CN102799939A - Biomass furnace combustion-optimized model updating method - Google Patents

Biomass furnace combustion-optimized model updating method Download PDF

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Publication number
CN102799939A
CN102799939A CN2012102450371A CN201210245037A CN102799939A CN 102799939 A CN102799939 A CN 102799939A CN 2012102450371 A CN2012102450371 A CN 2012102450371A CN 201210245037 A CN201210245037 A CN 201210245037A CN 102799939 A CN102799939 A CN 102799939A
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王春林
王再富
孔亚广
彭东亮
杨慧敏
钟哲科
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Hangzhou Dianzi University
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Hangzhou Dianzi University
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Abstract

The invention relates to a biomass furnace combustion-optimized model updating method which comprises the following steps of: firstly, building a forecast supper-allowable error database of an original model, building a new model, when the original model needs updating, selecting the working condition data which is larger than 10 sets in the forecast supper-allowable error database, randomly selecting a part of working condition data which is larger than 20 sets in a modeling sample of the original model, and taking two parts of the data as a training sample to further update and build the model; and ensuring the proportion between the new model and the original model, and combining the original model with the new model according to the optimal proportion coefficient, so that a new combined model can be formed, and the updating of the biomass furnace combustion-optimized model can be realized. According to the method provided by the invention, the data selecting and processing working capacity caused by updating the model can be reduced, the model updating efficiency can be improved, the actual requirement of the on-line optimization of the biomass furnace combustion can be met, and the instantaneity and the accuracy of the optimization of the biomass furnace combustion can be guaranteed.

Description

A kind of model update method of biomass stove burning optimization
Technical field
The invention belongs to the information Control technical field, relate to a kind of model update method of biomass stove burning optimization.
Background technology
The method of biomass stove burning optimization is the important technical of energy-saving and emission-reduction; Its target is under certain load (biomass fuel delivery rate) condition, obtains the running status of high-level efficiency, low pollution emission through the operational factor of adjustment biomass stove air distribution.The collocation of the air distribution parameter of biomass stove has direct influence to the biomass stove fired state, and the configuration of operating parameters such as different air distributions, oxygen amount can directly cause the situation of the discharge capacity of different burning efficiency and dusty gas.For given biomass stove; Under certain loading condiction,, there is a kind of air distribution scheme of optimum to different fired state characteristic indexs; Can make the characteristic index optimization of corresponding fired state; But, complicated coupled relation is arranged between the operating parameter of biomass stove, find optimum air distribution and be not easy.Along with continuous progress in science and technology, biomass stove burning automaticity is also improving constantly, but biomass stove burning optimization problem also well is not resolved.
The research focus of the burning optimization of biomass stove is through data mining at present; In a large amount of different actual operation parameters combinations; The method of applied for machines study; Excavate the relational model between the characteristic index of operational factor and biomass stove fired state, the model that utilizes optimized Algorithm to combine to excavate again carries out the online burning optimization of biomass stove.This method uses manpower and material resources sparingly; And can find than the more excellent parameter configuration of manual work experiment; But because the characteristic of the equipment of biomass stove changes along with the growth meeting of time to some extent, how guaranteeing that model can upgrade to adapt to news fast and efficiently becomes the bottleneck problem of this method.This problem and modeling method, sample data are chosen and update strategy etc. all has much relations.
Summary of the invention
The objective of the invention is to the bottleneck problem in the burning optimization of biomass stove, proposes a kind of model update method of taking into account model history learning outcome and new situation of change.
Technical scheme of the present invention is through utilizing original model prediction to exceed the data of extent of the error and the fraction data in the master mould jointly as sample; The model of setting up combines with original model and constructs new model; Established a kind of model update method of biomass stove burning optimization; Utilize the renewal of implementation model fast and efficiently of this method, make the model after the renewal take into account new burning feature and original burning feature, the model prediction ability is more comprehensive.
The step of the inventive method comprises:
Step (1). set up the ultra permissible error database of prediction of original model; According to concrete biomass stove combustion case with to the requirement of model prediction precision; The permission predicated error limit δ of setting model; When image data; Error between judgment models predicted value and the actual motion value and the size that allows predicated error limit δ; If predicated error is greater than δ; I.e.
Figure 2012102450371100002DEST_PATH_IMAGE002
; Wherein
Figure 2012102450371100002DEST_PATH_IMAGE004
is model predication value;
Figure 2012102450371100002DEST_PATH_IMAGE006
is actual operating data; The data that then will transfinite deposit in the ultra permissible error database of prediction, in order to the usefulness of model modification;
Step (2). set up new model; When master mould need upgrade; Select in the ultra permissible error database of prediction in the modeling sample of master mould, to select part floor data more at random greater than 20 groups greater than 100 groups floor data, with two parts data jointly as training sample; Prediction error data and master mould modeling data ratio are greater than 4; Carry out next step model modification modeling, make the model after the renewal can take into account new burning feature and original burning feature, the model prediction ability is more comprehensive; If data sample can be expressed as
Figure 2012102450371100002DEST_PATH_IMAGE008
; Wherein
Figure 2012102450371100002DEST_PATH_IMAGE010
expression
Figure 2012102450371100002DEST_PATH_IMAGE012
group is as the boiler operating parameter vector of input data; expression group adopts the radial base neural net algorithm to set up new model as the parameter of the sign boiler combustion status flag of output parameter:
It is output as
Figure 2012102450371100002DEST_PATH_IMAGE018
for the radial base neural net of
Figure 2012102450371100002DEST_PATH_IMAGE016
individual latent node:
Figure 2012102450371100002DEST_PATH_IMAGE020
;
Figure 2012102450371100002DEST_PATH_IMAGE022
is weight coefficient;
Figure 2012102450371100002DEST_PATH_IMAGE024
is
Figure 2012102450371100002DEST_PATH_IMAGE026
ties up input vector; is the center of
Figure 538487DEST_PATH_IMAGE012
individual basis function,
Figure 2012102450371100002DEST_PATH_IMAGE030
be the sound stage width degree parameter of function.The key of setting up the radial base neural net model is to confirm center
Figure 943054DEST_PATH_IMAGE028
, sound stage width degree
Figure 699658DEST_PATH_IMAGE030
and the weight coefficient
Figure 928383DEST_PATH_IMAGE022
of basis function.Adopt particle cluster algorithm iteration train RBF Neural Network; Definition particle cluster algorithm initial population
Figure 2012102450371100002DEST_PATH_IMAGE032
vector respectively tie up component; Be respectively the sound stage width degree and the weight coefficient of latent node, base function center, function; Objective function is:
Figure 2012102450371100002DEST_PATH_IMAGE034
; Wherein
Figure 2012102450371100002DEST_PATH_IMAGE036
is the radial base neural net output valve of individual sample, and
Figure 2012102450371100002DEST_PATH_IMAGE038
is the actual value of
Figure 607723DEST_PATH_IMAGE012
individual sample.When having reached minimum as
Figure 2012102450371100002DEST_PATH_IMAGE040
, reached setting value or having accomplished iterations; Training is accomplished; Obtain the sound stage width degree and the weight coefficient of latent node number, basis function center, function, thereby obtain the radial base neural net model.
Step (3). confirm the ratio of new model and original model.Gather data under the different running statuses of new biomass stove as test samples; Use the average weighted Forecasting Methodology of original model prediction and new model; Check data is predicted; I.e.
Figure 2012102450371100002DEST_PATH_IMAGE042
; The target prediction value of
Figure 2012102450371100002DEST_PATH_IMAGE044
the group test samples operating mode that is
Figure 671363DEST_PATH_IMAGE012
wherein;
Figure 2012102450371100002DEST_PATH_IMAGE046
is by being built the new model predicted value in the step (2);
Figure 2012102450371100002DEST_PATH_IMAGE048
is original model predication value;
Figure 2012102450371100002DEST_PATH_IMAGE050
is new model predicted value weight coefficient;
Figure 2012102450371100002DEST_PATH_IMAGE052
is the prediction weight coefficient of original model, and ;
Figure 805410DEST_PATH_IMAGE050
definite employing ant group algorithm iteration optimizing with is confirmed; Initialization ant crowd position vector respectively tie up component; Be respectively new model weight
Figure 146710DEST_PATH_IMAGE050
and original model weight ; Objective function is :
Figure 2012102450371100002DEST_PATH_IMAGE060
; The error of
Figure 2012102450371100002DEST_PATH_IMAGE062
be
Figure 58220DEST_PATH_IMAGE012
group operating mode real data and combination model predicted value wherein; When variance summation
Figure 2012102450371100002DEST_PATH_IMAGE064
has obtained minimum, reached setting value or has accomplished iterations; Optimizing is accomplished, and obtains optimum model weight coefficient; Empty the prediction error database, in order to upgrade the usefulness of image data next time.
Step (4). original model is combined with the scale-up factor of new model by optimum; Constitute new built-up pattern; I.e.
Figure 2012102450371100002DEST_PATH_IMAGE066
; Wherein E is the built-up pattern after upgrading, thereby realizes the renewal of biomass stove burning optimization model.
The data that the inventive method utilization exceeds original model prediction extent of the error combine with the medium and small partial data of master mould and build new model, the method that combines with original model again, and implementation model upgrades.This method has overcome and will have model data in traditional update method and abandon fully; Can not make full use of the shortcoming of existing model; The characteristics of master mould and new data have been made full use of; Shortened the data processing amount of calculation and the time of model modification, made the model after the renewal take into account new burning feature and original burning feature, the model prediction ability is more comprehensive.
The model update method that the present invention proposes takes into full account new data and former data conditions; Utilized the useful information that modeling data comprised of existing model; Reduced the workload that the model modification data are selected and handled; Improve the efficient of model modification, satisfied the actual requirement of biomass stove burning on-line optimization, guaranteed the real-time and the accuracy of biomass stove burning optimization; And making the model after the renewal take into account new burning feature and original burning feature, the model prediction ability is more comprehensive.
Embodiment
A kind of model update method of biomass stove burning optimization, concrete steps are:
(1) sets up the ultra permissible error database of prediction of original model; According to concrete biomass stove combustion case with to the requirement of model prediction precision; The permission predicated error limit δ of setting model; When image data; Error between judgment models predicted value and the actual motion value and the size that allows predicated error limit δ; If predicated error is greater than δ; I.e.
Figure 204029DEST_PATH_IMAGE002
; Wherein
Figure 754090DEST_PATH_IMAGE004
is model predication value;
Figure 277475DEST_PATH_IMAGE006
is actual operating data, and the data that then will transfinite deposit in the ultra permissible error database of prediction, in order to the usefulness of model modification;
(2) set up new model; When master mould need upgrade; Select in the ultra permissible error database of prediction in the modeling sample of master mould, to select part floor data more at random greater than 20 groups greater than 100 groups floor data, with two parts data jointly as training sample; Prediction error data and master mould modeling data ratio>4; Carry out next step model modification modeling, make the model after the renewal can take into account new burning feature and original burning feature, the model prediction ability is more comprehensive; If data sample can be expressed as
Figure 769637DEST_PATH_IMAGE008
; Wherein
Figure 322846DEST_PATH_IMAGE010
expression
Figure 660287DEST_PATH_IMAGE012
group is as the boiler operating parameter vector of input data; expression group adopts the radial base neural net algorithm to set up new model as the parameter of the sign boiler combustion status flag of output parameter:
It is output as for the radial base neural net of
Figure 243212DEST_PATH_IMAGE016
individual latent node:
Figure 633611DEST_PATH_IMAGE020
;
Figure 131588DEST_PATH_IMAGE022
is weight coefficient;
Figure 965552DEST_PATH_IMAGE024
is
Figure 729240DEST_PATH_IMAGE026
ties up input vector; is the center of
Figure 26546DEST_PATH_IMAGE012
individual basis function,
Figure 280679DEST_PATH_IMAGE030
be the sound stage width degree parameter of function.The key of setting up the radial base neural net model is to confirm center
Figure 780930DEST_PATH_IMAGE028
, sound stage width degree
Figure 201547DEST_PATH_IMAGE030
and the weight coefficient
Figure 487166DEST_PATH_IMAGE022
of basis function.Adopt particle cluster algorithm iteration train RBF Neural Network; Definition particle cluster algorithm initial population
Figure 662933DEST_PATH_IMAGE032
vector respectively tie up component; Be respectively the sound stage width degree and the weight coefficient of latent node, base function center, function; Objective function is:
Figure 905607DEST_PATH_IMAGE034
; Wherein
Figure 129915DEST_PATH_IMAGE036
is the radial base neural net output valve of
Figure 253729DEST_PATH_IMAGE012
individual sample, and
Figure 351129DEST_PATH_IMAGE038
is the actual value of
Figure 825973DEST_PATH_IMAGE012
individual sample.When having reached minimum as
Figure 853972DEST_PATH_IMAGE040
, reached setting value or having accomplished iterations; Training is accomplished; Obtain the sound stage width degree and the weight coefficient of latent node number, basis function center, function, thereby obtain the radial base neural net model.
(3) confirm the ratio of new model and original model.Gather data under the different running statuses of new biomass stove as test samples; Use the average weighted Forecasting Methodology of original model prediction and new model; Check data is predicted; I.e. ; The target prediction value of
Figure 599129DEST_PATH_IMAGE044
the group test samples operating mode that is
Figure 312001DEST_PATH_IMAGE012
wherein;
Figure 878111DEST_PATH_IMAGE046
is by being built the new model predicted value in the step (2); is original model predication value;
Figure 914255DEST_PATH_IMAGE050
is new model predicted value weight coefficient;
Figure 363691DEST_PATH_IMAGE052
is the prediction weight coefficient of original model, and ;
Figure 437138DEST_PATH_IMAGE050
definite employing ant group algorithm iteration optimizing with is confirmed; Initialization ant crowd position vector
Figure 216929DEST_PATH_IMAGE056
respectively tie up component; Be respectively new model weight
Figure 390422DEST_PATH_IMAGE050
and original model weight ; Objective function is: ; The error of
Figure 340557DEST_PATH_IMAGE062
be group operating mode real data and combination model predicted value wherein; When having obtained minimum as
Figure 285391DEST_PATH_IMAGE064
, reached setting value or having accomplished iterations; Optimizing is accomplished, and obtains optimum model weight coefficient; Empty the prediction error database, in order to upgrade the usefulness of image data next time.
(4) original model is combined with the scale-up factor of new model by optimum; Constitute new built-up pattern; I.e.
Figure 486565DEST_PATH_IMAGE066
; Wherein E is the built-up pattern after upgrading, thereby realizes the renewal of biomass stove burning optimization model.

Claims (1)

1. the model update method of a biomass stove burning optimization is characterized in that the concrete steps of this method are:
Step (1). set up the ultra permissible error database of prediction of original model: according to concrete biomass stove combustion case with to the requirement of model prediction precision; The permission predicated error limit δ of setting model; When image data; Error between judgment models predicted value and the actual motion value and the size that allows predicated error limit δ; If predicated error is greater than δ; I.e. ; The data that then will transfinite deposit in the ultra permissible error database of prediction; Usefulness in order to model modification; Wherein is model predication value, and
Figure 2012102450371100001DEST_PATH_IMAGE006
is actual operating data;
Step (2). set up new model: when master mould need upgrade; Select in the ultra permissible error database of prediction in the modeling sample of master mould, to select part floor data more at random greater than 20 groups greater than 100 groups floor data, with two parts data jointly as training sample; Prediction error data and master mould modeling data ratio are greater than 4; Carry out next step model modification modeling, make the model after the renewal can take into account new burning feature and original burning feature, the model prediction ability is more comprehensive; If data sample can be expressed as
Figure 2012102450371100001DEST_PATH_IMAGE008
; Wherein
Figure 2012102450371100001DEST_PATH_IMAGE010
expression
Figure 2012102450371100001DEST_PATH_IMAGE012
group is as the boiler operating parameter vector of input data;
Figure 2012102450371100001DEST_PATH_IMAGE014
expression
Figure 726076DEST_PATH_IMAGE012
group adopts the radial base neural net algorithm to set up new model as the parameter of the sign boiler combustion status flag of output parameter:
It is output as for the radial base neural net of
Figure 2012102450371100001DEST_PATH_IMAGE016
individual latent node: ;
Figure 2012102450371100001DEST_PATH_IMAGE022
is weight coefficient;
Figure 2012102450371100001DEST_PATH_IMAGE024
is
Figure 2012102450371100001DEST_PATH_IMAGE026
ties up input vector;
Figure 2012102450371100001DEST_PATH_IMAGE028
is the center of
Figure 406150DEST_PATH_IMAGE012
individual basis function,
Figure 2012102450371100001DEST_PATH_IMAGE030
be the sound stage width degree parameter of function; The key of setting up the radial base neural net model is to confirm the center
Figure 453741DEST_PATH_IMAGE028
of basis function, sound stage width degree
Figure 75084DEST_PATH_IMAGE030
and weight coefficient
Figure 145808DEST_PATH_IMAGE022
; Adopt particle cluster algorithm iteration train RBF Neural Network; Definition particle cluster algorithm initial population
Figure 2012102450371100001DEST_PATH_IMAGE032
vector respectively tie up component; Be respectively the sound stage width degree and the weight coefficient of latent node, base function center, function; Objective function is:
Figure 2012102450371100001DEST_PATH_IMAGE034
; Wherein
Figure 2012102450371100001DEST_PATH_IMAGE036
is the radial base neural net output valve of
Figure 136898DEST_PATH_IMAGE012
individual sample, and is the actual value of
Figure 350579DEST_PATH_IMAGE012
individual sample; When having reached minimum as
Figure 2012102450371100001DEST_PATH_IMAGE040
, reached setting value or having accomplished iterations; Training is accomplished; Obtain the sound stage width degree and the weight coefficient of latent node number, basis function center, function, thereby obtain the radial base neural net model;
Step (3). confirm the ratio of new model and original model: gather data under the new different running statuses of biomass stove as test samples; Use the average weighted Forecasting Methodology of original model prediction and new model; Check data is predicted; I.e.
Figure 2012102450371100001DEST_PATH_IMAGE042
; The target prediction value of
Figure 2012102450371100001DEST_PATH_IMAGE044
the group test samples operating mode that is wherein;
Figure 2012102450371100001DEST_PATH_IMAGE046
is by being built the new model predicted value in the step (2);
Figure 2012102450371100001DEST_PATH_IMAGE048
is original model predication value; is new model predicted value weight coefficient; is the prediction weight coefficient of original model, and
Figure 2012102450371100001DEST_PATH_IMAGE054
;
Figure 441124DEST_PATH_IMAGE050
definite employing ant group algorithm iteration optimizing with is confirmed; Initialization ant crowd position vector
Figure 2012102450371100001DEST_PATH_IMAGE056
respectively tie up component; Be respectively new model weight
Figure 366409DEST_PATH_IMAGE050
and original model weight
Figure 80288DEST_PATH_IMAGE052
; Objective function is
Figure 2012102450371100001DEST_PATH_IMAGE058
: ; The error of
Figure 2012102450371100001DEST_PATH_IMAGE062
be
Figure 312555DEST_PATH_IMAGE012
group operating mode real data and combination model predicted value wherein; When variance summation has obtained minimum, reached setting value or has accomplished iterations; Optimizing is accomplished, and obtains optimum model weight coefficient; Empty the prediction error database, in order to upgrade the usefulness of image data next time;
Step (4). original model is combined with the scale-up factor of new model by optimum; Constitute new built-up pattern; I.e.
Figure 2012102450371100001DEST_PATH_IMAGE066
; Wherein E is the built-up pattern after upgrading, thereby realizes the renewal of biomass stove burning optimization model.
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Cited By (4)

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CN103020455A (en) * 2012-12-17 2013-04-03 富通集团有限公司 Multi-target model updating method for optimizing operation of coaxial cable sheath machine
CN104318303A (en) * 2014-09-23 2015-01-28 杭州电子科技大学 Coking furnace temperature predication method of RBF (Radial Basis Function) neural network optimized by genetic algorithm
CN106594794A (en) * 2016-12-22 2017-04-26 杭州电子科技大学 Hybrid and intelligent updating method for boiler efficiency combustion optimization model
CN107016176A (en) * 2017-03-24 2017-08-04 杭州电子科技大学 A kind of hybrid intelligent overall boiler burning optimization method

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CN102252343A (en) * 2011-05-05 2011-11-23 浙江宜景环保科技有限公司 Method for optimizing combustion of porous medium combustor
CN102252342A (en) * 2011-05-05 2011-11-23 衢州远景资源再生科技有限公司 Model updating method for online combustion optimization of porous medium combustor

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CN101498458A (en) * 2009-03-02 2009-08-05 杭州电子科技大学 Model updating method for on-line boiler combustion optimization
CN101498457A (en) * 2009-03-02 2009-08-05 杭州电子科技大学 Boiler combustion optimizing method
CN102184450A (en) * 2011-05-05 2011-09-14 衢州远景资源再生科技有限公司 Modeling method for combustion optimization of porous medium combustor
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Publication number Priority date Publication date Assignee Title
CN103020455A (en) * 2012-12-17 2013-04-03 富通集团有限公司 Multi-target model updating method for optimizing operation of coaxial cable sheath machine
CN103020455B (en) * 2012-12-17 2015-07-22 富通集团有限公司 Multi-target model updating method for optimizing operation of coaxial cable sheath machine
CN104318303A (en) * 2014-09-23 2015-01-28 杭州电子科技大学 Coking furnace temperature predication method of RBF (Radial Basis Function) neural network optimized by genetic algorithm
CN106594794A (en) * 2016-12-22 2017-04-26 杭州电子科技大学 Hybrid and intelligent updating method for boiler efficiency combustion optimization model
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Application publication date: 20121128