaotic multi-scale intelligent optimal propylene polymerization process measuring instrument.
Background
Polypropylene is a thermoplastic resin prepared by polymerizing propylene, the most important downstream product of propylene is 50% of propylene in the world, 65% of propylene in China is used for preparing polypropylene, and the polypropylene is one of five common plastics and is closely related to daily life of people. Polypropylene is the fastest growing commodity thermoplastic resin in the world, second only to polyethylene and polyvinyl chloride. In order to make the polypropylene products in China have market competitiveness, the development of impact-resistant copolymerization products, random copolymerization products, BOPP and CPP film materials, fibers and non-woven fabrics with good balance of rigidity, toughness and fluidity and the application of polypropylene in the fields of automobiles and household appliances are important research subjects in the future.
The melt index is one of important quality indexes for determining the grade of a product of polypropylene, determines different purposes of the product, is an important link for controlling the product quality in the production of polypropylene in the measurement of the melt index, and has very important function and guiding significance for production and scientific research.
However, the online analysis and measurement of the melt index are difficult to achieve at present, on one hand, the lack of the online melt index analyzer is caused, and on the other hand, the existing online analyzer is difficult to use due to the fact that the online melt index analyzer is often blocked and inaccurate in measurement or even cannot be used normally. Therefore, currently, MI measurement in industrial production is mainly obtained by manual sampling and off-line assay analysis, and generally, MI can only be analyzed once every 2-4 hours, so that the time delay is large, which brings difficulty to quality control of propylene polymerization production and becomes a bottleneck problem to be solved urgently in production.
Disclosure of Invention
In order to overcome the defects that the measurement precision of the existing propylene polymerization production process is not high and is easily influenced by human factors, the invention aims to provide the chaotic multi-scale intelligent optimal propylene polymerization process measuring instrument which has the advantages of on-line measurement, consideration of the multi-scale characteristic and the chaotic characteristic of the polymerization process, strong system application and popularization capability and high precision.
The purpose of the invention is realized by the following technical scheme:
the invention provides a polypropylene quality detection system, which is used for online detection of the quality of a polypropylene product and is characterized by comprising a chaotic reconstruction module, a Gabor multi-scale analysis module and an extreme random tree measurement model module.
The polypropylene quality detection system provided by the invention is characterized in that the chaos reconstruction module is used for reconstructing the input variable of the model of the DCS database into a dynamic chaos system signal according to the chaos characteristic of the input variable.
The invention provides a polypropylene quality detection system, wherein a Gabor multi-scale analysis module is used for analyzing the multi-scale characteristics of a dynamic chaotic system signal by taking frequency as a reference, and carrying out multi-scale reconstruction on the dynamic chaotic system signal through a Gabor kernel function to obtain local texture characteristic information of each scale under different frequencies.
The polypropylene quality detection system provided by the invention is characterized in that the extreme random tree measurement model module is used for establishing a mapping relation between chaotic multi-scale characteristic information and a melt index, establishing a propylene polymerization process measurement system and further predicting the melt index of polypropylene.
In the polypropylene quality detection system provided by the invention, preferably, the input variables are a first propylene feeding flow rate, a second propylene feeding flow rate, a third propylene feeding flow rate, a main catalyst flow rate, an auxiliary catalyst flow rate, a temperature in the stirred tank, a pressure in the tank, a liquid level in the tank and a hydrogen volume concentration in the tank.
In the polypropylene quality detection system provided by the present invention, preferably, the chaotic system of the input variables is expressed as z (n) ═ s (n), s (n + T)1),s(n+T2),...,s(n+Td-1)]Wherein s (n) is the n-th sampling point signal of propylene polymerization process, T1,T2,...,Td-1Respectively the sampling time after the nth sampling point.
In the polypropylene quality detection system provided by the present invention, preferably, the expression of the Gabor kernel function is:
where z denotes coordinate information of a reconstruction variable, u denotes a direction of the Gabor filter, v denotes a scale of the Gabor filter, i is a complex symbol, exp (ik)u,vz) an oscillation function in the form of a complex exponential, σ2Is kernel function width, ku,vRepresenting the response of the Gabor filter in each direction of the respective scale.
In the polypropylene quality detection system provided by the present invention, preferably, the Gabor feature is obtained by convolution of a kernel function, and the expression is as follows:
Gu,v(z)＝f(z)*ψu,v(z)
wherein G isu,v(z) represents a convolution function of the corresponding dimension v and direction u around the coordinate z, ψ being a Gabor kernel function; analyzing the input variable by using a Gabor function to obtain a complex input characteristic signal:
Gu,v(z)＝Re(Gu,v(z))+jIm(Gu,v(z))
the amplitude and phase of the Gabor characteristic signal are respectively:
the polypropylene quality detection system provided by the invention has the advantages that the mapping relation is preferably established by taking input signals of a group of extreme random trees as local texture characteristic information under a single scale, and the chaotic multi-scale mapping modeling from input to output is completed by the multiple groups of extreme random trees based on an integrated learning framework.
The polypropylene quality detection system provided by the invention preferably further comprises a chaotic artificial bee colony optimization module, and the chaotic artificial bee colony optimization module is used for optimizing the bifurcation threshold parameter of the extreme random tree measurement model module by adopting a chaotic artificial bee colony algorithm.
The invention provides a polypropylene quality detection system, wherein, preferably, the optimization comprises the following steps:
(1) initializing parameters of the artificial bee colony algorithm, setting the honey source number p and the maximum iterative itermaxMinimum and maximum values L of the initial search spacedAnd Ud(ii) a The position of the honey source represents a feasible solution to the problem, p in a correlation vector measurement systemiThe dimension of (2) is 2, and the initial iteration time iter is set to be 0;
(2) is a honey source piDistributing a leading bee, and generating a new honey source V based on chaotic mappingi；
Wherein,
is a chaotic factor and is generated by a chaotic mapping dynamic system.
(3) Calculating ViDetermining the preserved honey source according to a greedy selection method;
(4) calculating the probability that the honey source found by the leading bee is followed;
(5) searching the follower bees in the same way as the leading bees, and determining a reserved honey source according to a greedy selection method;
(6) judgment of honey source ViWhether the abandoned condition is met or not, if so, the corresponding leading bee role is changed into the scout bee, otherwise, the step (8) is directly carried out;
(7) randomly generating a new honey source by the scout bees;
(8) if yes, outputting an optimal parameter, selecting a solution with an optimal fitness value as an optimal solution of the algorithm, and otherwise, turning to the step (2);
the number of honey sources is 100, the minimum value and the maximum value of the initial search space are 0 and 100, and the maximum iteration number is 100.
The polypropylene quality detection system provided by the invention preferably further comprises a system updating module, which is used for inputting offline experimental data into a training set so as to update the extreme random tree measurement model online.
According to some embodiments of the invention, the invention may also state the following:
the invention provides a chaotic multi-scale intelligent optimal propylene polymerization process measuring instrument which is used for detecting the quality of polypropylene production products. Wherein:
(1) a chaotic reconstruction module: the method is used for reconstructing model input variables input from a DCS database according to chaotic characteristics of the model input variables, wherein input signals of a propylene polymerization process measuring instrument are 9 operation variables of an industrial propylene polymerization process, namely a first propylene feeding flow rate, a second propylene feeding flow rate, a third propylene feeding flow rate, a main catalyst flow rate, an auxiliary catalyst flow rate, a temperature in a stirring kettle, a pressure in the kettle, a liquid level in the kettle and a volume concentration of hydrogen in the kettle. The chaotic system of the input signal is expressed as z (n) ═ s (n), s (n + T)1),s(n+T2),...,s(n+Td-1)]Wherein s (n) is a propylene polymerization processOf the nth sample point signal, T1,T2,...,Td-1Respectively the sampling time after the nth sampling point. In the chaotic system, the delay time satisfies TmCondition m τ where τ is the delay time, TmRepresents the mth sampling instant, therefore, the propylene process input signal can be reconstructed into a dynamic chaotic system signal z (n) (s (n), s (n + τ), s (n +2 τ), and]where z (n) is the chaos reconstructed signal at the nth moment, τ is the delay time, and d is the embedding dimension of the input signal. The delay time and the embedding dimension of the chaotic reconstruction are respectively obtained by a mutual information method and a pseudo-nearest neighbor method.
(2) Gabor multiscale analysis module: the method is used for analyzing the multi-scale characteristics of the chaotically reconstructed input variable by taking frequency as a reference, and performing multi-scale reconstruction on the variable through a Gabor kernel function to extract local texture feature information of the input variable in each scale and each direction under different frequencies, wherein the Gabor kernel function is defined as follows:
where z denotes coordinate information of a reconstruction variable, u denotes a direction of the Gabor filter, v denotes a scale of the Gabor filter, i is a complex symbol, exp (ik)u,vz) an oscillation function in the form of a complex exponential, σ2Is kernel function width, ku,vRepresenting the response of the Gabor filter in each direction of the respective scale. The partial function of the Gabor kernel functions is as follows: k is a radical ofu,v 2z2/2σ2Is a Gaussian envelope function, ku,v 2/σ2To compensate for energy spectrum attenuation, the envelope function can limit the range of the oscillating function, usually by windowing, preserve the locality of the wave, and extract the characteristic information near the coordinates. exp (ik)u,vz) is an oscillating function whose real part is even symmetric with respect to the cosine function and whose imaginary part is odd symmetric with respect to the sine function. exp (-sigma)2/2) represents the filtered DC component, [ exp (ik)u,vz)-exp(-σ2/2)]The purpose of the operation is to eliminate the DC component pairInfluence of the filtering effect, kernel function width σ2To determine the bandwidth size of the Gabor filter. k is a radical ofu,vRepresenting the response of the Gabor filter in the respective direction of the respective scale, each ku,vAll represent a Gabor filter, so that when a plurality of different k's are selectedu,vA plurality of different filter banks can be obtained.
The Gabor features are obtained by convolution of kernel functions, and the expression is as follows:
Gu,v(z)＝f(z)*ψu,v(z)
wherein G isu,v(z) represents the convolution function of the corresponding dimension v and direction u around the coordinate z, and ψ is the Gabor kernel function. Analyzing the input variable by using a Gabor function to obtain a complex input characteristic signal:
Gu,v(z)＝Re(Gu,v(z))+jIm(Gu,v(z))
the amplitude and phase of the Gabor characteristic signal are respectively:
(3) an extreme random tree measurement model module: the method is used for establishing a propylene polymerization process measurement system, and adopts an extreme random tree to complete input-to-output mapping modeling based on an integrated learning framework. The extreme random tree training splitting rule ensures the sparsity of the model by introducing the Gaussian prior distribution of the zero mean value of the weight vector given by the hyper-parameters, and the hyper-parameters can be estimated by adopting a method of maximizing the edge likelihood function. The purpose of the whole model is to design a system according to a sample set and prior knowledge, so that the system can predict the output of the polypropylene melt index for new data.
(4) The chaotic artificial bee colony optimization module: the chaotic artificial bee colony algorithm is adopted to optimize the bifurcation threshold parameter of the extreme random measuring instrument, and the following processes are adopted to complete the method:
(4.1) initializing parameters of the artificial bee colony algorithm, setting the honey source number P and the maximum iteration itermaxMinimum and maximum values L of the initial search spacedAnd Ud(ii) a The position of the honey source represents a feasible solution to the problem, p in a correlation vector measurement systemiThe dimension of (2) is 2, and the initial iteration time iter is set to be 0;
(4.2) is a honey source piDistributing a leading bee, and generating a new honey source V based on chaotic mappingi；
Wherein,
is a chaotic factor and is generated by a chaotic mapping dynamic system.
(4.3) calculation of ViDetermining the preserved honey source according to a greedy selection method;
(4.4) calculating the probability that the honey source found by the leading bees is followed;
(4.5) searching the follower bees in the same way as the leading bees, and determining a reserved honey source according to a greedy selection method;
(4.6) judgment of Honey Source ViWhether the condition of being abandoned is met, if so, the corresponding leading bee role is changed into the scout bee, otherwise, the operation is directly carried out to (4.8);
(4.7) randomly generating a new honey source by the scout bees;
(4.8) judging whether the iter is equal to iter +1, if so, outputting an optimal parameter, selecting a solution with an optimal fitness value as an optimal solution of the algorithm, and otherwise, turning to the step (4.2).
The number of honey sources is 100, the minimum value and the maximum value of the initial search space are 0 and 100, and the maximum iteration number is 100.
(5) The chaotic multi-scale intelligent optimal propylene polymerization process measuring instrument further comprises a system updating module, wherein the system updating module is used for updating the system on line, inputting offline experimental data into a training set regularly and updating the extreme random tree measuring model.
The technical conception of the invention is as follows: the method is characterized in that the melt index of an important quality index in the propylene polymerization process is forecasted on line, in order to overcome the defects of low measurement precision and nonlinear feature extraction of the existing polypropylene melt index measuring instrument, a chaotic phase space reconstruction and multi-scale analysis method is introduced for feature extraction and sequence reconstruction, and an intelligent method is introduced to adjust system parameters without manual experience or multiple tests, so that the chaos multi-scale intelligent optimal measuring instrument in the propylene polymerization production process is obtained. In order to overcome the problem of low measurement precision in the existing propylene polymerization production process, the invention aims to provide a chaotic multi-scale intelligent optimal propylene polymerization process measuring instrument.
The invention has the following beneficial effects:
1. the chaos reconstruction analysis and the Gabor multi-scale analysis in the propylene polymerization production process effectively represent the dynamic characteristics and nonlinearity under different scales in the actual industrial polymerization process, and the high-precision measurement of the product quality index melt index is realized;
2. the chaotic artificial bee colony optimization related vector system realizes the on-line measurement and matching of the detection system and improves the application and popularization capability of the system.
Drawings
FIG. 1 is a diagram of the overall architecture of a polypropylene quality inspection system according to an embodiment of the present invention;
fig. 2 is a functional block diagram of a polypropylene quality detection system according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
Examples
Referring to fig. 1, an overall architecture diagram of a polypropylene quality detection system of an embodiment relates to a propylene polymerization production process 1, an on-site intelligent instrument 2 for measuring easily-measured variables, a control station 3 for measuring operation variables, a DCS database 4 for storing data, and a melt index soft measurement value display 6, wherein the on-site intelligent instrument 2 and the control station 3 are connected with the propylene polymerization production process 1, the on-site intelligent instrument 2 and the control station 3 are connected with the DCS database 4, and further relates to a chaotic multi-scale intelligent optimal propylene polymerization process measurement instrument 5, wherein the DCS database 4 is connected with an input end of the chaotic multi-scale intelligent optimal propylene polymerization process measurement instrument 5, and an output end of the chaotic multi-scale intelligent optimal propylene polymerization process measurement instrument 5 is connected with the melt index soft measurement value display 6. Both the above-mentioned manipulated variables and easily measurable variables can be used as input variables.
According to the reaction mechanism and the process analysis, in consideration of various factors influencing the melt index in the production process of polypropylene, nine commonly used operation variables and easily-measured variables in the actual production process are taken as modeling variables, which are respectively as follows: three propylene feed flow rates, main catalyst flow rate, auxiliary catalyst flow rate, temperature, pressure, liquid level in the kettle, and hydrogen volume concentration in the kettle. Table 1 lists 9 modeling variables required as a chaotic multi-scale intelligent optimal propylene polymerization process measuring instrument, namely, the temperature in the kettle (T), the pressure in the kettle (p), the liquid level in the kettle (L), the volume concentration of hydrogen in the kettle (Xv), the propylene feed flow rate of 3 strands (the first propylene feed flow rate f1, the second propylene feed flow rate f2, the third propylene feed flow rate f3), and the catalyst feed flow rate of 2 strands (the main catalyst flow rate f4 and the auxiliary catalyst flow rate f 5). The polymerization reaction in the reaction kettle is carried out after reaction materials are repeatedly mixed, so that the process variable of the model input variable related to the materials adopts the average value of a plurality of previous moments. The data in this example were averaged over the previous hour. The melt index off-line test value is used as the output variable of the chaos multi-scale intelligent optimal propylene polymerization process measuring instrument 5. The test sample is obtained by manual sampling and offline assay analysis, and is analyzed and collected every 4 hours.
TABLE 1 chaos multiscale intelligent optimum propylene polymerization process measuring instrument required modeling variables
Referring to fig. 2, a functional block diagram of a polypropylene quality detection system of an embodiment includes:
(1) the chaos reconstructing module 7 is used for reconstructing the model input variables input from the DCS database according to the chaos characteristics thereof, and the chaos system of the input signals in the propylene polymerization production process is expressed as z (n) ═ s (n), and s (n + T)1),s(n+T2),...,s(n+Td-1)]Wherein s (n) is the n-th sampling point signal of propylene polymerization process, T1,T2,...,Td-1Respectively the sampling time after the nth sampling point. In the chaotic system, the delay time satisfies TmCondition m τ where τ is the delay time, TmRepresents the mth sampling instant, therefore, the propylene process input signal can be reconstructed into a dynamic chaotic system signal z (n) (s (n), s (n + τ), s (n +2 τ), and]where z (n) is the chaos reconstructed signal at the nth moment, τ is the delay time, and d is the embedding dimension of the input signal. The delay time and the embedding dimension of the chaotic reconstruction are respectively obtained by a mutual information method and a pseudo-nearest neighbor method.
(2) The Gabor multi-scale analysis module 8 is configured to analyze the input variable with frequency as a reference, perform multi-scale reconstruction on the variable through a Gabor kernel function, and represent local texture information of the input variable in each scale and each direction at different frequencies, where the Gabor kernel function is defined as follows:
where z denotes reconstruction variable coordinate information, u denotes a direction of the Gabor filter, v denotes a scale of the Gabor filter, i is a complex symbol, exp (ik)u,vz) an oscillation function in the form of a complex exponential, σ2Is kernel function width, ku,vRepresenting the response of the Gabor filter in each direction of the respective scale. The partial function of the Gabor kernel functions is as follows: k is a radical ofu,v 2z2/2σ2Is a Gaussian envelope function, ku,v 2/σ2To compensate for energy spectrum attenuation, the envelope function can limit the range of the oscillating function, usually by windowing, preserve the locality of the wave, and extract the characteristic information near the coordinates. exp (ik)u,vz) is an oscillating function whose real part is even symmetric with respect to the cosine function and whose imaginary part is odd symmetric with respect to the sine function. exp (-sigma)2/2) represents the filtered DC component, [ exp (ik)u,vz)-exp(-σ2/2)]The purpose of the operation is to eliminate the influence of the DC component on the filtering effect, the kernel function width sigma2To determine the bandwidth size of the Gabor filter. k is a radical ofu,vRepresenting the response of the Gabor filter in the respective direction of the respective scale, each ku,vAll represent a Gabor filter, so that when a plurality of different k's are selectedu,vA plurality of different filter banks can be obtained.
The Gabor features are obtained by convolution of kernel functions, and the expression is as follows:
Gu,v(z)＝f(z)*ψu,v(z)
wherein G isu,v(z) represents the convolution function of the corresponding dimension v and direction u around the coordinate z, and ψ is the Gabor kernel function. Analyzing the input variable by using a Gabor function to obtain a complex input characteristic signal:
Gu,v(z)＝Re(Gu,v(z))+jIm(Gu,v(z))
the amplitude and phase of the Gabor characteristic signal are respectively:
(3) and the extreme random tree measurement model module 9 is used for completing input-to-output mapping modeling based on an integrated learning framework by adopting an extreme random tree. The extreme random tree training splitting rule ensures the sparsity of the model by introducing the Gaussian prior distribution of the zero mean value of the weight vector given by the hyper-parameters, and the hyper-parameters can be estimated by adopting a method of maximizing the edge likelihood function. The purpose of the whole model is to design a system according to a sample set and prior knowledge, so that the system can predict the output of the polypropylene melt index for new data.
(4) The chaotic artificial bee colony optimization module 10 optimizes the bifurcation threshold parameter of the measurement system by adopting a chaotic artificial bee colony algorithm, and is completed by adopting the following processes:
(4.1) initializing parameters of the artificial bee colony algorithm, setting the honey source number P and the maximum iteration itermaxMinimum and maximum values L of the initial search spacedAnd Ud(ii) a The position of the honey source represents a feasible solution to the problem, p in a correlation vector measurement systemiThe dimension of (2) is 2, and the initial iteration time iter is set to be 0;
(4.2) is a honey source piDistributing a leading bee, and generating a new honey source V based on chaotic mappingi；
Wherein,
is a chaotic factor and is generated by a chaotic mapping dynamic system.
(4.3) calculation of ViDetermining the preserved honey source according to a greedy selection method;
(4.4) calculating the probability that the honey source found by the leading bees is followed;
(4.5) searching the follower bees in the same way as the leading bees, and determining a reserved honey source according to a greedy selection method;
(4.6) judgment of Honey Source ViWhether the condition of being abandoned is met, if so, the corresponding leading bee role is changed into the scout bee, otherwise, the operation is directly carried out to (4.8);
(4.7) randomly generating a new honey source by the scout bees;
(4.8) judging whether the iter is equal to iter +1, if so, outputting an optimal parameter, selecting a solution with an optimal fitness value as an optimal solution of the algorithm, and otherwise, turning to the step (4.2).
The number of honey sources is 100, the minimum value and the maximum value of the initial search space are 0 and 100, and the maximum iteration number is 100.
(5) The chaotic multi-scale intelligent optimal propylene polymerization process measuring instrument further comprises a system updating module, wherein the system updating module is used for updating a system on line, inputting offline experimental data into a training set regularly and updating an extreme random tree measuring model.
Take specific data as an example: in this embodiment, 9 modeling variables required for acquiring in the DCS system are extracted to obtain a variable input matrix:
inputting data into a propylene polymerization process measuring instrument 5, and obtaining predicted values of the melt index [2.4439,2.4011,2.4525,2.4796 and 2.4872 by a chaotic multi-scale detection module]. Melt index off-line assay values [2.42,2.37,2.46,2.48,2.51]The Error is used as a check value of the propylene polymerization process measuring instrument 5 for calculating a prediction Error to evaluate the prediction precision of the polypropylene production quality measuring system 5, wherein the prediction Error is Root Mean Square Error (RMSE), and the calculation formula is
Wherein,
for the measurement of the output value, y, of the instrument 5 in the propylene polymerization processiThe predicted deviation of the propylene polymerization process measuring instrument 5 is [0.0239,0.0311, -0.0075, -0.0004, -0.0228 ] for the melt index off-line assay value]The root mean square error is 0.0239, and the melt index prediction value and the prediction precision of the measurement system are obtained.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit of the invention and the scope of the appended claims.
Claims (9)
1. A polypropylene quality detection system for online detection of polypropylene product quality, comprising:
the chaotic reconstruction module is used for reconstructing input variables of the model of the DCS database into dynamic chaotic system signals according to chaotic characteristics of the input variables;
the Gabor multi-scale analysis module is used for analyzing the multi-scale characteristics of the dynamic chaotic system signal by taking the frequency as a reference, and carrying out multi-scale reconstruction on the dynamic chaotic system signal through a Gabor kernel function to obtain local texture feature information of each scale under different frequencies;
and the extreme random tree measurement model module is used for establishing a mapping relation between the chaotic multi-scale characteristic information and the melt index, establishing a propylene polymerization process measurement system and further predicting the polypropylene melt index.
2. The polypropylene quality inspection system of claim 1, wherein: the input variables are a first propylene feeding flow rate, a second propylene feeding flow rate, a third propylene feeding flow rate, a main catalyst flow rate, an auxiliary catalyst flow rate, the temperature in the stirred tank, the pressure in the tank, the liquid level in the tank and the volume concentration of hydrogen in the tank.
3. The polypropylene quality inspection system of claim 1, wherein: the chaotic system of the input variable is expressed as z (n) ═ s (n), s (n + T)1),s(n+T2),...,s(n+Td-1)]Wherein s (n) is the n-th sampling point signal of propylene polymerization process, T1,T2,...,Td-1Respectively the sampling time after the nth sampling point.
4. The polypropylene quality inspection system of claim 1, wherein: the expression of the Gabor kernel function is:
where z denotes coordinate information of a reconstruction variable, u denotes a direction of the Gabor filter, v denotes a scale of the Gabor filter, i is a complex symbol, exp (ik)u,vz) an oscillation function in the form of a complex exponential, σ2Is kernel function width, ku,vRepresenting the response of the Gabor filter in each direction of the respective scale.
5. The polypropylene quality inspection system of claim 4, wherein: the Gabor features are obtained by convolution of kernel functions, and the expression is as follows:
Gu,v(z)＝f(z)*ψu,v(z)
wherein G isu,v(z) represents a convolution function of the corresponding dimension v and direction u around the coordinate z, ψ being a Gabor kernel function; analyzing the input variable by using a Gabor function to obtain a complex input characteristic signal:
Gu,v(z)＝Re(Gu,v(z))+jIm(Gu,v(z))
the amplitude and phase of the Gabor characteristic signal are respectively:
6. the polypropylene quality detection system according to claim 1, wherein the mapping relationship is established by taking input signals of a set of extreme random trees as local texture feature information under a single scale, and the multiple sets of extreme random trees complete chaotic multi-scale mapping modeling from input to output based on an ensemble learning framework.
7. The polypropylene quality detection system of claim 1, further comprising a chaotic artificial bee colony optimization module that optimizes a bifurcation threshold parameter of the extreme random tree measurement model module using a chaotic artificial bee colony algorithm.
8. The polypropylene quality inspection system of claim 7, wherein: the optimization comprises the following steps:
(1) initializing parameters of the artificial bee colony algorithm, setting the honey source number p and the maximum iterative itermaxMinimum and maximum values L of the initial search spacedAnd Ud(ii) a The position of the honey source represents a feasible solution to the problem, p in a correlation vector measurement systemiThe dimension of (2) is 2, and the initial iteration time iter is set to be 0;
(2) is a honey source piDistributing a leading bee, and generating a new honey source V based on chaotic mappingi；
Wherein,
is a chaotic factor and is generated by a chaotic mapping dynamic system.
(3) Calculating ViDetermining the preserved honey source according to a greedy selection method;
(4) calculating the probability that the honey source found by the leading bee is followed;
(5) searching the follower bees in the same way as the leading bees, and determining a reserved honey source according to a greedy selection method;
(6) judgment of honey source ViWhether the abandoned condition is met or not, if so, the corresponding leading bee role is changed into the scout bee, otherwise, the step (8) is directly carried out;
(7) randomly generating a new honey source by the scout bees;
(8) if yes, outputting an optimal parameter, selecting a solution with an optimal fitness value as an optimal solution of the algorithm, and otherwise, turning to the step (2);
the number of honey sources is 100, the minimum value and the maximum value of the initial search space are 0 and 100, and the maximum iteration number is 100.
9. The polypropylene quality inspection system of claim 1, further comprising a system update module for inputting offline experimental data into a training set to update the extreme random tree measurement model online.
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