CN103472867A - Pesticide production waste liquid incinerator temperature optimization system and method of support vector machine - Google Patents

Abstract

The invention discloses a pesticide production waste liquid incinerator temperature optimization system and method of a support vector machine. According to the method, the support vector machine and a fuzzy system are combined, a plurality of output results of the support vector machine are subjected to fuzzification, and accordingly accurate furnace temperature control results are obtained. A training sample is subjected to standardization processing module processing and is used as input of a fuzzy system module; a furnace temperature predicted value and an operation variable value which enables the furnace temperature to be optimum are obtained in the fuzzy system module which is connected with a result displaying module, the results are transmitted to a DCS; a model updating model is used for collecting field intelligent instrument signals according to the set sampling time intervals. Accuracy control of the furnace temperature and on-line train sample data updating are achieved, and the phenomena that the furnace temperature is too high or too low are avoided.

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 is controlled and harmless minimization, not only becomes difficult point and the focus of various countries' scientific research, is also the science proposition that is related to the national active demand of 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, the 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 generation of volatile quantity and the nitrogen oxide of metal in discarded object.Special in chloride waste water, suitable furnace temperature more can reduce the corrosion of inwall.But the factor that affects furnace temperature in actual burning process is complicated and changeable, the phenomenon that furnace temperature is too low or too high easily appears.

At first nineteen sixty-five U.S. mathematician L.Zadeh has proposed the concept of fuzzy set.Fuzzy logic, in the mode of its problem closer to daily people and meaning of one's words statement, starts to replace adhering to the classical logic that all things can mean with the binary item subsequently.Fuzzy logic so far successful Application industry a plurality of fields among, fields such as household electrical appliances, Industry Control.2003, Demirci proposed the concept of fuzzy system, by use the fuzzy membership matrix and and its distortion build a new input matrix, the gravity model appoach of then usining in local equation in the Anti-fuzzy method show that analytic value is as last output.For the optimizing temperature of pesticide production waste liquid incinerator system and method, consider noise effect and operate miss in industrial processes, can use the fuzzy performance of fuzzy logic to reduce the impact of error on precision.

Support vector machine, introduced in 1998 by Vapnik, due to its good Generalization Ability, is widely used in pattern-recognition, matching and classification problem.The shortcomings such as speed of convergence is slow, precision is low are arranged, so proposed again afterwards least square method supporting vector machine while due to the standard support vector machine, processing data in enormous quantities.Least square method supporting vector machine can be processed sample data in enormous quantities better than the standard support vector machine, is selected as the local equation in fuzzy system here.

Summary of the invention

Be difficult to control in order to overcome existing incinerator furnace temperature, the deficiency that furnace temperature is too low or too high easily occurs, the invention provides and a kind ofly realize that furnace temperature accurately controls, avoids 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, comprise 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 the DCS system, and described DCS system is connected with host computer, and described host computer comprises:

The standardization module, for carrying out pre-service from the model training sample of DCS database input, to the training sample centralization, deduct the mean value of sample, then it carried out to standardization:

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 _ibeing i training sample, is the production that gathers from the DCS database key variables, furnace temperature when normal and the data that make the optimized performance variable of furnace temperature, and N is number of training,

for the average of training sample, X is the training sample after standardization.σ _xthe standard deviation that means training sample, σ ² _xthe variance that means training sample.

The fuzzy system module, the training sample X to from data preprocessing module passes the standardization of coming, carry out obfuscation.If c is arranged in fuzzy system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, the training sample X after i standardization _idegree of membership μ for 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 the fuzzy classification process, usually gets and does 2; ‖ ‖ is the norm expression formula.

Use above degree of membership value or its distortion to obtain new input matrix, for fuzzy group k, its input matrix is deformed into:

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

Func (μ wherein _ik) be degree of membership value μ _ikwarping function, generally get

exp (μ _ik) etc., Φ _ik(X _i, μ _ik) mean i 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 target of model training sample is output as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem to fitting problems by conversion:

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

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 the Nonlinear Mapping function, N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ii component of slack variable, α _i, i=1 ..., N is i component of i 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 are respectively weight and the penalty factors of support vector machine, the transposition of subscript T representing matrix, μ _ikmean i the training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) mean i 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 is output as at training sample i:

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

Wherein,

be the prediction output of k error back propagation fuzzy system output layer, K<be the kernel function of support vector machine, here K<the line taking kernel function, α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkmean m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) mean m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

Gravity model appoach in the Anti-fuzzy method obtains the output of last fuzzy system:

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

be corresponding to the training sample X after i standardization _ithe furnace 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: the 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 the normal bound scope of producing, the new data that makes furnace temperature the best of producing in the DCS database when normal is added to the training sample data, upgrade soft-sensing model.

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

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

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

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

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

2), will carry out pre-service from the model training sample of DCS database input, to the training sample centralization, deduct the mean value of sample, then it is carried out to standardization, making its average is 0, variance is 1.This processing adopts following formula process to complete:

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

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

Wherein, TX _ibeing i training sample, is the production that gathers from the DCS database key variables, furnace temperature when normal and the data that make the optimized performance variable of furnace temperature, and N is number of training, for the average of training sample, X is the training sample after standardization.σ _xthe standard deviation that means training sample, σ ² _xthe variance that means training sample.

3), to pass the training sample come from data preprocessing module, carry out obfuscation.If c is arranged in fuzzy system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, the training sample X after i standardization _idegree of membership μ for 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 the fuzzy classification process, usually gets and does 2; ‖ ‖ is the norm expression formula.

Use above degree of membership value or its distortion to obtain new input matrix, for fuzzy group k, its input matrix is deformed into:

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

Func (μ wherein _ik) be degree of membership value μ _ikwarping function, generally get exp (μ _ik) etc., Φ _ik(X _i, μ _ik) mean i 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 target of model training sample is output as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem to fitting problems by conversion:

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

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 the Nonlinear Mapping function, N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ii component of slack variable, α _i, i=1 ..., N is i component of i 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 are respectively weight and the penalty factors of support vector machine, the transposition of subscript T representing matrix, μ _ikmean i the training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) mean i 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 is output as at training sample i:

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

Wherein,

be the prediction output of k error back propagation fuzzy system output layer, K<be the kernel function of support vector machine, here K<the line taking kernel function, α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkmean m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) mean m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

Gravity model appoach in the Anti-fuzzy method obtains the output of last fuzzy system:

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

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

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

Further, calculate the Optimum Operation variate-value in described step 3), by the furnace temperature predicted value that obtains with make the performance variable value of furnace temperature the best pass to the DCS system, show at the control station of DCS, and be delivered to operator station by DCS system and fieldbus and shown; Simultaneously, the DCS system, using the resulting performance variable value that makes furnace temperature the best as new performance variable setting value, automatically performs the operation of furnace temperature optimization.

Further again, described key variables comprise the waste liquid flow that enters incinerator, enter the air mass flow of incinerator and enter the fuel flow rate of incinerator; Described performance variable comprises the air mass flow that enters incinerator and the fuel flow rate that enters 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 that makes furnace temperature the best.

Beneficial effect of the present invention is mainly manifested in: the online soft sensor model of 1, having set up quantitative relationship between system core variable and furnace temperature; 2, find rapidly the operating conditions that makes furnace temperature the best.

The 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 the present invention that explains, rather than limits the invention, and in the protection domain of spirit of the present invention and claim, any modification and change that the present invention is made, 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 the field intelligent instrument 2, DCS system and the host computer 6 that are 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, for carrying out pre-service from the model training sample of DCS database input, to the training sample centralization, deduct the mean value of sample, then it carried out to standardization:

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 _ibeing i training sample, is the production that gathers from the DCS database key variables, furnace temperature when normal and the data that make the optimized performance variable of furnace temperature, and N is number of training,

for the average of training sample, X is the training sample after standardization.σ _xthe standard deviation that means training sample, σ ² _xthe variance that means training sample.

Fuzzy system module 8, the training sample X to from data preprocessing module passes the standardization of coming, carry out obfuscation.If c is arranged in fuzzy system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, the training sample X after i standardization _idegree of membership μ for 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 the fuzzy classification process, usually gets and does 2; ‖ ‖ is the norm expression formula.

Use above degree of membership value or its distortion to obtain new input matrix, for fuzzy group k, its input matrix is deformed into:

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

Func (μ wherein _ik) be degree of membership value μ _ikwarping function, generally get

exp (μ _ik) etc., Φ _ik(X _i, μ _ik) mean i 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 target of model training sample is output as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem to fitting problems by conversion:

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

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 the Nonlinear Mapping function, N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ii component of slack variable, α _i, i=1 ..., N is i component of i 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 are respectively weight and the penalty factors of support vector machine, the transposition of subscript T representing matrix, μ _ikmean i the training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) mean i 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 is output as at training sample i:

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

Wherein,

be the prediction output of k error back propagation fuzzy system output layer, K<be the kernel function of support vector machine, here K<the line taking kernel function, α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkmean m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) mean m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

Gravity model appoach in the Anti-fuzzy method obtains the output of last fuzzy system:

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

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

Described host computer 6 also comprises: signal acquisition module 10, and for the time interval of the each sampling according to setting, 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 and the system predicted value that obtain are compared, if relative error be greater than 10% or furnace temperature exceed the normal bound scope of producing, the new data that makes furnace temperature the best of producing in the DCS database when normal is added to the training sample data, upgrade soft-sensing model.

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

Described system also comprises the DCS system, and described DCS system consists 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 display module 9 as a result, for calculating optimal result, passes to the DCS system, and, at the control station procedure for displaying state of DCS, by DCS system and fieldbus, process status information is delivered to operator station simultaneously and is shown.

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

When the liquid waste incineration process is not equipped with the DCS system, adopted data memory substitutes the data storage function of the real-time and historical data base of DCS system, and one of the DCS system that do not rely on that will obtain the furnace temperature predicted value and the function system of the performance variable value of furnace temperature the best is manufactured comprising I/O element, data-carrier store, program storage, arithmetical unit, several large members of display module complete SOC (system on a chip) independently, in the situation that no matter whether burning process is equipped with DCS, can both independently 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, gather to produce the input matrix of the data of described variable when normal as training sample TX from the DCS database, gather corresponding furnace temperature and make the optimized performance variable data of furnace temperature as output matrix O;

2), will carry out pre-service from the model training sample of DCS database input, to the training sample centralization, deduct the mean value of sample, then it is carried out to standardization, making its average is 0, variance is 1.This processing adopts following formula process to complete:

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

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

Wherein, TX _ibeing i training sample, is the production that gathers from the DCS database key variables, furnace temperature when normal and the data that make the optimized performance variable of furnace temperature, and N is number of training,

for the average of training sample, X is the training sample after standardization.σ _xthe standard deviation that means training sample, σ ² _xthe variance that means training sample.

3), to pass the training sample come from data preprocessing module, carry out obfuscation.If c is arranged in fuzzy system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, the training sample X after i standardization _idegree of membership μ for 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 the fuzzy classification process, usually gets and does 2; ‖ ‖ is the norm expression formula.

Use above degree of membership value or its distortion to obtain new input matrix, for fuzzy group k, its input matrix is deformed into:

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

Func (μ wherein _ik) be degree of membership value μ _ikwarping function, generally get

exp (μ _ik) etc., Φ _ik(X _i, μ _ik) mean i 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 target of model training sample is output as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem to fitting problems by conversion:

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

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 the Nonlinear Mapping function, N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ii component of slack variable, α _i, i=1 ..., N is i component of i 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 are respectively weight and the penalty factors of support vector machine, the transposition of subscript T representing matrix, μ _ikmean i the training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) mean i 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 is output as at training sample i:

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

Wherein, be the prediction output of k error back propagation fuzzy system output layer, K<be the kernel function of support vector machine, here K<the line taking kernel function; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkmean m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) mean m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

Gravity model appoach in the Anti-fuzzy method obtains the output of last fuzzy system:

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

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

4), by the sampling time interval of setting described method also comprises:, collection site intelligent instrument signal, the actual measurement furnace temperature and the system predicted value that obtain are compared, if relative error be greater than 10% or furnace temperature exceed the normal bound scope of producing, the new data that makes furnace temperature the best of producing in the DCS database when normal is added to the training sample data, upgrade soft-sensing model.

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

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

Claims (2)

1. the optimizing temperature of pesticide production waste liquid incinerator system of a support vector machine, comprise 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 the DCS system, and described DCS system is connected with host computer, it is characterized in that: described host computer comprises:

The standardization module, for carrying out pre-service from the model training sample of DCS database input, to the training sample centralization, deduct the mean value of sample, then it carried out to standardization:

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 _ibeing i training sample, is the production that gathers from the DCS database key variables, furnace temperature when normal and the data that make the optimized performance variable of furnace temperature, and N is number of training,

for the average of training sample, X is the training sample after standardization.σ _xthe standard deviation that means training sample, σ ² _xthe variance that means training sample.

The fuzzy system module, the training sample X to from data preprocessing module passes the standardization of coming, carry out obfuscation.If c is arranged in fuzzy system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, the training sample X after i standardization _idegree of membership μ for 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 the fuzzy classification process, usually gets and does 2; ‖ ‖ is the norm expression formula.

Use above degree of membership value or its distortion to obtain new input matrix, for fuzzy group k, its input matrix is deformed into:

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

Func (μ wherein _ik) be degree of membership value μ _ikwarping function, generally get

exp (μ _ik) etc., Φ _ik(X _i, μ _ik) mean i 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 target of model training sample is output as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem to fitting problems by conversion:

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

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 the Nonlinear Mapping function, N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ii component of slack variable, α _i, i=1 ..., N is i component of i 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 are respectively weight and the penalty factors of support vector machine, the transposition of subscript T representing matrix, μ _ikmean i the training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) mean i 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 is output as at training sample i:

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

Wherein, be the prediction output of k error back propagation fuzzy system output layer, K<be the kernel function of support vector machine, here K<the line taking kernel function; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkmean m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) mean m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

Gravity model appoach in the Anti-fuzzy method obtains the output of last fuzzy system:

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

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

Described host computer also comprises:

The model modification module, for the sampling time interval by setting, collection site intelligent instrument signal, the actual measurement furnace temperature and the system predicted value that obtain are compared, if relative error be greater than 10% or furnace temperature exceed the normal bound scope of producing, the new data that makes furnace temperature the best of producing in the DCS database when normal is added to the training sample data, upgrade soft-sensing model.

Display module as a result, for the furnace temperature predicted value by obtaining with make the performance variable value of furnace temperature the best pass to the DCS system, show at the control station of DCS, and be delivered to operator station by DCS system and fieldbus and shown; Simultaneously, the DCS system, using the resulting performance variable value that makes furnace temperature the best as new performance variable setting value, automatically performs the operation of furnace temperature optimization.Signal acquisition module, for the time interval of the each sampling according to setting, image data from database.

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

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

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

2), will carry out pre-service from the model training sample of DCS database input, to the training sample centralization, deduct the mean value of sample, then it is carried out to standardization, making its average is 0, variance is 1.This processing adopts following formula process to complete:

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

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

Wherein, TX _ibeing i training sample, is the production that gathers from the DCS database key variables, furnace temperature when normal and the data that make the optimized performance variable of furnace temperature, and N is number of training, for the average of training sample, X is the training sample after standardization.σ _xthe standard deviation that means training sample, σ ² _xthe variance that means training sample.

3), to pass the training sample come from data preprocessing module, carry out obfuscation.If c is arranged in fuzzy system ^*individual fuzzy group, the center of fuzzy group k, j is respectively v _k, v _j, the training sample X after i standardization _idegree of membership μ for 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 the fuzzy classification process, usually gets and does 2; ‖ ‖ is the norm expression formula.

Use above degree of membership value or its distortion to obtain new input matrix, for fuzzy group k, its input matrix is deformed into:

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

Func (μ wherein _ik) be degree of membership value μ _ikwarping function, generally get

exp (μ _ik) etc., Φ _ik(X _i, μ _ik) mean i 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 target of model training sample is output as O _i, the support vector machine of weighted is equivalent to following quadratic programming problem to fitting problems by conversion:

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

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 the Nonlinear Mapping function, N is number of training, ξ={ ξ ₁..., ξ _nslack variable, ξ _ii component of slack variable, α _i, i=1 ..., N is i component of i 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 are respectively weight and the penalty factors of support vector machine, the transposition of subscript T representing matrix, μ _ikmean i the training sample X after standardization _ifor the degree of membership of fuzzy group k, Φ _ik(X _i, μ _ik) mean i 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 is output as at training sample i:

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

Wherein,

be the prediction output of k error back propagation fuzzy system output layer, K<be the kernel function of support vector machine, here K<the line taking kernel function; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.μ _mkmean m training sample X _mfor the degree of membership of fuzzy group k, Φ _mk(X _m, μ _mk) mean m input variable X _mand the degree of membership μ of fuzzy group k _mkcorresponding new input matrix.

Gravity model appoach in the Anti-fuzzy method obtains the output of last fuzzy system:

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

be corresponding to the training sample X after i standardization _ithe furnace 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 the normal bound scope of producing, the new data that makes furnace temperature the best of producing in the DCS database when normal is added to the training sample data, upgrade soft-sensing model.

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

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

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