CN103062781A - Intelligent soot blowing method for heating surfaces of boilers on basis of principle of artificial neural network - Google Patents
Intelligent soot blowing method for heating surfaces of boilers on basis of principle of artificial neural network Download PDFInfo
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Abstract
The invention discloses an intelligent soot blowing method for heating surfaces of boilers on the basis of a principle of an artificial neural network. The intelligent soot blowing method particularly includes computing a model-BP (back propagation) neural network with high nonlinear mapping ability; training the network by the principle of the artificial neural network via temperature detection data and data of a DAS (data acquisition system ) of a boiler; simulating and computing fouling coefficients of heating surfaces of the boiler; monitoring soot formation and slag-bonding of the heating surfaces by the trained artificial soot blowing neural network in real time; enabling a system to automatically judge that a soot formation or slag-bonding condition of the certain heating surface is severe when the fouling coefficient of the certain heating surface exceeds a threshold value; and starting to blow slag and soot on the heating surface. The intelligent soot blowing method has the advantages that the soot is blown in an intelligent mode, accordingly, shortcomings of a timed soot blowing mode are overcome, and the purpose of saving energy is achieved.
Description
Technical field
The present invention relates to boiler heating surface and blow the gray technology field, relate in particular to a kind of boiler heating surface intelligent ash blowing method based on artificial neural network principle.
Background technology
In China's thermal power generation unit, along with the continuous increase of Coal-fired capacity and a large amount of uses of colm, the burner hearth coking problem of quite a few various degrees is arranged, it is bad that the lighter is conducted heat its heating surface, reduce exerting oneself and the thermal efficiency of boiler, increase NOx discharging etc.; Burner hearth coking when serious; pipe at the high temperature convection superheater is put up a bridge; destroy the aerodynamic field in the stove; stove internal combustion operating mode is worsened; force the operation of unit load down, fall simultaneously such as the bulk lime-ash what is more, also can cause the boiler protection action to cause blowing out or smash furnace hopper; cause being forced to blowing out even may cause the furnace explosion accident, have a strong impact on the safe operation of generating set.Simultaneously, the serious dust stratification of the long-pending also frequent appearance of heated surface at the end of boiler, the circulation of impact heat transfer and flue gas, especially the less convection heating surface of channel cross-section, even can stop up the passage of flue gas, cause exhaust gas temperature rising, air-introduced machine electric current sharply to rise, so that the boiler output reduction, even be forced to blowing out.Therefore, the station boiler major part has all disposed soot blower system burner hearth coking and back-end surfaces dust stratification has been purged.Soot-blowing mode commonly used has steam soot blowing, impulse soot blowing, rapping to blow ash and sonic soot blowing etc.
At present, the power station soot blower system generally adopts the method for operation that regularly comprehensively each heating surface of boiler is purged, although this soot-blowing mode is simple, exists very large blindness and many problems.The dust stratification of each heating surface of boiler, slagging scorification process are permitted multifactorial the impact, are not the relations linear with the time far.Regularly blow ash comprehensively and may cause a part of heating surface because excessively purged, tube wall outer oxide film suffers frequent destruction, has increased the wear extent of metal, causes easily the tube wall leakage accident; Perhaps because of blow ash, to blow slag untimely or not enough, causes exhaust gas temperature to raise, even cause boiler to tie large slag, causes boiler and fall burnt fire-putting-out emergency, has a strong impact on the safe and economical boiler operation.
Summary of the invention
Problem for the prior art existence, the object of the present invention is to provide a kind of based on artificial neural network principle, targetedly the concrete position of heating surface is blown ash, blown slag, can improve the station boiler thermal efficiency, can save the boiler heating surface intelligent ash blowing method of operating cost again.
For achieving the above object, the present invention is based on the boiler heating surface intelligent ash blowing method of artificial neural network principle, be specially:
1) there is one-to-one relationship according to heating surface temperature and local local heat flux, in order to obtain the boiler heating surface hot-fluid, at the other set temperature test point of boiler heating surface each several part, and the input detection variable of measured temperature data as the BP neutral net;
2) because the heating surface hot-fluid is subject to the impact of boiler operatiopn condition, the input conditional-variable of the DAS data of boiler operatiopn data collecting system as the BP neutral net;
3) adopt computation model with nonlinear ability-BP(Back propagation) neutral net, come the dirty coefficient of analog computation boiler heating surface ash; The BP neutral net with boiler operatiopn DAS data and boiler heating surface temperature detection data as input, with the dirty coefficient lambda of each heating surface ash of boiler
iAs output.The dirty coefficient lambda of ash
iBe defined as:
q
iThe hot-fluid at a certain heating surface place during for the boiler actual motion, there are one-to-one relationship in heating surface temperature and local local heat flux, according to detecting the heating surface temperature that obtains, obtain by neuron network simulation; q
OiHot-fluid during for the boiler heating surface cleaning, this value can obtain by boiler thermodynamic calculation;
4) by temperature detection data and DAS data, local heat flux is as sample point in the stove under some operating modes that the stove internal heating surface is monitored, utilize artificial neural network principle, network is trained, draw local heat flux and the grey dirty coefficient of each one of stove internal heating surface under the various operating modes, and to the grey dirty coefficient settings threshold values of each heating surface;
5) utilize the artificial neural network trained, monitoring fouling of heating surface and slagging scorification, when the dirty coefficient of certain heating surface ash surpassed a threshold values, system namely judges this heating surface automatically, and dust stratification or coking were serious, begin it is blown slag, blows ash.
Further, the heating surface in the boiler comprises water-cooling wall and convection heating surface in the described step 1).
Further, pass through at heating surface set temperature test point in the described step 1), Real-time Measuring gets the local actual hot-fluid at each position in the stove.
Further, the DAS data comprise load (steam flow), pressure, intake, wind-warm syndrome, coal amount, coal value, burner operation mode, convection heating surface import cigarette temperature described step 2).
Further, described BP neutral net is comprised of three layers of neuron, has an input layer, an output layer and an implicit intermediate layer.
Further, the training process of described artificial neural network is: the hot-fluid that calculates each heating surface according to heat transfer principle, record simultaneously the clean heating surface temperature difference, with this as learning sample, according to BP Neural Network Self-learning ability, by the weight factor between continuous adjustment input layer and the hidden layer and the weight factor between hidden layer and the output layer, reach the purpose of training network.
The present invention adopts the aptitude manner ash disposal, has avoided regularly blowing the defective of ash, reaches simultaneously energy-conservation purpose.
Description of drawings
Fig. 1 is water-cooling wall temperature monitoring schematic diagram;
Fig. 2 is the dirty coefficient analog computation artificial neural network block diagram of boiler heating surface ash.
The specific embodiment
Below, with reference to the accompanying drawings, the present invention is more fully illustrated, shown in the drawings of exemplary embodiment of the present invention.Yet the present invention can be presented as multiple multi-form, and should not be construed as the exemplary embodiment that is confined to narrate here.But, these embodiment are provided, thereby make the present invention comprehensively with complete, and scope of the present invention is fully conveyed to those of ordinary skill in the art.
In order to be easy to explanation, here can use such as " on ", the space relative terms such as D score " left side " " right side ", be used for element shown in the key diagram or feature with respect to the relation of another element or feature.It should be understood that except the orientation shown in the figure spatial terminology is intended to comprise the different azimuth of device in using or operating.For example, if the device among the figure is squeezed, be stated as the element that is positioned at other elements or feature D score will be positioned at other elements or feature " on ".Therefore, the exemplary term D score can comprise upper and lower orientation both.Device can otherwise be located (90-degree rotation or be positioned at other orientation), and the relative explanation in used space here can correspondingly be explained.
The present invention is based on the boiler heating surface intelligent ash blowing method of artificial neural network principle, be specially:
1) there is one-to-one relationship according to heating surface temperature and local local heat flux, in order to obtain the boiler heating surface hot-fluid, at the other set temperature test point of boiler heating surface each several part, and the input detection variable of measured temperature data as the BP neutral net, the heating surface in the boiler comprises water-cooling wall and convection heating surface;
2) because the heating surface hot-fluid is subject to the impact of boiler operatiopn condition, the input conditional-variable of the DAS data of boiler operatiopn data collecting system as the BP neutral net, the DAS data comprise load (steam flow), pressure, intake, wind-warm syndrome, coal amount, coal value, burner operation mode, convection heating surface import cigarette temperature etc.;
3) adopt computation model with nonlinear ability-BP(Back propagation) neutral net, come the dirty coefficient of analog computation boiler heating surface ash; The BP neutral net with boiler operatiopn DAS data and boiler heating surface temperature detection data as input, with the dirty coefficient lambda of each heating surface ash of boiler
iAs output.The dirty coefficient lambda of ash
iBe defined as:
q
iThe hot-fluid at a certain heating surface place during for the boiler actual motion, there are one-to-one relationship in heating surface temperature and local local heat flux, according to detecting the heating surface temperature that obtains, obtain by neuron network simulation, and this is modeled as field of neural networks general knowledge; q
OiHot-fluid during for the boiler heating surface cleaning, this value can obtain by boiler thermodynamic calculation, and boiler thermodynamic calculation is boiler professional and technical personnel's basic skills, and the application's book no longer is described in detail;
4) by temperature detection data and DAS data, local heat flux is as sample point in the stove under some operating modes that the stove internal heating surface is monitored, utilize artificial neural network principle, network is trained, draw local heat flux and the grey dirty coefficient of each one of stove internal heating surface under the various operating modes, and to the grey dirty coefficient settings threshold values of each heating surface;
5) utilize the artificial neural network trained, monitoring fouling of heating surface and slagging scorification, when the dirty coefficient of certain heating surface ash surpassed a threshold values, system namely judges this heating surface automatically, and dust stratification or coking were serious, begin it is blown slag, blows ash.
At first, in order to detect the heating surface heat flux distribution and to determine grey dirty distribution situation, arrange thermocouple at heating surface, shown in fin among Fig. 11, water-cooling wall 2, furnace wall 3, there are one-to-one relationship in 2 temperature difference of wall-cooling surface and local local heat flux, a, b two decorate thermocouple outside the easy slagging scorification of burner hearth position water-cooling wall 2 pipe, record the wall temperature of 2 of everywhere a, b, can obtain in real time by boiler thermodynamic calculation the local actual hot-fluid at each position in the stove.
Because the boiler heating surface heat flux distribution is not only relevant with dust stratification and slagging scorification, and also have relevant with boiler actual motion condition (load, pressure, intake, wind-warm syndrome, coal amount, coal value, burner operation mode etc.).The grey dirty coefficient that therefore, will obtain under the precondition according to the heating surface hot-fluid is very difficult.Here we have adopted computation model with nonlinear ability-BP(Back propagation) neutral net, be used for the dirty coefficient of analog computation boiler heating surface ash.This network design has an input layer for to be comprised of three layers of neuron, and an output layer and implicit (centre) layer are seen Fig. 2.This network with boiler operatiopn DAS data (load, pressure, intake, wind-warm syndrome, coal amount, coal value, burner operation mode etc.) and boiler heating surface temperature detection data as input, with the dirty coefficient of each heating surface ash of boiler
As output.
Integrated the neural network software of having set up and Spot Data Acquisition System, software can be divided into three bulks substantially: instruction is processed, sent to data acquisition (comprising DAS data and thermocouple signal acquisition and A/D conversion etc.), data.Measurement data and signal finally all are pooled to the monitoring microcomputer, are calculated and are processed by the software systems that load on the monitoring microcomputer.Monitoring system is connected on the existing control network of power plant by network interface card, and obtains the image data of existing DAS by grid.When each sampling period began, timer sent acquisition, and data acquisition program reads required operational factor from the DAS system.Simultaneously, by the thermal signal of thermocouple, through the A/D conversion, the temperature data acquisition of corresponding measuring point on the water-cooling wall is come again.Local heat flux utilizes artificial neural network principle as sample point in the stove under some operating modes that monitor when the stove internal heating surface is cleaned, and network is trained, and can draw the local heat flux of each one when the stove internal heating surface cleans under the various operating modes.The network training process is: after overhaul, can calculate according to heat transfer principle the hot-fluid of each heating surface, record simultaneously clean heating surface A, B two point (Fig. 1) temperature difference, with this as learning sample, training network, according to BP Neural Network Self-learning ability, by the weight factor between continuous adjustment input layer and the hidden layer and the weight factor between hidden layer and the output layer, reach the purpose of training network.Then can carry out real time on-line monitoring to the grey dirty situation of heating surface, instruct and blow ash, blow slag, and the sample size when heating surface cleaned can constantly replenish, regularly network be trained again, make it along with the increase meeting of sample size is more accurate.When monitoring certain fouling of heating surface or slagging scorification after to a certain degree, system both can send instruction, and it is blown targetedly slag, blows ash.When the dirty coefficient of boiler somewhere heating surface ash during more than or equal to the threshold values set, namely
Illustrate that this place's slagging scorification, dust stratification are serious, system sent instruction and blew ash this moment, saw Fig. 2.
Claims (6)
1. based on the boiler heating surface intelligent ash blowing method of artificial neural network principle, it is characterized in that the method is specially:
1) there is one-to-one relationship according to heating surface temperature and local local heat flux, in order to obtain the boiler heating surface hot-fluid, at the other set temperature test point of boiler heating surface each several part, and the input detection variable of measured temperature data as the BP neutral net;
2) because the heating surface hot-fluid is subject to the impact of boiler operatiopn condition, the input conditional-variable of the DAS data of boiler operatiopn data collecting system as the BP neutral net;
3) adopt computation model with nonlinear ability-BP(Back propagation) neutral net, come the dirty coefficient of analog computation boiler heating surface ash; The BP neutral net with boiler operatiopn DAS data and boiler heating surface temperature detection data as input, with the dirty coefficient lambda of each heating surface ash of boiler
iAs output.The dirty coefficient lambda of ash
iBe defined as:
q
iThe hot-fluid at a certain heating surface place during for the boiler actual motion, there are one-to-one relationship in heating surface temperature and local local heat flux, according to detecting the heating surface temperature that obtains, obtain by neuron network simulation; q
OiHot-fluid during for the boiler heating surface cleaning, this value can obtain by boiler thermodynamic calculation;
4) by temperature detection data and DAS data, local heat flux is as sample point in the stove under some operating modes that the stove internal heating surface is monitored, utilize artificial neural network principle, network is trained, draw local heat flux and the grey dirty coefficient of each one of stove internal heating surface under the various operating modes, and to the grey dirty coefficient settings threshold values of each heating surface;
5) utilize the artificial neural network trained, monitoring fouling of heating surface and slagging scorification, when the dirty coefficient of certain heating surface ash surpassed a threshold values, system namely judges this heating surface automatically, and dust stratification or coking were serious, begin it is blown slag, blows ash.
2. the boiler heating surface intelligent ash blowing method based on artificial neural network principle as claimed in claim 1 is characterized in that, the heating surface in the described step 1) in the boiler comprises water-cooling wall and convection heating surface.
3. the boiler heating surface intelligent ash blowing method based on artificial neural network principle as claimed in claim 1 is characterized in that, passes through at heating surface set temperature test point in the described step 1), and Real-time Measuring gets the local actual hot-fluid at each position in the stove.
4. the boiler heating surface intelligent ash blowing method based on artificial neural network principle as claimed in claim 1, it is characterized in that described step 2) in the DAS data comprise load (steam flow), pressure, intake, wind-warm syndrome, coal amount, coal value, burner operation mode, convection heating surface import cigarette temperature.
5. the boiler heating surface intelligent ash blowing method based on artificial neural network principle as claimed in claim 1 is characterized in that described BP neutral net is comprised of three layers of neuron, has an input layer, an output layer and an implicit intermediate layer.
6. the boiler heating surface intelligent ash blowing method based on artificial neural network principle as claimed in claim 1, it is characterized in that, the training process of described artificial neural network is: the hot-fluid that calculates each heating surface according to heat transfer principle, record simultaneously the clean heating surface temperature difference, with this as learning sample, according to BP Neural Network Self-learning ability, by the weight factor between continuous adjustment input layer and the hidden layer and the weight factor between hidden layer and the output layer, reach the purpose of training network.
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Cited By (12)
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| CN103604115A (en) * | 2013-10-15 | 2014-02-26 | 上海申能能源科技有限公司 | Method for reducing steam temperature deviation by using steam soot blowing for boiler of thermal power plant |
| CN105222115A (en) * | 2014-06-16 | 2016-01-06 | 艾默生过程管理电力水利解决方案公司 | For control method and the control system of fossil-fuel boiler |
| CN106322412A (en) * | 2016-08-30 | 2017-01-11 | 上海交通大学 | Coal-fired unit convection heating surface intelligent soot blowing method based on two-dimensional optimization |
| CN106402910A (en) * | 2016-10-31 | 2017-02-15 | 上海电力学院 | Intelligent soot blowing method for heat engine plant boiler |
| CN106524122A (en) * | 2016-11-08 | 2017-03-22 | 广东电网有限责任公司电力科学研究院 | Slagging analysis method and device for power station boiler |
| CN108205260A (en) * | 2017-11-23 | 2018-06-26 | 中材节能股份有限公司 | A kind of industrial silicon ash cleaner for exhaust-heating boiler intelligent control method |
| CN109140471A (en) * | 2018-04-10 | 2019-01-04 | 刘惠敏 | Boiler Cinder Surveying clears up alarm method |
| CN109654519A (en) * | 2019-02-19 | 2019-04-19 | 中国神华能源股份有限公司 | The operation method of soot blower system and soot blower system |
| CN111237789A (en) * | 2020-01-09 | 2020-06-05 | 京东城市(北京)数字科技有限公司 | Boiler soot blowing method, device and computer readable storage medium |
| CN112016754A (en) * | 2020-08-31 | 2020-12-01 | 哈电发电设备国家工程研究中心有限公司 | Power station boiler exhaust gas temperature advanced prediction system and method based on neural network |
| CN112833409A (en) * | 2021-01-18 | 2021-05-25 | 江苏方天电力技术有限公司 | Hearth soot blowing optimization method based on dynamic loss prediction |
| CN113847611A (en) * | 2021-08-18 | 2021-12-28 | 浙江大学 | Power station boiler furnace intelligent soot blowing system and method based on online monitoring of inner wall temperature of furnace |
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| CN103604115B (en) * | 2013-10-15 | 2015-08-05 | 上海申能能源科技有限公司 | A kind of steam soot blowing that utilizes for heat power plant boiler reduces the method for Temperature Deviation |
| CN103604115A (en) * | 2013-10-15 | 2014-02-26 | 上海申能能源科技有限公司 | Method for reducing steam temperature deviation by using steam soot blowing for boiler of thermal power plant |
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| CN105222115A (en) * | 2014-06-16 | 2016-01-06 | 艾默生过程管理电力水利解决方案公司 | For control method and the control system of fossil-fuel boiler |
| CN106322412A (en) * | 2016-08-30 | 2017-01-11 | 上海交通大学 | Coal-fired unit convection heating surface intelligent soot blowing method based on two-dimensional optimization |
| CN106322412B (en) * | 2016-08-30 | 2019-05-24 | 上海交通大学 | Coal unit convection heating surface intelligent ash blowing method based on two-dimentional optimizing |
| CN106402910B (en) * | 2016-10-31 | 2018-09-28 | 上海电力学院 | A kind of power plant boiler intelligent ash blowing method |
| CN106402910A (en) * | 2016-10-31 | 2017-02-15 | 上海电力学院 | Intelligent soot blowing method for heat engine plant boiler |
| CN106524122A (en) * | 2016-11-08 | 2017-03-22 | 广东电网有限责任公司电力科学研究院 | Slagging analysis method and device for power station boiler |
| CN108205260A (en) * | 2017-11-23 | 2018-06-26 | 中材节能股份有限公司 | A kind of industrial silicon ash cleaner for exhaust-heating boiler intelligent control method |
| CN108205260B (en) * | 2017-11-23 | 2021-02-26 | 中材节能股份有限公司 | Intelligent control method for ash removal device of industrial silicon waste heat boiler |
| CN109140471A (en) * | 2018-04-10 | 2019-01-04 | 刘惠敏 | Boiler Cinder Surveying clears up alarm method |
| CN109654519A (en) * | 2019-02-19 | 2019-04-19 | 中国神华能源股份有限公司 | The operation method of soot blower system and soot blower system |
| CN111237789A (en) * | 2020-01-09 | 2020-06-05 | 京东城市(北京)数字科技有限公司 | Boiler soot blowing method, device and computer readable storage medium |
| CN112016754A (en) * | 2020-08-31 | 2020-12-01 | 哈电发电设备国家工程研究中心有限公司 | Power station boiler exhaust gas temperature advanced prediction system and method based on neural network |
| CN112833409A (en) * | 2021-01-18 | 2021-05-25 | 江苏方天电力技术有限公司 | Hearth soot blowing optimization method based on dynamic loss prediction |
| CN113847611A (en) * | 2021-08-18 | 2021-12-28 | 浙江大学 | Power station boiler furnace intelligent soot blowing system and method based on online monitoring of inner wall temperature of furnace |
| CN113847611B (en) * | 2021-08-18 | 2022-04-12 | 浙江大学 | Intelligent soot blowing system and method for power station boiler furnace |
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Application publication date: 20130424 |