CN117950426A - Intelligent ammonia spraying control system based on partition smoke flow - Google Patents
Intelligent ammonia spraying control system based on partition smoke flow Download PDFInfo
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- CN117950426A CN117950426A CN202311577299.2A CN202311577299A CN117950426A CN 117950426 A CN117950426 A CN 117950426A CN 202311577299 A CN202311577299 A CN 202311577299A CN 117950426 A CN117950426 A CN 117950426A
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- 238000005192 partition Methods 0.000 title claims abstract description 118
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 title claims abstract description 53
- 238000005507 spraying Methods 0.000 title claims abstract description 43
- 239000000779 smoke Substances 0.000 title claims abstract description 33
- 229910021529 ammonia Inorganic materials 0.000 title claims abstract description 26
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims abstract description 76
- 239000003546 flue gas Substances 0.000 claims abstract description 75
- 230000002159 abnormal effect Effects 0.000 claims abstract description 67
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims abstract description 54
- 230000008859 change Effects 0.000 claims abstract description 41
- 206010022000 influenza Diseases 0.000 claims abstract description 29
- 238000012545 processing Methods 0.000 claims abstract description 29
- 229910052757 nitrogen Inorganic materials 0.000 claims abstract description 27
- 238000012544 monitoring process Methods 0.000 claims abstract description 17
- 238000013211 curve analysis Methods 0.000 claims abstract description 13
- 239000011159 matrix material Substances 0.000 claims description 15
- 239000007921 spray Substances 0.000 claims description 14
- 238000013507 mapping Methods 0.000 claims description 10
- 238000000034 method Methods 0.000 claims description 7
- 239000013598 vector Substances 0.000 claims description 7
- 238000009826 distribution Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 5
- 230000005856 abnormality Effects 0.000 claims description 4
- 238000000638 solvent extraction Methods 0.000 claims description 4
- 238000004140 cleaning Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000004868 gas analysis Methods 0.000 abstract description 7
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 23
- 238000004458 analytical method Methods 0.000 description 10
- 230000009286 beneficial effect Effects 0.000 description 9
- 239000007789 gas Substances 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 238000002347 injection Methods 0.000 description 3
- 239000007924 injection Substances 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000007599 discharging Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 229910002092 carbon dioxide Inorganic materials 0.000 description 1
- 239000001569 carbon dioxide Substances 0.000 description 1
- 229910002091 carbon monoxide Inorganic materials 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 229910000037 hydrogen sulfide Inorganic materials 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 239000002440 industrial waste Substances 0.000 description 1
- QJGQUHMNIGDVPM-UHFFFAOYSA-N nitrogen group Chemical group [N] QJGQUHMNIGDVPM-UHFFFAOYSA-N 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- XTQHKBHJIVJGKJ-UHFFFAOYSA-N sulfur monoxide Chemical class S=O XTQHKBHJIVJGKJ-UHFFFAOYSA-N 0.000 description 1
- 229910052815 sulfur oxide Inorganic materials 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 239000012855 volatile organic compound Substances 0.000 description 1
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Abstract
The invention provides an intelligent ammonia spraying control system based on partition smoke flow. Belongs to the technical field of intelligent monitoring. The system comprises: and a flow monitoring module: configuring sensors to the flues in different partitions to detect the flue gas in the corresponding flues and obtain the basic flue gas parameters in the flues in different partitions; and a data processing module: carrying out parameter pretreatment and standardization on basic flue gas parameters of the partitions to obtain first flue gas conditions in each partition flue; and a curve analysis module: constructing a change curve according to the first smoke condition of a specified time period of the current partition, analyzing abnormal points of the change curve and adjusting the smoke flow parameters of the current partition based on abnormal continuous information in a trigger time period of each abnormal point; and the control module is used for: and controlling and adjusting the spraying parameters of the ammonia spraying device according to the adjustment result, and reducing the nitrogen content of different partitions to be under a preset standard. The accuracy of flue gas analysis in each partition is guaranteed, and the efficiency of nitrogen content adjustment is improved.
Description
Technical Field
The invention relates to the technical field of intelligent monitoring, in particular to an intelligent ammonia spraying control system based on partition smoke flow.
Background
The intelligent control technology provides powerful support and guarantee for the research and development of ammonia spraying control of the flue gas flow in the flue. In the process of monitoring the flow of the industrial system, the monitoring data of all the flues are generally analyzed by adopting an integral analysis method, and finally only a numerical value is obtained, but the nitrogen content of different flues cannot be accurately analyzed by adopting the integral analysis method due to uneven ammonia spraying of the denitration cross section of the denitration device, so that the efficiency of adjusting the nitrogen content is low, and the stability of the system is further reduced.
Therefore, the invention provides an intelligent ammonia spraying control system based on the partitioned flue gas flow.
Disclosure of Invention
The invention provides an intelligent ammonia spraying control system based on partitioned flue gas flow, which is used for realizing ammonia spraying control of partitioned flue gas flow through partitioned collection of flue gas parameters and parameter analysis, ensuring the accuracy of flue gas analysis in each partition, improving the efficiency of the system on nitrogen content adjustment, and further ensuring the stability of the system.
The invention provides an intelligent ammonia spraying control system based on partitioned flue gas flow, which comprises the following components:
and a flow monitoring module: configuring sensors in the flues of different partitions to detect the flue gas in the corresponding flues and obtain basic flue gas parameters in the flues of different partitions;
And a data processing module: carrying out parameter pretreatment and standardization on basic flue gas parameters of all the subareas to obtain a first flue gas condition in each subarea flue;
and a curve analysis module: constructing a plurality of change curves according to the first smoke condition of a specified time period of a current partition, analyzing abnormal points of each change curve, and adjusting the smoke flow parameters of the current partition based on abnormal continuous information in a trigger time period of each abnormal point;
And the control module is used for: and controlling and adjusting the spraying parameters of the ammonia spraying device according to the adjustment result of the current partition to reduce the nitrogen content of different partitions to a preset standard.
In one possible implementation, the flow monitoring module includes:
Flue partition unit: partitioning all the flues according to the distribution condition of the flues, and determining the partition range of each partition and the partition flue structure;
Parameter acquisition unit: and configuring smoke sensors into the corresponding subareas according to the subarea range and the subarea flue structure to obtain basic smoke parameters in the flues of different subareas.
In one possible implementation manner, the parameter obtaining unit includes:
Numbering configuration subunit: setting unique first numbers for different partitions and unique second numbers for the smoke sensors;
Matching subunit: and matching the first number with the second number one by one to obtain parameters.
In one possible implementation, the data processing module includes:
Pretreatment unit: and carrying out parameter pretreatment on the basic flue gas parameters of all the subareas, wherein the pretreatment comprises the following steps: unifying time units, removing abnormal values, filling missing values in the previous item, and cleaning data;
Normalization unit: classifying basic flue gas parameters of all flues of each partition according to the types of measurement parameters of the flue gas sensor to obtain parameter data under different categories;
The same category parameter data under the same partition are standardized:
; wherein/> Represents the i-th parameter/>, under the same categoryNormalized parameter of/>Representing the minimum value in parameters under the same category,/>Represents the maximum value in the parameters under the same category,/>Represents the average value in the parameters under the same category, n represents the number of the parameters under the same category,/>Represents division of the ith parameter under the same category/>Variance of the remaining parameters of (a); ln represents the sign of the logarithmic function.
In one possible implementation manner, the data processing module further includes:
Matrix construction unit: constructing a standardized feature matrix E:
; wherein/> Represents the ith standardized parameter under the jth category, where j has a value of 1,2,3,/>; I has a value of 1,2,3,;
obtaining feature vectors of the standardized feature matrix E ：
; Wherein/>Representing characteristic parameters under category 1/>Characteristic parameters representing normalized mapping parameters under category 2,/>Characteristic parameters representing normalized mapping parameters under category I,/>Representing the smallest parameter under category 1,/>Represents the largest parameter under category 1,/>Representing the smallest parameter under category 2,/>Represents the largest parameter under category 1,/>Representing the smallest parameter under category I,/>Representing the largest parameter under the first category; t represents the transposed symbol of the matrix;
first flue gas condition unit: and according to all the standardized parameters and the corresponding characteristic parameters under each partition, the first smoke condition of each partition is formed together.
In one possible implementation, the curve analysis module includes:
Curve drawing unit: constructing a change curve according to the standardized parameters of the corresponding category related to the first smoke condition of each partition and the parameter occurrence time of each standardized parameter;
An abnormality determination unit: analyzing abnormal points in corresponding change curves according to the characteristic parameters under the corresponding categories, wherein the abnormal points comprise:
Acquiring a random point on the change curve Regarding as a central point, obtaining the distance between the central point and each point remained on the change curve:
; wherein i2 has a value of 1,2,3, & n-1; /(I) Representing the parameter value corresponding to the i2 th point,/>Indicates the time corresponding to the i2 nd point,/>Representing the parameter value corresponding to the center point,/>Representing the time corresponding to the center point; /(I)Representing the distance between the center point and the i2 point on the change curve;
Sorting all distances to obtain 9 nearest points closest to the center point, and calculating a distance threshold based on the center point The method comprises the following steps:
; wherein/> Characteristic parameters representing corresponding change curves,/>Representing the distance from the j1 st point to the center point on the change curve,/>Represent logarithms based on a constant e,/>Representing the distance from the (1+1) th point to the center point on the change curve,/>Representing the distance from the j1-1 point to the center point on the change curve;
the points with the distances larger than the distance threshold value corresponding to the adjacent points are regarded as abnormal points, otherwise, the points are regarded as normal points;
Marking the abnormal point and the occurrence time of the abnormal point to obtain the abnormal point Wherein/>Representing the outlier corresponding to the outlier,/>And representing the moment corresponding to the abnormal point, and acquiring abnormal continuous information in the abnormal point triggering time period.
In one possible implementation manner, the curve analysis module further includes:
And a processing unit: processing the existing abnormal points according to the abnormal point conditions of all the corresponding category parameters under the current partition and the parameter correlation coefficients under different categories;
The structural unit comprises: and forming an adjustment result of the current partition based on the processing results of the abnormal points under different categories.
In one possible implementation, the control module includes:
determining the spraying coefficient of each partition according to the adjustment result of each partition:
; wherein/> Representing the spray coefficient of the corresponding partition,/>Representing that the weight coefficient corresponding to the q-th parameter of the corresponding partition is obtained according to the parameter type-weight mapping table,/>Adjustment of the q-th spray parameter value by the adjustment parameter representing the abnormal point existing in the corresponding partition,/>Representing the q-th spraying parameter value of the current moment under the corresponding subarea; /(I)The number of spraying parameter values existing in the corresponding partition is represented;
According to Adjusting nitrogen content of the corresponding partition, wherein/>Nitrogen content for the corresponding partition; Is the preset standard nitrogen content of the corresponding partition.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of an intelligent ammonia injection control system based on zoned flue gas flow in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
The embodiment of the invention provides an intelligent ammonia spraying control system based on partition smoke flow, as shown in fig. 1, comprising:
and a flow monitoring module: configuring sensors in the flues of different partitions to detect the flue gas in the corresponding flues and obtain basic flue gas parameters in the flues of different partitions;
And a data processing module: carrying out parameter pretreatment and standardization on basic flue gas parameters of all the subareas to obtain a first flue gas condition in each subarea flue;
and a curve analysis module: constructing a plurality of change curves according to the first smoke condition of a specified time period of a current partition, analyzing abnormal points of each change curve, and adjusting the smoke flow parameters of the current partition based on abnormal continuous information in a trigger time period of each abnormal point;
And the control module is used for: and controlling and adjusting the spraying parameters of the ammonia spraying device according to the adjustment result of the current partition to reduce the nitrogen content of different partitions to a preset standard.
In this embodiment, partitioning refers to dividing the entire industrial waste gas treatment system into a plurality of areas, and performing individual analysis treatment on the flue gas in each area.
In this embodiment, the flue means a pipe for discharging exhaust gas from an industrial process, for discharging the exhaust gas from the production facility to the atmosphere.
In this embodiment, the sensor is a device capable of detecting a specific physical quantity and converting it into a readable signal, and the sensor in the flue is mainly used for acquiring parameters such as temperature, concentration, flow rate and the like.
In this embodiment, the flue gas refers to the exhaust gas produced in industrial production and containing gases, particulates and other pollutants, and common components include carbon dioxide, carbon monoxide, nitrogen oxides (such as nitrogen oxides and nitrogen monoxide), sulfur oxides (such as sulfur dioxide and hydrogen sulfide), volatile organic compounds, and the like.
In this embodiment, the basic flue gas parameters refer to basic parameters obtained when monitoring and detecting flue gas in the flue. Comprising the following steps: temperature, concentration, flow, etc.
In this embodiment, the parameter preprocessing refers to a series of processing and converting the original data of the flue gas, and the parameters are obtained after the data is cleaned.
In this embodiment, normalization refers to normalizing the base flue gas parameters of different partitions so that the parameters have the same scale and range.
In this embodiment, the first flue gas condition refers to the flue gas condition inside each partitioned flue obtained after pretreatment and standardization of the basic flue gas parameters of all partitions in the data processing module. Including temperature, pressure, flow rate, concentration, etc.
In this embodiment, the specified time period is a specific time range set for analyzing and processing the first smoke condition of the current zone.
In this embodiment, the change curve is a number of curves constructed from the first smoke situation for a specified period of time for the current zone. Each curve is constructed based on different flue gas parameter temperatures, pressures, flow rates, concentrations and the like, and reflects the variation trend and abnormal conditions of the current type of flue gas parameters under the current partition.
In this embodiment, the abnormal point is a point at which abnormal fluctuation or deviation of the smoke parameter occurs in comparison with the normal variation trend in the curve analysis module.
In this embodiment, the abnormal continuous information refers to an abnormal condition in a period triggered by an abnormal point detected for each change curve in the curve analysis module.
In this embodiment, the flue gas flow refers to the volume of flue gas that passes through the flue per unit time.
In this embodiment, the adjustment result refers to modification information of the flue gas flow parameter of the current partition according to the analysis result.
In this embodiment, the ammonia gas injection device is a device for controlling the emission of nitrogen oxides in the flue gas, and is used for reducing the concentration of harmful nitrogen oxides in the flue gas so as to reach the environmental emission standard.
In this embodiment, the spraying parameters refer to parameters related to controlling the spraying process of the ammonia spraying device, including: spray rate, spray position, spray angle, spray pattern, etc.
In this example, the ratio of the nitrogen oxides content of the nitrogen-containing flue gas to the total volume or mass of the flue gas.
In this embodiment, the preset standard refers to legal standards regarding the emission concentration of nitrogen oxides, which are established by the environmental protection agency.
The working principle and the beneficial effects of the technical scheme are as follows: through configuration sensor monitoring flue gas parameter, through data processing and curve analysis module's processing, can accurate adjustment flue gas flow parameter to control ammonia injection apparatus, reduce nitrogen content to the standard of predetermineeing. The monitoring and control precision is improved, the accuracy of flue gas analysis in each partition is guaranteed, the efficiency of the system for adjusting the nitrogen content is improved, and the emission concentration of nitrogen oxides is reduced.
Example 2:
on the basis of the above embodiment 1, the flow monitoring module includes:
Flue partition unit: partitioning all the flues according to the distribution condition of the flues, and determining the partition range of each partition and the partition flue structure;
Parameter acquisition unit: and configuring smoke sensors into the corresponding subareas according to the subarea range and the subarea flue structure to obtain basic smoke parameters in the flues of different subareas.
In this embodiment, the placement and location of the situation stack throughout the system is distributed. Comprising the following steps: the number and the position of the flues, the determination of the partition range and the partition flue structure.
In this embodiment, partition division is to divide the whole system into several partitions according to the position and layout of the flue, and determine the range and flue structure of each partition.
In this embodiment, the partition range is that the whole system is divided into a plurality of partitions according to the distribution condition of the flues, each partition contains a certain number of flues, and the range of each partition is determined.
In this embodiment, the flue structure is a specific shape, length, diameter and other parameters of the interior of the flue, including different types of flue structures such as straight-tube flue, bent-tube flue, T-shaped flue and the like.
The working principle and the beneficial effects of the technical scheme are as follows: the flue structure is determined by dividing the flue into sections, the sensor is configured to acquire parameters, an advantageous basis is provided for analysis of the flow of flue gas in the subsequent sections, the accuracy of flue gas analysis in each section is ensured, and the efficiency of the system on nitrogen content adjustment is improved.
Example 3:
on the basis of the above embodiment 1, the parameter acquisition unit includes:
Numbering configuration subunit: setting unique first numbers for different partitions and unique second numbers for the smoke sensors;
Matching subunit: and matching the first number with the second number one by one to obtain parameters.
In this embodiment, the first number refers to a unique identifier set for the different partitions, which is used to distinguish and mark each stack partition.
In this embodiment, the second number is a unique identifier for each smoke sensor setting, which is used to identify and distinguish between different sensor devices.
In this embodiment, the matching is to associate the unique second number of each smoke sensor with the unique first number of the corresponding zone, ensuring that the data of each sensor can correspond to the correct stack zone.
The working principle and the beneficial effects of the technical scheme are as follows: through the numbering configuration and the matching, the accurate acquisition of the flue gas parameters is realized, the monitoring precision and the system performance are improved, and meanwhile, the system maintenance and the data analysis are also convenient.
Example 4:
On the basis of the above embodiment 1, the data processing module includes:
Pretreatment unit: and carrying out parameter pretreatment on the basic flue gas parameters of all the subareas, wherein the pretreatment comprises the following steps: unifying time units, removing abnormal values, filling missing values in the previous item, and cleaning data;
Normalization unit: classifying basic flue gas parameters of all flues of each partition according to the types of measurement parameters of the flue gas sensor to obtain parameter data under different categories;
The same category parameter data under the same partition are standardized:
; wherein/> Represents the i-th parameter/>, under the same categoryNormalized parameter of/>Representing the minimum value in parameters under the same category,/>Represents the maximum value in the parameters under the same category,/>Represents the average value in the parameters under the same category, n represents the number of the parameters under the same category,/>Represents division of the ith parameter under the same category/>Variance of the remaining parameters of (a); ln represents the sign of the logarithmic function.
In this embodiment, unification is the unified processing of data, ensuring that they have similar dimensions and distributions.
In this embodiment, the missing values are the case of the absence of certain items present in the data.
In this embodiment, the outlier is a value in the dataset that is significantly different from other values.
In this embodiment, the data cleansing process is a process of recognizing and correcting errors, inconsistencies, or incomplete data existing in the data set.
In this embodiment, the measured parameter categories are different types of base flue gas parameters, including: temperature, pressure, flow rate, concentration, etc.
The working principle and the beneficial effects of the technical scheme are as follows: through the pretreatment and standardization unit, the flue gas parameter data are uniformly processed, the quality, the credibility and the analysis effect of the data are improved, the accuracy of flue gas analysis in each partition is ensured, and the efficiency of the system on nitrogen content adjustment is improved.
Example 5:
On the basis of the above embodiment 1, the data processing module further includes:
Matrix construction unit: constructing a standardized feature matrix E:
; wherein/> Represents the ith standardized parameter under the jth category, where j has a value of 1,2,3,/>; I has a value of 1,2,3,;
obtaining feature vectors of the standardized feature matrix E ：
; Wherein/>Representing characteristic parameters under category 1/>Characteristic parameters representing normalized mapping parameters under category 2,/>Characteristic parameters representing normalized mapping parameters under category I,/>Representing the smallest parameter under category 1,/>Represents the largest parameter under category 1,/>Representing the smallest parameter under category 2,/>Represents the largest parameter under category 1,/>Representing the smallest parameter under category I,/>Representing the largest parameter under the first category; t represents the transposed symbol of the matrix;
first flue gas condition unit: and according to all the standardized parameters and the corresponding characteristic parameters under each partition, the first smoke condition of each partition is formed together.
In this embodiment, the normalized feature matrix converts the normalized data into a feature representation for obtaining feature values corresponding to the normalized data under the classification.
In this embodiment, the normalization parameter is obtained by mapping the original data in the basic flue gas parameter to a new numerical standard range.
In this embodiment, the feature vector refers to a vector representing all parameter type feature values in one vector space in the normalized feature matrix.
The working principle and the beneficial effects of the technical scheme are as follows: by constructing a standardized feature matrix and acquiring the feature vector thereof, mathematical description and analysis of data features are realized, the accuracy of flue gas analysis in each partition is ensured, and the efficiency of the system on nitrogen content adjustment is improved.
Example 6:
On the basis of the above embodiment 1, the curve analysis module includes:
Curve drawing unit: constructing a change curve according to the standardized parameters of the corresponding category related to the first smoke condition of each partition and the parameter occurrence time of each standardized parameter;
An abnormality determination unit: analyzing abnormal points in corresponding change curves according to the characteristic parameters under the corresponding categories, wherein the abnormal points comprise:
Acquiring a random point on the change curve Regarding as a central point, obtaining the distance between the central point and each point remained on the change curve:
; wherein i2 has a value of 1,2,3, & n-1; /(I) Representing the parameter value corresponding to the i2 th point,/>Indicates the time corresponding to the i2 nd point,/>Representing the parameter value corresponding to the center point,/>Representing the time corresponding to the center point; /(I)Representing the distance between the center point and the i2 point on the change curve;
Sorting all distances to obtain 9 nearest points closest to the center point, and calculating a distance threshold based on the center point The method comprises the following steps:
; wherein/> Characteristic parameters representing corresponding change curves,/>Representing the distance from the j1 st point to the center point on the change curve,/>Represents a logarithm based on a constant e,Representing the distance from the (1+1) th point to the center point on the change curve,/>Representing the distance from the j1-1 point to the center point on the change curve;
the points with the distances larger than the distance threshold value corresponding to the adjacent points are regarded as abnormal points, otherwise, the points are regarded as normal points;
Marking the abnormal point and the occurrence time of the abnormal point to obtain the abnormal point Wherein/>Representing the outlier corresponding to the outlier,/>And representing the moment corresponding to the abnormal point, and acquiring abnormal continuous information in the abnormal point triggering time period.
In this embodiment, the center point refers to one random point selected on the change curve as a reference point of the abnormality determination unit.
In this embodiment, the neighboring points are points closest to the center point on the change curve, and are selected according to the calculation result of the distance threshold. When the distance threshold is calculated, the distances between all points on the curve and the center point are ordered, and the first 9 points closest to the center point are selected as the adjacent points.
In this embodiment, the distance threshold is a distance used to determine the outlier, and a standard distance is calculated based on the distance between the center point and other points on the change curve.
In this embodiment, the marking means an operation of marking an abnormal point and its appearance time.
The working principle and the beneficial effects of the technical scheme are as follows: based on the standardized parameters and the characteristic parameters, abnormal points are identified by constructing a change curve and setting a distance threshold. The abnormal condition is effectively identified, beneficial information is provided for subsequent processing and analysis, the accuracy of flue gas analysis in each partition is ensured, and the efficiency of the system for nitrogen content adjustment is improved.
Example 7:
On the basis of the above embodiment 1, the curve analysis module further includes:
And a processing unit: processing the existing abnormal points according to the abnormal point conditions of all the corresponding category parameters under the current partition and the parameter correlation coefficients under different categories;
The structural unit comprises: and forming an adjustment result of the current partition based on the processing results of the abnormal points under different categories.
In this embodiment, the correlation coefficient represents the strength of the linear relationship between the different category parameters.
In this embodiment, the processing result refers to a result obtained after the current partition having the outlier is corrected correspondingly.
The working principle and the beneficial effects of the technical scheme are as follows: by detecting and processing abnormal points of different types of parameters and combining the correlation coefficients, accurate judgment and processing are carried out, and an adjustment result of the current partition is formed. The data quality and reliability are effectively improved, a reliable basis is provided for data analysis and decision making, and the efficiency of the system for adjusting the nitrogen content is improved.
Example 8:
On the basis of the above embodiment 1, the control module includes:
determining the spraying coefficient of each partition according to the adjustment result of each partition:
; wherein/> Representing the spray coefficient of the corresponding partition,/>Representing that the weight coefficient corresponding to the q-th parameter of the corresponding partition is obtained according to the parameter type-weight mapping table,/>Adjustment of the q-th spray parameter value by the adjustment parameter representing the abnormal point existing in the corresponding partition,/>Representing the q-th spraying parameter value of the current moment under the corresponding subarea; /(I)The number of spraying parameter values existing in the corresponding partition is represented;
According to Adjusting nitrogen content of the corresponding partition, wherein/>Nitrogen content for the corresponding partition; /(I)Is the preset standard nitrogen content of the corresponding partition.
In this embodiment, the spray coefficient is an adjustment constant of the spray device for adjusting the parameter value of the abnormal point existing in the corresponding division.
In this embodiment, the parameter type-weight map is a map representing the relationship between the parameter type and the weight coefficient.
In this embodiment, the weighting coefficients are used to measure the degree of influence of different parameter types on the spray coefficient.
In this embodiment, the adjustment amount refers to an adjustment constant of the adjustment parameter corresponding to the abnormal point existing in the partition to the q-th spray parameter value.
In this embodiment, the spraying parameter value refers to the q-th spraying parameter value at the current time under the corresponding division, and indicates the number of spraying parameter values existing in the corresponding division.
In this embodiment, the predetermined standard nitrogen content is a standard nitrogen content predetermined according to the stack distribution of the partition prior to analysis.
The working principle and the beneficial effects of the technical scheme are as follows: by adjusting the spraying parameter value, the effective adjustment of the subareas is realized according to the preset nitrogen content of the subareas, the accurate parameter adjustment is completed, the monitoring precision and the system performance are improved, and meanwhile, the maintenance and the abnormal monitoring of the system are also facilitated.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. Intelligent ammonia spraying control system based on subregion flue gas flow, characterized by comprising:
and a flow monitoring module: configuring sensors in the flues of different partitions to detect the flue gas in the corresponding flues and obtain basic flue gas parameters in the flues of different partitions;
And a data processing module: carrying out parameter pretreatment and standardization on basic flue gas parameters of all the subareas to obtain a first flue gas condition in each subarea flue;
and a curve analysis module: constructing a plurality of change curves according to the first smoke condition of a specified time period of a current partition, analyzing abnormal points of each change curve, and adjusting the smoke flow parameters of the current partition based on abnormal continuous information in a trigger time period of each abnormal point;
And the control module is used for: and controlling and adjusting the spraying parameters of the ammonia spraying device according to the adjustment result of the current partition to reduce the nitrogen content of different partitions to a preset standard.
2. The intelligent ammonia spraying control system based on the partitioned flue gas flow according to claim 1,
The flow monitoring module is characterized by comprising:
Flue partition unit: partitioning all the flues according to the distribution condition of the flues, and determining the partition range of each partition and the partition flue structure;
Parameter acquisition unit: and configuring smoke sensors into the corresponding subareas according to the subarea range and the subarea flue structure to obtain basic smoke parameters in the flues of different subareas.
3. The intelligent ammonia spraying control system based on the partitioned flue gas flow according to claim 2,
The parameter obtaining unit is characterized by comprising:
Numbering configuration subunit: setting unique first numbers for different partitions and unique second numbers for the smoke sensors;
Matching subunit: and matching the first number with the second number one by one to obtain parameters.
4. The intelligent ammonia spraying control system based on the partitioned flue gas flow according to claim 1,
The data processing module is characterized by comprising:
Pretreatment unit: and carrying out parameter pretreatment on the basic flue gas parameters of all the subareas, wherein the pretreatment comprises the following steps: unifying time units, removing abnormal values, filling missing values in the previous item, and cleaning data;
Normalization unit: classifying basic flue gas parameters of all flues of each partition according to the types of measurement parameters of the flue gas sensor to obtain parameter data under different categories;
The same category parameter data under the same partition are standardized:
; wherein/> Representing the ith parameter under the same categoryNormalized parameter of/>Representing the minimum value in parameters under the same category,/>Represents the maximum value in the parameters under the same category,/>Represents the average value in the parameters under the same category, n represents the number of the parameters under the same category,/>Represents division of the ith parameter under the same category/>Variance of the remaining parameters of (a); ln represents the sign of the logarithmic function.
5. The intelligent ammonia spraying control system based on the partitioned flue gas flow of claim 4,
The data processing module is characterized by further comprising:
Matrix construction unit: constructing a standardized feature matrix E:
; wherein/> Represents the ith standardized parameter under the jth category, where j has a value of 1,2,3,/>; I has a value of 1,2,3,;
obtaining feature vectors of the standardized feature matrix E ：
; Wherein/>Representing the characteristic parameters under category 1,Characteristic parameters representing normalized mapping parameters under category 2,/>Characteristic parameters representing normalized mapping parameters under category I,/>Representing the smallest parameter under category 1,/>Represents the largest parameter under category 1,/>Representing the smallest parameter under category 2,/>Represents the largest parameter under category 1,/>Representing the smallest parameter under the first category,Representing the largest parameter under the first category; t represents the transposed symbol of the matrix;
first flue gas condition unit: and according to all the standardized parameters and the corresponding characteristic parameters under each partition, the first smoke condition of each partition is formed together.
6. The intelligent ammonia spraying control system based on the partitioned flue gas flow according to claim 5,
The curve analysis module is characterized by comprising:
Curve drawing unit: constructing a change curve according to the standardized parameters of the corresponding category related to the first smoke condition of each partition and the parameter occurrence time of each standardized parameter;
An abnormality determination unit: analyzing abnormal points in corresponding change curves according to the characteristic parameters under the corresponding categories, wherein the abnormal points comprise:
Acquiring a random point on the change curve Regarding as a central point, obtaining the distance between the central point and each point remained on the change curve:
; wherein i2 has a value of 1,2,3, & n-1; /(I) Representing the parameter value corresponding to the i2 th point,/>Indicates the time corresponding to the i2 nd point,/>Representing the parameter value corresponding to the center point,/>Representing the time corresponding to the center point; /(I)Representing the distance between the center point and the i2 point on the change curve;
Sorting all distances to obtain 9 nearest points closest to the center point, and calculating a distance threshold based on the center point The method comprises the following steps:
; wherein/> Characteristic parameters representing corresponding change curves,/>Representing the distance from the j1 st point to the center point on the change curve,/>Represent logarithms based on a constant e,/>Representing the distance from the (1+1) th point to the center point on the change curve,/>Representing the distance from the j1-1 point to the center point on the change curve;
the points with the distances larger than the distance threshold value corresponding to the adjacent points are regarded as abnormal points, otherwise, the points are regarded as normal points;
Marking the abnormal point and the occurrence time of the abnormal point to obtain the abnormal point Wherein/>Representing the outlier corresponding to the outlier,/>And representing the moment corresponding to the abnormal point, and acquiring abnormal continuous information in the abnormal point triggering time period.
7. The intelligent ammonia spraying control system based on the partitioned flue gas flow according to claim 1,
The curve analysis module is characterized by further comprising:
And a processing unit: processing the existing abnormal points according to the abnormal point conditions of all the corresponding category parameters under the current partition and the parameter correlation coefficients under different categories;
The structural unit comprises: and forming an adjustment result of the current partition based on the processing results of the abnormal points under different categories.
8. The intelligent ammonia spraying control system based on the partitioned flue gas flow according to claim 1,
The control module is characterized by comprising:
determining the spraying coefficient of each partition according to the adjustment result of each partition:
; wherein/> The spray coefficients of the corresponding partitions are indicated,Representing that the weight coefficient corresponding to the q-th parameter of the corresponding partition is obtained according to the parameter type-weight mapping table,/>Adjustment of the q-th spray parameter value by the adjustment parameter representing the abnormal point existing in the corresponding partition,/>Representing the q-th spraying parameter value of the current moment under the corresponding subarea; /(I)The number of spraying parameter values existing in the corresponding partition is represented;
According to Adjusting nitrogen content of the corresponding partition, wherein/>Nitrogen content for the corresponding partition; /(I)Is the preset standard nitrogen content of the corresponding partition.
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