CN103472867B - The optimizing temperature of pesticide production waste liquid incinerator system and method for support vector machine - Google Patents

Abstract

The invention discloses a kind of optimizing temperature of pesticide production waste liquid incinerator system and method for support vector machine.Support vector machine and fuzzy system combine by the method, carry out obfuscation to the Output rusults of multiple support vector machine, to obtain Control for Kiln Temperature result more accurately.In the present invention, training sample through standardization resume module, as the input of fuzzy system module; The furnace temperature predicted value obtained in fuzzy system module with make the performance variable value of furnace temperature the best and be connected with result display module, will DCS system be passed to for result; Model modification module, for the sampling time interval by setting, collection site intelligent instrument signal.Present invention achieves the accurate control of furnace temperature, online updating training sample data, avoid occurring that furnace temperature is too low or too high.

Description

The optimizing temperature of pesticide production waste liquid incinerator system and method for support vector machine

Technical field

The present invention relates to pesticide producing liquid waste incineration field, especially, relate to the optimizing temperature of pesticide production waste liquid incinerator system and method for support vector machine.

Background technology

Along with developing rapidly of pesticide industry, the problem of environmental pollution of emission has caused the great attention of national governments and corresponding environmental administration.The qualified discharge of research and solution agricultural chemicals organic liquid waste controls and harmless minimization, not only becomes difficult point and the focus of various countries' scientific research, is also the science proposition of the national active demand being related to social sustainable development simultaneously.

Burning method be process at present agricultural chemicals raffinate and waste residue the most effectively, thoroughly, the most general method of application.In burning process, incinerator furnace temperature must remain on a suitable temperature, and too low furnace temperature is unfavorable for the decomposition of poisonous and harmful element in discarded object; Too high furnace temperature not only increases fuel consumption, increases equipment operating cost, and easily damages inboard wall of burner hearth, shortens equipment life.In addition, excessive temperature may increase the volatile quantity of metal and the generation of nitrogen oxide in discarded object.Special in chloride waste water, suitable furnace temperature more can reduce the corrosion of inwall.But the factor affecting furnace temperature in actual burning process is complicated and changeable, easily there is the phenomenon that furnace temperature is too low or too high.

Nineteen sixty-five U.S. mathematician L.Zadeh first proposed the concept of fuzzy set.Subsequently fuzzy logic with its problem closer to daily people and the meaning of one's words statement mode, start the classical logic replacing adhering to that all things can represent with binary item.Fuzzy logic so far successful Application among multiple fields of industry, the such as field such as household electrical appliances, Industry Control.2003, Demirci proposed the concept of fuzzy system, by using fuzzy membership matrix and the input matrix new with its distortion structure one, then in local equation, showed that analytic value is as last output using the gravity model appoach in Anti-fuzzy method.For optimizing temperature of pesticide production waste liquid incinerator system and method, consider the noise effect in industrial processes and operate miss, the fuzzy performance of fuzzy logic can be used to reduce error to the impact of precision.

Support vector machine, was introduced in 1998 by Vapnik, due to the Generalization Ability that it is good, is widely used in pattern-recognition, matching and classification problem.The shortcomings such as speed of convergence is slow, precision is low are had, so also been proposed least square method supporting vector machine afterwards due to during standard support vector machine process data in enormous quantities.Least square method supporting vector machine can process sample data in enormous quantities better compared to standard support vector machine, is selected as the local equation in fuzzy system here.

Summary of the invention

Being difficult to control, easily occur the deficiency that furnace temperature is too low or too high to overcome existing incinerator furnace temperature, the invention provides a kind of furnace temperature that realizes and accurately controlling, avoid the optimizing temperature of pesticide production waste liquid incinerator system and method that occurs that furnace temperature is too low or too high.

The technical solution adopted for the present invention to solve the technical problems is:

The optimizing temperature of pesticide production waste liquid incinerator system of support vector machine, comprises incinerator, intelligent instrument, DCS system, data-interface and host computer, and described DCS system comprises control station and database; Described field intelligent instrument is connected with DCS system, and described DCS system is connected with host computer, and described host computer comprises:

Standardization module, carries out pre-service for the model training sample will inputted from DCS database, to training sample centralization, namely deducts the mean value of sample, then carries out standardization to it:

Computation of mean values: $\stackrel{\‾}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}T{X}_i---\left(1\right)$

Calculate variance: ${\mathrm{\σ}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}({\mathrm{TX}}_i-\stackrel{\‾}{\mathrm{TX}})---\left(2\right)$

Standardization: $X=\frac{\mathrm{TX}-\stackrel{\‾}{\mathrm{TX}}}{{\mathrm{\σ}}_x}---\left(3\right)$

Wherein, TX _ibe i-th training sample, be the production that gathers from DCS database normal time key variables, furnace temperature and make the data of the optimized performance variable of furnace temperature, N is number of training, for the average of training sample, X is the training sample after standardization.σ _xrepresent the standard deviation of training sample, σ ² _xrepresent the variance of training sample.

Fuzzy system module, to the training sample X passed from data preprocessing module after the standardization of coming, carries out obfuscation.If have c in fuzzy system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, then the training sample X after i-th standardization _ifor the degree of membership μ of fuzzy group k _ikfor:

${\mathrm{\μ}}_{\mathrm{ik}}={\left(\underset{j=1}{\overset{c^{*}}{\mathrm{\Σ}}}{\left(\frac{\left|\right|X_i-v_k\left|\right|}{\left|\right|X_i-v_j\left|\right|}\right)}^{\frac{2}{n-1}}\right)}^{-1}---\left(4\right)$

In formula, n is the partitioned matrix index needed in fuzzy classification process, is usually taken as 2; ‖ ‖ is norm expression formula.

Use and be subordinate to angle value or its distortion to obtain new input matrix above, for fuzzy group k, its input matrix is deformed into:

Φ _ik(X _i,μ _ik)=[1 func(μ _ik) X _i] （5）

Wherein func (μ _ik) for being subordinate to angle value μ _ikwarping function, generally get exp (μ _ik) etc., Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Support vector machine, as the local equation of fuzzy system, is optimized matching to each fuzzy group.If i-th of model training sample target exports as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem by conversion fitting problems:

$\min R(w,\mathrm{\ξ})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\γ}{\mathrm{\Σ}}_{i=1}^N{\mathrm{\ω}}_i{\mathrm{\ξ}}_i^2---\left(6\right)$

Define Lagrangian function simultaneously:

Wherein, R (w, ξ) is the objective function of optimization problem, and minR (w, ξ) is the minimum value of the objective function of optimization problem, be nonlinear mapping function, N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ii-th component of slack variable, α _i, i=1 ..., N is i-th component of i-th component of corresponding Lagrange multiplier, and w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and ω _i, i=1 ..., N and γ is the transposition of weight and the penalty factor of support vector machine, subscript T representing matrix respectively, μ _ikrepresent the training sample X after i-th standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Can derive fuzzy group k by (6) (7) (8) formula in the output of training sample i is:

${\hat{y}}_{\mathrm{ik}}=\underset{m=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_m\&times;K<{\mathrm{\&Phi;}}_{\mathrm{im}}(X_m,{\mathrm{\&mu;}}_{\mathrm{mk}}),{\mathrm{\&Phi;}}_{\mathrm{ik}}(X_i,{\mathrm{\&mu;}}_{\mathrm{ik}})>+b---\left(9\right)$

Wherein, for the prediction of a kth error back propagation fuzzy system output layer exports, K<> is the kernel function of support vector machine, and K<> gets linear kernel function here, α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkrepresent m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) represent m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

The output of last fuzzy system is obtained by the gravity model appoach in Anti-fuzzy method:

${\hat{y}}_i=\frac{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\hat{y}}_{\mathrm{ik}}}{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}}---\left(10\right)$

be the training sample X after corresponding to i-th standardization _ifurnace temperature predicted value and make the performance variable value of furnace temperature the best.

As preferred a kind of scheme: described host computer also comprises: model modification module, for the sampling time interval by setting, collection site intelligent instrument signal, the actual measurement furnace temperature function calculated value obtained is compared, if relative error be greater than 10% or furnace temperature exceed produce normal bound scope, then the new data of furnace temperature the best that makes when producing normal in DCS database is added training sample data, upgrade soft-sensing model.

Further, described host computer also comprises: result display module, for by the furnace temperature obtained predicted value and make the performance variable value of furnace temperature the best pass to DCS system, show at the control station of DCS, and be delivered to operator station by DCS system and fieldbus and show; Meanwhile, obtained makes the performance variable value of furnace temperature the best as new performance variable setting value by DCS system, automatically performs the operation of furnace temperature optimization.

Signal acquisition module, for the time interval of each sampling according to setting, image data from database.

Further again, described key variables comprise enter incinerator waste liquid flow, enter the air mass flow of incinerator and enter the fuel flow rate of incinerator; Described performance variable comprises the air mass flow entering incinerator and the fuel flow rate entering incinerator.

The furnace temperature optimization method of the optimizing temperature of pesticide production waste liquid incinerator system realization of support vector machine, described furnace temperature optimization method specific implementation step is as follows:

1), determine key variables used, from DCS database, gather the input matrix of data as training sample TX of described variable when producing normal, gather corresponding furnace temperature and make the optimized performance variable data of furnace temperature as output matrix O;

2), by the model training sample inputted from DCS database carry out pre-service, to training sample centralization, namely deduct the mean value of sample, then carry out standardization to it, make its average be 0, variance is 1.This process adopts following formula process:

2.1) computation of mean values: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}T{X}_i---\left(1\right)$

2.2) variance is calculated: ${\mathrm{\&sigma;}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{TX}}_i-\stackrel{\&OverBar;}{\mathrm{TX}})---\left(2\right)$

2.3) standardization: $X=\frac{\mathrm{TX}-\stackrel{\&OverBar;}{\mathrm{TX}}}{{\mathrm{\&sigma;}}_x}---\left(3\right)$

Wherein, TX _ibe i-th training sample, be the production that gathers from DCS database normal time key variables, furnace temperature and make the data of the optimized performance variable of furnace temperature, N is number of training, for the average of training sample, X is the training sample after standardization.σ _xrepresent the standard deviation of training sample, σ ² _xrepresent the variance of training sample.

3), to passing the training sample of coming from data preprocessing module, obfuscation is carried out.If have c in fuzzy system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, then the training sample X after i-th standardization _ifor the degree of membership μ of fuzzy group k _ikfor:

${\mathrm{\&mu;}}_{\mathrm{ik}}={\left(\underset{j=1}{\overset{c^{*}}{\mathrm{\&Sigma;}}}{\left(\frac{\left|\right|X_i-v_k\left|\right|}{\left|\right|X_i-v_j\left|\right|}\right)}^{\frac{2}{n-1}}\right)}^{-1}---\left(4\right)$

In formula, n is the partitioned matrix index needed in fuzzy classification process, is usually taken as 2; ‖ ‖ is norm expression formula.

Use and be subordinate to angle value or its distortion to obtain new input matrix above, for fuzzy group k, its input matrix is deformed into:

Φ _ik(X _i,μ _ik)=[1 func(μ _ik) X _i] （5）

Wherein func (μ _ik) for being subordinate to angle value μ _ikwarping function, generally get exp (μ _ik) etc., Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Support vector machine, as the local equation of fuzzy system, is optimized matching to each fuzzy group.If i-th of model training sample target exports as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem by conversion fitting problems:

$\min R(w,\mathrm{\&xi;})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\&gamma;}{\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&omega;}}_i{\mathrm{\&xi;}}_i^2---\left(6\right)$

Define Lagrangian function simultaneously:

Wherein, R (w, ξ) is the objective function of optimization problem, and minR (w, ξ) is the minimum value of the objective function of optimization problem, be nonlinear mapping function, N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ii-th component of slack variable, α _i, i=1 ..., N is i-th component of i-th component of corresponding Lagrange multiplier, and w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and ω _i, i=1 ..., N and γ is the transposition of weight and the penalty factor of support vector machine, subscript T representing matrix respectively, μ _ikrepresent the training sample X after i-th standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Can derive fuzzy group k by (6) (7) (8) formula in the output of training sample i is:

${\hat{y}}_{\mathrm{ik}}=\underset{m=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_m\&times;K<{\mathrm{\&Phi;}}_{\mathrm{im}}(X_m,{\mathrm{\&mu;}}_{\mathrm{mk}}),{\mathrm{\&Phi;}}_{\mathrm{ik}}(X_i,{\mathrm{\&mu;}}_{\mathrm{ik}})>+b---\left(9\right)$

Wherein, for the prediction of a kth error back propagation fuzzy system output layer exports, K<> is the kernel function of support vector machine, and K<> gets linear kernel function here, α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkrepresent m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) represent m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

The output of last fuzzy system is obtained by the gravity model appoach in Anti-fuzzy method:

${\hat{y}}_i=\frac{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\hat{y}}_{\mathrm{ik}}}{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}}---\left(10\right)$

be the training sample X after corresponding to i-th standardization _ifurnace temperature predicted value and make the performance variable value of furnace temperature the best.

As preferred a kind of scheme: described method also comprises: 4), by the sampling time interval set, collection site intelligent instrument signal, the actual measurement furnace temperature obtained is compared with predicts value, if relative error be greater than 10% or furnace temperature exceed produce normal bound scope, then the new data of furnace temperature the best that makes when producing normal in DCS database is added training sample data, upgrade soft-sensing model.

Further, Optimum Operation variate-value is calculated in described step 3), by the furnace temperature predicted value obtained and make the performance variable value of furnace temperature the best pass to DCS system, show at the control station of DCS, and be delivered to operator station by DCS system and fieldbus and show; Meanwhile, obtained makes the performance variable value of furnace temperature the best as new performance variable setting value by DCS system, automatically performs the operation of furnace temperature optimization.

Further again, described key variables comprise enter incinerator waste liquid flow, enter the air mass flow of incinerator and enter the fuel flow rate of incinerator; Described performance variable comprises the air mass flow entering incinerator and the fuel flow rate entering incinerator.

Technical conceive of the present invention is: the optimizing temperature of pesticide production waste liquid incinerator system and method for invention support vector machine, searches out furnace temperature predicted value and the performance variable value making furnace temperature the best.

Beneficial effect of the present invention is mainly manifested in: the online soft sensor model 1, establishing quantitative relationship between system core variable and furnace temperature; 2, the operating conditions making furnace temperature the best is found rapidly.

Accompanying drawing explanation

Fig. 1 is the hardware structure diagram of system proposed by the invention;

Fig. 2 is the functional structure chart of host computer proposed by the invention.

Embodiment

Below in conjunction with accompanying drawing, the invention will be further described.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

With reference to Fig. 1, Fig. 2, the optimizing temperature of pesticide production waste liquid incinerator system of support vector machine, comprise field intelligent instrument 2, DCS system and the host computer 6 be connected with incinerator object 1, described DCS system comprises data-interface 3, control station 4 and database 5, described field intelligent instrument 2 is connected with data-interface 3, described data-interface is connected with control station 4, database 5 and host computer 6, and described host computer 6 comprises:

Standardization module 7, carries out pre-service for the model training sample will inputted from DCS database, to training sample centralization, namely deducts the mean value of sample, then carries out standardization to it:

Computation of mean values: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}T{X}_i---\left(1\right)$

Calculate variance: ${\mathrm{\&sigma;}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{TX}}_i-\stackrel{\&OverBar;}{\mathrm{TX}})---\left(2\right)$

Standardization: $X=\frac{\mathrm{TX}-\stackrel{\&OverBar;}{\mathrm{TX}}}{{\mathrm{\&sigma;}}_x}---\left(3\right)$

Wherein, TX _ibe i-th training sample, be the production that gathers from DCS database normal time key variables, furnace temperature and make the data of the optimized performance variable of furnace temperature, N is number of training, for the average of training sample, X is the training sample after standardization.σ _xrepresent the standard deviation of training sample, σ ² _xrepresent the variance of training sample.

Fuzzy system module 8, to the training sample X passed from data preprocessing module after the standardization of coming, carries out obfuscation.If have c in fuzzy system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, then the training sample X after i-th standardization _ifor the degree of membership μ of fuzzy group k _ikfor:

${\mathrm{\&mu;}}_{\mathrm{ik}}={\left(\underset{j=1}{\overset{c^{*}}{\mathrm{\&Sigma;}}}{\left(\frac{\left|\right|X_i-v_k\left|\right|}{\left|\right|X_i-v_j\left|\right|}\right)}^{\frac{2}{n-1}}\right)}^{-1}---\left(4\right)$

In formula, n is the partitioned matrix index needed in fuzzy classification process, is usually taken as 2; ‖ ‖ is norm expression formula.

Use and be subordinate to angle value or its distortion to obtain new input matrix above, for fuzzy group k, its input matrix is deformed into:

Φ _ik(X _i,μ _ik)=[1 func(μ _ik) X _i] （5）

Wherein func (μ _ik) for being subordinate to angle value μ _ikwarping function, generally get exp (μ _ik) etc., Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Support vector machine, as the local equation of fuzzy system fuzzy system, is optimized matching to each fuzzy group.If i-th of model training sample target exports as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem by conversion fitting problems:

$\min R(w,\mathrm{\&xi;})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\&gamma;}{\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&omega;}}_i{\mathrm{\&xi;}}_i^2---\left(6\right)$

Define Lagrangian function simultaneously:

Wherein, R (w, ξ) is the objective function of optimization problem, and minR (w, ξ) is the minimum value of the objective function of optimization problem, be nonlinear mapping function, N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ii-th component of slack variable, α _i, i=1 ..., N is i-th component of i-th component of corresponding Lagrange multiplier, and w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and ω _i, i=1 ..., N and γ is the transposition of weight and the penalty factor of support vector machine, subscript T representing matrix respectively, μ _ikrepresent the training sample X after i-th standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Can derive fuzzy group k by (6) (7) (8) formula in the output of training sample i is:

${\hat{y}}_{\mathrm{ik}}=\underset{m=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_m\&times;K<{\mathrm{\&Phi;}}_{\mathrm{im}}(X_m,{\mathrm{\&mu;}}_{\mathrm{mk}}),{\mathrm{\&Phi;}}_{\mathrm{ik}}(X_i,{\mathrm{\&mu;}}_{\mathrm{ik}})>+b---\left(9\right)$

Wherein, for the prediction of a kth error back propagation fuzzy system output layer exports, K<> is the kernel function of support vector machine, and K<> gets linear kernel function here, α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkrepresent m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) represent m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

The output of last fuzzy system is obtained by the gravity model appoach in Anti-fuzzy method:

${\hat{y}}_i=\frac{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\hat{y}}_{\mathrm{ik}}}{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}}---\left(10\right)$

be the training sample X after corresponding to i-th standardization _ifurnace temperature predicted value and make the performance variable value of furnace temperature the best.

Described host computer 6 also comprises: signal acquisition module 10, for the time interval of each sampling according to setting, and image data from database.

Described host computer 6 also comprises: model modification module 11, by the sampling time interval of setting, collection site intelligent instrument signal, the actual measurement furnace temperature obtained is compared with predicts value, if relative error be greater than 10% or furnace temperature exceed produce normal bound scope, then the new data of furnace temperature the best that makes when producing normal in DCS database is added training sample data, upgrade soft-sensing model.

Described key variables comprise enter incinerator waste liquid flow, enter the air mass flow of incinerator and enter the fuel flow rate of incinerator; Described performance variable comprises the air mass flow entering incinerator and the fuel flow rate entering incinerator.

Described system also comprises DCS system, and described DCS system is made up of data-interface 3, control station 4, database 5; Intelligent instrument 2, DCS system, host computer 6 are connected successively by fieldbus; Host computer 6 also comprises result display module 9, for calculating optimal result is passed to DCS system, and in the control station procedure for displaying state of DCS, by DCS system and fieldbus, process state information is delivered to operator station simultaneously and shows.

When liquid waste incineration process has been furnished with DCS system, the detection of sample real-time dynamic data, the real-time of memory DCS system and historical data base, obtained furnace temperature predicted value and the function of the performance variable value of furnace temperature the best mainly completed on host computer.

When liquid waste incineration process is not equipped with DCS system, data-carrier store is adopted to carry out the data storage function of the real-time of alternative DCS system and historical data base, and do not rely on the independently complete SOC (system on a chip) of of DCS system by what obtain furnace temperature predicted value and make the function system of the performance variable value of furnace temperature the best manufacture to comprise I/O element, data-carrier store, program storage, arithmetical unit, several large component of display module, when whether being equipped with DCS regardless of burning process, independently can both use, more be of value to and promoting the use of.

Embodiment 2

With reference to Fig. 1, Fig. 2, the optimizing temperature of pesticide production waste liquid incinerator method of support vector machine, described method specific implementation step is as follows:

1), determine key variables used, from DCS database, gather the input matrix of data as training sample TX of described variable when producing normal, gather corresponding furnace temperature and make the optimized performance variable data of furnace temperature as output matrix O;

2), by the model training sample inputted from DCS database carry out pre-service, to training sample centralization, namely deduct the mean value of sample, then carry out standardization to it, make its average be 0, variance is 1.This process adopts following formula process:

2.1) computation of mean values: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}T{X}_i---\left(1\right)$

2.2) variance is calculated: ${\mathrm{\&sigma;}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{TX}}_i-\stackrel{\&OverBar;}{\mathrm{TX}})---\left(2\right)$

2.3) standardization: $X=\frac{\mathrm{TX}-\stackrel{\&OverBar;}{\mathrm{TX}}}{{\mathrm{\&sigma;}}_x}---\left(3\right)$

Wherein, TX _ibe i-th training sample, be the production that gathers from DCS database normal time key variables, furnace temperature and make the data of the optimized performance variable of furnace temperature, N is number of training, for the average of training sample, X is the training sample after standardization.σ _xrepresent the standard deviation of training sample, σ ² _xrepresent the variance of training sample.

3), to passing the training sample of coming from data preprocessing module, obfuscation is carried out.If have c in fuzzy system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, then the training sample X after i-th standardization _ifor the degree of membership μ of fuzzy group k _ikfor:

${\mathrm{\&mu;}}_{\mathrm{ik}}={\left(\underset{j=1}{\overset{c^{*}}{\mathrm{\&Sigma;}}}{\left(\frac{\left|\right|X_i-v_k\left|\right|}{\left|\right|X_i-v_j\left|\right|}\right)}^{\frac{2}{n-1}}\right)}^{-1}---\left(4\right)$

In formula, n is the partitioned matrix index needed in fuzzy classification process, is usually taken as 2; ‖ ‖ is norm expression formula.

Use and be subordinate to angle value or its distortion to obtain new input matrix above, for fuzzy group k, its input matrix is deformed into:

Φ _ik(X _i,μ _ik)=[1 func(μ _ik) X _i] （5）

Wherein func (μ _ik) for being subordinate to angle value μ _ikwarping function, generally get exp (μ _ik) etc., Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Support vector machine, as the local equation of fuzzy system, is optimized matching to each fuzzy group.If i-th of model training sample target exports as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem by conversion fitting problems:

$\min R(w,\mathrm{\&xi;})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\&gamma;}{\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&omega;}}_i{\mathrm{\&xi;}}_i^2---\left(6\right)$

Define Lagrangian function simultaneously:

Wherein, R (w, ξ) is the objective function of optimization problem, and minR (w, ξ) is the minimum value of the objective function of optimization problem, be nonlinear mapping function, N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ii-th component of slack variable, α _i, i=1 ..., N is i-th component of i-th component of corresponding Lagrange multiplier, and w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and ω _i, i=1 ..., N and γ is the transposition of weight and the penalty factor of support vector machine, subscript T representing matrix respectively, μ _ikrepresent the training sample X after i-th standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix.

Can derive fuzzy group k by (6) (7) (8) formula in the output of training sample i is:

${\hat{y}}_{\mathrm{ik}}=\underset{m=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_m\&times;K<{\mathrm{\&Phi;}}_{\mathrm{im}}(X_m,{\mathrm{\&mu;}}_{\mathrm{mk}}),{\mathrm{\&Phi;}}_{\mathrm{ik}}(X_i,{\mathrm{\&mu;}}_{\mathrm{ik}})>+b---\left(9\right)$

Wherein, for the prediction of a kth error back propagation fuzzy system output layer exports, K<> is the kernel function of support vector machine, and K<> gets linear kernel function here; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkrepresent m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) represent m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

The output of last fuzzy system is obtained by the gravity model appoach in Anti-fuzzy method:

${\hat{y}}_i=\frac{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\hat{y}}_{\mathrm{ik}}}{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}}---\left(10\right)$

be the training sample X after corresponding to i-th standardization _ifurnace temperature predicted value and make the performance variable value of furnace temperature the best.

Described method also comprises: 4), by the sampling time interval set, collection site intelligent instrument signal, the actual measurement furnace temperature obtained is compared with predicts value, if relative error be greater than 10% or furnace temperature exceed produce normal bound scope, then the new data of furnace temperature the best that makes when producing normal in DCS database is added training sample data, upgrade soft-sensing model.

5), in described step 3), Optimum Operation variate-value is calculated, by the furnace temperature predicted value obtained and make the performance variable value of furnace temperature the best pass to DCS system, show at the control station of DCS, and be delivered to operator station by DCS system and fieldbus and show; Meanwhile, obtained makes the performance variable value of furnace temperature the best as new performance variable setting value by DCS system, automatically performs the operation of furnace temperature optimization.

Described key variables comprise enter incinerator waste liquid flow, enter the air mass flow of incinerator and enter the fuel flow rate of incinerator; Described performance variable comprises the air mass flow entering incinerator and the fuel flow rate entering incinerator.

Claims (2)

1. an optimizing temperature of pesticide production waste liquid incinerator system for support vector machine, comprises incinerator, field intelligent instrument, DCS system, data-interface and host computer, and described DCS system comprises control station and database; Described field intelligent instrument is connected with DCS system, and described DCS system is connected with host computer, it is characterized in that: described host computer comprises:

Standardization module, carries out pre-service for the model training sample will inputted from DCS database, to training sample centralization, namely deducts the mean value of sample, then carries out standardization to it:

Computation of mean values $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

Calculate variance: ${\mathrm{\&sigma;}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{TX}}_i-\stackrel{\&OverBar;}{\mathrm{TX}})---\left(2\right)$

Standardization: $X=\frac{\mathrm{TX}-\stackrel{\&OverBar;}{\mathrm{TX}}}{{\mathrm{\&sigma;}}_x}---\left(3\right)$

Wherein, TX _ibe i-th training sample, be the production that gathers from DCS database normal time key variables, furnace temperature and make the data of the optimized performance variable of furnace temperature, N is number of training, for the average of training sample, X is the training sample after standardization; σ _xrepresent the standard deviation of training sample, σ ² _xrepresent the variance of training sample;

Fuzzy system module, to the training sample X passed from standardization module after the standardization of coming, carries out obfuscation; If have c in fuzzy system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, then the training sample X after i-th standardization _ifor the degree of membership μ of fuzzy group k _ikfor:

${\mathrm{\&mu;}}_{\mathrm{ik}}={\left(\underset{j=1}{\overset{c^{*}}{\mathrm{\&Sigma;}}}{\left(\frac{\left|\right|X_i-v_k\left|\right|}{\left|\right|X_i-v_j\left|\right|}\right)}^{\frac{2}{n-1}}\right)}^{-1}---\left(4\right)$

In formula, n is the partitioned matrix index needed in fuzzy classification process, is usually taken as 2; || || be norm expression formula;

Use and be subordinate to angle value or its distortion to obtain new input matrix above, for fuzzy group k, its input matrix is deformed into:

Φ _ik(X _i,μ _ik)＝[1 func(μ _ik) X _i] (5)

Wherein func (μ _ik) for being subordinate to angle value μ _ikwarping function, generally get exp (μ _ik), Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix;

Support vector machine, as the local equation of fuzzy system, is optimized matching to each fuzzy group; If i-th of model training sample target exports as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem by conversion fitting problems:

$\min R(w,\mathrm{\&xi;})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\&gamma;}{\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&omega;}}_i{\mathrm{\&xi;}}_i^2---\left(6\right)$

Define Lagrangian function simultaneously

Wherein, R (w, ξ) is the objective function of optimization problem, and min R (w, ξ) is the minimum value of the objective function of optimization problem, be nonlinear mapping function, N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ii-th component of slack variable, α _i, i=1 ..., N is i-th component of i-th component of corresponding Lagrange multiplier, and w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and ω _i, i=1 ..., N and γ is the transposition of weight and the penalty factor of support vector machine, subscript T representing matrix respectively, μ _ikrepresent the training sample X after i-th standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix; Can derive fuzzy group k by (6) (7) (8) formula in the output of training sample i is:

${\hat{y}}_{\mathrm{ik}}=\underset{m=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_m\&times;<{\mathrm{\&Phi;}}_{\mathrm{im}}(X_m,{\mathrm{\&mu;}}_{\mathrm{mk}}),{\mathrm{\&Phi;}}_{\mathrm{ik}}(X_i,{\mathrm{\&mu;}}_{\mathrm{ik}})>+b---\left(9\right)$

Wherein, for the prediction of a kth error back propagation fuzzy system output layer exports, K<> is the kernel function of support vector machine, and K<> gets linear kernel function here; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier; μ _mkrepresent m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) represent m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix;

The output of last fuzzy system is obtained by the gravity model appoach in Anti-fuzzy method:

${\hat{y}}_i=\frac{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\hat{y}}_{\mathrm{ik}}}{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}}---\left(10\right)$

be the training sample X after corresponding to i-th standardization _ifurnace temperature predicted value and make the performance variable value of furnace temperature the best;

Described host computer also comprises:

Model modification module, for the sampling time interval by setting, collection site intelligent instrument signal, the actual measurement furnace temperature obtained is compared with predicts value, if relative error be greater than 10% or furnace temperature exceed produce normal bound scope, then the new data of furnace temperature the best that makes when producing normal in DCS database is added training sample data, upgrade soft-sensing model;

Result display module, for by the furnace temperature obtained predicted value and make the performance variable value of furnace temperature the best pass to DCS system, shows at the control station of DCS, and is delivered to operator station by DCS system and fieldbus and shows; Meanwhile, obtained makes the performance variable value of furnace temperature the best as new performance variable setting value by DCS system, automatically performs the operation of furnace temperature optimization;

Signal acquisition module, for the time interval of each sampling according to setting, image data from database;

Described key variables comprise enter incinerator waste liquid flow, enter the air mass flow of incinerator and enter the fuel flow rate of incinerator; Described performance variable comprises the air mass flow entering incinerator and the fuel flow rate entering incinerator.

2. an optimizing temperature of pesticide production waste liquid incinerator method for support vector machine, is characterized in that: described furnace temperature optimization method specific implementation step is as follows:

1), determine key variables used, from DCS database, gather the input matrix of data as training sample TX of described variable when producing normal, gather corresponding furnace temperature and make the optimized performance variable data of furnace temperature as output matrix O;

2), by the model training sample inputted from DCS database carry out pre-service, to training sample centralization, namely deduct the mean value of sample, then carry out standardization to it, make its average be 0, variance is 1; This process adopts following formula process:

2.1) computation of mean values: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

2.2) variance is calculated: ${\mathrm{\&sigma;}}_x^2=\frac{1}{N-1}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}({\mathrm{TX}}_i-\stackrel{\&OverBar;}{\mathrm{TX}})---\left(2\right)$

2.3) standardization: $X=\frac{\mathrm{TX}-\stackrel{\&OverBar;}{\mathrm{TX}}}{{\mathrm{\&sigma;}}_x}---\left(3\right)$

Wherein, TX _ibe i-th training sample, be the production that gathers from DCS database normal time key variables, furnace temperature and make the data of the optimized performance variable of furnace temperature, N is number of training, for the average of training sample, X is the training sample after standardization; σ _xrepresent the standard deviation of training sample, σ ² _xrepresent the variance of training sample;

3), to passing the training sample of coming from standardization module, obfuscation is carried out; If have c in fuzzy system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, then the training sample X after i-th standardization _ifor the degree of membership μ of fuzzy group k _ikfor:

${\mathrm{\&mu;}}_{\mathrm{ik}}={\left(\underset{j=1}{\overset{c^{*}}{\mathrm{\&Sigma;}}}{\left(\frac{\left|\right|X_i-v_k\left|\right|}{\left|\right|X_i-v_j\left|\right|}\right)}^{\frac{2}{n-1}}\right)}^{-1}---\left(4\right)$

In formula, n is the partitioned matrix index needed in fuzzy classification process, is usually taken as 2; || || be norm expression formula;

Use and be subordinate to angle value or its distortion to obtain new input matrix above, for fuzzy group k, its input matrix is deformed into:

Φ _ik(X _i,μ _ik)＝[1 func(μ _ik) X _i] (5)

Wherein func (μ _ik) for being subordinate to angle value μ _ikwarping function, generally get exp (μ _ik), Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix;

Support vector machine, as the local equation of fuzzy system, is optimized matching to each fuzzy group; If i-th of model training sample target exports as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem by conversion fitting problems:

$\min R(w,\mathrm{\&xi;})=\frac{1}{2}{w}^{T}w+\frac{1}{2}\mathrm{\&gamma;}{\mathrm{\&Sigma;}}_{i=1}^N{\mathrm{\&omega;}}_i{\mathrm{\&xi;}}_i^2---\left(6\right)$

Define Lagrangian function simultaneously:

Wherein, R (w, ξ) is the objective function of optimization problem, and minR (w, ξ) is the minimum value of the objective function of optimization problem, be nonlinear mapping function, N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ii-th component of slack variable, α _i, i=1 ..., N is i-th component of i-th component of corresponding Lagrange multiplier, and w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and ω _i, i=1 ..., N and γ is the transposition of weight and the penalty factor of support vector machine, subscript T representing matrix respectively, μ _ikrepresent the training sample X after i-th standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) represent i-th input variable X _iand the degree of membership μ of fuzzy group k _ikcorresponding new input matrix; Can derive fuzzy group k by (6) (7) (8) formula in the output of training sample i is:

${\hat{y}}_{\mathrm{ik}}=\underset{m=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_m\&times;<{\mathrm{\&Phi;}}_{\mathrm{im}}(X_m,{\mathrm{\&mu;}}_{\mathrm{mk}}),{\mathrm{\&Phi;}}_{\mathrm{ik}}(X_i,{\mathrm{\&mu;}}_{\mathrm{ik}})>+b---\left(9\right)$

Wherein, for the prediction of a kth error back propagation fuzzy system output layer exports, K<> is the kernel function of support vector machine, and K<> gets linear kernel function here; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier; μ _mkrepresent m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) represent m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix;

The output of last fuzzy system is obtained by the gravity model appoach in Anti-fuzzy method:

${\hat{y}}_i=\frac{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}{\hat{y}}_{\mathrm{ik}}}{{\mathrm{\&Sigma;}}_{k=1}^{c^{*}}{\mathrm{\&mu;}}_{\mathrm{ik}}}---\left(10\right)$

be the training sample X after corresponding to i-th standardization _ifurnace temperature predicted value and make the performance variable value of furnace temperature the best;

Described method also comprises:

4), by the sampling time interval of setting, collection site intelligent instrument signal, the actual measurement furnace temperature function calculated value obtained is compared, if relative error be greater than 10% or furnace temperature exceed produce normal bound scope, then the new data of furnace temperature the best that makes when producing normal in DCS database is added training sample data, upgrade soft-sensing model;

5), in described step 3) in calculate Optimum Operation variate-value, by the furnace temperature predicted value obtained and make the performance variable value of furnace temperature the best pass to DCS system, show at the control station of DCS, and be delivered to operator station by DCS system and fieldbus and show; Meanwhile, obtained makes the performance variable value of furnace temperature the best as new performance variable setting value by DCS system, automatically performs the operation of furnace temperature optimization;

Described key variables comprise enter incinerator waste liquid flow, enter the air mass flow of incinerator and enter the fuel flow rate of incinerator; Described performance variable comprises the air mass flow entering incinerator and the fuel flow rate entering incinerator.