CN117169146A - NO (NO) x And SO 2 Flue gas monitoring method - Google Patents
NO (NO) x And SO 2 Flue gas monitoring method Download PDFInfo
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- CN117169146A CN117169146A CN202311010431.1A CN202311010431A CN117169146A CN 117169146 A CN117169146 A CN 117169146A CN 202311010431 A CN202311010431 A CN 202311010431A CN 117169146 A CN117169146 A CN 117169146A
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- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000012544 monitoring process Methods 0.000 title claims abstract description 12
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 title claims description 9
- 239000003546 flue gas Substances 0.000 title claims description 9
- 238000002835 absorbance Methods 0.000 claims abstract description 34
- 238000001228 spectrum Methods 0.000 claims abstract description 22
- 238000000862 absorption spectrum Methods 0.000 claims abstract description 19
- 239000000779 smoke Substances 0.000 claims abstract description 15
- 230000010354 integration Effects 0.000 claims abstract description 14
- 238000005259 measurement Methods 0.000 claims abstract description 13
- 238000013178 mathematical model Methods 0.000 claims abstract description 5
- 238000012549 training Methods 0.000 claims description 40
- 239000007789 gas Substances 0.000 claims description 39
- 238000012360 testing method Methods 0.000 claims description 16
- 238000010521 absorption reaction Methods 0.000 claims description 15
- 230000008859 change Effects 0.000 claims description 13
- 238000002474 experimental method Methods 0.000 claims description 5
- 230000007423 decrease Effects 0.000 claims description 3
- 238000007865 diluting Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 2
- 230000003287 optical effect Effects 0.000 description 5
- 238000011160 research Methods 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 229910052724 xenon Inorganic materials 0.000 description 2
- FHNFHKCVQCLJFQ-UHFFFAOYSA-N xenon atom Chemical compound [Xe] FHNFHKCVQCLJFQ-UHFFFAOYSA-N 0.000 description 2
- 235000008331 Pinus X rigitaeda Nutrition 0.000 description 1
- 235000011613 Pinus brutia Nutrition 0.000 description 1
- 241000018646 Pinus brutia Species 0.000 description 1
- 239000004566 building material Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000012895 dilution Substances 0.000 description 1
- 238000010790 dilution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005283 ground state Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
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Abstract
The present invention relates to a NO x And SO 2 A smoke monitoring method belongs to the technical field of smoke monitoring. The method comprises the steps of constructing a spectrum measurement platform of a smoke monitor and a concentration inversion model; the method comprises the steps that a spectrum measurement platform of the smoke monitor is built, and the spectrum measurement platform comprises a light source, an air chamber, spectrum acquisition equipment, a computer and a gas distribution system; the light source is emitted into the air chamber, and then emitted out of the air chamber after passing through the first reflecting mirror, the concave mirror and the second reflecting mirror, and then emitted into the spectrum acquisition equipment, wherein the spectrum acquisition equipment is connected with computer data; the air chamber comprises an air inlet and an air outlet; the air inlet is connected with the air distribution system; a line-by-line integration method is adopted when the inversion of the gas concentration is established; continuously recording the differential absorbance of 10 groups of NO at the same concentration and obtaining the standard deviation of the differential absorbance; by collecting absorption spectra under different concentrations, characteristic data affecting the concentration is obtained, a mathematical model of concentration inversion is established, and SO is realized 2 And NO x Is low in cost.
Description
Technical Field
The invention belongs to the technical field of flue gas monitoring, and relates to a NO x And SO 2 A flue gas monitoring method.
Background
The cost of the whole machine is up to about 15 ten thousand yuan due to the adoption of foreign imported light sources, absorption tanks and spectrometers. However, the coal-fired boiler discharge enterprises are more concentrated in building material industries other than the thermal power industry, and customers in the industry have higher cost performance requirements on flue gas monitoring equipment, and NO is generated under the condition of low cost as much as possible x 、SO 2 The flue gas monitor has the same precision and reliability. Therefore, there is an urgent need to develop a high cost performance smoke monitor.
Disclosure of Invention
In view of the above, an object of the present invention is to provide an NO x And SO 2 A flue gas monitoring method.
In order to achieve the above purpose, the present invention provides the following technical solutions:
NO (NO) x And SO 2 The method comprises the steps of constructing a spectrum measurement platform of a smoke monitor and a concentration inversion model;
the method comprises the steps that a spectrum measurement platform of the smoke monitor is built, and the spectrum measurement platform comprises a light source, an air chamber, spectrum acquisition equipment, a computer and a gas distribution system;
the light source is emitted into the air chamber, and then emitted out of the air chamber after passing through the first reflecting mirror, the concave mirror and the second reflecting mirror, and then emitted into the spectrum acquisition equipment, wherein the spectrum acquisition equipment is connected with computer data;
the air chamber comprises an air inlet and an air outlet;
the air inlet is connected with the air distribution system;
the gas distribution system is N 2 、SO 2 NO and NO 2 Mixing according to a certain proportion through valve control, inputting the mixture into an air inlet, and researching the influence of the absorption spectrum along with the concentration change and the gas type change by measuring the absorption spectrum of single and mixed gas to find out the characteristics of the absorption spectrum directly related to the concentration change; by collecting absorption spectra under different concentrations, characteristic data affecting the concentration is obtained, a mathematical model of concentration inversion is established, and SO is realized 2 And NO x Is a monitoring of (2);
the concentration inversion model is established as follows:
a line-by-line integration method is adopted when the inversion of the gas concentration is established; continuously recording the differential absorbance of 10 groups of NO at the same concentration and obtaining the standard deviation of the differential absorbance;
the integration interval selects absorption peaks 1 and 2;
obtaining 1, 2, 3, … … ppm SO 2 、NO、NO 2 Differentiating the integral value of absorbance in the interval, and diluting the standard gas with high concentration into a concentration value required by experiments by a high-precision gas diluter with 100ppm of standard gas; SO (SO) 2 And NO differential absorptionThe relationship between the photometric value and the concentration is nonlinear; a polynomial is adopted when the relation between the concentration and the differential absorbance integral value is established;
obtaining 200 groups of samples from each gas, randomly selecting 150 groups of sample data for model training, using the rest sample data for measuring the accuracy of the model, for the training process of NO, the concentration and differential absorbance integral value are nonlinear, for determining the order of a concentration inversion model, using a least square method to train the training data to carry out model training of 1-9 order polynomials, and training the obtained parameters and R 2 ；
With increasing training order, R 2 And also increases, R 2 The larger the training result is, the better the training result is, when the order is more than or equal to 3, R 2 The stable value is 0.9998, and does not change with the increase of the order, and the order is determined between 3 and 9; the trained concentration inversion model H (x) is shown in formula (1):
H NO (x)＝A 0 +A 1 x+A 2 x 2 +A 3 x 3 +A 4 x 4 +A 5 x 5 +A 6 x 6 +A 7 x 7 +A 8 x 8 +A 9 x 9 (1)
wherein x represents a differential absorbance integral value; a is that 0 ～A 9 Representing model coefficients obtained by training;
adopting a mean square error as a cost function of the model; the mean square error refers to the expectation of the square deviation of the predicted value of the model and the true value of the system, and the smaller the value of the cost function is, the higher the accuracy of the predicted value of the model is; the cost function of the modeling is shown in the formula (2):
where MSE represents the mean square error; h NO (x i ) Representing an estimate of the ith test data; y is i The true value of the ith test data is represented, and n is the training number;
when the order increases from 1 to 3, the MSE gradually decreases, and when n=3, the MSE reaches a minimum; as n continues to increase, the MSE of the training data does not improve; the MSE of the test data is minimized when n=3, when n >3, the MSE shows an increased phenomenon, the MSE of the test data shows that the n <3 training model is under-fitted, the n >3 training model is over-fitted, the NO concentration inversion model is finally determined to be a 3-order polynomial, and the NO concentration inversion model type (3) is shown as follows:
H NO (x)＝-0.1876+2.5204x+0.1308x 2 +0.0178x 3 (3)
the same method is adopted to obtain SO 2 And NO 2 Is shown in the concentration inversion model patterns (4) (5);
the invention has the beneficial effects that: by collecting absorption spectra under different concentrations, characteristic data affecting the concentration is obtained, a mathematical model of concentration inversion is established, and SO is realized 2 And NO x Is low in cost.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
FIG. 1 is a schematic diagram of the present invention;
FIG. 2 is a diagram of the spectrum measuring platform of the smoke monitor;
FIG. 3 is a NO differential absorbance integration interval selection;
FIG. 4 is SO 2 And NO 2 Selecting an integration interval; FIG. 4 (a) is SO 2 Selecting an integration interval; FIG. 4 (b) is NO 2 Selecting an integration interval;
FIG. 5 is SO 2 And an NO differential absorbance integral value; FIG. 5 (a) is SO 2 Differential absorbance integral; FIG. 5 (b) is the NO differential absorbance integral;
FIG. 6 is NO 2 Differential absorbance integral;
FIG. 7 is training data and test data for NO; FIG. 7 (a) is NO training data; fig. 7 (a) is NO test data.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present invention by way of illustration, and the following embodiments and features in the embodiments may be combined with each other without conflict.
Wherein the drawings are for illustrative purposes only and are shown in schematic, non-physical, and not intended to limit the invention; for the purpose of better illustrating embodiments of the invention, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the size of the actual product; it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numbers in the drawings of embodiments of the invention correspond to the same or similar components; in the description of the present invention, it should be understood that, if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc., that indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but not for indicating or suggesting that the referred device or element must have a specific azimuth, be constructed and operated in a specific azimuth, so that the terms describing the positional relationship in the drawings are merely for exemplary illustration and should not be construed as limiting the present invention, and that the specific meaning of the above terms may be understood by those of ordinary skill in the art according to the specific circumstances.
1. Technical route
The technical route of the smoke monitor is shown in fig. 1, a spectrum measuring platform of the smoke monitor is firstly built, as shown in fig. 2, the smoke monitor comprises a gas absorption tank, a light source, an optical fiber and spectrum measuring equipment, standard gas is diluted into mixed gas with different concentrations and different types through an auxiliary system for gas mixing, and the absorption spectrum of the single mixed gas is measured to study the influence of the absorption spectrum on the change of the concentration and the change of the gas type, so that the characteristics of the absorption spectrum directly related to the change of the concentration are found. By collecting absorption spectra under different concentrations, characteristic data affecting the concentration is obtained, a mathematical model of concentration inversion is established, and SO is realized 2 And NO x Is provided).
The absorption spectrum of the gas is affected by temperature, the higher the temperature is, the fewer electrons are in a ground state in gas molecules, the fewer the energy of absorbed photons is in electron transition, and the absorption spectrum of the gas is affected, so that the research on the change rule of the absorption spectrum of the gas along with the temperature is important to determine whether the concentration of the gas is monitored. The temperature control auxiliary system mainly heats the standard gas, measures the absorption spectrum of the gas at different temperatures, researches the law of the influence of the absorption spectrum on the temperature and establishes a temperature compensation model. The adaptability of the instrument to different working conditions is improved.
SO 2 And NO 2 Easily soluble in water and containing a large amount of water molecules in the flue gas, the gas molecules can combine with the water molecules to form new compounds, the new compounds absorb light wave bands which are not in the monitoring range, SO that the monitored gas concentration is lower than the actual concentration, therefore, the influence rules on the measurement result under different humidity conditions need to be researched, and a compensation model is built, SO that SO is carried out under different humidity conditions 2 And NO 2 Is an accurate monitor of (a).
2. Study procedure
(1) Replacement of imported equipment with domestic optical measurement equipment
The main components of the smoke monitor are all inlet devices, such as a high-end experimental-grade ocean optical spectrometer used by spectrum measuring equipment, which is quite expensive and has a unit price of about 8 ten thousand yuan. The light source adopts a xenon lamp of Japanese Korea pine, and the unit price of the light source is about 4 kiloyuan. The unit price of the domestic spectrometer is about 1.5 and Mo Zuo, the batch price is lower, the price of a xenon lamp is about 3 kiloyuan, and the performance is equivalent. The short plate of the domestic spectrometer is sensitive to ultraviolet light, and the defect mainly applies to the lower measurement limit and resolution of the smoke monitor. This disadvantage can be compensated for by increasing the optical path length of the gas absorption cell, which is much lower the longer the optical path length, the lower the measurement limit and higher the resolution, while the cost of increasing the optical path length is much lower than that of using a high performance spectrometer.
(2) Establishment of concentration inversion model
The method of line-by-line integration is adopted in establishing the inversion of the gas concentration. The selection of the integration interval is mainly based on two aspects, namely the intensity of the first differential absorbance and the intensity of the differential absorbance, and the stronger the differential absorbance under the same concentration, the more sensitive the wave band is, and the more beneficial to measuring the gas with ultra-low concentration is. Second, the differential absorbance in this interval is stable, and the selection of an interval will be described using NO as an example. The differential absorbance of 10 groups of NO was recorded continuously at the same concentration and the standard deviation was determined as shown in fig. 3:
as can be seen from fig. 3, both absorption peak 1 and absorption peak 2 are larger than absorption peak 3, and the standard deviation of absorption peaks 1 and 2 is significantly lower than that of absorption 3, which proves that the differential absorbance of absorption peak 1 and absorption peak 2 is more stable, so that the integration interval selects absorption peaks 1 and 2. The final selected wavelength point is shown in the red curve of fig. 3. By the same method, SO 2 And NO 2 The integrated wavelength points of (2) are shown in figure 4. FIG. 4 (a) is SO 2 Selecting an integration interval; FIG. 4 (b) is NO 2 And selecting an integration interval.
In the experiment, 1, 2 and 3 … … ppm SO are required to be obtained 2 、NO、NO 2 In the present study, the integrated value of the differential absorbance in the above section was obtained by diluting a standard gas of 100ppm to a concentration value required for the experiment by a high-precision gas dilution apparatus. SO obtained in the experiment 2 And the differential absorbance integral of NO is shown in fig. 5. FIG. 5 (a) is SO 2 Differential absorbance integral; fig. 5 (b) shows the integrated value of NO differential absorbance. SO (SO) 2 And the relationship between the NO differential absorbance integral value and the concentration is nonlinear. A polynomial is required to be used in establishing the relationship between the concentration and the differential absorbance integral value. FIG. 6 is NO 2 And SO 2 NO compared with NO 2 The non-linear behavior is not obvious, which is comparable to NO 2 Is related to the absorption characteristics of NO 2 Absorption ratio of light SO 2 And NO is much weaker.
200 sets of samples were obtained for each gas, 150 sets of sample data were randomly selected for model training, the remaining sample data were used for model accuracy testing, the model training process was described by taking NO as an example, fig. 7 (a) is the randomly selected 150 sets of training data, and fig. 7 (b) is the test sample.
As can be seen from FIG. 7, the concentration and the differential absorbance integral value are nonlinear, training data is model-trained by using a least square method to determine the order of a concentration inversion model, and 1-9 order polynomials are trained to obtain parameters and R 2 As shown in table 1:
table 11-9 training results
As can be seen from Table 1, as the training order increases, R 2 And also increases, R 2 The larger the training result is, the better the training result is, when the order n is more than or equal to 3, R 2 The stable value is 0.9998, which does not change with increasing n, and the order is between 3 and 9 to be further determined. The trained concentration inversion model H (x) is shown in formula (1):
H NO (x)＝A 0 +A 1 x+A 2 x 2 +A 3 x 3 +A 4 x 4 +A 5 x 5 +A 6 x 6 +A 7 x 7 +A 8 x 8 +A 9 x 9 (1)
wherein X represents a differential absorbance integral value; a is that 0 ～A 9 Representing model coefficients obtained by training.
The cost function is a method for evaluating the quality of the model, and the mean square error is adopted as the cost function of the model in the research. The mean square error is the expectation of the square of the deviation of the predicted value of the model and the true value of the system, and the smaller the value of the cost function, the higher the accuracy of the predicted value of the model. The cost function of the modeling is shown in the formula (2):
where MSE represents the mean square error; h NO (x i ) Representing an estimate of the ith test data; y is i Representing the true value of the ith test data.
The MSE gradually decreases as the order increases from 1 to 3, and becomes minimal when n=3. When n continues to increase, the MSE of the training data is not significantly improved and is minimized at n=3, when n >3, the MSE of the test data shows an increase, the MSE of the test data shows that the n <3 training model shows a lack of fit, and the n >3 training model shows a lack of fit, and finally, the NO concentration inversion model is determined to be a polynomial of order 3, and the NO concentration inversion model type (3) shows that:
H NO (x)＝-0.1876+2.5204x+0.1308x 2 +0.0178x 3 (3)
the same method is adopted to obtain SO 2 And NO 2 Is shown in the concentration inversion model patterns (4) (5);
finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (1)
1. NO (NO) x And SO 2 The flue gas monitoring method is characterized in that: the method comprises the steps of constructing a spectrum measurement platform of a smoke monitor and a concentration inversion model;
the method comprises the steps that a spectrum measurement platform of the smoke monitor is built, and the spectrum measurement platform comprises a light source, an air chamber, spectrum acquisition equipment, a computer and a gas distribution system;
the light source is emitted into the air chamber, and then emitted out of the air chamber after passing through the first reflecting mirror, the concave mirror and the second reflecting mirror, and then emitted into the spectrum acquisition equipment, wherein the spectrum acquisition equipment is connected with computer data;
the air chamber comprises an air inlet and an air outlet;
the air inlet is connected with the air distribution system;
the gas distribution system is N 2 、SO 2 NO and NO 2 Mixing according to a certain proportion through valve control, inputting the mixture into an air inlet, and researching the influence of the absorption spectrum along with the concentration change and the gas type change by measuring the absorption spectrum of single and mixed gas to find out the characteristics of the absorption spectrum directly related to the concentration change; by collecting absorption spectra under different concentrations, characteristic data affecting the concentration is obtained, a mathematical model of concentration inversion is established, and SO is realized 2 And NO x Is a monitoring of (2);
the concentration inversion model is established as follows:
a line-by-line integration method is adopted when the inversion of the gas concentration is established; continuously recording the differential absorbance of 10 groups of NO at the same concentration and obtaining the standard deviation of the differential absorbance;
the integration interval selects absorption peaks 1 and 2;
obtaining 1, 2, 3, … … ppm SO 2 、NO、NO 2 Differentiating the integral value of absorbance in the interval, and diluting the standard gas with high concentration into a concentration value required by experiments by a high-precision gas diluter with 100ppm of standard gas; SO (SO) 2 And the relationship between the NO differential absorbance integral and the concentration is nonlinear; a polynomial is adopted when the relation between the concentration and the differential absorbance integral value is established;
obtaining 200 groups of samples from each gas, randomly selecting 150 groups of sample data for model training, using the rest sample data for measuring the accuracy of the model, for the training process of NO, the concentration and differential absorbance integral value are nonlinear, for determining the order of a concentration inversion model, using a least square method to train the training data to carry out model training of 1-9 order polynomials, and training the obtained parameters and R 2 ；
With increasing training order, R 2 And also increases, R 2 The larger the training result is, the better the training result is, when the order is more than or equal to 3, R 2 The stable value is 0.9998, and does not change with the increase of the order, and the order is determined between 3 and 9; the trained concentration inversion model H (x) is shown in formula (1):
H NO (x)＝A 0 +A 1 x+A 2 x 2 +A 3 x 3 +A 4 x 4 +A 5 x 5 +A 6 x 6 +A 7 x 7 +A 8 x 8 +A 9 x 9 (1)
wherein x represents a differential absorbance integral value; a is that 0 ～A 9 Representing model coefficients obtained by training;
adopting a mean square error as a cost function of the model; the mean square error refers to the expectation of the square deviation of the predicted value of the model and the true value of the system, and the smaller the value of the cost function is, the higher the accuracy of the predicted value of the model is; the cost function of the modeling is shown in the formula (2):
where MSE represents the mean square error; h NO (x i ) Representing an estimate of the ith test data; y is i The true value of the ith test data is represented, and n is the training number;
when the order increases from 1 to 3, the MSE gradually decreases, and when n=3, the MSE reaches a minimum; as n continues to increase, the MSE of the training data does not improve; the MSE of the test data is minimized when n=3, when n >3, the MSE shows an increased phenomenon, the MSE of the test data shows that the n <3 training model is under-fitted, the n >3 training model is over-fitted, the NO concentration inversion model is finally determined to be a 3-order polynomial, and the NO concentration inversion model type (3) is shown as follows:
H NO (x)＝-0.1876+2.5204x+0.1308x 2 +0.0178x 3 (3)
the same method is adopted to obtain SO 2 And NO 2 Is shown in the concentration inversion model patterns (4) (5);
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117388204A (en) * | 2023-12-13 | 2024-01-12 | 杭州泽天春来科技股份有限公司 | Nitric oxide gas analysis system, method and computer readable storage medium |
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| CN116223417A (en) * | 2023-03-21 | 2023-06-06 | 重庆理工大学 | Method for detecting concentration of NO gas based on DOAS method |
| CN117969436A (en) * | 2023-12-27 | 2024-05-03 | 重庆理工大学 | Monitoring low concentration NO and SO2Is a method of (2) |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116223417A (en) * | 2023-03-21 | 2023-06-06 | 重庆理工大学 | Method for detecting concentration of NO gas based on DOAS method |
| CN117969436A (en) * | 2023-12-27 | 2024-05-03 | 重庆理工大学 | Monitoring low concentration NO and SO2Is a method of (2) |
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| CN117388204A (en) * | 2023-12-13 | 2024-01-12 | 杭州泽天春来科技股份有限公司 | Nitric oxide gas analysis system, method and computer readable storage medium |
| CN117388204B (en) * | 2023-12-13 | 2024-04-12 | 杭州泽天春来科技股份有限公司 | Nitric oxide gas analysis system, method and computer readable storage medium |
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