CN108982576A - A kind of high-precision propylene polymerization production process optimal soft survey instrument of chaos - Google Patents
A kind of high-precision propylene polymerization production process optimal soft survey instrument of chaos Download PDFInfo
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Abstract
The invention discloses a kind of high-precision propylene polymerization production process optimal soft survey instruments of chaos, including propylene polymerization production process, field intelligent instrument, control station, DCS database, the optimal soft measurement model based on chaos support vector machines and the melt index flexible measured value display instrument for storing data, field intelligent instrument and control station are connected with propylene polymerization production process, are connected with DCS database；Optimal soft measurement model is connected with DCS database and hard measurement value display instrument.The optimal soft measurement model based on chaos support vector machines includes data preprocessing module, chaos analysis module, support vector machines module, model modification module.Propylene polymerization production process optimal soft survey instrument of the invention realizes chaos forecast, has high-precision.
Description
Technical field
It is specifically a kind of to be supported based on chaos the present invention relates to a kind of high-precision optimal soft survey instrument of chaos and method
The propylene polymerization processes optimal soft survey instrument of vector machine.
Background technique
Polypropylene is a kind of thermoplastic resin as prepared by propylene polymerization, the most important downstream product of propylene, the world third
The 50% of alkene, the 65% of China's propylene are all for polypropylene processed, are one of five big general-purpose plastics, close with our daily life
Cut phase is closed.Polypropylene is that fastest-rising general thermoplastic resin, total amount are only only second to polyethylene and polyvinyl chloride in the world.For
Make China's polypropylene product that there is the market competitiveness, exploitation rigidity, flows the good crushing-resistant copolymerization product of sexual balance, is random toughness
Copolymerized product, BOPP and CPP film material, fiber, nonwoven cloth, and develop polypropylene in the application of automobile and field of household appliances, all
It is research topic important from now on.
Melt index is that polypropylene product determines one of important quality index of product grade, it determines the difference of product
Purposes, the measurement to melt index are an important links of control of product quality in polypropylene production, to production and scientific research, all
There are very important effect and directive significance.
However, the on-line analysis measurement of melt index is difficult to accomplish at present, it is on the one hand online melt index analysis instrument
Lack, be on the other hand existing in-line analyzer measured often blocking it is inaccurate even can not be caused by normal use
Use on difficulty.Therefore, at present in industrial production MI measurement, mainly obtained by manual sampling, offline assay
, and general every 2-4 hours can only analyze once, and time lag is big, brings to the quality control that propylene polymerization produces tired
Difficulty becomes a bottleneck problem urgently to be solved in production.The online forecasting system and method for polypropylene melt index is studied, from
And become a forward position and the hot spot of academia and industry.
Summary of the invention
Measurement accuracy in order to overcome the shortcomings of current existing propylene polymerization production process is not high, and the purpose of the present invention exists
In providing the propylene polymerization production process optimal soft survey instrument of a kind of forecast of chaos, high precision.
The technical solution adopted by the present invention to solve the technical problems is: a kind of high-precision propylene polymerization of chaos produced
Journey optimal soft survey instrument, for carrying out hard measurement, the hard measurement unit to the melt index in propylene polymerization production process
Including the data preprocessing module, chaos analysis module and support vector machines module being sequentially connected, in which:
Data preprocessing module: the mode input variable inputted from DCS database is pre-processed, with specific reference to following formula
It is standardized:
Wherein, mean indicates that the arithmetic mean of instantaneous value of each variable, std indicate the standard deviation of each variable,Indicate input variable
Value, subscript i indicate i-th detection, j respectively indicate jth dimension variable, xijInput variable after indicating standardization, S indicate model
Input variable.
Chaos analysis module: according to chaology to sample X={ xijCarry out chaotic Property Analysis.It first has to determine mutually empty
Between two important parameters reconstructing, i.e. delay time and Embedded dimensions.If the maximum Lyapunov exponent of system is greater than 0,
The system must be chaos, it is possible to the chaotic characteristic of system is judged according to maximum Lyapunov exponent.
(1) mutual information method determines the delay time T of reconstruct, is under nonlinear case, to sometime t and another moment
Relationship between the information content of t+ τ is analyzed.If A, B two systems, ar、bkIt is the measurement result of a certain physical quantity, PA
(ar)、PB(bk) it is a respectivelyrWith bkProbability, PAB(ar,bk) it is expressed as the joint probability of A and B.Then mutual information is defined as
Average is defined as follows:
If A and B are uncorrelated, IAB(ar,bk)=0.If A represents time series y (t), B represents time series y (t+
τ), then Average are as follows:
τ~I (τ) curve is drawn, delay of the first minimum point corresponding time of curve as phase space reconfiguration is taken
Time τ.
(2) Cao method determines the Embedded dimensions of reconstruct, and in m dimension phase space, i-th of phase point vector is denoted as
Xm(i)=[x (i), x (i+ τ) ..., x (i+ (m-1) τ)]T (5)
It defines first
Wherein,For Xm(i) nearest neighbor point, Xm+1(i) andIt is phase point Xm(i) andIt is tieed up in m+1
The continuation of phase space.Range formula uses maximum norm, i.e.,
Remember a2(i, m) is about the mean value of i
For the change situation for studying E (m), definition
If time series is generated by attractor, when m is greater than some m0When, E1(m) it is no longer changed, then
m0It is exactly the minimum embedding dimension number of phase space reconstruction.
(3) maximum Lyapunov exponent is calculated using small data sets arithmetic, process is as follows:
1. carrying out Fast Fourier Transform (FFT) to time series { x (t) | t=1,2 ..., n }, its average period of P is calculated；
2. the delay time T and Embedded dimensions m of time series are determined, if reconstructing later phase space is { Xi| i=1,
2 ..., K }, wherein K=n- (m-1) τ represents the number of phase point in phase space；
3. finding phase point XiThe corresponding nearest neighbor point in phase spaceIt is limited simultaneously to separate in short-term in phase space:
4. for any phase point X in phase spacei, acquire its correspond to adjoint point pair j discrete time after distance di(j)
Are as follows:
5. finding out all ln (d to each ji(j)) average y (j), it may be assumed that
Wherein, q is record di(j) ≠ 0 number.It is fitted with least square method, then when the slope of regression straight line is exactly this
Between sequence maximum Lyapunov exponent λ1。
(4) phase space reconfiguration is carried out to sample:
Wherein, N=n- (m-1) τ is phase point sum.
Support vector machines module: for establishing soft-sensing model, modeling process is as follows:
In support vector machines, the regression problem such as minor function is considered:
Give M sample dataWherein xiFor model sample input, yiFor sample output.It is reflected by non-linear
It penetratesInput data is projected into high-dimensional feature space, to convert nonlinear regression problem in high-dimensional feature space
Linear regression problem.The dimension of w is characterized space dimensionality (may be Infinite-dimensional), and b is amount of bias.Define ε insensitive loss letter
Number is
Wherein, ε > 0 is the directly related design parameter of and function estimated accuracy, and solving purpose is construction f (x), is made and mesh
The distance between scale value is less than ε.The parameters equivalent in above formula is found in the following optimization problem of solution:
Wherein, ξ, ξ * are relaxation factor, and γ is penalty coefficient.Solving this using method of Lagrange multipliers has linearly not
The quadratic programming problem of equality constraint, i.e.,
Wherein, αi,βi,I=1,2 ..., M is Lagrange multiplier.It can thus be concluded that primal-dual optimization problem:
0≤αi≤γ
Wherein,Referred to as kernel function.It can thus be concluded that w and function to be estimated:
Wherein, amount of bias b can be calculated by KTT (Karush-Kuhn-Tucker) condition.The selection of kernel function is necessary
Meet Mercer condition, using radial basis function K (xi, x)=exp (- | | x-xi||/σ2)。
The high-precision propylene polymerization production process optimal soft survey instrument of chaos further includes model modification module, is used for
Offline analysis data is periodically input in training set by the online updating of model, updates supporting vector machine model.
Beneficial effects of the present invention are mainly manifested in: the present invention melts the important quality index of propylene polymerization production process
Index carries out online optimal hard measurement, overcomes the shortcomings of that existing polypropylene melt index measuring instrumentss measurement accuracy is not high, draws
Enter chaology to analyze melt index time series, it be extended to from one by hyperspace by phase space reconfiguration,
The more information that timing is included can be probed into, to obtain that there is the optimal soft of high-precision melt index chaos forecast function
Measuring instrumentss.
Detailed description of the invention
Fig. 1 is the basic structure signal of the high-precision propylene polymerization production process optimal soft survey instrument of chaos and method
Figure；
Fig. 2 is the optimal soft measurement model structural schematic diagram based on chaos support vector machines；
Fig. 3 is propylene polymerization production process Hypol technique flow sheet.
Specific embodiment
The present invention is illustrated below according to attached drawing.
Referring to Fig.1, the high-precision propylene polymerization production process optimal soft survey instrument of a kind of chaos, including propylene polymerization are raw
Production process 1, the control station 3 for measuring performance variable, stores data at the field intelligent instrument 2 for measuring easy survey variable
DCS database 4 and melt index flexible measured value display instrument 6, the field intelligent instrument 2, control station 3 and propylene polymerization produce
Process 1 connects, and the field intelligent instrument 2, control station 3 are connect with DCS database 4, and the soft measuring instrument further includes being based on
The optimal soft measurement model 5 of chaos support vector machines, the DCS database 4 is with described based on the optimal of chaos support vector machines
The input terminal of soft-sensing model 5 connects, the output end of the optimal soft measurement model 5 based on chaos support vector machines and melting
Index hard measurement value display instrument 6 connects.
It referring to Fig. 3, is analyzed according to reaction mechanism and flow process, takes common nine behaviour in propylene polymerization production process
Make variable and easily surveys variable as modeling variable, comprising: 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 kettle, as shown in table 1, respectively temperature in the kettle (T), pressure (p) in kettle,
Liquid level (L) in kettle, hydrogen volume concentration (X in kettlev), 3 bursts of propylene feed flow rates (first gang of propylene feed flow rates f1, second strand
Propylene feed flow rates f2, third stock propylene feed flow rates f3), 2 bursts of catalyst charge flow rates (major catalyst flow rate f4, auxiliary catalysis
Agent flow rate f5).Input variable of the variable data of propylene polymerization processes as optimal soft measurement model 5, melt index are changed offline
Output variable of the value as optimal soft measurement model 5 is tested, is obtained by manual sampling, offline assay, analysis in every 4 hours is adopted
Collection is primary.
The variable of 1 propylene polymerization processes of table
| Variable symbol | Variable meaning | Variable symbol | Variable meaning |
| T | Temperature in the kettle | f1 | First burst of propylene feed flow rates |
| p | Pressure in kettle | f2 | Second burst of propylene feed flow rates |
| L | Liquid level in kettle | f3 | Third stock propylene feed flow rates |
| Xv | Hydrogen volume concentration in kettle | f4 | Major catalyst flow rate |
| f5 | Cocatalyst flow rate |
Referring to Fig. 2, the optimal soft measurement model 5 based on chaos support vector machines further include:
Data preprocessing module 7: the mode input variable inputted from DCS database is pre-processed, with specific reference under
Formula is standardized:
Wherein, mean indicates that the arithmetic mean of instantaneous value of each variable, std indicate the standard deviation of each variable,Indicate input variable
Value, subscript i indicate i-th detection, j respectively indicate jth dimension variable, xijInput variable after indicating standardization, S indicate model
Input variable.
Chaos analysis module 8: according to chaology to sample X={ xijCarry out chaotic Property Analysis.It first has to determine phase
Two important parameters of Space Reconstruction, i.e. delay time and Embedded dimensions.If the maximum Lyapunov exponent of system is greater than 0,
Then the system must be chaos, it is possible to the chaotic characteristic of system is judged according to maximum Lyapunov exponent.
(1) mutual information method determines the delay time T of reconstruct, is under nonlinear case, to sometime t and another moment
Relationship between the information content of t+ τ is analyzed.If A, B two systems, ar、bkIt is the measurement result of a certain physical quantity, PA
(ar)、PB(bk) it is a respectivelyrWith bkProbability, PAB(ar,bk) it is expressed as the joint probability of A and B.Then mutual information is defined as
Average is defined as follows:
If A and B are uncorrelated, IAB(ar,bk)=0.If A represents time series y (t), B represents time series y (t+
τ), then Average are as follows:
τ~I (τ) curve is drawn, delay of the first minimum point corresponding time of curve as phase space reconfiguration is taken
Time τ.
(2) Cao method determines the Embedded dimensions of reconstruct, and in m dimension phase space, i-th of phase point vector is denoted as
Xm(i)=[x (i), x (i+ τ) ..., x (i+ (m-1) τ)]T (5)
It defines first
Wherein,For Xm(i) nearest neighbor point, Xm+1(i) andIt is phase point Xm(i) andIt is tieed up in m+1
The continuation of phase space.Range formula uses maximum norm, i.e.,
Remember a2(i, m) is about the mean value of i
For the change situation for studying E (m), definition
If time series is generated by attractor, when m is greater than some m0When, E1(m) it is no longer changed, then
m0It is exactly the minimum embedding dimension number of phase space reconstruction.
(3) maximum Lyapunov exponent is calculated using small data sets arithmetic, process is as follows:
1. carrying out Fast Fourier Transform (FFT) to time series { x (t) | t=1,2 ..., n }, its average period of P is calculated；
2. the delay time T and Embedded dimensions m of time series are determined, if reconstructing later phase space is { Xi| i=1,
2 ..., K }, wherein K=n- (m-1) τ represents the number of phase point in phase space；
3. finding phase point XiThe corresponding nearest neighbor point in phase spaceIt is limited simultaneously to separate in short-term in phase space:
4. for any phase point X in phase spacei, acquire its correspond to adjoint point pair j discrete time after distance di(j)
Are as follows:
5. finding out all ln (d to each ji(j)) average y (j), it may be assumed that
Wherein, q is record di(j) ≠ 0 number.It is fitted with least square method, then when the slope of regression straight line is exactly this
Between sequence maximum Lyapunov exponent λ1。
(4) phase space reconfiguration is carried out to sample:
Wherein, N=n- (m-1) τ is phase point sum.
Support vector machines module 9: for establishing soft-sensing model:
In support vector machines, the regression problem such as minor function is considered:
Give M sample dataWherein xiFor model sample input, yiFor sample output.It is reflected by non-linear
It penetratesInput data is projected into high-dimensional feature space, to convert nonlinear regression problem in high-dimensional feature space
Linear regression problem.The dimension of w is characterized space dimensionality (may be Infinite-dimensional), and b is amount of bias.Define ε insensitive loss letter
Number is
Wherein, ε > 0 is the directly related design parameter of and function estimated accuracy, and solving purpose is construction f (x), is made and mesh
The distance between scale value is less than ε.The parameters equivalent in above formula is found in the following optimization problem of solution:
Wherein, ξ, ξ * are relaxation factor, and γ is penalty coefficient.Solving this using method of Lagrange multipliers has linearly not
The quadratic programming problem of equality constraint, i.e.,
Wherein, αi,βi,I=1,2 ..., M is Lagrange multiplier.It can thus be concluded that primal-dual optimization problem:
0≤αi≤γ
Wherein,Referred to as kernel function.It can thus be concluded that w and function to be estimated:
Wherein, amount of bias b can be calculated by KTT (Karush-Kuhn-Tucker) condition.The selection of kernel function is necessary
Meet Mercer condition, using radial basis function K (xi, x)=exp (- | | x-xi||/σ2)。
Offline analysis data is periodically input in training set, more by model modification module 10 for the online updating of model
New supporting vector machine model.
The embodiment of the present invention is used to illustrate the present invention, rather than limits the invention, in spirit of the invention
In scope of protection of the claims, to any modifications and changes that the present invention makes, protection scope of the present invention is both fallen within.
Claims (4)
1. a kind of high-precision propylene polymerization production process optimal soft survey instrument of chaos, for in propylene polymerization production process
Melt index carry out hard measurement, which is characterized in that including field intelligent instrument, control station, DCS database, hard measurement unit,
Display instrument；After field intelligent instrument and control station measure easy survey variable and performance variable in propylene polymerization production process respectively,
It is stored in DCS database, after hard measurement unit carries out hard measurement processing to the data in DCS database, is output to display instrument；
The hard measurement unit includes the data preprocessing module being sequentially connected, chaos analysis module and support vector machines module, DCS number
After being pre-processed according to the mode input variable that library inputs by data preprocessing module, chaos point is carried out in chaos analysis module
Then analysis carries out Modeling and Prediction in support vector machines module.
2. the high-precision propylene polymerization production process optimal soft survey instrument of chaos according to claim 1, which is characterized in that
The data preprocessing module pre-processes the mode input variable inputted from DCS database, carries out with specific reference to following formula
Standardization:
Wherein, mean indicates that the arithmetic mean of instantaneous value of each variable, std indicate the standard deviation of each variable,Indicate the value of input variable,
Subscript i indicates that i-th detection, j respectively indicate jth dimension variable, xijInput variable after indicating standardization, S indicate mode input
Variable.
3. the high-precision propylene polymerization production process optimal soft survey instrument of chaos according to claim 1, which is characterized in that
The chaos analysis module is according to chaology to sample X={ xijCarry out chaotic Property Analysis.It first has to determine phase space weight
Two important parameters of structure, i.e. delay time and Embedded dimensions.If the maximum Lyapunov exponent of system is greater than 0, this is
Unified is chaos surely, it is possible to the chaotic characteristic of system is judged according to maximum Lyapunov exponent.
(1) mutual information method determines the delay time T of reconstruct, is under nonlinear case, to sometime t and another moment t+ τ
Information content between relationship analyzed.If A, B two systems, ar、bkIt is the measurement result of a certain physical quantity, PA(ar)、PB
(bk) it is a respectivelyrWith bkProbability, PAB(ar,bk) it is expressed as the joint probability of A and B.Then mutual information is defined as
Average is defined as follows:
If A and B are uncorrelated, IAB(ar,bk)=0.If A represents time series y (t), B represents time series y (t+ τ), then puts down
Equal mutual information are as follows:
τ~I (τ) curve is drawn, delay time T of the first minimum point corresponding time of curve as phase space reconfiguration is taken.
(2) Cao method determines the Embedded dimensions of reconstruct, and in m dimension phase space, i-th of phase point vector is denoted as
Xm(i)=[x (i), x (i+ τ) ..., x (i+ (m-1) τ)]T (5)
It defines first
Wherein,For Xm(i) nearest neighbor point, Xm+1(i) andIt is phase point Xm(i) andIt is tieed up in m+1 mutually empty
Between continuation.Range formula uses maximum norm, i.e.,
Remember a2(i, m) is about the mean value of i
For the change situation for studying E (m), definition
If time series is generated by attractor, when m is greater than some m0When, E1(m) it is no longer changed, then m0Just
It is the minimum embedding dimension number of phase space reconstruction.
(3) maximum Lyapunov exponent is calculated using small data sets arithmetic, process is as follows:
1. carrying out Fast Fourier Transform (FFT) to time series { x (t) | t=1,2 ..., n }, its average period of P is calculated；
2. the delay time T and Embedded dimensions m of time series are determined, if reconstructing later phase space is { Xi| i=1,2 ..., K },
Wherein K=n- (m-1) τ represents the number of phase point in phase space；
3. finding phase point XiThe corresponding nearest neighbor point in phase spaceIt is limited simultaneously to separate in short-term in phase space:
4. for any phase point X in phase spacei, acquire its correspond to adjoint point pair j discrete time after distance di(j) are as follows:
5. finding out all ln (d to each ji(j)) average y (j), it may be assumed that
Wherein, q is record di(j) ≠ 0 number.It is fitted with least square method, then the slope of regression straight line is exactly the time series
Maximum Lyapunov exponent λ1。
(4) phase space reconfiguration is carried out to sample:
Wherein, N=n- (m-1) τ is phase point sum.
4. the high-precision propylene polymerization production process optimal soft survey instrument of chaos according to claim 1, which is characterized in that
The support vector machines module is for establishing soft-sensing model.
In support vector machines, the regression problem such as minor function is considered:
Give M sample dataWherein xiFor model sample input, yiFor sample output.Pass through Nonlinear MappingInput data is projected into high-dimensional feature space, to convert nonlinear regression problem on the line in high-dimensional feature space
Property regression problem.The dimension of w is characterized space dimensionality (may be Infinite-dimensional), and b is amount of bias.Define ε insensitive loss function
For
Wherein, ε > 0 is the directly related design parameter of and function estimated accuracy, and solving purpose is construction f (x), is made and target value
The distance between be less than ε.The parameters equivalent in above formula is found in the following optimization problem of solution:
Wherein, ξ, ξ * are relaxation factor, and γ is penalty coefficient.This is solved with linear inequality using method of Lagrange multipliers
The quadratic programming problem of constraint, i.e.,
Wherein,For Lagrange multiplier.It can thus be concluded that primal-dual optimization problem:
0≤αi≤γ
Wherein,Referred to as kernel function.It can thus be concluded that w and function to be estimated:
Wherein, amount of bias b can be calculated by KTT (Karush-Kuhn-Tucker) condition.The selection of kernel function must satisfy
Mercer condition, using radial basis function K (xi, x)=exp (- | | x-xi||/σ2)。
The high-precision propylene polymerization production process optimal soft survey instrument of chaos further includes model modification module, is used for model
Online updating, periodically offline analysis data is input in training set, update supporting vector machine model.
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