CN112697977A - Thermal power station boiler flue gas NOx index prediction method - Google Patents
Thermal power station boiler flue gas NOx index prediction method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 29
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 title claims abstract description 19
- 239000003546 flue gas Substances 0.000 title claims abstract description 19
- 229910052760 oxygen Inorganic materials 0.000 claims abstract description 13
- 239000001301 oxygen Substances 0.000 claims abstract description 11
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims abstract description 10
- 239000003245 coal Substances 0.000 claims abstract description 9
- 230000001419 dependent effect Effects 0.000 claims abstract description 8
- 238000012417 linear regression Methods 0.000 claims abstract description 8
- 238000004088 simulation Methods 0.000 claims abstract description 7
- 239000000779 smoke Substances 0.000 claims abstract description 4
- 238000004458 analytical method Methods 0.000 claims description 9
- 238000007689 inspection Methods 0.000 claims description 6
- 238000000513 principal component analysis Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 230000002146 bilateral effect Effects 0.000 claims description 3
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 abstract description 28
- 229910021529 ammonia Inorganic materials 0.000 abstract description 14
- 238000007405 data analysis Methods 0.000 abstract description 11
- 238000002347 injection Methods 0.000 abstract description 8
- 239000007924 injection Substances 0.000 abstract description 8
- 238000004364 calculation method Methods 0.000 abstract description 6
- 230000004044 response Effects 0.000 abstract description 6
- 238000012360 testing method Methods 0.000 abstract description 6
- 230000009471 action Effects 0.000 abstract description 3
- 238000005259 measurement Methods 0.000 abstract description 3
- 238000010248 power generation Methods 0.000 abstract description 2
- 238000000611 regression analysis Methods 0.000 abstract description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 7
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000002485 combustion reaction Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 238000005507 spraying Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006555 catalytic reaction Methods 0.000 description 1
- 238000010531 catalytic reduction reaction Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000006722 reduction reaction Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—General constructional details of gas analysers, e.g. portable test equipment concerning the detector specially adapted to detect a particular component
- G01N33/0037—NOx
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method or the display, e.g. intermittent measurement or digital display
- G01N33/0068—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method or the display, e.g. intermittent measurement or digital display using a computer specifically programmed
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- Combustion & Propulsion (AREA)
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- Analytical Chemistry (AREA)
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Abstract
The invention discloses a thermal power station boiler flue gas NOx index prediction method which comprises the steps of establishing a NOx prediction model by utilizing SPSS software to perform linear regression, performing big data analysis on a plurality of power generation load sections respectively, performing linear/nonlinear regression analysis by utilizing covariance, correlation coefficient, Pearson correlation and significance test, taking a NOx value as a dependent variable, taking primary air pressure, oxygen quantity, air quantity, coal quantity, water supply flow and smoke temperature as independent variables, and fitting the NOx prediction model. After configuration comparison, the action of a fitting curve calculated by mathematical simulation is prior to an actual NOx measurement curve, and according to calculation, the advanced response time is about 40 seconds, so that the advanced response time can be controlled in advance to a certain extent, and the consumption of ammonia injection amount is reduced.
Description
Technical Field
The invention relates to the technical field of thermal power plant NOx index prediction, in particular to a thermal power plant boiler flue gas NOx index prediction method.
Background
The SPSS software is called "statistical product and service solution" software, and is a general term for software products and related services proposed by IBM for statistical operations, data mining, predictive analysis, and decision support tasks. The software is innovatively used for modeling research of the nitrogen oxides of the thermal power plant, so that complicated manual calculation is saved, the model prediction accuracy is improved, and advanced control is performed.
At present, after denitration ammonia injection of a machine set is automatically put into operation, the fluctuation of NOx is large, oscillation is not easy to be stable, and the dynamic deviation of NOx is large especially when a coal mill is started and stopped under the condition of variable load. In order to avoid examination, an operator can only reduce the set value of NOx, so that excessive ammonia injection is easily caused, and ammonia escape is easily caused. Most of domestic power stations select an ammonia spraying device of a catalytic reduction (SCR) denitration system, and an ammonia spraying adjusting valve is automatically controlled. Due to the typical multi-input, large inertia, nonlinear and strong coupling characteristics of boiler combustion of a power station and the influence of SCR catalytic reaction delay, conventional PID controlled ammonia injection control cannot achieve a good control effect. Therefore, how to accurately predict the mass concentration of the NOx at the inlet of the SCR denitration system, improve the advance of the ammonia injection automatic control, and reduce the ammonia slip is a technical problem which needs to be solved urgently.
Disclosure of Invention
In order to solve the problems, the invention provides a thermal power station boiler flue gas NOx index prediction method, which comprises the following steps: selecting historical trends of related variables under different load sections to establish a sample, and screening the load sections with proper linearity by using a curve estimation method; utilizing SPSS software to calculate Pearson correlation coefficients and carry out significance bilateral inspection, and determining appropriate variables related to NOx after inspection; selecting dependent variable NOx and screened independent variables, carrying out curve estimation, and further confirming the correlation; on the basis of principal component analysis, a denitration inlet NOx concentration prediction model is constructed through linear regression analysis; establishing an actual model in the DCS: and establishing a load-related piecewise function simulation model by using the obtained formula configuration.
Preferably, the more the absolute value of the Pearson correlation coefficient is close to 1, the more the correlation exists among the variables, the more the significance is close to zero, and the more the overall similarity and the sample similarity are shown.
Preferably, the thermal power station boiler flue gas NOx index prediction method selects a NOx value as a dependent variable, and takes primary air pressure, oxygen quantity, air quantity, coal quantity, feedwater flow and smoke temperature as independent variables.
Preferably, in order to further improve the accuracy of model prediction, the thermal power station boiler flue gas NOx index prediction method is researched by adopting a segmented load analysis method.
Preferably, sample data is selected in a common load section, and in order to ensure the accuracy of data prediction, the thermal power station boiler flue gas NOx index prediction method selects a load section with stable load and characteristic representativeness of oxygen content to perform SPSS data analysis.
Preferably, the thermal power station boiler flue gas NOx index prediction method selects load sections of 200MW, 250MW, 270MW, 300MW and 350MW5 to sample, and utilizes SPSS software to calculate, and on the basis of principal component analysis, a denitration inlet NOx concentration prediction model is constructed through linear regression analysis, and data analysis is carried out on the load sections for improving model accuracy, wherein the model formula is as follows:
NOx（A）=56.867-0.108×F-7.133×O+0.4×Q+1.089×M
in the formula, (A) is a predicted value of NOx at the denitration inlet at the side A, and F, O, Q, M represents air volume, oxygen volume, main steam flow and coal volume respectively.
Preferably, the mathematical model is established by performing data analysis on 180MW, 200MW, 220MW, 250MW, 300MW, 320MW and 350MW respectively based on the method.
The invention has the beneficial effects that: according to the thermal power station boiler flue gas NOx index prediction method, the SPSS software is used for establishing a nonlinear model of the mass concentration of NOx at the inlet of the SCR denitration system of the unit, the mass concentration of NOx at the inlet of the SCR denitration system is accurately predicted, the advance of automatic ammonia injection control is improved, ammonia escape is reduced, combustion adjustment measures can be guided, and the blockage phenomenon of an air preheater is improved; after configuration comparison, the action of a fitting curve calculated by mathematical simulation is prior to an actual NOx measurement curve, and according to calculation, the advanced response time is about 40 seconds, so that the advanced response time can be controlled in advance to a certain extent, and the consumption of ammonia injection amount is reduced. The method has the characteristics of low investment cost, small operation difficulty, high reduction degree of the fitting curve and good advance, and is convenient to popularize and apply.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of the present invention utilizing SPSS correlation checking;
FIG. 3 is a schematic diagram of curve estimation using SPSS according to the present invention;
FIG. 4 is a schematic diagram of a broken line function model established by load segments according to the present invention.
Detailed Description
For the purpose of describing the embodiments of the present invention in detail, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention mainly aims to provide a thermal power station boiler flue gas NOx index prediction method, and aims to solve the technical problems that the mass concentration of NOx at an inlet of an SCR denitration system cannot be accurately predicted, high ammonia escape occurs and the like in the prior art.
In order to achieve the above object, an embodiment of the present invention is provided, as shown in fig. 1, where fig. 1 is a schematic flowchart of the present invention, and the method for predicting a NOx index in flue gas of a thermal power station boiler includes the following steps:
selecting historical trends of related variables under different load sections to establish a sample, and screening the load sections with proper linearity by using a curve estimation method;
utilizing SPSS software to calculate Pearson correlation coefficients and carry out significance bilateral inspection, and determining appropriate variables related to NOx after inspection;
selecting dependent variable NOx and screened independent variables, carrying out curve estimation, and further confirming the correlation;
on the basis of principal component analysis, a denitration inlet NOx concentration prediction model is constructed through linear regression analysis;
establishing an actual model in the DCS: and establishing a load-related piecewise function simulation model by using the obtained formula configuration.
Specifically, the method establishes an NOX prediction model by linear regression through SPSS software, performs big data analysis on a plurality of power generation load sections respectively, and performs covariance, correlation coefficient, Pearson correlation and significance test. And (3) taking the NOX value as a dependent variable, taking primary air pressure, oxygen quantity, air quantity, coal quantity, water supply flow and smoke temperature as independent variables, performing linear/nonlinear regression analysis, fitting a NOX estimation model, and achieving the purposes of advanced prediction and control.
Further, as shown in fig. 2, fig. 2 is a schematic diagram of the present invention using SPSS correlation test, using SPSS software to perform big data analysis, performing correlation test on a plurality of variables, classifying and screening, and determining the related variables.
Furthermore, historical trends of a plurality of related variables under different load sections are selected to establish a sample, and a linear and proper load section is screened out by utilizing a curve estimation method. And then verifying the correlation between the NOx and each parameter of the boiler, performing Pearson correlation coefficient calculation and significance double-side test by using SPSS software (the closer the absolute value of the Pearson correlation coefficient is to 1, the more the correlation exists among the variables, the closer the significance is to zero, the more the similarity between the total body and the sample is shown), and determining the suitable variable related to the NOx after the test.
Further, as shown in fig. 3, fig. 3 is a schematic diagram of curve estimation by using SPSS according to the present invention, curve estimation is performed by using SPSS software, a linear relationship of related variables is further confirmed, and an interference item can be analyzed and eliminated by using this method when a load segment is selected in the previous period to perform data screening, so as to confirm a proper historical data segment. Selecting dependent variable NOx and screened independent variable, and performing curve estimation; correlation is further confirmed, for example, after research, a clear linear correlation exists between oxygen amount and denitration inlet NOx value.
Further, in the process of data analysis, when different load data are selected to be studied together, interference terms are too many, and the obtained linear regression function is not representative. In order to further improve the accuracy of model prediction, a segmented load analysis method is adopted for research. Sample data is selected in a common load section, and in order to ensure the accuracy of data prediction, the load section with stable load and characteristic representativeness of oxygen content is selected for SPSS data analysis. Here, 5 load segments of 200MW, 250MW, 270MW, 300MW, 350MW and the like are selected for sampling, a relatively representative load segment is selected, corresponding data is called, and calculation is performed by using SPSS software. On the basis of principal component analysis, a denitration inlet NOx concentration prediction model is constructed through linear regression analysis. To improve model accuracy, we perform data analysis on the load segments. For example, we call the trend and then analyze to obtain a model data formula under 270 MW:
NOx（A）=56.867-0.108×F-7.133×O+0.4×Q+1.089×M
in the formula, (A) is a predicted value of NOx at the denitration inlet at the side A, and F, O, Q, M respectively represents air volume, oxygen volume, main steam flow and coal volume; based on the method, data analysis is respectively carried out on 180MW, 200MW, 220MW, 250MW, 300MW, 320MW and 350MW, and a mathematical model is established.
Further, as shown in fig. 4, fig. 4 is a schematic diagram of a broken line function model established by load segments, data analysis is performed by a plurality of load segments, and a coefficient matrix is established by using the broken line function, so that different load segments respond to different functions conveniently. Specifically, according to the obtained coefficient matrix, a corresponding broken line function is established on the DCS, and meanwhile, an amplitude limiting and speed limiting module is added, so that the problem that the accuracy of the whole function is influenced by large deviation of individual values is avoided. Wherein, C-constant, xo-oxygen amount, xf-air volume and xm-coal amount.
Furthermore, a piecewise function simulation model related to the load is established by using the obtained formula configuration, and the model is characterized by adopting the air volume, the oxygen volume, the main steam flow and the coal volume as independent variables, so that the change state of the NOx can be reflected in advance. After configuration comparison, the action of a fitting curve calculated by mathematical simulation is prior to an actual NOx measurement curve, and according to calculation, the advanced response time is about 40 seconds, so that the advanced response time can be controlled in advance to a certain extent, and the consumption of ammonia injection amount is reduced.
It should be noted that the above-mentioned preferred embodiments are only illustrative and should not be construed as limiting the scope of the invention, and that modifications and variations can be made by those skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (4)
1. A thermal power station boiler flue gas NOx index prediction method is characterized by comprising the following steps:
selecting historical trends of related variables under different load sections to establish a sample, and screening the load sections with proper linearity by using a curve estimation method;
utilizing SPSS software to calculate Pearson correlation coefficients and carry out significance bilateral inspection, and determining appropriate variables related to NOx after inspection;
selecting dependent variable NOx and screened independent variables, carrying out curve estimation, and further confirming the correlation;
on the basis of principal component analysis, a denitration inlet NOx concentration prediction model is constructed through linear regression analysis;
establishing an actual model in the DCS: and establishing a load-related piecewise function simulation model by using the obtained formula configuration.
2. The thermal power station boiler flue gas NOx index prediction method of claim 1, wherein the thermal power station boiler flue gas NOx index prediction method selects a NOx value as a dependent variable, and takes primary air pressure, oxygen amount, air volume, coal amount, feedwater flow and smoke temperature as independent variables.
3. The thermal power station boiler flue gas NOx index prediction method of claim 1, characterized in that the thermal power station boiler flue gas NOx index prediction method is researched by a segmented load analysis method.
4. The thermal power station boiler flue gas NOx index prediction method of claim 1, wherein the thermal power station boiler flue gas NOx index prediction method selects load sections of 200MW, 250MW, 270MW, 300MW, 350MW5 to sample, and utilizes SPSS software to construct a denitration inlet NOx concentration prediction model.
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Application publication date: 20210423 |