CN103472865A - Intelligent least-square system and method for optimizing incinerator temperature of pesticide waste liquid incinerator - Google Patents

Abstract

The invention discloses an intelligent least-square system and method for optimizing incinerator temperature of a pesticide waste liquid incinerator. According to the method, a least-square support vector machine is adopted as a local equation of a fuzzy system, defuzzification output is carried out on the output of the least-square support vector machine, and accurate control over the incinerator temperature is achieved. In the system and the method, training samples are processed through a standardization processing module and used as the input of a fuzzy system module; an incinerator temperature predicted value obtained from the fuzzy system module and an operating variable value optimizing the incinerator temperature are connected with a result display module, and the result display module is used for transmitting results to a DCS; a model updating module is used for collecting the signals of a field intelligent instrument according to a set sampling time interval. According to the system and the method, the fact that the incinerator temperature is calculated in real time and controlled accurately is achieved, and overshooting of the incinerator temperature is avoided.

Description

Pesticide waste liquid incinerator furnace temperature optimization system and the method for intelligence least square

Technical field

The present invention relates to pesticide producing liquid waste incineration field, especially, relate to pesticide waste liquid incinerator furnace temperature optimization system and the method for intelligent least square.

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 pesticide waste liquid incinerator furnace temperature optimization 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.Due to the standard support vector machine to isolated point and noise sensitivity, so proposed again afterwards Weighted Support Vector.Weighted Support Vector can be processed the sample data with noise better than the standard support vector machine, is selected as the local equation in fuzzy system here.

Particle cluster algorithm, Particle Swarm Optimization, be a kind of a kind of biological intelligence optimizing algorithm of seeking global optimum by imitating the Bird Flight behavior put forward by Kennedy and professor Eberhart, is called for short PSO.This algorithm, by interparticle influencing each other in colony, has reduced searching algorithm and has been absorbed in the risk of locally optimal solution, has good global search performance.Particle cluster algorithm is used to the best parameter group of search weighted support vector machine, to reach the purpose of Optimized model.

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 pesticide waste liquid incinerator furnace temperature optimization system and the method that occur that furnace temperature is too low or too high.

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

The pesticide waste liquid incinerator furnace temperature optimization system of intelligence least square, 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: $\stackrel{\‾}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{TX}}_i---\left(1\right)$

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 get and do 2, || || be 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)=[1func(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.

Weighted Support Vector, 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:

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

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 corresponding Lagrange multiplier, and w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and γ is the penalty factor of least square method supporting vector machine, the transposition of subscript T representing matrix, μ _iki training sample X after the expression 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,

the output of fuzzy group k at training sample i, 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.K<be the kernel function of Weighted Support Vector, here K<the line taking kernel function; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.

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

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

In formula,

the output of fuzzy system,

the output of fuzzy group k at training sample i

The intelligent optimization module, be optimized penalty factor and the error margin value of fuzzy system Weighted Support Vector local equation for adopting particle cluster algorithm, and the specific implementation step is as follows:

1. the penalty factor that the Optimal Parameters of determining population is the Weighted Support Vector local equation and error margin value, population individual amount popsize, largest loop optimizing number of times iter _max, a p particle initial position r _p, initial velocity v _p, local optimum Lbest _pand the global optimum Gbest of whole population.

2. set the optimization aim function, be converted into fitness, each On Local Fuzzy equation is estimated; Calculate fitness function by corresponding error function, and think that the large particle fitness of error is little, the fitness function of particle p is expressed as:

f _p=1/(E _p+1) （11）

In formula, E _pbe the error function of fuzzy system, be expressed as:

$E_p=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{({\hat{y}}_i-O_i)}^2}---\left(12\right)$

In formula,

the prediction output of fuzzy system, O _itarget output for fuzzy system;

3. according to following formula, speed and the position of each particle upgraded in circulation,

v _p(iter+1)=ω×v _p(iter)+m ₁a ₁(Lbest _p-r _p(iter))+m ₂a ₂(Gbest-r _p(iter))

（13）

r _p(iter+1)=r _p(iter)+v _p(iter+1) （14）

In formula, v _pmean the more speed of new particle p, r _pmean the more position of new particle p, Lbest _pmean the more individual optimal value of new particle p, Gbest is the global optimum of whole population, and iter means cycle index, and ω is the inertia weight in particle cluster algorithm, m ₁, m ₂corresponding accelerator coefficient, a ₁, a ₂it is the random number between [0,1];

4. for particle p, if new fitness is greater than original individual optimal value, the individual optimal value of new particle more:

Lbest _p=f _p （15）

If the 5. individual optimal value Lbest of particle p _pbe greater than original population global optimum Gbest, upgrade original population global optimum Gbest:

Gbest=Lbest _p （16）

6. judge whether to meet performance requirement, if, finish optimizing, obtain the local equation parameter of one group of fuzzy system of optimizing; Otherwise return to step 3., continue the iteration optimizing, until reach maximum iteration time iter _max.

Gbest is 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 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, 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 pesticide waste liquid incinerator furnace temperature optimization system of intelligence least square 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: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

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 get and do 2, || || be 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)=[1func(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.

Weighted Support Vector, 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:

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

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 corresponding Lagrange multiplier, and w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and γ is the penalty factor of least square method supporting vector machine, the transposition of subscript T representing matrix, μ _iki training sample X after the expression 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,

the output of fuzzy group k at training sample i, 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.K<be the kernel function of Weighted Support Vector, here K<the line taking kernel function; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.

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

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

In formula,

the output of fuzzy system, the output of fuzzy group k at training sample i

4), adopt particle cluster algorithm to be optimized penalty factor and the error margin value of Weighted Support Vector local equation in fuzzy system, the specific implementation step is as follows:

1. the penalty factor that the Optimal Parameters of determining population is the Weighted Support Vector local equation and error margin value, population individual amount popsize, largest loop optimizing number of times iter _max, a p particle initial position r _p, initial velocity v _p, local optimum Lbest _pand the global optimum Gbest of whole population.

2. set the optimization aim function, be converted into fitness, each On Local Fuzzy equation is estimated; Calculate fitness function by corresponding error function, and think that the large particle fitness of error is little, the fitness function of particle p is expressed as:

f _p=1/(E _p+1) （11）

In formula, E _pbe the error function of fuzzy system, be expressed as:

$E_p=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{({\hat{y}}_i-O_i)}^2}---\left(12\right)$

In formula,

the prediction output of fuzzy system, O _itarget output for fuzzy system;

3. according to following formula, speed and the position of each particle upgraded in circulation,

v _p(iter+1)=ω×v _p(iter)+m ₁a ₁(Lbest _p-r _p(iter))+m ₂a ₂(Gbest-r _p(iter))

（13）

r _p(iter+1)=r _p(iter)+v _p(iter+1) （14）

In formula, v _pmean the more speed of new particle p, r _pmean the more position of new particle p, Lbest _pmean the more individual optimal value of new particle p, Gbest is the global optimum of whole population, and iter means cycle index, and ω is the inertia weight in particle cluster algorithm, m ₁, m ₂corresponding accelerator coefficient, a ₁, a ₂it is the random number between [0,1];

4. for particle p, if new fitness is greater than original individual optimal value, the individual optimal value of new particle more:

Lbest _p=f _p （15）

If the 5. individual optimal value Lbest of particle p _pbe greater than original population global optimum Gbest, upgrade original population global optimum Gbest:

Gbest=Lbest _p （16）

6. judge whether to meet performance requirement, if, finish optimizing, obtain the local equation parameter of one group of fuzzy system of optimizing; Otherwise return to step 3., continue the iteration optimizing, until reach maximum iteration time iter _max.

Gbest is 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.

5), 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 4), result is passed 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: invent pesticide waste liquid incinerator furnace temperature optimization system and the method for intelligent least square, search 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 pesticide waste liquid incinerator furnace temperature optimization system of intelligence least square, 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: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

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 _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 get and do 2, || || be 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)=[1func(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.

Weighted Support Vector, 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:

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

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 corresponding Lagrange multiplier, and w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and γ is the penalty factor of least square method supporting vector machine, the transposition of subscript T representing matrix, μ _iki training sample X after the expression 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,

the output of fuzzy group k at training sample i, 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.K<be the kernel function of Weighted Support Vector, here K<the line taking kernel function; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.

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

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

In formula,

the output of fuzzy system,

the output of fuzzy group k at training sample i

Intelligent optimization module 9, be optimized penalty factor and the error margin value of fuzzy system Weighted Support Vector local equation for adopting particle cluster algorithm, and the specific implementation step is as follows:

1. the penalty factor that the Optimal Parameters of determining population is the Weighted Support Vector local equation and error margin value, population individual amount popsize, largest loop optimizing number of times iter _max, a p particle initial position r _p, initial velocity v _p, local optimum Lbest _pand the global optimum Gbest of whole population.

2. set the optimization aim function, be converted into fitness, each On Local Fuzzy equation is estimated; Calculate fitness function by corresponding error function, and think that the large particle fitness of error is little, the fitness function of particle p is expressed as:

f _p=1/(E _p+1) （11）

In formula, E _pbe the error function of fuzzy system, be expressed as:

$E_p=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{({\hat{y}}_i-O_i)}^2}---\left(12\right)$

In formula, the prediction output of fuzzy system, O _itarget output for fuzzy system;

3. according to following formula, speed and the position of each particle upgraded in circulation,

v _p(iter+1)=ω×v _p(iter)+m ₁a ₁(Lbest _p-r _p(iter))+m ₂a ₂(Gbest-r _p(iter))

（13）

r _p(iter+1)=r _p(iter)+v _p(iter+1) （14）

In formula, v _pmean the more speed of new particle p, r _pmean the more position of new particle p, Lbest _pmean the more individual optimal value of new particle p, Gbest is the global optimum of whole population, and iter means cycle index, and ω is the inertia weight in particle cluster algorithm, m ₁, m ₂corresponding accelerator coefficient, a ₁, a ₂it is the random number between [0,1];

4. for particle p, if new fitness is greater than original individual optimal value, the individual optimal value of new particle more:

Lbest _p=f _p （15）

If the 5. individual optimal value Lbest of particle p _pbe greater than original population global optimum Gbest, upgrade original population global optimum Gbest:

Gbest=Lbest _p （16）

6. judge whether to meet performance requirement, if, finish optimizing, obtain the local equation parameter of one group of fuzzy system of optimizing; Otherwise return to step 3., continue the iteration optimizing, until reach maximum iteration time iter _max.

Gbest is 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 11, 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 12, 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 10 as a result, be used for the furnace temperature predicted value will obtained and make the performance variable value of furnace temperature the best pass 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 pesticide waste liquid incinerator furnace temperature optimization method of intelligent least square, 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: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

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 after standardization 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 get and do 2, || || be 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)=[1func(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.

Weighted Support Vector, 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:

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

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 corresponding Lagrange multiplier, and w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and γ is the penalty factor of least square method supporting vector machine, the transposition of subscript T representing matrix, μ _iki training sample X after the expression 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, the output of fuzzy group k at training sample i, 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.K<be the kernel function of Weighted Support Vector, here K<the line taking kernel function; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.

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

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

In formula,

the output of fuzzy system,

the output of fuzzy group k at training sample i

4), adopt particle cluster algorithm to be optimized penalty factor and the error margin value of Weighted Support Vector local equation in fuzzy system, the specific implementation step is as follows:

1. the penalty factor that the Optimal Parameters of determining population is the Weighted Support Vector local equation and error margin value, population individual amount popsize, largest loop optimizing number of times iter _max, a p particle initial position r _p, initial velocity v _p, local optimum Lbest _pand the global optimum Gbest of whole population.

2. set the optimization aim function, be converted into fitness, each On Local Fuzzy equation is estimated; Calculate fitness function by corresponding error function, and think that the large particle fitness of error is little, the fitness function of particle p is expressed as:

f _p=1/(E _p+1) （11）

In formula, E _pbe the error function of fuzzy system, be expressed as:

$E_p=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{({\hat{y}}_i-O_i)}^2}---\left(12\right)$

In formula, the prediction output of fuzzy system, O _itarget output for fuzzy system;

3. according to following formula, speed and the position of each particle upgraded in circulation,

v _p(iter+1)=ω×v _p(iter)+m ₁a ₁(Lbest _p-r _p(iter))+m ₂a ₂(Gbest-r _p(iter))

（13）

r _p(iter+1)=r _p(iter)+v _p(iter+1) （14）

In formula, v _pmean the more speed of new particle p, r _pmean the more position of new particle p, Lbest _pmean the more individual optimal value of new particle p, Gbest is the global optimum of whole population, and iter means cycle index, and ω is the inertia weight in particle cluster algorithm, m ₁, m ₂corresponding accelerator coefficient, a ₁, a ₂it is the random number between [0,1];

4. for particle p, if new fitness is greater than original individual optimal value, the individual optimal value of new particle more:

Lbest _p=f _p （15）

If the 5. individual optimal value Lbest of particle p _pbe greater than original population global optimum Gbest, upgrade original population global optimum Gbest:

Gbest=Lbest _p （16）

6. judge whether to meet performance requirement, if, finish optimizing, obtain the local equation parameter of one group of fuzzy system of optimizing; Otherwise return to step 3., continue the iteration optimizing, until reach maximum iteration time iter _max.

Gbest is 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.

5), 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.

6), calculate the furnace temperature predicted value and make the performance variable value of furnace temperature the best in described step 4), result is passed to the DCS system, show at the control station of DCS, and be delivered to operator station by DCS system and fieldbus and shown.

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. pesticide waste liquid incinerator furnace temperature optimization system and the method for an intelligent least square, 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: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

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 _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 get and do 2, || || be 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)=[1func(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.

Weighted Support Vector, 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:

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

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 corresponding Lagrange multiplier, and w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and γ is the penalty factor of least square method supporting vector machine, the transposition of subscript T representing matrix, μ _iki training sample X after the expression 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, the output of fuzzy group k at training sample i, 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.K<be the kernel function of Weighted Support Vector, here K<the line taking kernel function; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.

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

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

In formula,

the output of fuzzy system,

be the output intelligent optimization module of fuzzy group k at training sample i, for adopting particle cluster algorithm, penalty factor and the error margin value of fuzzy system Weighted Support Vector local equation be optimized, the specific implementation step is as follows:

1. the penalty factor that the Optimal Parameters of determining population is the Weighted Support Vector local equation and error margin value, population individual amount popsize, largest loop optimizing number of times iter _max, a p particle initial position r _p, initial velocity v _p, local optimum Lbest _pand the global optimum Gbest of whole population.

2. set the optimization aim function, be converted into fitness, each On Local Fuzzy equation is estimated; Calculate fitness function by corresponding error function, and think that the large particle fitness of error is little, the fitness function of particle p is expressed as:

f _p=1/(E _p+1) （11）

In formula, E _pbe the error function of fuzzy system, be expressed as:

$E_p=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{({\hat{y}}_i-O_i)}^2}---\left(12\right)$

In formula, the prediction output of fuzzy system, O _itarget output for fuzzy system;

3. according to following formula, speed and the position of each particle upgraded in circulation,

v _p(iter+1)=ω×v _p(iter)+m ₁a ₁(Lbest _p-r _p(iter))+m ₂a ₂(Gbest-r _p(iter))

（13）

r _p(iter+1)=r _p(iter)+v _p(iter+1) （14）

In formula, v _pmean the more speed of new particle p, r _pmean the more position of new particle p, Lbest _pmean the more individual optimal value of new particle p, Gbest is the global optimum of whole population, and iter means cycle index, and ω is the inertia weight in particle cluster algorithm, m ₁, m ₂corresponding accelerator coefficient, a ₁, a ₂it is the random number between [0,1];

4. for particle p, if new fitness is greater than original individual optimal value, the individual optimal value of new particle more:

Lbest _p=f _p （15）

If the 5. individual optimal value Lbest of particle p _pbe greater than original population global optimum Gbest, upgrade original population global optimum Gbest:

Gbest=Lbest _p （16）

6. judge whether to meet performance requirement, if, finish optimizing, obtain the local equation parameter of one group of fuzzy system of optimizing; Otherwise return to step 3., continue the iteration optimizing, until reach maximum iteration time iter _max.

Gbest is 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.

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 pesticide waste liquid incinerator furnace temperature optimization method of an intelligent least square, 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: $\stackrel{\&OverBar;}{\mathrm{TX}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{TX}}_i---\left(1\right)$

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 get and do 2, || || be 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)=[1func(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.

Weighted Support Vector, 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:

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

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 corresponding Lagrange multiplier, and w is the normal vector of support vector machine lineoid, and b is corresponding side-play amount, and γ is the penalty factor of least square method supporting vector machine, the transposition of subscript T representing matrix, μ _iki training sample X after the expression 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,

the output of fuzzy group k at training sample i, 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.K<be the kernel function of Weighted Support Vector, here K<the line taking kernel function; α _m, m=1 ..., N is m component of corresponding Lagrange multiplier.

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

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

In formula, the output of fuzzy system,

the output of fuzzy group k at training sample i

4), adopt particle cluster algorithm to be optimized penalty factor and the error margin value of Weighted Support Vector local equation in fuzzy system, the specific implementation step is as follows:

1. the penalty factor that the Optimal Parameters of determining population is the Weighted Support Vector local equation and error margin value, population individual amount popsize, largest loop optimizing number of times iter _max, a p particle initial position r _p, initial velocity v _p, local optimum Lbest _pand the global optimum Gbest of whole population.

2. set the optimization aim function, be converted into fitness, each On Local Fuzzy equation is estimated; Calculate fitness function by corresponding error function, and think that the large particle fitness of error is little, the fitness function of particle p is expressed as:

f _p=1/(E _p+1) （11）

In formula, E _pbe the error function of fuzzy system, be expressed as:

$E_p=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{({\hat{y}}_i-O_i)}^2}---\left(12\right)$

In formula,

the prediction output of fuzzy system, O _itarget output for fuzzy system;

3. according to following formula, speed and the position of each particle upgraded in circulation,

v _p(iter+1)=ω×v _p(iter)+m ₁a ₁(Lbest _p-r _p(iter))+m ₂a ₂(Gbest-r _p(iter))

（13）

r _p(iter+1)=r _p(iter)+v _p(iter+1) （14）

In formula, v _pmean the more speed of new particle p, r _pmean the more position of new particle p, Lbest _pmean the more individual optimal value of new particle p, Gbest is the global optimum of whole population, and iter means cycle index, and ω is the inertia weight in particle cluster algorithm, m ₁, m ₂corresponding accelerator coefficient, a ₁, a ₂it is the random number between [0,1];

4. for particle p, if new fitness is greater than original individual optimal value, the individual optimal value of new particle more:

Lbest _p=f _p （15）

If the 5. individual optimal value Lbest of particle p _pbe greater than original population global optimum Gbest, upgrade original population global optimum Gbest:

Gbest=Lbest _p （16）

6. judge whether to meet performance requirement, if, finish optimizing, obtain the local equation parameter of one group of fuzzy system of optimizing; Otherwise return to step 3., continue the iteration optimizing, until reach maximum iteration time iter _max.

Gbest is 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:

5), 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.

6), calculate the furnace temperature predicted value and make the performance variable value of furnace temperature the best in described step 4), result is passed 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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