CN109508832A - Power plant SO based on variable compression BP neural network2Discharge flexible measurement method - Google Patents
Power plant SO based on variable compression BP neural network2Discharge flexible measurement method Download PDFInfo
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- 230000006835 compression Effects 0.000 title claims abstract description 16
- 238000007906 compression Methods 0.000 title claims abstract description 16
- 238000000691 measurement method Methods 0.000 title claims abstract description 13
- 230000001537 neural effect Effects 0.000 title description 3
- 238000000034 method Methods 0.000 claims abstract description 52
- 230000003009 desulfurizing effect Effects 0.000 claims abstract description 35
- 239000003517 fume Substances 0.000 claims abstract description 33
- 239000003546 flue gas Substances 0.000 claims abstract description 32
- 239000002002 slurry Substances 0.000 claims abstract description 32
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims abstract description 30
- 238000013528 artificial neural network Methods 0.000 claims abstract description 28
- 239000007921 spray Substances 0.000 claims abstract description 22
- 230000008569 process Effects 0.000 claims abstract description 9
- 210000002569 neuron Anatomy 0.000 claims abstract description 7
- 238000010219 correlation analysis Methods 0.000 claims abstract description 5
- 230000008859 change Effects 0.000 claims abstract description 4
- 238000010521 absorption reaction Methods 0.000 claims description 24
- 239000010440 gypsum Substances 0.000 claims description 7
- 229910052602 gypsum Inorganic materials 0.000 claims description 7
- 235000019738 Limestone Nutrition 0.000 claims description 6
- 239000003245 coal Substances 0.000 claims description 6
- 239000006028 limestone Substances 0.000 claims description 6
- 238000006477 desulfuration reaction Methods 0.000 claims description 5
- 235000019504 cigarettes Nutrition 0.000 claims description 4
- 238000012549 training Methods 0.000 claims description 4
- 230000023556 desulfurization Effects 0.000 claims description 3
- 239000007788 liquid Substances 0.000 claims description 3
- 239000000779 smoke Substances 0.000 abstract description 3
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 description 17
- 238000010586 diagram Methods 0.000 description 11
- 238000005516 engineering process Methods 0.000 description 5
- OSGAYBCDTDRGGQ-UHFFFAOYSA-L calcium sulfate Chemical compound [Ca+2].[O-]S([O-])(=O)=O OSGAYBCDTDRGGQ-UHFFFAOYSA-L 0.000 description 4
- 238000013461 design Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- VTYYLEPIZMXCLO-UHFFFAOYSA-L Calcium carbonate Chemical compound [Ca+2].[O-]C([O-])=O VTYYLEPIZMXCLO-UHFFFAOYSA-L 0.000 description 3
- 230000002745 absorbent Effects 0.000 description 3
- 239000002250 absorbent Substances 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 235000008733 Citrus aurantifolia Nutrition 0.000 description 1
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 description 1
- 235000011941 Tilia x europaea Nutrition 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 229910000019 calcium carbonate Inorganic materials 0.000 description 1
- JGIATAMCQXIDNZ-UHFFFAOYSA-N calcium sulfide Chemical compound [Ca]=S JGIATAMCQXIDNZ-UHFFFAOYSA-N 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000002425 crystallisation Methods 0.000 description 1
- 230000008025 crystallization Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000018044 dehydration Effects 0.000 description 1
- 238000006297 dehydration reaction Methods 0.000 description 1
- 150000004683 dihydrates Chemical class 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000002386 leaching Methods 0.000 description 1
- 239000004571 lime Substances 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000011593 sulfur Substances 0.000 description 1
- 229910052717 sulfur Inorganic materials 0.000 description 1
- 230000005619 thermoelectricity Effects 0.000 description 1
- 239000011800 void material Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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Abstract
The present invention relates to a kind of power plant SO based on variable compression BP neural network2Flexible measurement method is discharged, including step S1: the sample data in acquisition wet method fume desulfurizing system about time change, and the input layer of dynamic neural network and the neuron of output layer are determined according to collected sample data；Step S2: carrying out correlation analysis to the sample data, compress to original sample variable, removal and SO in the flue gas of the wet method fume desulfurizing system exit2Sample data of the correlation less than 0.2；Step S3: wet method fume desulfurizing system is modeled using the variable compression-BP neural network, establishes wet method fume desulfurizing system prediction model；Step S4: the SO of the smoke outlet of wet method fume desulfurizing system is calculated using the wet process of FGD prediction model established in step S32The predicted value of concentration；Step S5: the slurries spray flow of wet method fume desulfurizing system is controlled using the predicted value of step S4.
Description
Technical field
The invention belongs to flue gas desulphurization Prediction and Control Technology fields, and in particular to one kind is based on variable compression BP mind
Power plant SO through network2Discharge flexible measurement method.
Background technique
Lime stone-gypsum wet flue gas desulfurizing technology is to add water that slurries are made as absorbent agstone to be pumped into suction
It receives tower to come into full contact with and mixes with flue gas, calcium carbonate in sulfur dioxide and slurries in flue gas and the air blasted from tower lower part
It carries out oxidation reaction and generates calcium sulfate, after calcium sulfate reaches certain saturation degree, crystallization forms dihydrate gypsum.
Through absorption tower be discharged gypsum slurries it is concentrated, dehydration, make its water content less than 10%, then with conveyer send to
Gypsum bunker is stacked, and the flue gas after desulfurization removes droplet by demister and is discharged into after heat exchanger heat temperature raising by chimney
Atmosphere.
Since absorption tower inner absorbent slurries are by circulating pump iterative cycles and smoke contacts, absorbent utilization rate is very high,
Calcium sulfur ratio is lower, and desulfuration efficiency can be greater than 95%.
Current most of thermoelectricity companies use wet type desulfurizing technology, and reaction principle is substantially similar, and the main distinction is to inhale
In the structure for receiving tower, some sprays void tower using single loop, and some is using double-return circuit spray column and bubble tower etc..
Different sulfur removal technologies, desulfuration efficiency would also vary from.When unit load is in stable state, can obtain preferably
Control effect, but under the conditions of exchanging work, system shows non-linear, large time delay, is difficult to preferably control spray at this time
The flow of slurries in tower.If the slurry hypovolia of spray, can be difficult to ensure the discharge standard that can reach flue gas；If spray
Slurry hypervolia, will result in the waste of resource.Therefore, conventional control method is difficult to realize timely control, and reaches reason
The smoke exhaust effect thought.This is in place of the deficiencies in the prior art.
Therefore, in view of the foregoing drawbacks, provide and design a kind of power plant SO based on variable compression BP neural network2Discharge soft survey
Amount method；To solve problems of the prior art, it is necessary.
Summary of the invention
It is an object of the present invention to design one kind based on variable compression in view of the above-mentioned drawbacks of the prior art, providing
The power plant SO of BP neural network2Flexible measurement method is discharged, to solve the above technical problems.
To achieve the above object, the present invention provides following technical scheme:
A kind of power plant SO based on variable compression BP neural network2Discharge flexible measurement method, which is characterized in that including following
Step:
Step S1: the sample data in acquisition wet method fume desulfurizing system about time change, and according to collected sample
Notebook data determines the input layer of dynamic neural network and the neuron of output layer；
Step S2: to the sample data carry out correlation analysis, original sample variable is compressed, removal with it is described wet
SO in the flue gas of method flue gas desulphurization system exit2Sample data of the correlation less than 0.2 improves to reduce calculation amount and calculates speed
Degree；
In the step, the acquiring method of correlation are as follows:
Step S3: wet method fume desulfurizing system is modeled using the variable compression-BP neural network, is established wet
Method flue gas desulphurization system prediction model；
Step S4: the cigarette of wet method fume desulfurizing system is calculated using the wet process of FGD prediction model established in step S3
The SO of gas outlet2The predicted value of concentration；
Step S5: the slurries spray flow of wet method fume desulfurizing system is controlled using the predicted value of step S4；By mesh
Scale value is compared with predicted value, if predicted value is greater than target value and the two difference is bigger, the spray flow of corresponding slurries is also
It is bigger；Spray flow is reduced if target value is greater than predicted value.
Preferably, the sample data in the step S1 includes the SO of wet method fume desulfurizing system inlet2Concentration, machine
Group load, No. 1 absorption tower gypsum slurries pH value, lime stone slurry remove the flow on No. 1 absorption tower, No. 1 absorption tower inlet flue gas temperature
Degree, No. 2 absorption towers go out for slurry flow, total blast volume, total coal amount, No. 1 absorbing tower liquid-level calculated value and wet method fume desulfurizing system
SO in flue gas at mouthful2Concentration.
Preferably, the input layer of the variable compression-BP neural network is wet method fume desulfurizing system entrance
The SO at place2Concentration and NOx concentration, unit load, lime stone slurry remove the flow on No. 1 absorption tower, No. 1 absorption tower exiting flue gas SO2
Concentration, No. 2 absorption tower pH values, total blast volume and total coal amount；
Preferably, the neuron of the output layer of the variable compression-BP neural network is the wet process of FGD system
SO in system exit flue gas2Concentration.
Preferably, the frequency of collecting sample data is every five minutes primary, continuous acquisition ten days, totally 2880 data,
Using the first eight day in sample number as training set, last two days data are as verifying collection.
The beneficial effects of the present invention are:
Principal element is extracted from many factors for influencing wet process of FGD efficiency, is obtained according to the principal element of extraction
Notebook data is sampled, the sample data got in this way has the characteristics that compactness, ergodic and compatibility.
Larger, non-linear strong, the biggish wet method fume desulfurizing system of delay is disturbed, the present invention controls the effect of slurries spray flow
Fruit is excellent, robust performance is good, has adaptive, Self-tuning System ability, and regulating time is short, dynamic error is small.
Using Prediction and Control Technology of the invention, requirement to model is low, online convenience of calculation, control effect are good.
Variable compression processing has been carried out to data collected.Since collected data volume is bigger at the scene, if directly
It connects for dynamic neural network training and predicts, will lead to that neural computing is slow, be unfavorable for realizing to slurries spray flow
Real-time control.After correlation analysis and variable compression processing, data volume reduces, and increases the calculating of dynamic neural network
Speed can provide real-time accurate reference data for the control of slurries spray flow.
The present invention can control SO in the flue gas of exit substantially2Concentration is maintained at an a small range fluctuation, sprays in slurries
It can accurately be met the requirements in real time in the spray flow of leaching amount, substantially increase the accuracy of slurries spray flow PREDICTIVE CONTROL, benefit
It is compared with true sample and simulation result, accuracy can reach 97.3%.
In addition, design principle of the present invention is reliable, structure is simple, has very extensive application prospect.
It can be seen that compared with prior art, the present invention have substantive distinguishing features outstanding and it is significant ground it is progressive, implementation
Beneficial effect be also obvious.
Detailed description of the invention
Fig. 1 is No. 1 absorption tower schematic diagram in wet method fume desulfurizing system of the embodiment of the present invention.
Fig. 2 is a kind of power plant SO based on variable compression BP neural network provided by the invention2Discharge flexible measurement method stream
Cheng Tu.
Fig. 3 is the flue gas in the wet method fume desulfurizing system exit being calculated in the embodiment of the present invention according to prediction model
The predicted value of middle sulfur dioxide concentration and the comparison diagram of true value.
Fig. 4 is Model Predictive Control frame diagram in the embodiment of the present invention.
Fig. 5 is the PREDICTIVE CONTROL block diagram of neural network in the embodiment of the present invention.
Fig. 6 is control system block diagram in the embodiment of the present invention.
Specific embodiment
The present invention will be described in detail with reference to the accompanying drawing and by specific embodiment, and following embodiment is to the present invention
Explanation, and the invention is not limited to following implementation.
As shown in Fig. 2, a kind of power plant SO based on variable compression BP neural network provided in this embodiment2Discharge hard measurement
Method, comprising the following steps:
Step S1: the sample data in acquisition wet method fume desulfurizing system about time change, and according to collected sample
Notebook data determines the input layer of dynamic neural network and the neuron of output layer；
Step S2: to the sample data carry out correlation analysis, original sample variable is compressed, removal with it is described wet
SO in the flue gas of method flue gas desulphurization system exit2Sample data of the correlation less than 0.2 improves to reduce calculation amount and calculates speed
Degree；
In the step, the acquiring method of correlation are as follows:
Step S3: wet method fume desulfurizing system is modeled using the variable compression-BP neural network, is established wet
Method flue gas desulphurization system prediction model；
Step S4: the cigarette of wet method fume desulfurizing system is calculated using the wet process of FGD prediction model established in step S3
The SO of gas outlet2The predicted value of concentration；
Step S5: the slurries spray flow of wet method fume desulfurizing system is controlled using the predicted value of step S4；By mesh
Scale value is compared with predicted value, if predicted value is greater than target value and the two difference is bigger, the spray flow of corresponding slurries is also
It is bigger；Spray flow is reduced if target value is greater than predicted value.
In the present embodiment, the sample data in the step S1 includes the SO of wet method fume desulfurizing system inlet2Concentration,
Unit load, No. 1 absorption tower gypsum slurries pH value, lime stone slurry remove the flow on No. 1 absorption tower, No. 1 absorption tower inlet flue gas
Temperature, No. 2 absorption towers are for slurry flow, total blast volume, total coal amount, No. 1 absorbing tower liquid-level calculated value and wet method fume desulfurizing system
SO in the flue gas of exit2Concentration.The structural schematic diagram on No. 1 absorption tower is as shown in Figure 1.
In the present embodiment, the input layer of the variable compression-BP neural network enters for wet method fume desulfurizing system
SO at mouthful2Concentration and NOx concentration, unit load, lime stone slurry remove the flow on No. 1 absorption tower, No. 1 absorption tower exiting flue gas
SO2Concentration, No. 2 absorption tower pH values, total blast volume and total coal amount；
In the present embodiment, the neuron of the output layer of the variable compression-BP neural network is the wet process of FGD
SO in flue gas at system outlet2Concentration.
In the present embodiment, the frequencies of collecting sample data is every five minutes primary, continuous acquisition ten days, totally 2880 stroke count
According to using the first eight day in sample number as training set, last two days data are as verifying collection.
Fig. 3 be the sulfur dioxide concentration in wet method fume desulfurizing system exit that prediction model is calculated predicted value with
The comparison diagram of actual value.As Fig. 3 can the predicted value that is obtained using prediction model described in the present embodiment and actual value fitting compared with
Good, root-mean-square error 3.9645 is overall to be not much different although larger in data variation larger part deviation.
Fig. 4 is Model Predictive Control frame diagram, wherein can measure disturbance is that can pass through sensor measurement in real system
Disturbance out, directly acts on controlled device, this variable is not intended to be exported；Setting value exports target value, in reality
It is the concentration of exit sulfur dioxide in flue gas in system；Can performance variable be slurries spray flow, controller tune can be passed through
The size for saving it, makes it act on target object, and output is made to reach desired value；Immeasurability disturbance has centainly target output value
Influence；Measurement output is the concentration of the sulfur dioxide in flue gas measured at system middle outlet, can be used to assess practical defeated
Whether be worth out accurate；Noise indicates to influence the factor of measurement accuracy；Real output value is the wet method fume desulfurizing system outlet
Locate the actual value of sulfur dioxide in flue gas concentration.
Based on above-mentioned PREDICTIVE CONTROL Frame Design network response surface block diagram, as shown in figure 5, including referring to mould
Type, data prediction, selector, network response surface model and controlled device can pass through selection according to the actual situation
Device 1 and 2 come choose whether using network response surface model to controlled device carry out PREDICTIVE CONTROL.
On the basis of being based on network response surface principle, control system functional block diagram is built, as shown in Figure 6.
System block diagram mainly includes three parts: P1 stimulus part, P2 optimal control part, P3 are that sulfur dioxide absorbs part.
Disclosed above is only the preferred embodiment of the present invention, but the present invention is not limited to this, any this field
What technical staff can think does not have creative variation, and without departing from the principles of the present invention made by several improvement and
Retouching, should all be within the scope of the present invention.
Claims (5)
1. a kind of power plant SO based on variable compression BP neural network2Discharge flexible measurement method, which is characterized in that including following step
It is rapid:
Step S1: the sample data in acquisition wet method fume desulfurizing system about time change, and according to collected sample number
According to the neuron for the input layer and output layer for determining dynamic neural network；
Step S2: carrying out correlation analysis to the sample data, compress to original sample variable, removal and the wet process cigarette
SO in flue gas at desulfurization system outlet2Sample data of the correlation less than 0.2；
In the step, the acquiring method of correlation are as follows:
Step S3: wet method fume desulfurizing system is modeled using the variable compression-BP neural network, establishes wet process cigarette
Desulfurization system prediction model；
Step S4: gone out using the flue gas that the wet process of FGD prediction model established in step S3 calculates wet method fume desulfurizing system
SO at mouthful2The predicted value of concentration；
Step S5: the slurries spray flow of wet method fume desulfurizing system is controlled using the predicted value of step S4；By target value
It is compared with predicted value, if predicted value is greater than target value and the two difference is bigger, the spray flow of corresponding slurries is also bigger；
Spray flow is reduced if target value is greater than predicted value.
2. a kind of power plant SO based on variable compression BP neural network according to claim 12Flexible measurement method is discharged,
It is characterized in that, the sample data in the step S1 includes the SO of wet method fume desulfurizing system inlet2Concentration, unit load, 1
Number absorption tower gypsum slurries pH value, lime stone slurry go the flow on No. 1 absorption tower, No. 1 absorption tower entrance flue gas temperature, No. 2 suctions
Tower is received for slurry flow, total blast volume, total coal amount, No. 1 absorbing tower liquid-level calculated value and wet method fume desulfurizing system exit flue gas
Middle SO2Concentration.
3. a kind of power plant SO based on variable compression BP neural network according to claim 22Flexible measurement method is discharged,
It is characterized in that, the input layer of the variable compression-BP neural network is the SO of wet method fume desulfurizing system inlet2It is dense
Degree and NOx concentration, unit load, lime stone slurry remove the flow on No. 1 absorption tower, No. 1 absorption tower exiting flue gas SO2Concentration, No. 2
Absorption tower pH value, total blast volume and total coal amount.
4. a kind of power plant SO based on variable compression BP neural network according to claim 32Flexible measurement method is discharged,
It is characterized in that, the neuron of the output layer of the variable compression-BP neural network is the wet method fume desulfurizing system exit
SO in flue gas2Concentration.
5. a kind of power plant SO based on variable compression BP neural network according to claim 42Flexible measurement method is discharged,
It being characterized in that, the frequencies of collecting sample data is every five minutes primary, continuous acquisition ten days, totally 2880 data, by sample number
In the first eight day as training set, last two days data are as verifying collection.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109948869A (en) * | 2019-04-22 | 2019-06-28 | 东南大学 | Desulphurization system SO based on orderly cluster discretization2Exit concentration prediction technique |
| CN110442991A (en) * | 2019-08-12 | 2019-11-12 | 江南大学 | A kind of dynamic sulfur recovery soft-measuring modeling method based on parametrization FIR model |
| CN111013370A (en) * | 2019-11-08 | 2020-04-17 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Wet desulphurization slurry supply amount prediction method based on deep neural network |
| CN111068950A (en) * | 2019-12-26 | 2020-04-28 | 华南理工大学 | Flow velocity control method for spray head of LED coating machine |
| CN112415148A (en) * | 2020-08-18 | 2021-02-26 | 北京国电龙源环保工程有限公司 | Wet flue gas desulfurization system CaSO based on online learning3Soft measurement method |
| CN112585466A (en) * | 2018-09-10 | 2021-03-30 | 宇部兴产株式会社 | Inspection method and inspection apparatus |
| CN113144844A (en) * | 2021-03-22 | 2021-07-23 | 国家能源集团国源电力有限公司 | Desulfurizer flow control method and device and coal combustion system |
| CN113450880A (en) * | 2021-08-31 | 2021-09-28 | 大唐环境产业集团股份有限公司 | Desulfurization system inlet SO2Intelligent concentration prediction method |
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