CN103472865B - The pesticide waste liquid incinerator furnace temperature optimization system of intelligence least square and method - Google Patents
The pesticide waste liquid incinerator furnace temperature optimization system of intelligence least square and method Download PDFInfo
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- 238000005457 optimization Methods 0.000 title claims abstract description 67
- 238000000034 method Methods 0.000 title claims abstract description 52
- 239000007788 liquid Substances 0.000 title claims abstract description 23
- 239000010914 pesticide waste Substances 0.000 title claims abstract description 14
- 238000012549 training Methods 0.000 claims abstract description 124
- 238000005070 sampling Methods 0.000 claims abstract description 10
- 239000002245 particle Substances 0.000 claims description 117
- 239000011159 matrix material Substances 0.000 claims description 50
- 238000012706 support-vector machine Methods 0.000 claims description 48
- 230000008569 process Effects 0.000 claims description 18
- 238000004519 manufacturing process Methods 0.000 claims description 16
- 239000000446 fuel Substances 0.000 claims description 13
- 238000004422 calculation algorithm Methods 0.000 claims description 12
- 238000007781 pre-processing Methods 0.000 claims description 11
- 239000002699 waste material Substances 0.000 claims description 11
- 238000005259 measurement Methods 0.000 claims description 7
- 230000001133 acceleration Effects 0.000 claims description 6
- 230000000903 blocking effect Effects 0.000 claims description 6
- 125000004122 cyclic group Chemical group 0.000 claims description 6
- 230000005484 gravity Effects 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 230000009466 transformation Effects 0.000 claims description 6
- 238000012986 modification Methods 0.000 abstract description 2
- 230000004048 modification Effects 0.000 abstract description 2
- 238000002620 method output Methods 0.000 abstract 1
- 230000006870 function Effects 0.000 description 54
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 3
- 239000000575 pesticide Substances 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 1
- ZAMOUSCENKQFHK-UHFFFAOYSA-N Chlorine atom Chemical compound [Cl] ZAMOUSCENKQFHK-UHFFFAOYSA-N 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000000460 chlorine Substances 0.000 description 1
- 229910052801 chlorine Inorganic materials 0.000 description 1
- 230000007797 corrosion Effects 0.000 description 1
- 238000005260 corrosion Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 230000024703 flight behavior Effects 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 239000010815 organic waste Substances 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 239000000447 pesticide residue Substances 0.000 description 1
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- 231100000331 toxic Toxicity 0.000 description 1
- 230000002588 toxic effect Effects 0.000 description 1
- 239000002351 wastewater Substances 0.000 description 1
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Abstract
The invention discloses a kind of pesticide waste liquid incinerator furnace temperature optimization system and method for intelligent least square.The method adopts least square method supporting vector machine as the local equation of fuzzy system, by carrying out anti fuzzy method output to the output of least square method supporting vector machine, realizes the accurate control of furnace temperature.In the present invention, training sample through standardization resume module, as the input of fuzzy system module; The furnace temperature predicted value obtained in fuzzy system module with make the performance variable value of furnace temperature the best and be connected with result display module, will DCS system be passed to for result; Model modification module, for the sampling time interval by setting, collection site intelligent instrument signal.Present invention achieves the real-time calculating of furnace temperature, accurately control and avoid occurring the overshoot of furnace temperature.
Description
Technical Field
The invention relates to the field of pesticide production waste liquid incineration, in particular to an intelligent least square system and method for optimizing the incinerator temperature of a pesticide waste liquid incinerator.
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.
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 pesticide waste liquid incinerator temperature optimization system and method, noise influence and operation error in the industrial production process are considered, and the influence of fuzzy performance of fuzzy logic on accuracy can be reduced.
Support vector machines, introduced by Vapnik in 1998, are widely used in pattern recognition, fitting and classification problems due to their good generalization ability. Since the standard support vector machine is sensitive to isolated points and noise points, a weighted support vector machine was proposed later. The weighted support vector machine is better able to process noisy sample data than the standard support vector machine, and is chosen here as the local equation in the fuzzy system.
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 weighted support vector machine 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 pesticide waste liquid incinerator temperature optimization system and method for realizing accurate control of the furnace temperature and avoiding too low or too high furnace temperature.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the intelligent least square pesticide waste liquid incinerator temperature optimization system comprises an 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>
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calculating the variance: <math>
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and (3) standardization: <math>
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wherein, TXiThe 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. SigmaxRepresenting the standard deviation, σ, of the training samples2 xRepresenting variance of 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 vk、vjThe ith normalized training sample XiMembership mu for fuzzy group kikComprises 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:
Φik(Xi,μik)=[1func(μik)Xi] （5）
wherein func (. mu.)ik) Is a membership value muikDeformation function of, in general, takeexp(μik) Equal, phiik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix.
And the weighted support vector machine is used as a local equation of the fuzzy system and performs optimal fitting on each fuzzy group. Let the ith target output of the model training sample be OiThe weighted support vector machine equates the fitting problem to the following quadratic programming problem by transformation:
the lagrangian function is also defined:
where R (w, ξ) is the objective function of the optimization problem, minR (w, ξ) is the minimum of the objective function of the optimization problem,a non-linear mapping function, N is the number of training samples, ξ = { ξ =1,…,ξNIs the relaxation variable, ξiIs the i-th component of the relaxation variable, αiI =1, …, N being the i-th component of the corresponding lagrange multiplier, w being the normal vector of the hyperplane of the support vector machine, b being the corresponding offset, γ being the penalty factor of the least squares support vector machine, superscript T representing the transpose of the matrix, μikRepresents the normalized ith training sample XiMembership, Φ, for fuzzy group kik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix. From equations (6), (7) and (8), the output of the fuzzy group k in the training sample i is derived as:
wherein,is the output of the fuzzy group k on the training sample i, μmkRepresents the m-th training sample XmMembership, Φ, for fuzzy group kmk(Xm,μmk) Denotes the m-th input variable XmAnd membership mu of its fuzzy group kmkThe corresponding new input matrix. K<·>Is the kernel function of a weighted support vector machine, where K<·>Taking a linear kernel function; alpha is alphamM =1, …, N is the mth component of the corresponding lagrange multiplier.
The final fuzzy system output is obtained by the center of gravity method in the anti-fuzzy method:
in the formula,the output of the fuzzy system is then used,is the output of the fuzzy group k on the training sample i
The intelligent optimization module is used for optimizing a penalty factor and an error tolerance value of a local equation of a weighted support vector machine in the fuzzy system by adopting a particle swarm algorithm, and the specific implementation steps are as follows:
firstly, determining optimization parameters of the particle swarm as a penalty factor and an error tolerance value of a local equation of a weighted support vector machine, the individual number of the particle swarm popsize and the maximum cyclic optimization number itermaxInitial position r of the p-th particlepInitial velocity vpLocal optimum value LbestpAnd 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:
fp=1/(Ep+1) （11）
in the formula, EpIs the error function of the fuzzy system, expressed as:
in the formula,is the prediction output of the fuzzy system, OiIs the target output of the fuzzy system;
thirdly, according to the following formula, the speed and the position of each particle are circularly updated,
vp(iter+1)=ω×vp(iter)+m1a1(Lbestp-rp(iter))+m2a2(Gbest-rp(iter))
（13）
rp(iter+1)=rp(iter)+vp(iter+1) （14）
in the formula, vpIndicates the velocity, r, of the update particle ppIndicating the position of the update particle p, LbestpRepresenting the individual optimal value of the updated particle p, Gbest being the global optimal value of the whole particle swarm, iter representing the cycle number, omega being the inertial weight in the 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:
Lbestp=fp （15）
if the individual optimum value Lbest of the particle ppAnd (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=Lbestp （16）
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 itermax。
Gbest is the training sample X corresponding to the ith standardiAnd 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 waste liquid incinerator by the intelligent least square 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: <math>
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2.2) calculating the variance: <math>
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2.3) standardization: <math>
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wherein, TXiThe 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. SigmaxRepresenting the standard deviation, σ, of the training samples2 xRepresenting variance of training samples
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 vk、vjThe ith normalized training sample XiMembership mu for fuzzy group kikComprises 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:
Φik(Xi,μik)=[1func(μik)Xi] （5）
wherein func (. mu.)ik) Is a membership value muikDeformation function of, in general, takeexp(μik) Equal, phiik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix.
And the weighted support vector machine is used as a local equation of the fuzzy system and performs optimal fitting on each fuzzy group. Let the ith target output of the model training sample be OiThe weighted support vector machine equates the fitting problem to the following quadratic programming problem by transformation:
the lagrangian function is also defined:
where R (w, ξ) is the objective function of the optimization problem, minR (w, ξ) is the minimum of the objective function of the optimization problem,a non-linear mapping function, N is the number of training samples, ξ = { ξ =1,…,ξNIs the relaxation variable, ξiIs the i-th component of the relaxation variable, αiI =1, …, N being the i-th component of the corresponding lagrange multiplier, w being the normal vector of the hyperplane of the support vector machine, b being the corresponding offset, γ being the penalty factor of the least squares support vector machine, superscript T representing the transpose of the matrix, μikRepresents the normalized ith training sample XiMembership, Φ, for fuzzy group kik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix. From equations (6), (7) and (8), the output of the fuzzy group k in the training sample i is derived as:
wherein,is the output of the fuzzy group k on the training sample i, μmkRepresents the m-th training sample XmMembership, Φ, for fuzzy group kmk(Xm,μmk) Denotes the m-th input variable XmAnd membership mu of its fuzzy group kmkThe corresponding new input matrix. K<·>Is the kernel function of a weighted support vector machine, where K<·>Taking a linear kernel function; alpha is alphamM =1, …, N is the mth component of the corresponding lagrange multiplier.
The final fuzzy system output is obtained by the center of gravity method in the anti-fuzzy method:
in the formula,the output of the fuzzy system is then used,is the output of the fuzzy group k on the training sample i
4) Optimizing a penalty factor and an error tolerance value of a local equation of a weighted support vector machine in the fuzzy system by adopting a particle swarm optimization, and specifically realizing the following steps:
firstly, determining optimization parameters of the particle swarm as a penalty factor and an error tolerance value of a local equation of a weighted support vector machine, the individual number of the particle swarm popsize and the maximum cyclic optimization number itermaxInitial position r of the p-th particlepInitial velocity vpLocal optimum value LbestpAnd 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:
fp=1/(Ep+1) （11）
in the formula, EpIs the error function of the fuzzy system, expressed as:
in the formula,is the prediction output of the fuzzy system, OiIs the target output of the fuzzy system;
thirdly, according to the following formula, the speed and the position of each particle are circularly updated,
vp(iter+1)=ω×vp(iter)+m1a1(Lbestp-rp(iter))+m2a2(Gbest-rp(iter))
（13）
rp(iter+1)=rp(iter)+vp(iter+1) （14）
in the formula, vpIndicates the velocity, r, of the update particle ppIndicating the position of the update particle p, LbestpRepresenting the individual optimal value of the updated particle p, Gbest being the global optimal value of the whole particle swarm, iter representing the cycle number, omega being the inertial weight in the 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:
Lbestp=fp （15）
if the individual optimum value Lbest of the particle ppAnd (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=Lbestp （16）
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 itermax。
Gbest is the training sample X corresponding to the ith standardiAnd an operating variable value for optimizing the furnace temperature.
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.
Further, the optimal operation variable value is obtained through calculation in the step 4), the result is transmitted to the DCS system, displayed in a control station of the DCS, and transmitted to a field operation station through the DCS system 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.
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 technical conception of the invention is as follows: the invention discloses an intelligent least square system and a method for optimizing the furnace temperature of a pesticide 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 pesticide waste liquid incinerator with intelligent least square 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: <math>
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<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>
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<mn>1</mn>
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and (3) standardization: <math>
<mrow>
<mi>X</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mi>TX</mi>
<mo>-</mo>
<mover>
<mi>TX</mi>
<mo>‾</mo>
</mover>
</mrow>
<msub>
<mi>σ</mi>
<mi>x</mi>
</msub>
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<mo>-</mo>
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<mo>(</mo>
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wherein, TXiThe 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. SigmaxRepresenting the standard deviation, σ, of the training samples2 xRepresenting variance of training samples
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 vk、vjThe ith normalized training sample XiMembership mu for fuzzy group kikComprises 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:
Φik(Xi,μik)=[1func(μik)Xi] （5）
wherein func (. mu.)ik) Is a membership value muikDeformation function of, in general, takeexp(μik) Equal, phiik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix.
And the weighted support vector machine is used as a local equation of the fuzzy system and performs optimal fitting on each fuzzy group. Let the ith target output of the model training sample be OiThe weighted support vector machine equates the fitting problem to the following quadratic programming problem by transformation:
the lagrangian function is also defined:
where R (w, ξ) is the objective function of the optimization problem, minR (w, ξ) is the minimum of the objective function of the optimization problem,a non-linear mapping function, N is the number of training samples, ξ = { ξ =1,…,ξNIs the relaxation variable, ξiIs the i-th component of the relaxation variable, αiI =1, …, N being the i-th component of the corresponding lagrange multiplier, w being the normal vector of the hyperplane of the support vector machine, b being the corresponding offset, γ being the penalty factor of the least squares support vector machine, superscript T representing the transpose of the matrix, μikRepresents the normalized ith training sample XiMembership, Φ, for fuzzy group kik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix. From equations (6), (7) and (8), the output of the fuzzy group k in the training sample i is derived as:
wherein,is the output of the fuzzy group k on the training sample i, μmkRepresents the m-th training sample XmMembership, Φ, for fuzzy group kmk(Xm,μmk) Denotes the m-th input variable XmAnd membership mu of its fuzzy group kmkThe corresponding new input matrix. K<·>Is the kernel function of a weighted support vector machine, where K<·>Taking a linear kernel function; alpha is alphamM =1, …, N is the mth component of the corresponding lagrange multiplier.
The final fuzzy system output is obtained by the center of gravity method in the anti-fuzzy method:
in the formula,the output of the fuzzy system is then used,is the output of the fuzzy group k on the training sample i
The intelligent optimization module 9 is configured to optimize a penalty factor and an error tolerance value of a local equation of a weighted support vector machine in the fuzzy system by using a particle swarm algorithm, and specifically includes the following steps:
firstly, determining optimization parameters of the particle swarm as a penalty factor and an error tolerance value of a local equation of a weighted support vector machine, the individual number of the particle swarm popsize and the maximum cyclic optimization number itermaxInitial position r of the p-th particlepInitial velocity vpLocal optimum value LbestpAnd 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:
fp=1/(Ep+1) （11）
in the formula, EpIs the error function of the fuzzy system, expressed as:
in the formula,is the prediction output of the fuzzy system, OiIs the target output of the fuzzy system;
thirdly, according to the following formula, the speed and the position of each particle are circularly updated,
vp(iter+1)=ω×vp(iter)+m1a1(Lbestp-rp(iter))+m2a2(Gbest-rp(iter))
（13）
rp(iter+1)=rp(iter)+vp(iter+1) （14）
in the formula, vpIndicates the velocity, r, of the update particle ppIndicating the position of the update particle p, LbestpRepresenting the individual optimal value of the updated particle p, Gbest being the global optimal value of the whole particle swarm, iter representing the cycle number, omega being the inertial weight in the 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:
Lbestp=fp （15）
if the individual optimum value Lbest of the particle ppAnd (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=Lbestp （16）
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 itermax。
Gbest is the training sample X corresponding to the ith standardiAnd an operating variable value for optimizing the furnace temperature.
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 obtained furnace temperature forecast value and the operation variable value which enables the furnace temperature to be optimal to the DCS, displaying the process state at a control station of the DCS, and transmitting the process state information to a field operation station for displaying through the DCS and a 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 intelligent least square method for optimizing the furnace temperature of the pesticide waste liquid incinerator specifically comprises the following implementation steps:
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: <math>
<mrow>
<mover>
<mi>TX</mi>
<mo>‾</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>
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<mo>-</mo>
<mrow>
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</mrow>
</math>
2.2) calculating the variance: <math>
<mrow>
<msubsup>
<mi>σ</mi>
<mi>x</mi>
<mn>2</mn>
</msubsup>
<mo>=</mo>
<mfrac>
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<mi>N</mi>
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<mn>1</mn>
</mrow>
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</math>
2.3) standardization: <math>
<mrow>
<mi>X</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mi>TX</mi>
<mo>-</mo>
<mover>
<mi>TX</mi>
<mo>‾</mo>
</mover>
</mrow>
<msub>
<mi>σ</mi>
<mi>x</mi>
</msub>
</mfrac>
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<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
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</math>
wherein, TXiThe 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. SigmaxRepresenting the standard deviation, σ, of the training samples2 xRepresenting variance of training samples
3) And fuzzifying the standardized 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 vk、vjThe ith normalized training sample XiMembership mu for fuzzy group kikComprises 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:
Φik(Xi,μik)=[1func(μik)Xi] （5）
wherein func (. mu.)ik) Is a membership value muikDeformation function of, in general, takeexp(μik) Equal, phiik(Xi,μik) Representing the ith input variableXiAnd membership mu of its fuzzy group kikThe corresponding new input matrix.
And the weighted support vector machine is used as a local equation of the fuzzy system and performs optimal fitting on each fuzzy group. Let the ith target output of the model training sample be OiThe weighted support vector machine equates the fitting problem to the following quadratic programming problem by transformation:
the lagrangian function is also defined:
where R (w, ξ) is the objective function of the optimization problem, minR (w, ξ) is the minimum of the objective function of the optimization problem,a non-linear mapping function, N is the number of training samples, ξ = { ξ =1,…,ξNIs the relaxation variable, ξiIs the i-th component of the relaxation variable, αiI =1, …, N being the i-th component of the corresponding lagrange multiplier, w being the normal vector of the hyperplane of the support vector machine, b being the corresponding offset, γ being the penalty factor of the least squares support vector machine, superscript T representing the transpose of the matrix, μikRepresents the normalized ith training sample XiMembership, Φ, for fuzzy group kik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix. From equations (6), (7) and (8), the output of the fuzzy group k in the training sample i is derived as:
wherein,is the output of the fuzzy group k on the training sample i, μmkRepresents the m-th training sample XmMembership, Φ, for fuzzy group kmk(Xm,μmk) Denotes the m-th input variable XmAnd membership mu of its fuzzy group kmkThe corresponding new input matrix. K<·>Is the kernel function of a weighted support vector machine, where K<·>Taking a linear kernel function; alpha is alphamM =1, …, N is the mth component of the corresponding lagrange multiplier.
The final fuzzy system output is obtained by the center of gravity method in the anti-fuzzy method:
in the formula,the output of the fuzzy system is then used,is the output of the fuzzy group k on the training sample i
4) Optimizing a penalty factor and an error tolerance value of a local equation of a weighted support vector machine in the fuzzy system by adopting a particle swarm optimization, and specifically realizing the following steps:
firstly, determining optimization parameters of the particle swarm as a penalty factor and an error tolerance value of a local equation of a weighted support vector machine, the individual number of the particle swarm popsize and the maximum cyclic optimization number itermaxInitial position r of the p-th particlepInitial velocity vpLocal optimum value LbestpAnd 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:
fp=1/(Ep+1) （11）
in the formula, EpIs the error function of the fuzzy system, expressed as:
in the formula,is the prediction output of the fuzzy system, OiIs the target output of the fuzzy system;
thirdly, according to the following formula, the speed and the position of each particle are circularly updated,
vp(iter+1)=ω×vp(iter)+m1a1(Lbestp-rp(iter))+m2a2(Gbest-rp(iter))
（13）
rp(iter+1)=rp(iter)+vp(iter+1) （14）
in the formula, vpIndicates the velocity, r, of the update particle ppIndicating the position of the update particle p, LbestpRepresenting the individual optimal value of the updated particle p, Gbest being the global optimal value of the whole particle swarm, iter representing the cycle number, omega being the inertial weight in the 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:
Lbestp=fp （15）
if the individual optimum value Lbest of the particle ppAnd (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=Lbestp （16）
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 itermax。
Gbest is the training sample X corresponding to the ith standardiAnd an operating variable value for optimizing the furnace temperature.
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) And 4) calculating to obtain a furnace temperature forecast value and an operation variable value for optimizing the furnace temperature in the step 4), transmitting the result to the DCS, displaying the result on a control station of the DCS, and transmitting the result to a field operation station for displaying through the DCS and a field bus.
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.
Claims (2)
1. An intelligent least square system for optimizing the furnace temperature of a pesticide waste liquid incinerator comprises the incinerator, an on-site 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:
calculating the variance:
and (3) standardization:
wherein TX is a training sample, TXiThe 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; sigmaxRepresenting 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 data preprocessing module; let c in fuzzy system*A fuzzy group, the centers of the fuzzy groups k and j are vk、vjThe ith normalized training sample XiMembership mu for fuzzy group kikComprises the following steps:
in the formula, n is a blocking matrix index required in the fuzzy classification process, and is usually taken as 2, | · | | is 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:
Φik(Xi,μik)＝[1 func(μik) Xi] (5)
wherein func (. mu.)ik) Is a membership value muikDeformation function of, in general, takeexp(μik)，Φik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix;
the weighted support vector machine is used as a local equation of the fuzzy system, and optimal fitting is carried out on each fuzzy group; let the ith target output of the model training sample be OiThe weighted support vector machine equates the fitting problem to the following quadratic programming problem by transformation:
the lagrangian function is also defined:
wherein R (w, ξ) is the objective function of the optimization problem, min R (w, ξ) is the minimum of the objective function of the optimization problem,a non-linear mapping function, N is the number of training samples, ξ ═ ξ1,...,ξNIs the relaxation variable, ξiIs the i-th component of the relaxation variable, αiWhere i is 1, …, N is the ith component of the corresponding lagrange multiplier, w is the normal vector of the hyperplane of the support vector machine, b is the corresponding offset, γ is the penalty factor of the least squares support vector machine, superscript T represents the transpose of the matrix, μikRepresents the normalized ith training sample XiMembership, Φ, for fuzzy group kik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix; from equations (6), (7) and (8), the output of the fuzzy group k in the training sample i is derived as:
wherein,is the output of the fuzzy group k on the training sample i, μmkRepresents the m-th training sample XmMembership, Φ, for fuzzy group kmk(Xm,μmk) Denotes the m-th input variable XmAnd membership mu of its fuzzy group kmkThe corresponding new input matrix; k<·>Is the kernel function of a weighted support vector machine, where K<·>Taking a linear kernel function; alpha is alphamM is 1, …, N is the mth component of the corresponding lagrange multiplier;
the final fuzzy system output is obtained by the center of gravity method in the anti-fuzzy method:
in the formula,is the output of the fuzzy system and is,is the output of the fuzzy group k in the training sample i;
the intelligent optimization module is used for optimizing a penalty factor and an error tolerance value of a local equation of a weighted support vector machine in the fuzzy system by adopting a particle swarm algorithm, and the specific implementation steps are as follows:
firstly, determining optimization parameters of the particle swarm as a penalty factor and an error tolerance value of a local equation of a weighted support vector machine, the individual number of the particle swarm popsize and the maximum cyclic optimization number itermaxInitial position r of the p-th particlepInitial velocity vpLocal optimum value LbestpAnd the global optimum value Gbest of the whole particle swarm;
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:
fp＝1/(Ep+1) (11)
in the formula, EpIs the error function of the fuzzy system, expressed as:
in the formula,is the prediction output of the fuzzy system, OiIs the target output of the fuzzy system;
thirdly, according to the following formula, the speed and the position of each particle are circularly updated,
vp(iter+1)＝ω×vp(iter)+m1a1(Lbestp-rp(iter))+m2a2(Gbest-rp(iter))
(13)
rp(iter+1)＝rp(iter)+vp(iter+1) (14)
in the formula, vpIndicates the velocity, r, of the update particle ppIndicating the position of the update particle p, LbestpRepresenting the individual optimum value of the update particle p, Gbest being the global optimum value of the whole particle swarm, iter representing the number of cycles, and ω being the particleInertial weight, m, in a group algorithm1、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:
Lbestp＝fp (15)
if the individual optimum value Lbest of the particle ppAnd (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＝Lbestp (16)
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 itermax；
Gbest is the training sample X corresponding to the ith standardiThe furnace temperature predicted value and the operation variable value for optimizing the furnace temperature;
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;
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.
2. The intelligent least square method for optimizing the temperature of the pesticide waste liquid incinerator is characterized by comprising the following steps of: the furnace temperature optimization method comprises the following specific implementation steps:
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 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:
wherein, TXiThe 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; sigmaxRepresenting the standard deviation, σ, of the training samples2 xRepresenting the variance of the training samples;
3) fuzzification is carried out on the training samples transmitted from the data preprocessing module; let c in fuzzy system*Fuzzy groups, of fuzzy groups k, jCenters are respectively vk、vjThe ith normalized training sample XiMembership mu for fuzzy group kikComprises the following steps:
in the formula, n is a blocking matrix index required in the fuzzy classification process, and is usually taken as 2, | · | | is 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:
Φik(Xi,μik)＝[1 func(μik) Xi] (5)
wherein func (. mu.)ik) Is a membership value muikDeformation function of, in general, takeexp(μik)，Φik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix;
the weighted support vector machine is used as a local equation of the fuzzy system, and optimal fitting is carried out on each fuzzy group; let the ith target output of the model training sample be OiThe weighted support vector machine equates the fitting problem to the following quadratic programming problem by transformation:
the lagrangian function is also defined:
wherein R (w, ξ) is the objective function of the optimization problem, min R (w, ξ) is the minimum of the objective function of the optimization problem,a non-linear mapping function, N is the number of training samples, ξ ═ ξ1,...,ξNIs the relaxation variable, ξiIs the i-th component of the relaxation variable, αiWhere i is 1, …, N is the ith component of the corresponding lagrange multiplier, w is the normal vector of the hyperplane of the support vector machine, b is the corresponding offset, γ is the penalty factor of the least squares support vector machine, superscript T represents the transpose of the matrix, μikRepresents the normalized ith training sample XiMembership, Φ, for fuzzy group kik(Xi,μik) Denotes the ith input variable XiAnd membership mu of its fuzzy group kikThe corresponding new input matrix; from equations (6), (7) and (8), the output of the fuzzy group k in the training sample i is derived as:
wherein,is the output of the fuzzy group k on the training sample i, μmkRepresents the m-th training sample XmMembership, Φ, for fuzzy group kmk(Xm,μmk) Denotes the m-th input variable XmAnd membership mu of its fuzzy group kmkThe corresponding new input matrix; k<·>Is the kernel function of a weighted support vector machine, where K<·>Taking a linear kernel function; alpha is alphamM is 1, …, N is the mth component of the corresponding lagrange multiplier;
the final fuzzy system output is obtained by the center of gravity method in the anti-fuzzy method:
in the formula,is the output of the fuzzy system and is,is the output of the fuzzy group k in the training sample i;
4) optimizing a penalty factor and an error tolerance value of a local equation of a weighted support vector machine in the fuzzy system by adopting a particle swarm optimization, and specifically realizing the following steps:
firstly, determining optimization parameters of the particle swarm as a penalty factor and an error tolerance value of a local equation of a weighted support vector machine, the individual number of the particle swarm popsize and the maximum cyclic optimization number itermaxInitial position r of the p-th particlepInitial velocity vpLocal optimum value LbestpAnd the global optimum value Gbest of the whole particle swarm;
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:
fp＝1/(Ep+1) (11)
in the formula, EpIs the error function of the fuzzy system, expressed as:
in the formula,predicted output of fuzzy system, OiIs the target output of the fuzzy system;
thirdly, according to the following formula, the speed and the position of each particle are circularly updated,
vp(iter+1)＝ω×vp(iter)+m1a1(Lbestp-rp(iter))+m2a2(Gbest-rp(iter))
(13)
rp(iter+1)＝rp(iter)+vp(iter+1) (14)
in the formula, vpIndicates the velocity, r, of the update particle ppIndicating the position of the update particle p, LbestpRepresenting the individual optimal value of the updated particle p, Gbest being the global optimal value of the whole particle swarm, iter representing the cycle number, omega being the inertial weight in the 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:
Lbestp＝fp (15)
if the individual optimum value Lbest of the particle ppAnd (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＝Lbestp (16)
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 itermax；
Gbest is the training sample X corresponding to the ith standardiThe furnace temperature predicted value and the operation variable value for optimizing the furnace temperature;
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 a furnace temperature forecast value and an operation variable value for optimizing the furnace temperature in the step 4), transmitting the result to a DCS (distributed control system), displaying the result on a control station of the DCS, and transmitting the result to a field operation station for displaying through the DCS and a field bus; 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;
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.
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| CN110427715B (en) * | 2019-08-08 | 2022-10-14 | 内蒙古科技大学 | Method for predicting furnace hearth thermal state trend based on time sequence and multiple dimensions of blast furnace |
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