CN110263452B - Flue gas time distribution characteristic analysis method and system in flue and denitration system - Google Patents
Flue gas time distribution characteristic analysis method and system in flue and denitration system Download PDFInfo
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Abstract
The invention provides a method and a system for analyzing time distribution characteristics of flue gas in a flue and a denitration system, wherein the method comprises the steps of determining theoretical data of boiler flue gas parameters; establishing a flue gas parameter change rule model; and obtaining the time distribution characteristic of the flue gas in the flue. The flue gas parameter change rule model established by the invention and the determined time distribution characteristic of the flue gas in the flue can be used as control signals of an SCR denitration system, so that the aim of accurately controlling the ammonia injection amount is fulfilled; meanwhile, the support can be provided for denitration analysis of parameters of the same path of flue gas, the analysis of all data of the same path of flue gas is guaranteed, and the analysis precision is improved.
Description
Technical Field
The invention relates to the technical field of boiler combustion, in particular to a method and a system for analyzing time distribution characteristics of flue gas in a flue and a denitration system.
Background
During the combustion process of the coal-fired boiler, a large amount of nitrogen oxides are generated. The state sets up the emission standard of atmospheric pollutants in different industries, and in order to meet the emission standard of the atmospheric pollutants and reduce the emission of nitrogen oxides, a plurality of boilers are provided with denitration systems. At present, large boilers, especially coal-fired boilers of thermal power plants, basically adopt selective catalysisA chemical reduction (SCR) denitration technology is characterized in that a denitration reactor is arranged in a flue, flue gas generated by boiler combustion passes through the denitration reactor, a catalyst is arranged in the denitration reactor, and a certain reducing agent ammonia (NH) is sprayed into the flue gas 3 ) Under the action of a catalyst, the reducing agent NH 3 Selectively reacts with nitrogen oxide in the flue gas to generate N 2 And H 2 And O, thereby achieving the purpose of reducing the emission of nitrogen oxides. In a denitration system, NH sprayed into flue gas is controlled by monitoring parameters such as flue gas quantity and nitric oxide concentration at the inlet of an SCR (selective catalytic reduction) reactor 3 The denitration efficiency is ensured; and monitoring parameters (flue gas nitrogen oxide concentration, ammonia escape concentration and the like) at the outlet of the SCR reactor, and supervising the ammonia spraying condition. The technology has the advantages of mature process, strong adaptability, high denitration efficiency and relatively low price.
At present, the automatic ammonia spraying of domestic denitration systems is mainly realized by controlling the ammonia nitrogen molar ratio or controlling the concentration of outlet nitrogen oxides, namely, NH of the required chemical reaction is calculated according to the concentration of inlet nitrogen oxides and the flow rate of flue gas of an SCR reactor 3 Thereby controlling an ammonia injection regulating valve or adjusting the ammonia injection amount by detecting the concentration of nitrogen oxides at the outlet of the denitration SCR. In actual operation, because flue gas characteristics and measurement reason, the nitrogen oxide concentration measurement needs 3-5 minutes, and the flue gas only needs ten seconds of time through denitration SCR reactor, and the nitrogen oxide concentration of surveying lags behind the actual operating concentration of denitration far away, consequently, causes to spout ammonia volume and actual operating condition and does not accord with, causes to spout ammonia excessive or spout ammonia inadequately. The ammonia injection amount is too low, the denitration efficiency is low, the emission of nitrogen oxides exceeds the standard, and the environmental pollution is caused; the ammonia injection amount is excessive, ammonia gas escaping from the outlet of the reactor reacts with sulfur trioxide in the flue gas to generate ammonium bisulfate, the ammonium bisulfate is easy to deposit in the empty expectation of denitration downstream equipment, the plant power consumption rate is increased, the power generation load of a unit is influenced, even the result of shutdown cleaning is caused, and the problems of waste of a denitration agent of the unit, poor economy, reduced catalyst performance and the like can be caused due to excessive ammonia injection.
The method for solving the problems comprises the steps of canceling automatic ammonia spraying control of denitration and adopting a manual adjustment mode, but the actual operation effect has a great relationship with the responsibility and the service level of operators, and the workload is increased. The other method is that the machine set carries out automatic ammonia injection transformation, and the denitration SCR pre-injection ammonia adjustment is realized through feed-forward technologies such as the prediction of the concentration of nitrogen oxides at the inlet of the denitration system, so that the denitration ammonia injection error caused by the problems of measurement lag and the like is reduced.
For example, the invention patent application with the application number of 201710002535.6 discloses an intelligent feedforward signal-based system and method for optimally controlling the ammonia injection amount of an SCR denitration system, which are based on historical data of a thermal power plant, adopt the idea of data modeling, take boiler side adjustable parameters as input, take the concentration of NOx at a hearth outlet as output, and construct a prediction model by using a least square support vector machine algorithm, wherein the model can be used for constructing an intelligent feedforward controller in an ammonia injection amount control strategy. The method is characterized in that dynamic matrix control is used as a main controller, PID (proportion integration differentiation) is used as an auxiliary controller, a cascade feedback control structure is constructed, in the operation process, an intelligent feedforward controller outputs a feedforward control signal in real time according to parameter change of a boiler side, the change of the working condition of a unit is quickly responded, an SCR (selective catalytic reduction) system ammonia injection amount optimization control strategy is formed together with feedback control, and the quick and accurate control of the ammonia injection amount is realized. However, in the method, only the adjustable parameters at the side of the hearth are taken as input, the concentration of NOx at the outlet of the hearth is taken as output, other flue gas parameters such as flue gas flow are not predicted, other factors influencing the flue gas parameters are not considered, and the time distribution characteristics of the flue gas in the flue cannot be determined.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a method and a system for analyzing the time distribution characteristics of flue gas in a flue, and a denitration system, which can determine the time distribution characteristics of the flue gas in the flue, and further provide more accurate information for denitration control and denitration analysis.
A method for analyzing time distribution characteristics of flue gas in a flue comprises the following steps:
determining the data of boiler flue gas parameters;
establishing a flue gas parameter change rule model;
and obtaining the time distribution characteristic of the flue gas in the flue.
Preferably, the boiler flue gas parameter includes at least one of flue gas flow rate, nitrogen oxide concentration and nitrogen oxide generation amount.
Preferably, in any of the above schemes, the determining the flue gas flow rate includes:
determining the average carbon content in ash, the percentage b of coal ash and the excess air coefficient apy;
determining a theoretical dry air amount Vgkb according to the b;
determining theoretical dry smoke quantity Vgyb according to the Vgkb;
determining dry flue gas quantity Vgy according to the theoretical dry air quantity, the theoretical dry flue gas quantity and the excess air coefficient;
determining the volume VH2O of the water vapor in the smoke according to the theoretical dry air quantity and the excess air coefficient;
and determining the flue gas flow.
In any of the above aspects, preferably, the concentration of nitrogen oxide is determined based on data measured by a nitrogen oxide concentration detection device at an inlet position of the denitration SCR reactor.
In any of the above schemes, preferably, the nitrogen oxide generation amount is determined according to the determined flue gas flow rate and the determined nitrogen oxide concentration.
Preferably, in any of the above schemes, the establishing of the flue gas parameter change rule model includes the steps of:
analyzing factors influencing the flue gas parameters during boiler combustion by adopting a grey correlation degree analysis method, and determining a maximum influence factor set;
and determining the relation between the flue gas parameters and all influence factors in the maximum influence factor set by adopting a Least Square Support Vector Machine (LSSVM) method to obtain a flue gas parameter change rule model.
Preferably, the factors influencing the flue gas parameters during the boiler combustion include at least one of unit load, total coal quantity, coal quality, total air quantity, hot primary air quantity, hot primary air quantity main pipe pressure, hot primary air quantity air door opening, secondary air box pressure, secondary air baffle opening of each layer, SOFA air baffle opening of each layer, primary fan current, blower current, draught fan current, boiler oxygen quantity, grinding combination mode, coal quantity of each coal mill, air quantity of each coal mill, water supply quantity, water supply temperature, temperature reduction water quantity, main steam temperature, main steam pressure, reheat steam temperature, reheat steam pressure, denitration inlet flue gas temperature and economizer excess air coefficient.
Preferably, in any of the above schemes, the determining the maximum influence factor set by analyzing the factors influencing the flue gas parameters during the boiler combustion by using a grey correlation analysis method comprises the steps of:
inputting all N influencing factors influencing the flue gas parameters during boiler combustion, wherein N is a natural number;
determining a reference influence factor data column and a plurality of comparison influence factor data columns;
carrying out dimensionless processing on the historical operation data of all N influencing factors;
calculating and comparing a correlation coefficient between the influence factor data column and the reference influence factor data column according to the historical operating data after the non-dimensionalization processing;
calculating and comparing the association degrees between the influence factors and the reference influence factors and sequencing the association degrees;
and determining the maximum influence factor set according to the result of the relevance ranking.
Preferably, in any of the above schemes, the first M comparison influence factors with high correlation are taken to form a maximum influence factor set, where M is a natural number and M is less than or equal to N.
In any of the above schemes, preferably, the thresholds K1 and K2 are set, where K1 is greater than or equal to K1 and K2 is greater than or equal to 0, and the largest correlation influence factor among all the correlation influence factors having a correlation greater than or equal to K1 and all other correlation influence factors having a correlation less than K1 and greater than K2 form the largest influence factor set.
In any of the above embodiments, preferably, the reference influencing factor includes at least one of a flue gas flow rate or a nitrogen oxide concentration or a nitrogen oxide generation amount.
Preferably, in any of the above schemes, before determining the relationship between the flue gas parameter and each influence factor in the maximum influence factor set by using a least square support vector machine and obtaining a flue gas parameter change rule model, the method further includes the steps of: and determining the influence weight of each influence factor in the maximum influence factor set on the smoke parameters by adopting an analytic hierarchy process, and further determining the sequence of each influence factor and the operation level of each influence factor in a least square support vector machine.
Preferably, in any of the above schemes, determining the influence weight of each influence factor in the maximum influence factor set on the flue gas parameter by using an analytic hierarchy process, and further determining the ranking of each influence factor and the operation level thereof in the least square support vector machine includes the steps of:
and establishing a hierarchical structure model, wherein a target layer of the hierarchical structure model is a smoke parameter, and a scheme layer of the hierarchical structure model comprises a maximum influence factor set.
Constructing the judgment matrix of the maximum influence factor set;
calculating the weight of each influence factor in the maximum influence factor set to the target layer;
and determining the ranking of the influence factors and the operation level of the influence factors in the least square support vector machine according to the weight, wherein the higher the weight of the influence factors is, the higher the ranking of the influence factors is, and the higher the operation level of the influence factors in the least square support vector machine is.
In any of the above schemes, preferably, consistency check is performed after the weight of each influence factor in the maximum influence factor set on the target layer is calculated.
And verifying the determined weight according to the online monitoring data of the smoke parameters, if the error is within a set threshold range, the weight meets the requirement, and if the error exceeds the set threshold range, the weight is adjusted until the weight meets the requirement.
Preferably, in any of the above schemes, the online monitoring data includes at least one of monitoring data of flue gas flow, nitrogen oxide concentration and nitrogen oxide generation amount.
Preferably, in any of the above schemes, the least square support vector machine is trained and verified by using the flue gas parameters and the historical operating parameters of the factors in the corresponding maximum influence factor set, so as to obtain the flue gas parameter change rule model.
In any of the above schemes, preferably, the nox generation amount change rule model is determined according to the method, or after the flue gas flow rate change rule model and the nox concentration change rule model are determined, the nox generation amount change rule model is determined according to the nox generation amount = nox concentration × flue gas flow rate.
Preferably, in any of the above schemes, the flow distribution of the flue gas is calculated according to the flue gas parameter change rule model, the flow characteristics of the flue gas in the denitration reactor and the front and rear flues, and the geometric shape of the flues, so as to obtain the time distribution characteristics of the flue gas in the flues.
The invention also provides a flue gas time distribution characteristic analysis system in a flue, which comprises a processor and a storage medium, wherein a program is stored in the storage medium, the program is run by the processor, the program executes the flue gas time distribution characteristic analysis method in the flue, and the method comprises the following steps:
determining the data of boiler flue gas parameters;
establishing a flue gas parameter change rule model;
and obtaining the time distribution characteristic of the flue gas in the flue.
Preferably, still store in the storage medium influence flue gas parameter's factor and its historical operating data during the boiler burning, the factor includes at least one in unit load, total coal volume, coal quality, total amount of wind, hot primary air amount of wind, the female pipe pressure of hot primary air amount of wind, hot primary air amount of wind air door aperture, secondary air box pressure, every layer of overgrate air baffle aperture, every layer of SOFA wind baffle aperture, primary air fan current, forced draught blower current, draught fan current, boiler oxygen volume, the combination mode of grinding, every coal pulverizer coal volume, every coal pulverizer volume, feed water volume, feedwater temperature, the amount of temperature reduction water, main steam temperature, main steam pressure, reheat steam temperature, reheat steam pressure, denitration entry flue gas temperature, the surplus air coefficient of economizer.
In any of the above schemes, preferably, the system further includes a display device connected to the processor for displaying various data during the program running process.
In another aspect, the invention provides a denitration system, which includes an SCR reactor and an ammonia injection control device, and uses the time distribution characteristic of the flue gas in a flue as a control signal of the ammonia injection device, and the time distribution characteristic of the flue gas in the flue is determined by using a flue gas time distribution characteristic analysis method in the flue.
Preferably, the time T of the flue gas reaching the inlet of the SCR reactor is calculated according to the time distribution characteristics of the flue gas in the flue, and the ammonia injection amount is determined according to the time T and the flue gas amount.
The invention has the beneficial effects that:
1. when a smoke parameter change rule model is established, various influence factors are analyzed through a grey correlation degree analysis method, then the maximum influence factor set influencing the smoke parameters is reserved, and meanwhile, the factors with small influence or even no influence are abandoned, so that the dimensionality of the established model is reduced, and the calculated amount is reduced;
2. the established time distribution characteristic of the flue gas in the flue can be used as a control signal of the SCR denitration system, so that the aim of accurately controlling the ammonia injection amount is fulfilled;
3. the established time distribution characteristics of the flue gas in the flue can be used for denitration data analysis, so that the data of the flue gas in the same path are analyzed, and the analysis precision is improved.
Drawings
Fig. 1 is a schematic flow chart of a preferred embodiment of a method for analyzing the time distribution characteristics of flue gas in a flue according to the present invention.
Fig. 2A is a schematic flow chart of a gray correlation analysis method according to a preferred embodiment of the method for analyzing time distribution characteristics of flue gas in a flue.
Fig. 2B is a correlation between the factors calculated according to the embodiment shown in fig. 2 of the gray correlation analysis method of the method for analyzing the time distribution characteristics of flue gas in a flue according to the present invention.
Fig. 3 is a schematic diagram of a preferred embodiment of the analytic hierarchy process of the time distribution characteristics of the flue gas in the flue according to the present invention.
FIG. 4 is a schematic diagram of a preferred embodiment of establishing a correlation model between boiler combustion parameters and flue gas parameter change rules according to the method for analyzing the time distribution characteristics of flue gas in a flue.
Fig. 5 is a schematic diagram of a time distribution curve of the concentration of nitrogen oxides in the flue, which is obtained by adopting a preferred embodiment of the system for analyzing the time distribution characteristics of the flue gas in the flue.
Fig. 6 is a schematic diagram of a preferred embodiment of applying the time distribution characteristics of the flue gas in the flue to the analysis of the denitration data, which is obtained by the method for analyzing the time distribution characteristics of the flue gas in the flue according to the present invention.
Detailed Description
For a better understanding of the present invention, reference will now be made in detail to the following examples. The flues in the invention all refer to a section from a denitration inlet in the flue to a unit discharge port.
Example 1
As shown in fig. 1, a method for analyzing time distribution characteristics of flue gas in a flue includes the steps of:
s1, determining data of boiler flue gas parameters;
s2, establishing a flue gas parameter change rule model;
and S3, obtaining the time distribution characteristic of the flue gas in the flue.
In step S1, the boiler flue gas parameter includes at least one of a flue gas flow rate, a nitrogen oxide concentration, and a nitrogen oxide generation amount.
Determining the flue gas flow comprises the following steps:
s111, determining the average carbon content in ash, the percentage b of coal ash and the excess air coefficient apy;
s112, determining a theoretical dry air amount Vgkb according to the b;
s113, determining theoretical dry flue gas volume Vgyb according to the Vgkb;
s114, determining dry flue gas quantity Vgy according to the theoretical dry air quantity, the theoretical dry flue gas quantity and the excess air coefficient;
s115, determining the volume VH2O of the water vapor in the smoke according to the theoretical dry air quantity and the excess air coefficient;
and S116, determining the flue gas flow.
Determining the concentration of the nitrogen oxide according to data measured by a nitrogen oxide concentration detection device at the inlet position of the denitration SCR reactor; and determining the nitrogen oxide generation amount according to the determined flue gas flow and the nitrogen oxide concentration.
For a certain furnace type, the specific steps of determining the flue gas flow comprise:
the average carbon content in the ash and the coal ash content b are determined according to the formula b = (alz × Clz)/(100-Clz) + (afh × Cfh)/(100-Cfh), wherein alz represents the mass percentage of the slag to the total ash content of the coal, clz represents the mass percentage of the carbon to the total ash content of the coal, afh represents the mass percentage of the ash content of the fly ash to the total ash content of the coal, and Cfh represents the mass percentage of the carbon to the total ash content of the coal.
The excess air factor apy is determined according to the formula apy = 21/(21-O2 py), where O2py represents the amount of exhaust smoke oxygen, which is the value obtained by actual detection.
The theoretical dry air amount Vgkb is determined according to a formula Vgkb = gl, K2/1000 (gl, qnetar, 1000-3.3727 aar, b), wherein gl, K2 represents a dry air calculation coefficient determined according to the fuel type and the ashless dry base volatile matter of the fuel, and specific values are shown in the following table, wherein gl, qnetar represents the lower calorific value of the fuel per kilogram, and is obtained by analyzing the coal quality entering the furnace, and Aar represents the received base ash of the fuel, and is obtained by analyzing the coal quality entering the furnace.
TABLE 1 Dry air quantity calculation coefficient table
The theoretical dry flue gas quantity Vgyb is determined according to the formula Vgyb =0.98 × vgkb.
The dry flue gas quantity is determined according to the formula Vgy = Vgyb + (apy-1) × Vgkb.
The volume of water vapor in the flue gas, VH2O, was determined according to the formula VH2O =1.24 ((9 Har + Mar)/100 +0.01293 apy + vgkb), wherein Har represents the received basal hydrogen content of the fire coal in% and Mar represents the received basal total water of the fire coal in% of the total water.
Calculating the flue gas flow according to a formula of flue gas flow = (dry flue gas amount Vgy + volume of water vapor in flue gas VH 2O) × (actual coal combustion amount × 1000) × 0.92, wherein the unit of the actual coal combustion amount is ton.
In step S2, establishing a flue gas parameter change rule model includes the steps of:
s21, analyzing factors influencing the flue gas parameters during boiler combustion by adopting a grey correlation degree analysis method, and determining a maximum influence factor set;
s22, determining the influence weight of each influence factor in the maximum influence factor set on the smoke parameters by adopting an analytic hierarchy process, and further determining the sequence of each influence factor and the operation level of the influence factor in a least square support vector machine;
and S23, determining the relation between the flue gas parameters and all the influence factors in the maximum influence factor set by adopting a least square support vector machine to obtain a flue gas parameter change rule model.
Step S21 specifically includes the steps of:
s211, inputting all N influencing factors influencing the flue gas parameters during boiler combustion, wherein N is a natural number;
s212, determining a reference influence factor data column and a plurality of comparison influence factor data columns;
s213, carrying out dimensionless processing on the historical operating data of all N influencing factors;
s214, calculating and comparing a correlation coefficient between the influence factor data column and the reference influence factor data column according to the historical operating data after the non-dimensionalization processing;
s215, calculating and comparing the association degrees between the influence factors and the reference influence factors and sequencing the association degrees;
s216, selecting the first M comparative influence factors with high association degree as a maximum influence factor set according to the association degree sorting result, wherein M is a natural number and M is less than or equal to N.
Factors influencing flue gas parameters during boiler combustion include at least one of unit load, total coal quantity, coal quality, total air quantity, hot primary air quantity, hot primary air quantity main pipe pressure, hot primary air quantity air door opening degree, secondary air box pressure, each layer of secondary air baffle opening degree, each layer of SOFA air baffle opening degree, primary air fan current, air feeder current, draught fan current, boiler oxygen quantity, grinding combination mode, coal quantity of each coal mill, air quantity of each coal mill, water supply quantity, water supply temperature, desuperheating water quantity, main steam temperature, main steam pressure, reheat steam temperature, reheat steam pressure, denitration inlet flue gas temperature and economizer excess air coefficient.
Because the SCR denitration reactor includes a side a and a side B, the denitration inlet flue gas temperature includes an SCR denitration reactor a side inlet flue gas temperature and an SCR denitration reactor B side inlet flue gas temperature.
Step S22 specifically includes the steps of:
s221, establishing a hierarchical structure model, wherein a target layer of the hierarchical structure model is a smoke parameter, and a scheme layer of the hierarchical structure model comprises a maximum influence factor set.
S222, constructing a judgment matrix of the maximum influence factor set;
s223, calculating the weight of each influence factor in the maximum influence factor set to the target layer;
and S224, determining the ranking of the influence factors and the operation level of the influence factors in the least squares support vector machine according to the weight, wherein the higher the weight of the influence factors is, the higher the ranking of the influence factors is, and the higher the operation level of the influence factors in the least squares support vector machine is.
And after the weight of each influence factor in the maximum influence factor set on the target layer is calculated, consistency check is required. And verifying the determined weight according to the online monitoring data of the smoke parameters, if the error is within a set threshold range, the weight meets the requirement, and if the error exceeds the set threshold range, the weight is adjusted until the weight meets the requirement.
In step S23, a Least Square Support Vector Machine (LSSVM) is used to determine the relationship between the flue gas parameter and each influence factor in the maximum influence factor set, so as to obtain a flue gas parameter change rule model. And training and verifying the least square support vector machine by adopting the flue gas parameters and the historical operating parameters of the factors in the corresponding maximum influence factor set to obtain the flue gas parameter change rule model. The input of the LSSVM is the value of each factor in the maximum influence factor set, and the output is the value of the smoke parameter.
In particular, in view of the relationship between the nitrogen oxide production amount = nitrogen oxide concentration and flue gas flow rate, the determination of the nitrogen oxide production amount change law model may be performed according to the above steps S1 to S21 to S22 to S23, or may be performed according to the flue gas flow rate change law model and the nitrogen oxide concentration change law model determined according to the above steps S1 to S21 to S22 to S23, and then the nitrogen oxide production amount change law model may be determined according to the nitrogen oxide production amount = nitrogen oxide concentration and flue gas flow rate.
And S3, calculating the flow distribution of the flue gas according to the flue gas parameter change rule model, the flow characteristics of the flue gas in the denitration reactor and the front and rear flues and the geometric shape of the flues, and obtaining the time distribution characteristics of the flue gas in the flues.
Example 2
An in-flue gas time distribution characteristic analysis system, the system comprising a processor and a storage medium, the storage medium having stored therein a program, the program being executed by the processor, the program performing the in-flue gas time distribution characteristic analysis method, the method comprising the steps of:
s1, determining data of boiler flue gas parameters;
s2, establishing a flue gas parameter change rule model;
and S3, obtaining the time distribution characteristic of the flue gas in the flue.
Still store in the storage medium influence the factor of nitrogen oxide formation volume and the historical operating data of every factor during boiler combustion, the factor includes unit load, total coal volume, coal quality, total amount of wind, the hot primary air amount of wind, the female pipe pressure of the hot primary air amount of wind, hot primary air amount of wind air door aperture, secondary air box pressure, every layer of overgrate air baffle aperture, every layer of SOFA wind baffle aperture, primary air machine current, forced draught blower current, draught fan current, boiler oxygen volume, the combination mode of grinding, every coal pulverizer coal volume, every coal pulverizer amount of wind, the water feeding, feedwater temperature, the amount of temperature reduction, main steam temperature, main steam pressure, reheat steam temperature, reheat steam pressure, denitration entry flue gas temperature, at least one among the economizer surplus air factor
Because the SCR denitration reactor includes a side a and a side B, the denitration inlet flue gas temperature includes an SCR denitration reactor a side inlet flue gas temperature and an SCR denitration reactor B side inlet flue gas temperature.
The system also comprises a display device which is connected with the processor and is used for displaying various data in the program running process.
Example 3
The factors influencing the generation amount of nitrogen oxides during boiler combustion are more, the influence among the factors is more complex, if all the influencing factors are analyzed to establish a flue gas parameter change rule model, the analysis difficulty is very high, and the data calculation amount is huge, so that a grey correlation degree analysis method is adopted to determine a factor set which has the largest influence on the flue gas parameters during boiler combustion.
And storing historical operation data of each unit in the flue gas time distribution characteristic analysis system, selecting the historical operation data of the unit in a period of time, and confirming that the unit is in a stable operation stage in the period of time.
Taking the concentration of the nitrogen oxides in the flue gas parameters as an example, a specific process for establishing a flue gas parameter change rule model is introduced. In this embodiment, a model of the change law of the concentration of nitrogen oxides at the position of the denitration inlet is established.
As shown in fig. 2A, determining N influencing factors related to the concentration of nitrogen oxides during boiler combustion, for example, determining 19 parameters such as unit load, basic low calorific value received in a boiler, basic ash received in a boiler, air-dried basic volatile matter in a boiler, hot primary air flow, secondary air baffle opening (each layer relates to the parameter and totally three layers), boiler oxygen amount, primary air fan current, air blower current, induced draft fan current, coal feeding amount, warm air amount, economizer surplus coefficient, theoretical flue gas flow, denitration inlet nitrogen oxide concentration, denitration inlet flue gas temperature, nitrogen oxide generation amount, and taking historical data of the 19 influencing factors in the same time period as input; and carrying out non-dimensionalization on the data of all the factors by adopting an initial value method, or carrying out non-dimensionalization on all the data by adopting an average value method or an interval method. Calculating the degree of association between the various factors is shown in fig. 2B.
As can be seen from fig. 2B, the correlation degree between the nox concentration at the denitration inlet position and other factors is ranked as:
| serial number | Comparing the influencing factors | Degree of |
| 1 | Inlet nitrogen oxide production | 0.91 |
| 2 | Coal feeding amount | 0.9 |
| 3 | Primary fan current | 0.89 |
| 4 | Hot primary air quantity | 0.82 |
| 5 | Theoretical flue gas flow | 0.82 |
| 6 | Opening degree of secondary windshield at third layer | 0.78 |
| 7 | Opening degree of secondary windshield on second floor | 0.72 |
| 8 | Oxygen content of boiler | 0.72 |
| 9 | Warm air quantity | 0.71 |
| 10 | Current of draught fan | 0.66 |
| 11 | Load of unit | 0.64 |
| 12 | Opening degree of secondary windshield of first layer | 0.56 |
| 13 | Economizer excess coefficient | 0.43 |
| 14 | Volatile matter of charged air dry basis | 0.41 |
| 15 | Denitration inlet flue gas temperature | 0.37 |
| 16 | Current of blower | 0.34 |
| 17 | Ash of the received base | 0.13 |
| 18 | Low calorific value after entering furnace | 0.05 |
The threshold values K1=0.9 and K2=0.72 are set, and among the factors having a coefficient of correlation with the nox concentration at the nox removal inlet position exceeding 0.9, the element having a coal amount as the largest influence factor set is selected, and all the factors having a coefficient of correlation with the nox concentration at the nox removal inlet position of less than 0.9 and not less than 0.72 are the elements having the largest influence factor set.
The treatment is carried out because the elements with high correlation degree (more than or equal to the threshold value K1) with the concentration of the nitrogen oxide at the denitration inlet position have larger mutual influence, one of the elements is selected for analysis, and meanwhile, the influence factors are subjected to dimension reduction, so that the subsequent calculation amount is reduced; and for the factors with high correlation degree with the concentration of the nitrogen oxides at the denitration inlet position, because the influence is small, the analysis is not carried out, and the purposes of reducing the dimension of the influencing factors and reducing the subsequent calculation amount are also realized.
The influence factors related to the flue gas parameters during the combustion of the boiler are many, the threshold values K1 and K2 are set according to needs, and the influence factors and the values of K1 and K2 listed in the embodiment are only used for explaining the determination method of the maximum influence factor set, and have no limiting effect.
The maximum influence factor set determined according to the method comprises 7 factors such as coal feeding amount, primary fan current, hot primary air volume, theoretical flue gas flow, third-layer secondary windshield opening degree, second-layer secondary windshield opening degree and boiler oxygen amount.
The calculation method of the correlation coefficient and the correlation degree is a mature technology, and is not described herein.
And determining the influence weight of each influence factor in the maximum influence factor set on the concentration of the nitric oxide by adopting an analytic hierarchy process according to the determined maximum influence factor set, and further determining the sequence of each influence factor and the operation level of the influence factor in a least square support vector machine.
As shown in fig. 3, a hierarchical structure model is established, wherein a target layer of the hierarchical structure model is the concentration of nitrogen oxides, a criterion layer of the hierarchical structure model comprises nitrogen oxide parameters, unit operation parameters, air quantity and oxygen quantity parameters and boiler combustion parameters, and a scheme layer of the hierarchical structure model comprises all the influence factors of the maximum influence factor set.
And constructing the judgment matrix of the maximum influence factor set by adopting a pair-wise comparison mode, calculating the weight of each influence factor in the maximum influence factor set on the target layer, carrying out consistency check, determining the ordering of each influence factor and the operation level of each influence factor in the least square support vector machine according to the weight, wherein the larger the weight of each influence factor is, the more the ordering of the influence factor is, the higher the operation level of the influence factor in the least square support vector machine is.
In view of the fact that the specific process of the analytic hierarchy process is mature, the specific calculation process for determining the influence weight of each influence factor in the maximum influence factor set on the concentration of the nitric oxide by using the method is not repeated, and only the weight of each influence factor determined by using the method is provided as follows:
Y＝0.268X1+0.243X2+0.147X3+0.140X4+0.093X5+0.080X6+0.029X7 (1)
wherein Y represents the amount of nitrogen oxides generated, X1 represents the amount of coal, the weight thereof is 0.268, X2 represents the primary fan current, the weight thereof is 0.243, X3 represents the amount of hot primary air, the weight thereof is 0.147, X4 represents the theoretical flue gas flow rate, the weight thereof is 0.140, X5 represents the opening degree of the secondary windshield of the third layer, the weight thereof is 0.093, X6 represents the opening degree of the secondary windshield of the second layer, the weight thereof is 0.080, X6 represents the amount of boiler oxygen, the weight thereof is 0.029.
And verifying the determined weight according to the online monitoring data of the concentration of the nitrogen oxide, bringing the data of each influencing factor into a formula (1), if the error between the calculated concentration of the nitrogen oxide and the monitoring data is within a set threshold range, the weight meets the requirement, and if the error exceeds the set threshold range, the weight is adjusted until the error meets the requirement.
The Least Square Support Vector Machine (LSSVM) has the advantages of simplified operation process, arbitrary controllable precision approximation to arbitrary nonlinear function, good nonlinear fitting capability and generalization capability and the like, so that the nitrogen oxide concentration change rule model is established by adopting the Least Square Support Vector Machine (LSSVM) method. And training and verifying the least square support vector machine by adopting the flue gas parameters and the historical operating parameters of the factors in the corresponding maximum influence factor set to obtain the flue gas parameter change rule model.
As shown in fig. 4, historical operating data of the maximum influence factor set and historical operating data of the nitrogen oxide concentration are input into the least squares support vector machine, where the data of the maximum influence factor set is input by an LSSVM model and the nitrogen oxide concentration is input by an LSSVM model.
The initial model of the LSSVM model is as follows:
the model of the change rule of the concentration of the nitrogen oxides after training is as follows:
the specific implementation of establishing the change rule model for other parameters in the flue gas parameters can be realized by referring to the implementation process of establishing the nitrogen oxide concentration change rule model.
Example 4
Flue gas generated after combustion of the boiler is processed by a denitration, dedusting and desulfurization system, is discharged into the atmosphere through a chimney, and can be determined according to the actual combustion parameters of the boiler and the actual combustion parameters of the boiler under the conditions that the geometric shape of a flue and the flow characteristics of the flue gas in a denitration reactor and in front and rear flues are known, so that the time distribution characteristics of the flue gas parameters in the flue can be obtained.
As shown in fig. 5, it is a time distribution curve of the concentration of nitrogen oxides at the inlet and outlet positions of the denitration SCR reactor, where L1 is a time distribution curve of the concentration of nitrogen oxides at the inlet position of the denitration SCR reactor a; l2 is a nitrogen oxide concentration time distribution curve at the inlet position of the denitration SCR reactor B; l3 is a time distribution curve of the concentration of nitrogen oxides at the outlet position of the denitration SCR reactor A; and L4 is a nitrogen oxide concentration time distribution curve at the outlet position of the denitration SCR reactor B. In the figure, the horizontal axis represents time, and the vertical axis represents density values.
When flue gas analysis is usually carried out, data collected by monitoring points in a flue at the same time point are mostly used as data of flue gas in the same path for denitration analysis, and actually, because the flow of the flue gas needs a certain time, the data detected by the monitoring points in the flue at the same time are inevitably different from the data of the flue gas in the same path, so that the result of denitration analysis is inaccurate, especially under the conditions of large load change of a unit and unstable combustion working conditions, the range of front and back change of flue gas parameters is large, and the influence on the accuracy of denitration analysis is larger. By determining the time distribution characteristics of the flue gas in the flue and positioning different positions according to the time labels, the data detected by each monitoring point is determined to be the data of the same flue gas, so that the synchronous management of the parameters such as the concentration of nitrogen oxides at the denitration inlet, the concentration of nitrogen oxides at the outlet, the ammonia injection amount, the concentration of nitrogen oxides discharged by the flue gas and the like is realized, the parameters of the same flue gas are ensured to be analyzed, and the accuracy of denitration analysis is ensured.
In fig. 5, data at different positions at the same time are the same channel of nitrogen oxide concentration data, and when the denitration analysis is performed, the same channel of nitrogen oxide concentration data is selected and analyzed.
As shown in fig. 6, the flue gas generated at time t during the combustion process of the boiler is exhausted to the atmosphere through the boiler, the economizer, the denitrator, the air preheater, the dry dust collector, the desulfurizer and the chimney. Five flue gas parameter monitoring points are distributed in the flue, namely a denitrator inlet position, a denitrator outlet position, a desulfurization inlet position, a desulfurization outlet position and a chimney inlet position (the same unit discharge port position). The method comprises the steps of determining the change condition of flue gas parameters according to the change condition of boiler combustion parameters, determining the flue gas condition generated at the t moment of boiler combustion, obtaining the time that the flue gas generated at the t moment flows through five flue gas parameter monitoring points as t1, t2, t3, t4 and t5 respectively according to the time distribution characteristics of the flue gas in a flue, selecting the boiler parameters at the t moment, the data Z1 detected at the denitration inlet monitoring point position at the t1 moment, the data Z2 detected at the denitration outlet monitoring point position at the t2 moment, the data Z3 detected at the desulfuration inlet monitoring point position at the t3 moment, the data Z4 detected at the desulfuration outlet monitoring point position at the t4 moment and the data Z5 detected at the chimney inlet monitoring point position (unit discharge port position) at the t5 moment as the monitoring data of the same path of flue gas during denitration analysis, and analyzing the denitration data. The flue gas parameters which can be collected by the flue gas parameter monitoring points comprise the information of nitrogen oxide concentration, flue gas flow, temperature, oxygen content, pressure and the like in the flue gas.
Example 5
A denitration system comprises an SCR reactor and an ammonia injection control device, wherein the time distribution characteristic of flue gas in a flue is used as a control signal of the ammonia injection device, and the time distribution characteristic of the flue gas in the flue is determined by a flue gas time distribution characteristic analysis method in the flue. And calculating the time T of the flue gas reaching the inlet of the SCR reactor according to the time distribution characteristic of the flue gas in the flue, and determining the ammonia spraying amount according to the time T and the flue gas amount.
And for the flue gas generated at the moment t, the flue gas reaches the inlet position of the denitrator at the moment t1, the generated amount of the nitric oxide generated at the moment t can be determined according to a correlation model between boiler combustion parameters and a variation rule of the generated amount of the nitric oxide, the generated amount of the nitric oxide and the time distribution characteristic of the flue gas in a flue are used as feedforward control signals of a denitration control device, the ammonia amount which is adaptive to the generated amount of the nitric oxide generated at the moment t is sprayed at the moment t1, and the accurate control of the ammonia spraying amount in the denitration process is realized.
It should be noted that the above embodiments are only used for illustrating the technical solution of the present invention, and not for limiting the same; although the foregoing embodiments illustrate the invention in detail, those skilled in the art will appreciate that: it is possible to modify the technical solutions described in the foregoing embodiments or to substitute some or all of the technical features thereof, without departing from the scope of the technical solutions of the present invention.
Claims (8)
1. A method for analyzing time distribution characteristics of flue gas in a flue comprises the following steps:
determining data of boiler flue gas parameters;
establishing a flue gas parameter change rule model;
obtaining the time distribution characteristic of the flue gas in the flue;
the method for establishing the flue gas parameter change rule model comprises the following steps:
analyzing factors influencing the flue gas parameters during boiler combustion by adopting a grey correlation degree analysis method, and determining a maximum influence factor set;
determining the relation between the flue gas parameters and all the influence factors in the maximum influence factor set by adopting a least square support vector machine method to obtain a flue gas parameter change rule model;
and training and verifying a least square support vector machine by using the flue gas parameters and the historical operating parameters of the factors in the corresponding maximum influence factor set to obtain the flue gas parameter change rule model.
2. The method for analyzing the time distribution characteristics of the flue gas in the flue according to claim 1, wherein: the boiler flue gas parameter comprises at least one of flue gas flow, nitrogen oxide concentration and nitrogen oxide generation amount.
3. The method for analyzing the time distribution characteristics of the flue gas in the flue according to claim 1, wherein: the method for analyzing the factors influencing the flue gas parameters during the boiler combustion by adopting a grey correlation degree analysis method and determining the maximum influencing factor set comprises the following steps:
inputting historical operation data of all N influencing factors influencing flue gas parameters during boiler combustion, wherein N is a natural number;
determining a reference influence factor data column and a plurality of comparison influence factor data columns;
carrying out dimensionless processing on the historical operating data of all N influencing factors;
calculating and comparing a correlation coefficient between the influence factor data column and the reference influence factor data column according to the historical operating data after the non-dimensionalization processing;
calculating and comparing the association degrees between the influence factors and the reference influence factors and sequencing the association degrees;
and determining the maximum influence factor set according to the result of the relevance ranking.
4. The method for analyzing the time distribution characteristics of the flue gas in the flue according to claim 1, wherein: before determining the relationship between the flue gas parameters and the influence factors in the maximum influence factor set by adopting a least square support vector machine method and obtaining a flue gas parameter change rule model, the method further comprises the following steps: and determining the influence weight of each influence factor in the maximum influence factor set on the smoke parameters by adopting an analytic hierarchy process, and further determining the sequence of each influence factor and the operation level of the influence factor in a least square support vector machine.
5. The method for analyzing the time distribution characteristics of the flue gas in the flue according to claim 4, wherein: determining the influence weight of each influence factor in the maximum influence factor set on the flue gas parameters by adopting an analytic hierarchy process, and further determining the ordering of each influence factor and the operation level of each influence factor in a least square support vector machine comprises the following steps:
establishing a hierarchical structure model, wherein a target layer of the hierarchical structure model is a smoke parameter, and a scheme layer of the hierarchical structure model comprises a maximum influence factor set;
constructing a judgment matrix of the maximum influence factor set;
calculating the weight of each influence factor in the maximum influence factor set to the target layer;
and determining the ranking of the influence factors and the operation level of the influence factors in the least square support vector machine according to the weight, wherein the higher the weight of the influence factors is, the higher the ranking of the influence factors is, and the higher the operation level of the influence factors in the least square support vector machine is.
6. The method for analyzing the time distribution characteristics of the flue gas in the flue according to claim 1, wherein: and calculating the flow distribution of the flue gas according to the flue gas parameter change rule model, the flow characteristics of the flue gas in the denitration reactor and the front and rear flues and the geometric shape of the flues to obtain the time distribution characteristics of the flue gas in the flues.
7. A system for analyzing time distribution characteristics of flue gas in a flue, comprising a processor and a storage medium, wherein a program is stored in the storage medium, and the program is executed by the processor, the system comprising: the program performs a method of analyzing temporal distribution characteristics of flue gas in a flue according to any one of claims 1 to 6, the method comprising the steps of:
determining the data of boiler flue gas parameters;
establishing a flue gas parameter change rule model;
and obtaining the time distribution characteristic of the flue gas in the flue.
8. The utility model provides a deNOx systems, includes the SCR reactor and spouts ammonia controlling means, its characterized in that: the time distribution characteristics of the flue gas in the flue are used as control signals of the ammonia spraying device, and the time distribution characteristics of the flue gas in the flue are determined by the method for analyzing the time distribution characteristics of the flue gas in the flue according to any one of claims 1 to 6.
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