CN103488206B

CN103488206B - The optimizing temperature of pesticide production waste liquid incinerator system and method for intelligence radial basis

Info

Publication number: CN103488206B
Application number: CN201310436865.8A
Authority: CN
Inventors: 刘兴高; 李见会; 张明明; 孙优贤
Original assignee: Zhejiang University ZJU
Current assignee: Zhejiang University ZJU
Priority date: 2013-09-22
Filing date: 2013-09-22
Publication date: 2015-09-09
Anticipated expiration: 2033-09-22
Also published as: CN103488206A

Abstract

The invention discloses a kind of optimizing temperature of pesticide production waste liquid incinerator system and method for intelligent radial basis.The method uses radial base neural net as the local equation of fuzzy system, carrying out Fuzzy processing, reducing the impact of noise on Control for Kiln Temperature precision by exporting the mapping of training sample.In the present invention, standardization module, for training sample centralization; Fuzzy system module, for soft sensor modeling; Intelligent optimization module, for optimization system parameter; Model modification module presses the sampling time interval of setting, collection site intelligent instrument signal; Result display module, for by the furnace temperature obtained predicted value and make the performance variable value of furnace temperature the best pass to DCS system; Signal acquisition module, for the time interval according to setting, image data from database.The real-time control, the control accuracy that present invention achieves furnace temperature are high, by avoiding to the suppression of noise the overshoot occurring furnace temperature.

Description

Intelligent radial-based pesticide production waste liquid incinerator temperature optimization system and method

Technical Field

The invention relates to the field of pesticide production waste liquid incineration, in particular to an intelligent radial-based pesticide production waste liquid incinerator temperature optimization system and method.

Background

With the rapid development of the pesticide industry, the environmental pollution problem of the emissions has attracted high attention from governments and corresponding environmental protection departments of various countries. The research and the solution of the standard-reaching discharge control and the harmless minimization treatment of the pesticide organic waste liquid not only become the difficulty and the hot point of scientific research of various countries, but also are the scientific proposition of the national urgent need related to the sustainable development of society.

The incineration method is the most effective and thorough method for treating pesticide residue and waste residue at present and is the most common method for application. The temperature of the incinerator must be kept at a proper temperature in the incineration process, and the excessively low incinerator temperature is not beneficial to the decomposition of toxic and harmful components in the waste; the overhigh furnace temperature not only increases the fuel consumption and the equipment operation cost, but also easily damages the inner wall of the hearth and shortens the service life of the equipment. In addition, excessive temperatures may increase the amount of metal volatilization and nitrogen oxide formation in the waste. Particularly for chlorine-containing wastewater, the corrosion of the inner wall can be reduced by proper furnace temperature. However, factors influencing the furnace temperature in the actual incineration process are complex and changeable, and the phenomenon that the furnace temperature is too low or too high is easy to occur.

In recent years, artificial neural networks, especially radial basis function neural networks, have achieved good results in terms of system optimization. The neural network has strong self-adaption, self-organization and self-learning capabilities and large-scale parallel operation capabilities. In practical applications, however, neural networks also expose some inherent drawbacks: the initialization of the weight is random and is easy to fall into local minimum; the selection of the number of nodes and other parameters of the hidden layer in the learning process can be selected only according to experience and experiments; too long convergence time, poor robustness, etc. Secondly, the DCS data collected in the industrial field also has certain uncertain errors due to noise, manual operation errors, and the like, so that a model using an artificial neural network with strong certainty is generally not strong in popularization capability.

Zadeh first proposed the concept of fuzzy aggregation in 1965 by american mathematician l. Fuzzy logic then begins to replace the classical logic that persists in that everything can be represented in terms of binary terms in a way that it more closely resembles the question and semantic statement of everyday people. Fuzzy logic has been successfully applied in various fields of industry, such as home appliances, industrial control, etc. In 2003, Demirci proposed the concept of fuzzy system, which constructs a new input matrix by using fuzzy membership matrix and its variants, and then derives the analytic value as the final output in the local equation by the centroid method in the inverse fuzzy method. For the system and the method for optimizing the temperature of the pesticide production waste liquid incinerator, the influence of noise and operation errors in the industrial production process are considered, and the influence of fuzzy performance of fuzzy logic on the accuracy can be reduced.

Particle Swarm Optimization, namely Particle Swarm Optimization, is a biological intelligent Optimization algorithm proposed by professors Kennedy and Eberhart to seek global optimality by simulating bird flight behavior, called PSO for short. The algorithm reduces the risk that the search algorithm is trapped in the local optimal solution through the mutual influence among particles in the group, and has good global search performance. The particle swarm algorithm is used for searching the optimal parameter combination of the radial basis fuzzy system so as to achieve the aim of optimizing the model.

Disclosure of Invention

In order to overcome the defects that the furnace temperature of the conventional incinerator is difficult to control and is easy to be too low or too high, the invention provides the system and the method for optimizing the furnace temperature of the pesticide production waste liquid incinerator, which realize accurate control of the furnace temperature and avoid the occurrence of too low or too high furnace temperature.

The technical scheme adopted by the invention for solving the technical problems is as follows:

the intelligent radial-base system for optimizing the furnace temperature of the pesticide production waste liquid incinerator comprises the incinerator, an intelligent instrument, a DCS (distributed control system), a data interface and an upper computer, wherein the DCS comprises a control station and a database; on-spot intelligent instrument and DCS headtotail, the DCS system is connected with the host computer, the host computer include: the standardization processing module is used for preprocessing the model training samples input from the DCS database, centralizing the training samples, namely subtracting the average value of the samples, and then standardizing the training samples:

calculating an average value:

<math> <mrow> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> <mo>=</mo> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <munderover> <mi>Σ</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </math>

calculating the variance:

<math> <mrow> <msubsup> <mi>σ</mi> <mi>x</mi> <mn>2</mn> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mi>N</mi> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <munderover> <mi>Σ</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </math>

and (3) standardization:

<math> <mrow> <mi>X</mi> <mo>=</mo> <mfrac> <mrow> <mi>TX</mi> <mo>-</mo> <mover> <mi>TX</mi> <mo>&OverBar;</mo> </mover> </mrow> <msub> <mi>σ</mi> <mi>x</mi> </msub> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein, TX_iThe ith training sample is data of key variables, furnace temperature and operating variables for optimizing the furnace temperature during normal production collected from the DCS database, N is the number of training samples,is the mean of the training samples, and X is the normalized training sample. σ x denotes the standard deviation of the training samples, σ²x represents the variance of the training samples.

And the fuzzy system module is used for fuzzifying the standardized training sample X transmitted from the data preprocessing module. Let c in fuzzy system^*A fuzzy group, the centers of the fuzzy groups k and j are v_k、v_jThe ith normalized training sample X_iMembership mu for fuzzy group k_ikComprises the following steps:

in the formula, n is a blocking matrix index required in the fuzzy classification process, and is usually taken as 2, | | · |, as a norm expression.

Using the above membership values or its variants to obtain a new input matrix, for the fuzzy group k, its input matrix variant is:

Φ_ik(X_i,μ_ik)=[1 func(μ_ik) X_i] （5）

wherein func (. mu.)_ik) Is a membership value mu_ikDeformation function of, in general, takeexp(μ_ik) Equal, phi_ik(X_i,μ_ik) Denotes the ith input variable X_iAnd membership mu of its fuzzy group k_ikThe corresponding new input matrix.

The radial basis function neural network is used as a local equation of the fuzzy system, and optimal fitting is carried out on each fuzzy group. The output of the kth particle swarm optimization radial basis fuzzy system is:

wherein C is_lkAnd w_lkOptimizing the center and output weight, phi, of the ith node of the radial basis fuzzy system by the kth particle swarm algorithm_lk(||X_i-C_lk| |) is the radial basis function of the ith node of the k particle swarm optimization radial basis fuzzy system, and is determined by the following formula:

wherein sigma_lkIs the gaussian width of the corresponding radial basis function, | | | · | |, is a norm expression. Finally, the center of gravity method in the anti-fuzzy method is used to obtain the final prediction output of the fuzzy equation system

：

In the formula,is the predicted output of the fuzzy equation system.

An intelligent optimization module for adopting particle swarm optimization to perform C of the radial basis function neural network local equation in the fuzzy system_pk、σ_pk、w_pkThe optimization is carried out, and the specific steps are as follows:

firstly, determining optimized parameters of particle swarm as C of radial basis function neural network local equation_lk、σ_lk、w_lkNumber of individual particle groups popsize, maximum number of cyclic optimization iter_maxInitial position r of the p-th particle_pInitial velocity v_pLocal optimum value Lbest_pAnd a global optimum Gbest for the entire population of particles.

Secondly, setting an optimization objective function, converting the optimization objective function into fitness, and evaluating each local fuzzy equation; calculating a fitness function through a corresponding error function, considering that the fitness of the particle with large error is small, and expressing the fitness function of the particle p as follows:

f_p=1/(E_p+1) （9）

in the formula, E_pIs the error function of the fuzzy system, expressed as:

in the formula,is the predicted output of a system of fuzzy equations, O_iThe target output of the fuzzy equation system is shown, and N is the number of training samples;

thirdly, according to the following formula, the speed and the position of each particle are circularly updated,

v_p(iter+1)=ω×v_p(iter)+m₁a₁(Lbest_p-r_p(iter))+m₂a₂(Gbest-r_p(iter))

（11）

r_p(iter+1)=r_p(iter)+v_p(iter+1) （12）

in the formula, v_pIndicates the velocity, r, of the update particle p_pIndicating the position of the update particle p, Lbest_pRepresenting the individual optimum value of the update particle p, Gbest being that corresponding to the ith normalizedTraining sample X_iIs a value of an operating variable for optimizing the furnace temperature, iter represents the number of cycles, ω is an inertial weight in a particle swarm algorithm, m₁、m₂Is the corresponding acceleration factor, a₁、a₂Is [0,1 ]]A random number in between;

and fourthly, for the particle p, if the new fitness is larger than the original individual optimal value, updating the individual optimal value of the particle:

Lbest_p=f_p （13）

if the individual optimum value Lbest of the particle p_pAnd (3) updating the original particle swarm global optimum value Gbest if the particle swarm global optimum value Gbest is larger than the original particle swarm global optimum value Gbest:

Gbest=Lbest_p （14）

sixthly, judging whether the performance requirement is met, if so, finishing the optimization to obtain a group of optimized local equation parameters of the fuzzy system; otherwise, returning to the step III, continuing the iteration optimization until reaching the maximum iteration number iter_max。

Gbest is the training sample X corresponding to the ith standard_iAnd an operating variable value for optimizing the furnace temperature.

As a preferred solution: the host computer still include: and the model updating module is used for acquiring field intelligent instrument signals according to a set sampling time interval, comparing the obtained actually-measured furnace temperature with a system forecast value, and adding new data which enables the furnace temperature to be optimal when the furnace temperature is normally produced in the DCS database into the training sample data to update the soft measurement model if the relative error is more than 10% or the furnace temperature exceeds the upper and lower normal production limit ranges.

Further, the host computer still include: the result display module is used for transmitting the obtained furnace temperature forecast value and the operation variable value which enables the furnace temperature to be optimal to the DCS, displaying the values at a control station of the DCS and transmitting the values to a field operation station for displaying through the DCS and a field bus; at the same time, the DCS system automatically executes the furnace temperature optimization operation by using the obtained value of the operation variable that optimizes the furnace temperature as a new operation variable set value.

And the signal acquisition module is used for acquiring data from the database according to the set time interval of each sampling.

Still further, the key variables include the flow of waste liquid into the incinerator, the flow of air into the incinerator, and the flow of fuel into the incinerator; the manipulated variables include air flow into the incinerator and fuel flow into the incinerator.

The method for optimizing the furnace temperature of the pesticide production waste liquid incinerator by using the intelligent radial basis system comprises the following specific implementation steps of:

1) determining used key variables, collecting data of the variables in normal production from a DCS (distributed control system) database as an input matrix of a training sample TX, and collecting corresponding furnace temperature and operation variable data for optimizing the furnace temperature as an output matrix O;

2) preprocessing a model training sample input from a DCS database, centralizing the training sample, namely subtracting the average value of the sample, and then normalizing the training sample so that the average value is 0 and the variance is 1. The processing is accomplished using the following mathematical process:

2.1) calculating the mean value:

2.2) calculating the variance:

2.3) standardization:

wherein, TX_iThe ith training sample is data of key variables, furnace temperature and operating variables for optimizing the furnace temperature during normal production collected from the DCS database, N is the number of training samples,is the mean of the training samples, and X is the normalized training sample. σ x denotes the standard deviation of the training samples, and σ 2x denotes the variance of the training samples.

3) And fuzzifying the training sample transmitted from the data preprocessing module. Let c in fuzzy system^*One fuzzy group, among fuzzy groups k, jHeart is v respectively_k、v_jThe ith normalized training sample X_iMembership mu for fuzzy group k_ikComprises the following steps:

Φ_ik(X_i,μ_ik)=[1 func(μ_ik) X_i] （5）

wherein sigma_lkIs the gaussian width of the corresponding radial basis function, | | | · | |, is a norm expression. And finally, obtaining the final prediction output of the fuzzy equation system by a gravity center method in an anti-fuzzy method:

in the formula,is the predicted output of the fuzzy equation system.

4) C for radial basis function neural network local equation in fuzzy system by adopting particle swarm optimization_pk、σ_pk、w_pkThe optimization is carried out, and the specific implementation steps are as follows:

f_p=1/(E_p+1) （9）

in the formula, E_pIs the error function of the fuzzy system, expressed as:

in the formula,is the prediction output of the fuzzy system, O_iThe target output of the fuzzy equation system is shown, and N is the number of training samples;

v_p(iter+1)=ω×v_p(iter)+m₁a₁(Lbest_p-r_p(iter))+m₂a₂(Gbest-r_p(iter))

（11）

r_p(iter+1)=r_p(iter)+v_p(iter+1) （12）

in the formula, v_pIndicates the velocity, r, of the update particle p_pIndicating the position of the update particle p, Lbest_pRepresenting the individual optimum value of the update particle p, Gbest being the training sample X corresponding to the ith normalization_iIs a value of an operating variable for optimizing the furnace temperature, iter represents the number of cycles, ω is an inertial weight in a particle swarm algorithm, m₁、m₂Is the corresponding acceleration factor, a₁、a₂Is [0,1 ]]A random number in between;

Lbest_p=f_p （13）

Gbest=Lbest_p （14）

As a preferred solution: the method further comprises the following steps: 5) acquiring on-site intelligent instrument signals according to a set sampling time interval, comparing the obtained actually-measured furnace temperature with a system forecast value, and if the relative error is more than 10% or the furnace temperature exceeds the upper and lower production normal limit ranges, adding new data which enables the furnace temperature to be optimal when the furnace temperature is normally produced in the DCS database into training sample data, and updating the soft measurement model.

6) Further, calculating to obtain an optimal operating variable value in the step 4), transmitting the obtained furnace temperature forecast value and the operating variable value which enables the furnace temperature to be optimal to a DCS (distributed control system), displaying the values on a control station of the DCS, and transmitting the values to a field operating station through the DCS and a field bus for displaying; at the same time, the DCS system automatically executes the furnace temperature optimization operation by using the obtained value of the operation variable that optimizes the furnace temperature as a new operation variable set value.

The technical conception of the invention is as follows: the invention discloses an intelligent radial basis system and method for optimizing the furnace temperature of a pesticide production waste liquid incinerator, and aims to find a furnace temperature forecast value and an operation variable value for optimizing the furnace temperature.

The invention has the following beneficial effects: 1. establishing an online soft measurement model of a quantitative relation between a system key variable and furnace temperature; 2. the operating conditions that optimize the furnace temperature are quickly found.

Drawings

FIG. 1 is a hardware block diagram of the system proposed by the present invention;

fig. 2 is a functional structure diagram of the upper computer according to the present invention.

Detailed Description

The invention is further described below with reference to the accompanying drawings. The examples are intended to illustrate the invention, but not to limit the invention, and any modifications and variations of the invention within the spirit and scope of the claims are intended to fall within the scope of the invention.

Example 1

Referring to fig. 1 and 2, the system for optimizing the furnace temperature of the intelligent radial-based pesticide production waste liquid incinerator comprises a field intelligent instrument 2 connected with an incinerator object 1, a DCS system and an upper computer 6, wherein the DCS system comprises a data interface 3, a control station 4 and a database 5, the field intelligent instrument 2 is connected with the data interface 3, the data interface is connected with the control station 4, the database 5 and the upper computer 6, and the upper computer 6 comprises:

a normalization processing module 7, configured to pre-process the model training samples input from the DCS database, centralize the training samples, that is, subtract the average value of the samples, and then normalize them:

calculating an average value:

calculating the variance:

and (3) standardization:

And the fuzzy system module 8 is used for fuzzifying the standardized training sample X transmitted from the data preprocessing module. Let c in fuzzy system^*A fuzzy group, the centers of the fuzzy groups k and j are v_k、v_jThe ith normalized training sample X_iMembership mu for fuzzy group k_ikComprises the following steps:

Φ_ik(X_i,μ_ik)=[1 func(μ_ik) X_i] （5）

in the formula,is the predicted output of the fuzzy equation system.

An intelligent optimization module 9 for adopting the particle swarm algorithm to perform C on the radial basis function neural network local equation in the fuzzy system_pk、σ_pk、w_pkThe optimization is carried out, and the specific implementation steps are as follows:

f_p=1/(E_p+1) （9）

in the formula, E_pIs the error function of the fuzzy system, expressed as:

v_p(iter+1)=ω×v_p(iter)+m₁a₁(Lbest_p-r_p(iter))+m₂a₂(Gbest-r_p(iter))

(11)

r_p(iter+1)=r_p(iter)+v_p(iter+1) （12）

in the formula, v_pIndicates the velocity, r, of the update particle p_pIndicating the position of the update particle p, Lbest_pTo representUpdating the individual optimal value of the particle p, wherein Gbest is the training sample X corresponding to the ith normalization_iIs a value of an operating variable for optimizing the furnace temperature, iter represents the number of cycles, ω is an inertial weight in a particle swarm algorithm, m₁、m₂Is the corresponding acceleration factor, a₁、a₂Is [0,1 ]]A random number in between;

Lbest_p=f_p （13）

Gbest=Lbest_p （14）

The upper computer 6 further comprises: and the signal acquisition module 11 is used for acquiring data from the database according to a set time interval of each sampling.

The upper computer 6 further comprises: and the model updating module 12 is used for acquiring field intelligent instrument signals according to a set sampling time interval, comparing the obtained actually-measured furnace temperature with a system forecast value, and adding new data which enables the furnace temperature to be optimal when the furnace temperature is normally produced in the DCS database into the training sample data to update the soft measurement model if the relative error is more than 10% or the furnace temperature exceeds the upper and lower normal production limit ranges.

The key variables include the flow of waste liquid into the incinerator, the flow of air into the incinerator and the flow of fuel into the incinerator; the manipulated variables include air flow into the incinerator and fuel flow into the incinerator.

The system also comprises a DCS (distributed control system), wherein the DCS is composed of a data interface 3, a control station 4 and a database 5; the intelligent instrument 2, the DCS system and the upper computer 6 are sequentially connected through a field bus; the upper computer 6 further comprises a result display module 10, which is used for transmitting the calculation optimal result to the DCS system, displaying the process state at the control station of the DCS, and transmitting the process state information to the field operation station for displaying through the DCS system and the field bus.

When the waste liquid incineration process is provided with the DCS system, the functions of obtaining the furnace temperature forecast value and the operation variable value for optimizing the furnace temperature are mainly completed on the upper computer by utilizing the real-time and historical databases of the DCS system to detect and store the real-time dynamic data of the sample.

When the waste liquid incineration process is not provided with the DCS system, the data memory is adopted to replace the data storage function of a real-time and historical database of the DCS system, and the functional system for obtaining the furnace temperature forecast value and the operation variable value for optimizing the furnace temperature is manufactured into an independent complete system-on-chip which comprises an I/O element, a data memory, a program memory, an arithmetic unit and a display module and does not depend on the DCS system, so that the system-on-chip can be independently used regardless of whether the incineration process is provided with the DCS or not, and is more beneficial to popularization and use.

Example 2

Referring to fig. 1 and 2, the method for optimizing the temperature of the pesticide production waste liquid incinerator with the intelligent radial basis specifically comprises the following implementation steps:

2.1) calculating the mean value:

2.2) calculating the variance:

2.3) standardization:

3) And fuzzifying the training sample transmitted from the data preprocessing module. Let c in fuzzy system^*A fuzzy group, the centers of the fuzzy groups k and j are v_k、v_jThe ith normalized training sample X_iMembership mu for fuzzy group k_ikComprises the following steps:

Φ_ik(X_i,μ_ik)=[1 func(μ_ik) X_i] （5）

in the formula,is the predicted output of the fuzzy equation system.

f_p=1/(E_p+1) （9）

in the formula, E_pIs the error function of the fuzzy system, expressed as:

v_p(iter+1)=ω×v_p(iter)+m₁a₁(Lbest_p-r_p(iter))+m₂a₂(Gbest-r_p(iter))

（11）

r_p(iter+1)=r_p(iter)+v_p(iter+1)（12）

Lbest_p=f_p （13）

Gbest=Lbest_p （14）

The method further comprises the following steps: 5) acquiring on-site intelligent instrument signals according to a set sampling time interval, comparing the obtained actually-measured furnace temperature with a system forecast value, and if the relative error is more than 10% or the furnace temperature exceeds the upper and lower production normal limit ranges, adding new data which enables the furnace temperature to be optimal when the furnace temperature is normally produced in the DCS database into training sample data, and updating the soft measurement model.

Calculating to obtain an optimal operation variable value in the step 4), transmitting the obtained furnace temperature forecast value and the operation variable value which enables the furnace temperature to be optimal to the DCS, displaying the values at a control station of the DCS, and transmitting the values to a field operation station for displaying through the DCS and a field bus; at the same time, the DCS system automatically executes the furnace temperature optimization operation by using the obtained value of the operation variable that optimizes the furnace temperature as a new operation variable set value.

Claims

1. An intelligent radial-based pesticide production waste liquid incinerator temperature optimization system comprises an incinerator, a field intelligent instrument, a DCS (distributed control system), a data interface and an upper computer, wherein the DCS comprises a control station and a database; on-spot intelligent instrument and DCS headtotail, the DCS system is connected with the host computer, its characterized in that: the host computer include:

the standardization processing module is used for preprocessing the model training samples input from the DCS database, centralizing the training samples, namely subtracting the average value of the samples, and then standardizing the training samples:

calculating an average value:

<math> <mrow> <mover> <mrow> <mi>T</mi> <mi>X</mi> </mrow> <mo>&OverBar;</mo> </mover> <mo>=</mo> <mfrac> <mn>1</mn> <mi>N</mi> </mfrac> <munderover> <mo>Σ</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </math>

calculating the variance:

<math> <mrow> <msubsup> <mi>σ</mi> <mi>x</mi> <mn>2</mn> </msubsup> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mi>N</mi> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <munderover> <mo>Σ</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>TX</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mrow> <mi>T</mi> <mi>X</mi> </mrow> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow> </math>

and (3) standardization:

<math> <mrow> <mi>X</mi> <mo>=</mo> <mfrac> <mrow> <mi>T</mi> <mi>X</mi> <mo>-</mo> <mover> <mrow> <mi>T</mi> <mi>X</mi> </mrow> <mo>&OverBar;</mo> </mover> </mrow> <msub> <mi>σ</mi> <mi>x</mi> </msub> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow> </math>

wherein, TX_iThe ith training sample is data of key variables, furnace temperature and operating variables for optimizing the furnace temperature during normal production collected from the DCS database, N is the number of training samples,is the mean value of the training sample, and X is the training sample after standardization; sigma_xRepresenting the standard deviation, σ, of the training samples² _xRepresenting the variance of the training samples;

the fuzzy system module is used for fuzzifying the standardized training sample X transmitted from the standardization processing module; let c in fuzzy system^*A fuzzy group, the centers of the fuzzy groups k and j are v_k、v_jThe ith normalized training sample X_iMembership mu for fuzzy group k_ikComprises the following steps:

in the formula, n is a blocking matrix index required in the fuzzy classification process, and is taken as 2, | | · | | is a norm expression;

Φ_ik(X_i,μ_ik)＝[1 func(μ_ik) X_i] (5)

wherein func (. mu.)_ik) Is a membership value mu_ikIs taken as a deformation function ofOr exp (μ)_ik)，Φ_ik(X_i,μ_ik) Denotes the ith input variable X_iAnd membership mu of its fuzzy group k_ikThe corresponding new input matrix;

the radial basis function neural network is used as a local equation of the fuzzy equation system, and optimization fitting is carried out on each fuzzy group; let the output of the kth radial basis blurring system be:

wherein,is the output of the kth radial basis blur System, C_lkAnd w_lkIs the center and output weight, phi, of the ith node of the kth radial basis ambiguity system_lk(||X_i-C_lk| |) is the radial basis function of the kth node of the kth radial basis fuzzy system, determined by the following equation:

wherein sigma_lkIs the gaussian width of the corresponding radial basis function, | | | · | |, is a norm expression; and finally, obtaining the final prediction output of the fuzzy equation system by a gravity center method in an anti-fuzzy method:

in the formula,is the predicted output of the fuzzy equation system;

an intelligent optimization module for adopting particle swarm optimization to perform C on the radial basis function neural network local equation in the fuzzy equation_pk、σ_pk、w_pkThe optimization is carried out, and the specific steps are as follows:

firstly, determining optimized parameters of particle swarm as C of radial basis function neural network local equation_lk、σ_lk、w_lkNumber of individual particle groups popsize, maximum number of cyclic optimization iter_maxInitial position r of the p-th particle_pInitial velocity v_pLocal optimum value Lbest_pAnd the global optimum value Gbest of the whole particle swarm;

setting an optimization objective function, converting the optimization objective function into fitness, and evaluating each local fuzzy equation; calculating a fitness function through a corresponding error function, considering that the fitness of the particle with large error is small, and expressing the fitness function of the particle p as follows:

f_p＝1/(E_p+1) (9)

in the formula, E_pIs the error function of the fuzzy equation system, expressed as:

v_p(iter+1)＝ω×v_p(iter)+m₁a₁(Lbest_p-r_p(iter))+m₂a₂(Gbest-r_p(iter))

(11)

r_p(iter+1)＝r_p(iter)+v_p(iter+1) (12)

in the formula, v_pIndicates the velocity, r, of the update particle p_pIndicating the position of the update particle p, Lbest_pRepresenting the individual optimum of the update particle p, iter representing the number of cycles, ω being the inertial weight in the particle swarm optimization, m₁、m₂Is the corresponding acceleration factor, a₁、a₂Is [0,1 ]]A random number in between;

Lbest_p＝f_p (13)

Gbest＝Lbest_p (14)

sixthly, judging whether the performance requirement is met, if so, finishing the optimization to obtain a set of optimized local equation parameters of the fuzzy equation; otherwise, returning to the step III, continuing iterative optimization until reaching the maximum cyclic optimization times iter_max；

The host computer still include:

the model updating module is used for acquiring field intelligent instrument signals according to a set sampling time interval, comparing the obtained actually-measured furnace temperature with a system forecast value, and if the relative error is more than 10% or the furnace temperature exceeds the upper and lower production normal limit ranges, adding new data which enables the furnace temperature to be optimal when the furnace temperature is normally produced in the DCS database into training sample data, and updating the soft measurement model;

the result display module is used for transmitting the obtained furnace temperature forecast value and the operation variable value which enables the furnace temperature to be optimal to the DCS, displaying the values at a control station of the DCS and transmitting the values to a field operation station for displaying through the DCS and a field bus;

the signal acquisition module is used for acquiring data from the database according to the set time interval of each sampling;

2. An intelligent radial-based pesticide production waste liquid incinerator temperature optimization method is characterized by comprising the following steps: the furnace temperature optimization method comprises the following specific implementation steps:

2) preprocessing a model training sample input from a DCS database, centralizing the training sample, namely subtracting the average value of the sample, and then standardizing the training sample to ensure that the average value is 0 and the variance is 1; the processing is accomplished using the following mathematical process:

2.1) calculating the mean value:

2.2) calculating the variance:

2.3) standardization:

3) fuzzification is carried out on the training samples transmitted from the standardization processing module; let c in fuzzy system^*A fuzzy group, the centers of the fuzzy groups k and j are v_k、v_jThe ith normalized training sample X_iMembership mu for fuzzy group k_ikComprises the following steps:

Φ_ik(X_i,μ_ik)＝[1 func(μ_ik) X_i] (5)

in the formula,is the predicted output of the fuzzy equation system;

4) c for radial basis function neural network local equation in fuzzy equation by adopting particle swarm optimization_pk、σ_pk、w_pkThe optimization is carried out, and the specific steps are as follows:

f_p＝1/(E_p+1) (9)

in the formula, E_pIs a fuzzy squareThe error function of the equation system, expressed as:

(11)

r_p(iter+1)＝r_p(iter)+v_p(iter+1) (12)

Lbest_p＝f_p (13)

Gbest＝Lbest_p (14)

The method further comprises the following steps:

5) acquiring field intelligent instrument signals according to a set sampling time interval, comparing the obtained actually-measured furnace temperature with a system forecast value, and if the relative error is more than 10% or the furnace temperature exceeds the upper and lower production normal limit ranges, adding new data which enables the furnace temperature to be optimal when the furnace temperature is normally produced in a DCS database into training sample data, and updating a soft measurement model;

6) calculating to obtain an optimal operating variable value in the step 4), transmitting the obtained furnace temperature forecast value and the operating variable value which enables the furnace temperature to be optimal to the DCS, displaying the values at a control station of the DCS, and transmitting the values to a field operating station through the DCS and a field bus for displaying; meanwhile, the DCS system takes the obtained operating variable value which enables the furnace temperature to be optimal as a new operating variable set value, and automatically executes furnace temperature optimization operation;