CN103838142B - Based on propylene polymerization production process optimal soft measuring system and the method for mixing optimizing - Google Patents
Based on propylene polymerization production process optimal soft measuring system and the method for mixing optimizing Download PDFInfo
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Abstract
The invention discloses a kind of propylene polymerization production process optimal soft measuring system based on mixing optimizing, comprise propylene polymerization production process, field intelligent instrument, control station, store data DCS database, based on the mixing optimum hard measurement system of optimizing and melt index flexible measured value display instrument.Field intelligent instrument and control station are connected with propylene polymerization production process, are connected with DCS database; Optimum hard measurement system is connected with DCS database and hard measurement value display instrument.The described optimum hard measurement system based on mixing optimizing comprises model modification module, data preprocessing module, PCA principal component analysis (PCA) module, neural network model module and mixing optimizing and optimizes module.And provide the flexible measurement method of a kind of hard measurement system realization.The present invention realizes on-line measurement, on-line parameter optimization, hard measurement speed is fast, model upgrades automatically, antijamming capability is strong, precision is high.
Description
Technical field
The present invention relates to a kind of optimum hard measurement system and method, specifically a kind of propylene polymerization production process optimal soft measuring system based on mixing optimizing and method.
Background technology
Polypropylene is a kind of thermoplastic resin obtained by propylene polymerization, the most important downstream product of propylene, 50% of World Propylene, and 65% of China's propylene is all used to polypropylene processed, is one of five large general-purpose plastics, closely related with our daily life.Polypropylene is fastest-rising interchangeable heat plastic resin in the world, and total amount is only only second to tygon and Polyvinylchloride.For making China's polypropylene product, there is the market competitiveness, crushing-resistant copolymerization product, random copolymerization product, BOPP and CPP film material, fiber, nonwoven cloth that exploitation rigidity, toughness, mobility balance, and exploitation polypropylene is in the application of automobile and field of household appliances, is all research topic important from now on.
Melting index is one of important quality index of polypropylene product determination product grade, which determine the different purposes of product, be an important step of production quality control during polypropylene is produced to the measurement of melting index, to production and scientific research, have very important effect and directive significance.
But; the on-line analysis of melting index is measured and is difficult at present accomplish; being the shortage of online melting index analyser on the one hand, is that the inaccurate difficulty that even cannot normally use in caused use measured by existing in-line analyzer owing to often can block on the other hand.Therefore, the measurement of MI in current commercial production, is mainly obtained by hand sampling, off-line assay, and general every 2-4 hour can only analyze once, time lag is large, and the quality control of producing to propylene polymerization brings difficulty, becomes in production the bottleneck problem being badly in need of solving.The online soft sensor system and method research of polypropylene melt index, thus become a forward position and the focus of academia and industry member.
Summary of the invention
In order to the deficiency of the impact that the measuring accuracy overcoming current existing propylene polymerization production process is not high, be subject to human factor, the object of the present invention is to provide a kind of on-line measurement, on-line parameter optimization, hard measurement speed is fast, model upgrades automatically, antijamming capability is strong, precision is high based on mixing optimizing the optimum hard measurement system and method for propylene polymerization production process melting index.
The technical solution adopted for the present invention to solve the technical problems is:
A kind of based on mixing optimizing propylene polymerization production process optimal soft measuring system, comprise propylene polymerization production process, for measuring the field intelligent instrument easily surveying variable, for measuring the control station of performance variable, the DCS database of store data, based on optimum hard measurement system and the melting index hard measurement display instrument of mixing optimizing, described field intelligent instrument, control station is connected with propylene polymerization production process, described field intelligent instrument, control station is connected with DCS database, described DCS database is connected with the input end based on the optimum hard measurement system mixing optimizing, the output terminal of the described optimum hard measurement system based on mixing optimizing is connected with melting index hard measurement display instrument, it is characterized in that: the described optimum hard measurement system based on mixing optimizing comprises: (1), data preprocessing module, mode input variable for inputting from DCS database carries out pre-service, to input variable centralization, namely the mean value of variable is deducted, be normalized again, namely divided by the constant interval of variate-value,
(2), PCA principal component analysis (PCA) module, for by input variable pre-whitening processing and variable decorrelation, realize by applying a linear transformation to input variable, namely major component is obtained by C=MU, wherein M is input variable, and C is principal component scores matrix, and U is loading matrix.If be reconstructed raw data, can by M=CU
tcalculate, the wherein transposition of subscript T representing matrix.When the major component number chosen is less than the variable number of input variable, M=CU
t+ E, wherein E is residual matrix;
(3), neural network model module, for adopting RBF neural, having been minimized a kind of nonlinear being input to output by error function, in mapping, keep topological invariance;
(4), mixing optimizing optimization module, for adopting the optimization module based on HPSO-SA algorithm to be optimized neural network, comprising:
(4.1) algorithm initialization, constructs initial particle colony X=(x according to RBF neural structure to be optimized
1, x
2..., x
n), initial movable speed V=(v
1, v
2..., v
n), initial each particle successive dynasties optimal value OP=(p
1, x
2..., p
n) and global optimum p
g;
(4.2) perform HPSO algorithm by following formula, allow population restrain:
In formula, x is the position vector of particle, and i is the sequence number of particle, and k is algorithm iteration algebraically, and v is particle rapidity, and p represents initial each particle successive dynasties optimal value OP=(p
1, p
2..., p
n) and global optimum p
goptimal value set.V
k ifor the speed of i-th particle in kth time iteration algebraically; x
k ithe position vector of i-th particle in kth time iteration algebraically; p
k ifor the successive dynasties optimal location of i-th particle in kth time iteration algebraically, p
ibe the successive dynasties optimum solution of i-th particle, w is speed weight coefficient, c
1, c
2be respectively the attraction coefficient of particle successive dynasties optimum solution and group optimal solution, r
1, r
2be respectively random number.
(4.3) when population converges to suitable degree, the set (best of the successive dynasties optimum solution composition to each particle is started
1, best
2..., best
n) performing SA algorithm, the optimum solution obtained returns as the end product of algorithm.
As preferred a kind of scheme, described based on mixing optimizing optimal soft measurement model also comprise: model modification module, for the online updating of model, will regularly be input in training set by off-line analysis data, renewal neural network model.
As another scheme preferred: in PCA principal component analysis (PCA) module, PCA method realizes the pre-whitening processing of input variable, can simplify the input variable of neural network model, and then improve the performance of model.
Based on mixing optimizing the flexible measurement method that realizes of the optimum hard measurement system of polypropylene production process, described flexible measurement method specific implementation step is as follows:
(1) to propylene polymerization production process object, according to industrial analysis and Operations Analyst, select performance variable and easily survey the input of variable as model, performance variable and easily survey variable are obtained by DCS database;
(2) pre-service is carried out to sample data, to input variable centralization, namely deduct the mean value of variable; Be normalized again, namely divided by the constant interval of variate-value;
(3) PCA principal component analysis (PCA) module, for by input variable pre-whitening processing and variable decorrelation, realizes by applying a linear transformation to input variable, namely major component is obtained by C=xU, wherein x is input variable, and P is principal component scores matrix, and U is loading matrix.If be reconstructed raw data, can by x=CU
tcalculate, the wherein transposition of subscript T representing matrix.When the major component number chosen is less than the variable number of input variable, x=CU
t+ E, wherein E is residual matrix;
(4) set up initial neural network model based on mode input, output data, adopt RBF neural, be input to a kind of nonlinear of output by error minimize, in mapping, keep topological invariance;
(5) adopt the optimization module based on HPSO-SA algorithm to be optimized neural network, comprising:
(5.1) algorithm initialization, constructs initial particle colony X=(x according to RBF neural structure to be optimized
1, x
2..., x
n), initial movable speed V=(v
1, v
2..., v
n), initial each particle successive dynasties optimal value OP=(p
1, p
2..., p
n) and global optimum p
g;
(5.2) perform HPSO algorithm by following formula, allow population restrain:
In formula, x is the position vector of particle, and i is the sequence number of particle, and k is algorithm iteration algebraically, and v is particle rapidity, and p represents initial each particle successive dynasties optimal value OP=(p
1, p
2..., p
n) and global optimum p
goptimal value set.V
k ifor the speed of i-th particle in kth time iteration algebraically; x
k ithe position vector of i-th particle in kth time iteration algebraically; p
k ifor the successive dynasties optimal location of i-th particle in kth time iteration algebraically, p
ibe the successive dynasties optimum solution of i-th particle, w is speed weight coefficient, c
1, c
2be respectively the attraction coefficient of particle successive dynasties optimum solution and group optimal solution, r
1, r
2be respectively random number.
(5.3) when population converges to suitable degree, the set (best of the successive dynasties optimum solution composition to each particle is started
1, best
2..., best
n) performing SA algorithm, the optimum solution obtained returns as the end product of algorithm.
As preferred a kind of scheme, described flexible measurement method also comprises: be regularly input in training set by off-line analysis data, upgrades neural network model.
Further, in described step (3), adopt PCA principal component analytical method to realize the pre-whitening processing of input variable, the input variable of neural network model can be simplified, and then improve the performance of model.
Technical conceive of the present invention is: carry out online optimum hard measurement to the important quality index melting index of propylene polymerization production process, overcome that existing polypropylene melt index measurement instrument measuring accuracy is not high, the deficiency of the impact that is subject to human factor, introduce mixing optimizing optimization module and Automatic Optimal is carried out to neural network parameter and structure, do not need artificial experience or repeatedly test to adjust neural network, thus obtain the online optimum hard measurement system with optimum melt index flexible measurement function.
Beneficial effect of the present invention is mainly manifested in: 1, on-line measurement; 2, on-line parameter Automatic Optimal; 3, hard measurement speed is fast; 4, model upgrades automatically; 5, antijamming capability is strong; 6, precision is high.
Accompanying drawing explanation
Fig. 1 be based on mixing optimizing propylene polymerization production process optimal soft measuring system and the basic structure schematic diagram of method;
Fig. 2 is the optimum hard measurement system architecture schematic diagram based on mixing optimizing;
Fig. 3 is propylene polymerization production process Hypol explained hereafter process flow diagram.
Embodiment
Below in conjunction with accompanying drawing, the present invention is described further.The embodiment of the present invention is used for explaining and the present invention is described, instead of limits the invention, and in the protection domain of spirit of the present invention and claim, any amendment make the present invention and change, all fall into protection scope of the present invention.
Embodiment 1
1. with reference to Fig. 1, Fig. 2 and Fig. 3, a kind of based on mixing optimizing propylene polymerization production process optimal soft measuring system, comprise propylene polymerization production process 1, for measuring the field intelligent instrument 2 easily surveying variable, for measuring the control station 3 of performance variable, the DCS database 4 of store data, based on optimum hard measurement system 5 and the melt index flexible measured value display instrument 6 of mixing optimizing, described field intelligent instrument 2, control station 3 is connected with propylene polymerization production process 1, described field intelligent instrument 2, control station 3 is connected with DCS database 4, described DCS database 4 is connected with the input end based on the optimum hard measurement system 5 mixing optimizing, the output terminal of the described optimum hard measurement system 5 based on mixing optimizing is connected with melt index flexible measured value display instrument 6, described based on mixing optimizing optimum hard measurement system comprise:
(1), data preprocessing module, carry out pre-service for the mode input variable will inputted from DCS database, to input variable centralization, namely deduct the mean value of variable; Be normalized again, namely divided by the constant interval of variate-value;
(2), PCA principal component analysis (PCA) module, for by input variable pre-whitening processing and variable decorrelation, realize by applying a linear transformation to input variable, namely major component is obtained by C=MU, wherein M is input variable, and C is principal component scores matrix, and U is loading matrix.If be reconstructed raw data, can by M=CU
tcalculate, the wherein transposition of subscript T representing matrix.When the major component number chosen is less than the variable number of input variable, M=CU
t+ E, wherein E is residual matrix;
(3), neural network model module, for adopting RBF neural, having been minimized a kind of nonlinear being input to output by error function, in mapping, keep topological invariance;
(4), mixing optimizing optimization module, for adopting the optimization module based on HPSO-SA algorithm to be optimized neural network, comprising:
(4.1) algorithm initialization, constructs initial particle colony X=(x according to RBF neural structure to be optimized
1, x
2..., x
n), initial movable speed V=(v
1, v
2..., v
n), initial each particle successive dynasties optimal value OP=(p
1, p
2..., p
n) and global optimum p
g;
(4.2) perform HPSO algorithm by following formula, allow population restrain:
In formula, x is the position vector of particle, and i is the sequence number of particle, and k is algorithm iteration algebraically, and v is particle rapidity, and p represents initial each particle successive dynasties optimal value OP=(p
1, p
2..., p
n) and global optimum p
goptimal value set.V
k ifor the speed of i-th particle in kth time iteration algebraically; x
k ithe position vector of i-th particle in kth time iteration algebraically; p
k ifor the successive dynasties optimal location of i-th particle in kth time iteration algebraically, p
ibe the successive dynasties optimum solution of i-th particle, w is speed weight coefficient, c
1, c
2be respectively the attraction coefficient of particle successive dynasties optimum solution and group optimal solution, r
1, r
2be respectively random number.
(4.3) when population converges to suitable degree, the set (best of the successive dynasties optimum solution composition to each particle is started
1, best
2..., best
n) performing SA algorithm, the optimum solution obtained returns as the end product of algorithm.
The described optimum hard measurement system based on mixing optimizing also comprises: model modification module, for the online updating of model, will regularly be input in training set by off-line analysis data, upgrades neural network model.
In PCA principal component analysis (PCA) module, PCA method realizes the pre-whitening processing of input variable, can simplify the input variable of neural network model, and then improves the performance of model.
2. propylene polymerization production process process flow diagram as shown in Figure 3, according to reaction mechanism and flow process analysis, consider in polypropylene production process each factor that melting index has an impact, get nine performance variables conventional in actual production process and easily survey variable as mode input variable, have: three bursts of propylene feed flow rates, major catalyst flow rate, cocatalyst flow rate, temperature in the kettle, pressure, liquid level, hydrogen volume concentration in still.
Table 1 based on mixing optimizing optimum hard measurement system needed for mode input variable
Table 1 lists 9 the mode input variablees inputted as the optimum hard measurement system 5 based on mixing optimizing, to be respectively in temperature in the kettle (T), still in pressure (p), still hydrogen volume concentration (X in liquid level (L), still
v), 3 bursts of propylene feed flow rates (first gang of propylene feed flow rate f1, second gang of propylene feed flow rate f2, the 3rd gang of propylene feed flow rate f3), 2 bursts of catalyst charge flow rates (major catalyst flow rate f4, cocatalyst flow rate f5).Polyreaction in reactor is that reaction mass mixes rear participation reaction repeatedly, and therefore mode input variable relates to the mean value in process variable employing front some moment of material.The data acquisition mean value of last hour in this example.Melting index off-line laboratory values is as the output variable of the optimum hard measurement system 5 based on mixing optimizing.Obtained by hand sampling, off-line assay, within every 4 hours, analyze and gather once.
Field intelligent instrument 2 and control station 3 are connected with propylene polymerization production process 1, are connected with DCS database 4; Optimum hard measurement system 5 is connected with DCS database 4 and hard measurement value display instrument 6.Field intelligent instrument 2 measures the easy survey variable that propylene polymerization produces object, is transferred to DCS database 4 by easily surveying variable; Control station 3 controls the performance variable that propylene polymerization produces object, performance variable is transferred to DCS database 4.The variable data recorded in DCS database 4 is as the input of the optimum hard measurement system 5 based on mixing optimizing, and hard measurement value display instrument 6 is for showing the output of the optimum hard measurement system 5 based on mixing optimizing, i.e. hard measurement value.
Based on the optimum hard measurement system 5 of mixing optimizing, comprising:
(1) data preprocessing module 7, for carrying out pre-service to mode input, i.e. centralization and normalization.To input variable centralization, deduct the mean value of variable exactly, make variable be the variable of zero-mean, thus shortcut calculation; To input variable normalization, be exactly the constant interval divided by input variable value, be that the value of variable is fallen within-0.5 ~ 0.5, simplify further.
(2) PCA principal component analysis (PCA) module 8, for to input variable pre-whitening processing and variable decorrelation, a linear transformation is applied to input variable, make between each component of variable after converting uncorrelated mutually, its covariance matrix is unit battle array simultaneously, and namely major component is obtained by C=MU, and wherein M is input variable, C is principal component scores matrix, and U is loading matrix.If be reconstructed raw data, can by M=CU
tcalculate, the wherein transposition of subscript T representing matrix.When the major component number chosen is less than the variable number of input variable, M=CU
t+ E, wherein E is residual matrix.
(3) neural network model module 9, adopt RBF neural, multilayer feedforward neural network is usually made up of input layer, hidden layer and output layer in network structure.On network characterization, main manifestations is both without neuronic interconnected in layer, also without the anti-contact of interlayer.This network is in fact a kind of static network, and it exports the function of just existing input, and irrelevant with inputing or outputing of future in the past.RBF neural model has an input layer, an output layer and a hidden layer.Can prove in theory, RBF neural can approach nonlinear system arbitrarily.RBF neural training algorithm has minimized a kind of nonlinear being input to output by error function, keep topological invariance in mapping.
(4) mix optimizing and optimize module 10: adopt and based on mixing optimizing optimization module, neural network is optimized, by mixing optimizing powerful global optimizing ability come input and the hidden layer structure of optimization neural network, and carry out neural network learning with this, thus the optimum hard measurement system of the RBF neural setting up the optimization of the mixing optimizing of propylene polymerization melting index.Specific implementation step is as follows:
(4.1) algorithm initialization, constructs initial particle colony X=(x according to RBF neural structure to be optimized
1, x
2..., x
n), initial movable speed V=(v
1, v
2..., v
n), initial each particle successive dynasties optimal value OP=(p
1, p
2..., p
n) and global optimum p
g;
(4.2) perform HPSO algorithm by following formula, allow population restrain:
In formula, x is the position vector of particle, and i is the sequence number of particle, and k is algorithm iteration algebraically, and v is particle rapidity, and p represents initial each particle successive dynasties optimal value OP=(p
1, p
2..., p
n) and global optimum p
goptimal value set.V
k ifor the speed of i-th particle in kth time iteration algebraically; x
k ithe position vector of i-th particle in kth time iteration algebraically; p
k ifor the successive dynasties optimal location of i-th particle in kth time iteration algebraically, p
ibe the successive dynasties optimum solution of i-th particle, w is speed weight coefficient, c
1, c
2be respectively the attraction coefficient of particle successive dynasties optimum solution and group optimal solution, r
1, r
2be respectively random number.
(4.3) when population converges to suitable degree, the set (best of the successive dynasties optimum solution composition to each particle is started
1, best
2..., best
n) performing SA algorithm, the optimum solution obtained returns as the end product of algorithm.
(5) model modification module 11, for the online updating of model, is regularly input in training set by off-line analysis data, upgrades neural network model.
Embodiment 2
1. with reference to Fig. 1, Fig. 2 and Fig. 3, a kind of based on mixing optimizing propylene polymerization production process optimal soft measuring method comprise the following steps:
(1) to propylene polymerization production process object, according to industrial analysis and Operations Analyst, select performance variable and easily survey the input of variable as model, performance variable and easily survey variable are obtained by DCS database;
(2) pre-service is carried out to sample data, to input variable centralization, namely deduct the mean value of variable; Be normalized again, namely divided by the constant interval of variate-value;
(3) PCA principal component analysis (PCA) module, for by input variable pre-whitening processing and variable decorrelation, realizes by applying a linear transformation to input variable, namely major component is obtained by C=MU, wherein M is input variable, and C is principal component scores matrix, and U is loading matrix.If be reconstructed raw data, can by M=CU
tcalculate, the wherein transposition of subscript T representing matrix.When the major component number chosen is less than the variable number of input variable, M=CU
t+ E, wherein E is residual matrix;
(4) set up initial neural network model based on mode input, output data, adopt RBF neural, be input to a kind of nonlinear of output by error minimize, in mapping, keep topological invariance;
(5) adopt the optimization module based on HPSO-SA algorithm to be optimized neural network, comprising:
(5.1) algorithm initialization, constructs initial particle colony X=(x according to RBF neural structure to be optimized
1, x
2..., x
n), initial movable speed V=(v
1, v
2..., v
n), initial each particle successive dynasties optimal value OP=(p
1, p
2..., p
n) and global optimum p
g;
(5.2) perform HPSO algorithm by following formula, allow population restrain:
In formula, x is the position vector of particle, and i is the sequence number of particle, and k is algorithm iteration algebraically, and v is particle rapidity, and p represents initial each particle successive dynasties optimal value OP=(p
1, p
2..., p
n) and global optimum p
goptimal value set.V
k ifor the speed of i-th particle in kth time iteration algebraically; x
k ithe position vector of i-th particle in kth time iteration algebraically; p
k ifor the successive dynasties optimal location of i-th particle in kth time iteration algebraically, p
ibe the successive dynasties optimum solution of i-th particle, w is speed weight coefficient, c
1, c
2be respectively the attraction coefficient of particle successive dynasties optimum solution and group optimal solution, r
1, r
2be respectively random number.
(5.3) when population converges to suitable degree, the set (best of the successive dynasties optimum solution composition to each particle is started
1, best
2..., best
n) performing SA algorithm, the optimum solution obtained returns as the end product of algorithm.
Described flexible measurement method also comprises: be regularly input in training set by off-line analysis data, upgrades neural network model.
Further, in described step (3), adopt PCA principal component analytical method to realize the pre-whitening processing of input variable, the input variable of neural network model can be simplified, and then improve the performance of model.
2. the concrete implementation step of the method for the present embodiment is as follows:
Step 1: to propylene polymerization production process object 1, according to industrial analysis and Operations Analyst, selects performance variable and easily surveys the input of variable as model.
Step 2: carry out pre-service to sample data, is completed by data preprocessing module 7.
Step 3: carry out principal component analysis (PCA) to through pretreated data, is completed by PCA principal component analysis (PCA) module 8.
Step 4: based on mode input, export and set up initial neural network model 9.Input data obtain as described in step 1, export data and chemically examine acquisition by off-line.
Step 5: optimize input and the hidden layer structure that module 10 optimizes initial neural network 9 by mixing optimizing.
Step 6: off-line analysis data is regularly input in training set by model modification module 11, upgrades neural network model, and the optimum hard measurement system 5 based on mixing optimizing has been set up.
Step 7: the real-time model input variable data that the optimum hard measurement system 5 based on the optimizing of HPSO-SA algorithm established transmits based on DCS database 4 carry out the optimum hard measurement based on the optimizing of HPSO-SA algorithm to the melting index of propylene polymerization production process 1.
Step 8: melting index hard measurement display instrument 6 shows the output of the optimum hard measurement system 5 based on mixing optimizing, completes the display of the optimum hard measurement to propylene polymerization production process melting index.
Claims (2)
1. the propylene polymerization production process optimal soft measuring system based on mixing optimizing, comprise propylene polymerization production process, for measuring the field intelligent instrument easily surveying variable, for measuring the control station of performance variable, the DCS database of store data, based on optimum hard measurement system and the melting index hard measurement display instrument of mixing optimizing, described field intelligent instrument, control station is connected with propylene polymerization production process, described field intelligent instrument, control station is connected with DCS database, described DCS database with based on mix optimizing the input end of optimal soft measurement model be connected, the output terminal of the described optimum hard measurement system based on mixing optimizing is connected with melting index hard measurement display instrument, it is characterized in that: the described optimum hard measurement system based on mixing optimizing comprises:
(1), data preprocessing module, carry out pre-service for the mode input variable will inputted from DCS database, to input variable centralization, namely deduct the mean value of variable; Be normalized again, namely divided by the constant interval of variate-value;
(2), PCA principal component analysis (PCA) module, for by input variable pre-whitening processing and variable decorrelation, realize by applying a linear transformation to input variable, namely major component is obtained by C=MU, wherein M is input variable, and C is principal component scores matrix, and U is loading matrix; If be reconstructed raw data, can by M=CU
tcalculate, the wherein transposition of subscript T representing matrix; When the major component number chosen is less than the variable number of input variable, M=CU
t+ E, wherein E is residual matrix;
(3), neural network model module, for adopting RBF neural, having been minimized a kind of nonlinear being input to output by error function, in mapping, keep topological invariance;
(4), mix optimizing optimization module, for adopting the optimization module based on coordinating population-simulated annealing to be optimized neural network, described coordination population-simulated annealing is HPSO-SA hybrid algorithm; Comprise:
(4.1) algorithm initialization, constructs initial particle colony X=(x according to RBF neural structure to be optimized
1, x
2..., x
n), initial movable speed V=(v
1, v
2..., v
n), initial each particle successive dynasties optimal value OP=(p
1, p
2..., p
n) and global optimum p
g;
(4.2) perform HPSO algorithm by following formula, allow population restrain:
In formula, x is the position vector of particle, and i is the sequence number of particle, and k is algorithm iteration algebraically, and v is particle rapidity, and p represents initial each particle successive dynasties optimal value OP=(p
1, p
2..., p
n) and global optimum p
goptimal value set; v
k ifor the speed of i-th particle in kth time iteration; x
k ithe position vector of i-th particle in kth time iteration; p
k ifor the successive dynasties optimal location of i-th particle in kth time iteration, p
ibe the successive dynasties optimum solution of i-th particle, w is speed weight coefficient, c
1, c
2be respectively the attraction coefficient of particle successive dynasties optimum solution and group optimal solution, r
1, r
2be respectively random number;
(4.3) when population converges to suitable degree, the set (best of the successive dynasties optimum solution composition to each particle is started
1, best
2..., best
n) performing SA algorithm, the optimum solution obtained returns as the end product of algorithm;
The described optimum hard measurement system based on mixing optimizing also comprises: model modification module, for the online updating of model, is regularly input in training set by off-line analysis data, upgrades neural network model;
Described based on mixing optimizing propylene polymerization production process optimal soft measuring system PCA principal component analysis (PCA) module in, PCA method realizes the pre-whitening processing of input variable, can simplify the input variable of neural network model, and then improves the performance of model.
2. with as claimed in claim 1 based on mixing optimizing the flexible measurement method that realizes of the optimum hard measurement system of polypropylene production process, it is characterized in that: described flexible measurement method specific implementation step is as follows:
(4.1) to propylene polymerization production process object, according to industrial analysis and Operations Analyst, select performance variable and easily survey the input of variable as model, performance variable and easily survey variable get temperature, pressure, liquid level, hydrogen gas phase percentage, 3 strands of propylene feed flow velocitys and 2 strands of these variablees of catalyst charge flow velocity, are obtained by DCS database;
(4.2) pre-service is carried out to sample data, to input variable centralization, namely deduct the mean value of variable; Be normalized again, namely divided by the constant interval of variate-value;
(4.3) PCA principal component analysis (PCA) module, for by input variable pre-whitening processing and variable decorrelation, realizes by applying a linear transformation to input variable, namely major component is obtained by C=MU, wherein M is input variable, and C is principal component scores matrix, and U is loading matrix; If be reconstructed raw data, can by M=CU
tcalculate, the wherein transposition of subscript T representing matrix; When the major component number chosen is less than the variable number of input variable, M=CU
t+ E, wherein E is residual matrix;
(4.4) set up initial neural network model based on mode input, output data, adopt RBF neural, be input to a kind of nonlinear of output by error minimize, in mapping, keep topological invariance;
(4.5) adopt the optimization module based on HPSO-SA algorithm to be optimized neural network, comprising:
(4.5.1) algorithm initialization, constructs initial particle colony X=(x according to RBF neural structure to be optimized
1, x
2..., x
n), initial movable speed V=(v
1, v
2..., v
n), initial each particle successive dynasties optimal value OP=(p
1, p
2..., p
n) and global optimum p
g;
(4.5.2) perform HPSO algorithm by following formula, allow population restrain:
In formula, x is the position vector of particle, and i is the sequence number of particle, and k is algorithm iteration algebraically, and v is particle rapidity, and p represents initial each particle successive dynasties optimal value OP=(p
1, p
2..., p
n) and global optimum p
goptimal value set; v
k ifor the speed of i-th particle in kth time iteration; x
k ithe position vector of i-th particle in kth time iteration; p
k ifor the successive dynasties optimal location of i-th particle in kth time iteration, p
ibe the successive dynasties optimum solution of i-th particle, w is speed weight coefficient, c
1, c
2be respectively the attraction coefficient of particle successive dynasties optimum solution and group optimal solution, r
1, r
2be respectively random number;
(4.5.3) when population converges to suitable degree, the set (best of the successive dynasties optimum solution composition to each particle is started
1, best
2..., best
n) performing SA algorithm, the optimum solution obtained returns as the end product of algorithm;
Described flexible measurement method also comprises: be regularly input in training set by off-line analysis data, upgrades neural network model;
Described flexible measurement method, adopts PCA principal component analytical method to realize the pre-whitening processing of input variable, can simplify the input variable of neural network model, and then improve the performance of model in described step (4.3).
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