CN108775921A - Industrial smoke on-line continuous monitoring device - Google Patents
Industrial smoke on-line continuous monitoring device Download PDFInfo
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- CN108775921A CN108775921A CN201810591890.6A CN201810591890A CN108775921A CN 108775921 A CN108775921 A CN 108775921A CN 201810591890 A CN201810591890 A CN 201810591890A CN 108775921 A CN108775921 A CN 108775921A
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- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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Abstract
The present invention provides industrial smoke on-line continuous monitoring devices, including the continuous monitoring subsystem of particulate matter, it is made of particulate matter measuring instrument and school zero standard instrument, data analysis early warning subsystem is transferred to for being measured to particle content in flue, and by monitoring result；Gas Parameters measure subsystem, and data analysis early warning subsystem is transferred to for acquiring the Gas Parameters data in flue gas, and after being pre-processed to Gas Parameters data；The continuous monitoring subsystem of gaseous pollutant, for collecting gaseous pollutant sample by gaseous pollutant sampler, enter Gas controller by Flue Gas Pretreatment Device, it is analyzed into gaseous pollutant analyzer after classifying to the polluted gas of different component in Gas controller, the concentration data of each polluted gas is obtained, and concentration data is transmitted to data analysis early warning subsystem；Data analysis early warning subsystem, for being stored, being shown to data and analyzing processing.
Description
Technical field
The present invention relates to environmental technology fields, and in particular to industrial smoke on-line continuous monitoring device.
Background technology
With the rapid development of global industry process, environmental pollution and ecological disruption getting worse causes countries in the world
Great attention.Flue gas monitoring system can particulate pollutant continuous, in real time, in on-line monitoring discharge of pollutant sources flue gas, gas
The concentration and total emission volumn of state pollutant, may be implemented the target of monitoring pollution object total emission volumn.Based on current state's presence of pollution sources
The actual state of flue gas emission, national, flue gas emission increasingly strict to stationary source Air Pollutant Emission and detection requirement
The application development continuously monitored will be trend of the times.
Invention content
In view of the above-mentioned problems, the present invention provides industrial smoke on-line continuous monitoring device.
The purpose of the present invention is realized using following technical scheme：
Industrial smoke on-line continuous monitoring device, including the continuous monitoring subsystem of particulate matter are provided, is measured by particulate matter
Instrument and school zero standard instrument composition, for being measured to particle content in flue, and are transferred to data analysis by monitoring result
Early warning subsystem；
Gas Parameters measure subsystem, for acquiring including flue gas includes temperature, pressure, flow, humidity and oxygen content
Gas Parameters data, and data analysis early warning subsystem is transferred to after being pre-processed to Gas Parameters data；
The continuous monitoring subsystem of gaseous pollutant, including the control of gaseous pollutant sampler, Flue Gas Pretreatment Device, gas
Device and gaseous state pollutant analysis instrument collect gaseous pollutant sample, by Flue Gas Pretreatment Device by gaseous pollutant sampler
Enter gaseous pollutant into Gas controller, after classifying to the polluted gas of different component in Gas controller to analyze
Instrument is analyzed, and obtains the concentration data of each polluted gas, and concentration data is transmitted to data analysis early warning subsystem；
Data analysis early warning subsystem, for the monitoring result, the Gas Parameters data and the concentration data
It stored, shown and analyzing processing, in the monitoring result, the Gas Parameters data or the concentration data beyond corresponding to
Alarm is executed when the data area of setting.
Preferably, it is connected with a probe on the continuous monitoring subsystem of the particulate matter, which is mounted in flue, to cigarette
Particle content measures in road.
Preferably, it includes for joining to the abnormal flue gas in the Gas Parameters data that the Gas Parameters, which measure subsystem,
Number data and missing Gas Parameters data carry out pretreated pretreatment unit.
Further, the Gas Parameters measure subsystem further include temperature measuring set, pressure-measuring device, oxygen measuring device and
With corresponding transmitter.
Beneficial effects of the present invention are：The on-line continuous monitoring to industrial smoke is realized, and improves the automatic of system
Change degree reduces maintenance workload, keeps system stability good, data processing is quick and convenient.
Description of the drawings
Using attached drawing, the invention will be further described, but the embodiment in attached drawing does not constitute any limit to the present invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is the structural schematic block diagram of the industrial smoke on-line continuous monitoring device of an illustrative embodiment of the invention；
Fig. 2 is that the Gas Parameters of an illustrative embodiment of the invention measure the structural schematic block diagram of subsystem.
Reference numeral：
The continuous monitoring subsystem 1 of particulate matter, Gas Parameters measure subsystem 2, the continuous monitoring subsystem 3 of gaseous pollutant,
Data analysis early warning subsystem 4, pretreatment unit 10, temperature measuring set 20, pressure-measuring device 30, oxygen measuring device 40, transmitter
50。
Specific implementation mode
The invention will be further described with the following Examples.
Referring to Fig. 1, an embodiment of the present invention provides industrial smoke on-line continuous monitoring devices, including particulate matter continuously to monitor
Subsystem 1 is made of particulate matter measuring instrument and school zero standard instrument, for being measured to particle content in flue, and will prison
It surveys result and is transferred to data analysis early warning subsystem 4；
Gas Parameters measure subsystem 2, for acquiring including flue gas includes temperature, pressure, flow, humidity and oxygen content
Gas Parameters data, and data analysis early warning subsystem 4 is transferred to after being pre-processed to Gas Parameters data；
The continuous monitoring subsystem 3 of gaseous pollutant, including gaseous pollutant sampler, Flue Gas Pretreatment Device 10, gas
Controller and gaseous state pollutant analysis instrument are collected gaseous pollutant sample by gaseous pollutant sampler, are pre-processed by flue gas
Device 10 enters Gas controller, and gaseous contamination is entered after classifying to the polluted gas of different component in Gas controller
Object analyzer is analyzed, and obtains the concentration data of each polluted gas, and concentration data is transmitted to data analysis early warning subsystem
System 4；
Data analysis early warning subsystem 4, for the monitoring result, the Gas Parameters data and the concentration data
It stored, shown and analyzing processing, in the monitoring result, the Gas Parameters data or the concentration data beyond corresponding to
Alarm is executed when the data area of setting.
In one embodiment, it is connected with a probe on the continuous monitoring subsystem of the particulate matter 1, which is mounted on cigarette
In road, particle content in flue is measured.
In one embodiment, as shown in Fig. 2, it includes for joining to the flue gas that the Gas Parameters, which measure subsystem 2,
Abnormal Gas Parameters data and missing Gas Parameters data in number data carry out pretreated pretreatment unit 10.Further
Ground, the Gas Parameters measure subsystem 2 further include temperature measuring set 20, pressure-measuring device 30, oxygen measuring device 40 and with it is respective
Corresponding transmitter 50.
The above embodiment of the present invention realizes the on-line continuous monitoring to industrial smoke, and improves the automation journey of system
It spends, reduce maintenance workload, keep system stability good, data processing is quick and convenient.
In one embodiment, abnormal Gas Parameters data are pre-processed, including：
(1) collected Gas Parameters data time series are set as { yt, t=1,2 ..., n }, when to the Gas Parameters data
Between sequence carry out abnormality detection, determine exception Gas Parameters data；
(2) number of samples threshold value R is set, Gas Parameters data time series { y is located att, t=1,2 ..., n } in one
Occurs abnormal Gas Parameters data y when moment t=ii, select the R before the t=i moment a without pretreated Gas Parameters
Data are as exception Gas Parameters data yiReplacement handle sample；If before the t=i moment without pretreated flue gas
Supplemental characteristic number be less than R when, take before the t=i moment it is all without pretreated Gas Parameters data as the exception cigarette
Gas supplemental characteristic yiReplacement handle sample；
(3) abnormal Gas Parameters data y is setiReplacement processing sample be { yi-u,yi-u+1,…,yi-1, calculate the exception cigarette
Gas supplemental characteristic yiReplacement values, be used in combination the replacement values to replace abnormal Gas Parameters data yi；
Wherein, the calculation formula of replacement values is：
In formula, YiIndicate exception Gas Parameters data yiReplacement values, Yi-1For abnormal Gas Parameters data yiUpper one
The corresponding replacement values of abnormal Gas Parameters data, ui-1For corresponding the replacements processing sample of the upper abnormal Gas Parameters data
In Gas Parameters data amount check,For { yi-u,yi-u+1,…,yi-1Average value,For { yi-u,yi-u+1,…,yi-1}
Intermediate value, x be setting weight coefficient, as abnormal Gas Parameters data yiIt is arranged when before without other abnormal Gas Parameters data
Yi-1=0 and x=0.
Due to the limitation of environmental factor or system itself, often there is data exception in collected Gas Parameters data
With data deletion condition, the Gas Parameters data of acquisition are anticipated for situation needs.
The present embodiment sets the new pretreatment mechanism that abnormal Gas Parameters data are replaced with processing, the preprocessor
In system, the formula for replacing processing is innovatively set, wherein selecting without pretreated Gas Parameters data as abnormal
The replacement of Gas Parameters data handles sample, and the transmission for replacing error in calculating, the formula can be avoided to be additionally contemplates that one is different
The case where number that the replacement processing number of samples of normal Gas Parameters data may be unsatisfactory for setting requires, in a upper replacement values
It is improved on weight coefficient so that calculated current replacement values are more in line with the overall trend of Gas Parameters data sequence,
Further improve the accuracy that abnormal Gas Parameters data are replaced.
In one embodiment, Gas Parameters data time series are carried out abnormality detection, including：If Gas Parameters data
Meet exceptional condition with preset abnormal data list, then the Gas Parameters data is considered as abnormal Gas Parameters data, wherein
Abnormal data list selects history exception Gas Parameters data to be built according to actual conditions；
Wherein, the exceptional condition is：
In formula, ypIndicate { yt, t=1,2 ..., n } in the p moment Gas Parameters data,It is arranged for the abnormal data
The intermediate value of table,For the average value of the abnormal data list, σvFor the standard variance of the abnormal data list, a is setting
Threshold value.
The present embodiment selects history exception Gas Parameters data according to actual conditions, carries out structure abnormal data list, and
Gas Parameters data are carried out abnormality detection according to abnormal data list, relative to more complex Outlier Detection Algorithm, detection
It is more efficient, and meet actual monitoring situation.
In one embodiment, missing Gas Parameters data are pre-processed, including：
(1) if collected Gas Parameters data time series { yt, t=1,2 ..., n } in continuously there are multiple zeros, then
The corresponding position of multiple zero is considered as Gas Parameters shortage of data, the Gas Parameters data time sequence that multiple zero is constituted
It is classified as deletion sequence；
(2) select the R before the deletion sequence without pretreated Gas Parameters data as the first smoothing processing
Sample；If before the deletion sequence without pretreated Gas Parameters data number be less than R when, take before the deletion sequence
It is all without pretreated Gas Parameters data as the first smoothing processing sample；Select the R after the deletion sequence
Without pretreated Gas Parameters data as the second smoothing processing sample, and calculate the mean value of the second smoothing processing sample
And intermediate value
(3) it according to the corresponding first smoothing processing sample of the deletion sequence, is built and is predicted using Holter exponential smoothing
Model calculates the Gas Parameters data prediction value corresponding to each deletion sites of the deletion sequence using the prediction model of structure,
Middle prediction model correlation formula is：
Qj=dyj+(1-d)(Qj-1+Gj-1)
Gj=f (Qj-Qj-1)+(1-f)Gj-1
yj+1'=Qj+Gj
In formula, yjIndicate the Gas Parameters data at the jth moment；D, f is smoothing factor, reflects the first smoothing processing sample
Influence of the Gas Parameters data to prediction result in this, the value range of d, f are (0,1)；Qj、Qj-1Respectively indicate jth,
The smooth value at j-1 moment reflects the integral level of Gas Parameters data；Gj、Gj-1Jth, the trend at j-1 moment are indicated respectively
Value, has reacted the variation tendency of Gas Parameters data；yj+1The Gas Parameters data prediction value of ' expression at+1 moment of jth；
(4) the Gas Parameters data prediction value corresponding to k-th of deletion sites in the deletion sequence is set as yk', under
Row formula calculates the inserted value of k-th of deletion sites：
In formula, YkThe inserted value of ' expression k-th of deletion sites, h are the weight coefficient of setting；
(5) calculated each inserted value is inserted into corresponding deletion sites in the deletion sequence, forms complete flue gas
Supplemental characteristic time series.
When the present embodiment pre-processes missing Gas Parameters data, on the basis of Holter exponential smoothing, if
Determine new Gas Parameters shortage of data treatment mechanism, in the mechanism, the prediction model based on Holter exponential smoothing structure
The Gas Parameters data prediction value corresponding to each deletion sites of the deletion sequence is calculated, and in view of the flue gas ginseng after deletion sequence
Relevance between number data sequence and the deletion sequence, by the Gas Parameters data sequence and Gas Parameters number after deletion sequence
It is predicted that value calculates inserted value together as the initial data for calculating inserted value relative to Holter exponential smoothing is depended merely on
Mode, the more time response close to Gas Parameters data are beneficial to improve the precision of Gas Parameters shortage of data processing, for number
Good data source is provided according to analysis and early warning subsystem 4.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although being explained in detail to the present invention with reference to preferred embodiment, those skilled in the art answer
Work as understanding, technical scheme of the present invention can be modified or replaced equivalently, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (5)
1. industrial smoke on-line continuous monitoring device, characterized in that including：
The continuous monitoring subsystem of particulate matter is made of particulate matter measuring instrument and school zero standard instrument, for containing to particulate matter in flue
Amount measures, and monitoring result is transferred to data analysis early warning subsystem；
Gas Parameters measure subsystem, include the cigarette including temperature, pressure, flow, humidity and oxygen content for acquiring flue gas
Gas supplemental characteristic, and data analysis early warning subsystem is transferred to after being pre-processed to Gas Parameters data；
The continuous monitoring subsystem of gaseous pollutant, including gaseous pollutant sampler, Flue Gas Pretreatment Device, Gas controller and
Gaseous pollutant analyzer is collected gaseous pollutant sample by gaseous pollutant sampler, is entered by Flue Gas Pretreatment Device
Gas controller, after classifying to the polluted gas of different component in Gas controller enter gaseous pollutant analyzer into
Row analysis, obtains the concentration data of each polluted gas, and concentration data is transmitted to data analysis early warning subsystem；
Data analysis early warning subsystem, for being carried out to the monitoring result, the Gas Parameters data and the concentration data
Storage, display and analyzing processing are set in the monitoring result, the Gas Parameters data or the concentration data beyond corresponding
Data area when execute alarm.
2. industrial smoke on-line continuous monitoring device according to claim 1, characterized in that the particulate matter continuously monitors
A probe is connected on subsystem, which is mounted in flue, is measured to particle content in flue.
3. industrial smoke on-line continuous monitoring device according to claim 1 or 2, characterized in that the Gas Parameters are surveyed
Quantized system includes for being carried out with missing Gas Parameters data to the abnormal Gas Parameters data in the Gas Parameters data
Pretreated pretreatment unit.
4. industrial smoke on-line continuous monitoring device according to claim 3, characterized in that the Gas Parameters measure son
System further include temperature measuring set, pressure-measuring device, oxygen measuring device and with corresponding transmitter.
5. industrial smoke on-line continuous monitoring device according to claim 3, characterized in that lacking Gas Parameters data
It is pre-processed, including：
(1) if collected Gas Parameters data time series { yt, t=1,2 ..., n } in continuously there are multiple zeros, then should
The corresponding position of multiple zeros is considered as Gas Parameters shortage of data, and the Gas Parameters data time series that multiple zero is constituted are
Deletion sequence；
(2) select the R before the deletion sequence without pretreated Gas Parameters data as the first smoothing processing sample；
If before the deletion sequence without pretreated Gas Parameters data number be less than R when, take the institute before the deletion sequence
Have without pretreated Gas Parameters data as the first smoothing processing sample；Select R after the deletion sequence without
Pretreated Gas Parameters data are crossed as the second smoothing processing sample, and calculate the mean value of the second smoothing processing sampleWith in
Value
(3) according to the corresponding first smoothing processing sample of the deletion sequence, prediction model is built using Holter exponential smoothing,
The Gas Parameters data prediction value corresponding to each deletion sites of the deletion sequence is calculated using the prediction model of structure, wherein predicting
Model correlation formula is：
Qj=dyj+(1-d)(Qj-1+Gj-1)
Gj=f (Qj-Qj-1)+(1-f)Gj-1
yj+1'=Qj+Gj
In formula, yjIndicate the Gas Parameters data at the jth moment；D, f is smoothing factor, is reflected in the first smoothing processing sample
Influence of the Gas Parameters data to prediction result, the value range of d, f are (0,1)；Qj、Qj-1When indicating jth, j-1 respectively
The smooth value at quarter reflects the integral level of Gas Parameters data；Gj、Gj-1Jth, the Trend value at j-1 moment are indicated respectively, instead
The variation tendency of Gas Parameters data is answered；yj+1The Gas Parameters data prediction value of ' expression at+1 moment of jth；
(4) the Gas Parameters data prediction value corresponding to k-th of deletion sites in the deletion sequence is set as yk', according to the following formula
Calculate the inserted value of k-th of deletion sites：
In formula, YkThe inserted value of ' expression k-th of deletion sites, h are the weight coefficient of setting；
(5) calculated each inserted value is inserted into corresponding deletion sites in the deletion sequence, forms complete Gas Parameters
Data time series.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108990006A (en) * | 2018-07-02 | 2018-12-11 | 深圳众厉电力科技有限公司 | industrial smoke real-time wireless monitoring device |
| CN110793896A (en) * | 2019-12-03 | 2020-02-14 | 承德石油高等专科学校 | Short-term prediction method for dust concentration in tail gas |
| CN116992244A (en) * | 2023-09-26 | 2023-11-03 | 山东益来环保科技有限公司 | Intelligent monitoring system of cems |
| CN117950426A (en) * | 2023-11-23 | 2024-04-30 | 华能临沂发电有限公司 | Intelligent ammonia spraying control system based on partition smoke flow |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07180528A (en) * | 1993-12-24 | 1995-07-18 | Toyota Autom Loom Works Ltd | Exhaust emission control device |
| US20020146350A1 (en) * | 2001-02-02 | 2002-10-10 | Jiunn-Guang Lo | Real time monitoring system |
| CN201233390Y (en) * | 2008-07-28 | 2009-05-06 | 宇星科技发展(深圳)有限公司 | Flue gas on-line continuous monitoring apparatus |
| CN201335967Y (en) * | 2009-01-14 | 2009-10-28 | 张保森 | On-line monitoring system for emission of industrial fumes and flue gases |
| CN104502550A (en) * | 2015-01-20 | 2015-04-08 | 成都海兰天澄科技有限公司 | Continuous online detection system for plurality of types of pollution gas |
| CN105675801A (en) * | 2014-11-18 | 2016-06-15 | 姚秋丽 | Continuous flue gas emission monitoring system |
| CN107943558A (en) * | 2017-11-21 | 2018-04-20 | 山东科技大学 | State Forecasting Model generation method based on Holter exponential smoothing |
-
2018
- 2018-06-11 CN CN201810591890.6A patent/CN108775921A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH07180528A (en) * | 1993-12-24 | 1995-07-18 | Toyota Autom Loom Works Ltd | Exhaust emission control device |
| US20020146350A1 (en) * | 2001-02-02 | 2002-10-10 | Jiunn-Guang Lo | Real time monitoring system |
| CN201233390Y (en) * | 2008-07-28 | 2009-05-06 | 宇星科技发展(深圳)有限公司 | Flue gas on-line continuous monitoring apparatus |
| CN201335967Y (en) * | 2009-01-14 | 2009-10-28 | 张保森 | On-line monitoring system for emission of industrial fumes and flue gases |
| CN105675801A (en) * | 2014-11-18 | 2016-06-15 | 姚秋丽 | Continuous flue gas emission monitoring system |
| CN104502550A (en) * | 2015-01-20 | 2015-04-08 | 成都海兰天澄科技有限公司 | Continuous online detection system for plurality of types of pollution gas |
| CN107943558A (en) * | 2017-11-21 | 2018-04-20 | 山东科技大学 | State Forecasting Model generation method based on Holter exponential smoothing |
Non-Patent Citations (1)
| Title |
|---|
| 张杰: "基于云计算数据集成模式的矿井瓦斯预警研究", 《中国博士学位论文全文数据库 工程科技I辑》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108990006A (en) * | 2018-07-02 | 2018-12-11 | 深圳众厉电力科技有限公司 | industrial smoke real-time wireless monitoring device |
| CN110793896A (en) * | 2019-12-03 | 2020-02-14 | 承德石油高等专科学校 | Short-term prediction method for dust concentration in tail gas |
| CN110793896B (en) * | 2019-12-03 | 2022-04-08 | 承德石油高等专科学校 | Short-term prediction method for dust concentration in tail gas |
| CN116992244A (en) * | 2023-09-26 | 2023-11-03 | 山东益来环保科技有限公司 | Intelligent monitoring system of cems |
| CN116992244B (en) * | 2023-09-26 | 2023-12-22 | 山东益来环保科技有限公司 | Intelligent monitoring system of cems |
| CN117950426A (en) * | 2023-11-23 | 2024-04-30 | 华能临沂发电有限公司 | Intelligent ammonia spraying control system based on partition smoke flow |
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