WO2016004577A1 - 一种基于火焰光谱强度在线辨识锅炉煤种的方法及系统 - Google Patents

一种基于火焰光谱强度在线辨识锅炉煤种的方法及系统 Download PDF

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WO2016004577A1
WO2016004577A1 PCT/CN2014/081828 CN2014081828W WO2016004577A1 WO 2016004577 A1 WO2016004577 A1 WO 2016004577A1 CN 2014081828 W CN2014081828 W CN 2014081828W WO 2016004577 A1 WO2016004577 A1 WO 2016004577A1
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Prior art keywords
spectral intensity
line
coal
unit
peak
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PCT/CN2014/081828
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English (en)
French (fr)
Inventor
尹峰
罗志浩
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国网浙江省电力公司电力科学研究院
国家电网公司
杭州意能电力技术有限公司
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Application filed by 国网浙江省电力公司电力科学研究院, 国家电网公司, 杭州意能电力技术有限公司 filed Critical 国网浙江省电力公司电力科学研究院
Priority to GB1600756.9A priority Critical patent/GB2541759B/en
Priority to PCT/CN2014/081828 priority patent/WO2016004577A1/zh
Publication of WO2016004577A1 publication Critical patent/WO2016004577A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/71—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited
    • G01N21/72—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited using flame burners
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/71—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/22—Fuels; Explosives
    • G01N33/222—Solid fuels, e.g. coal

Definitions

  • the present invention relates to the field of coal quality testing technology, and more particularly to a method and system for online identification of boiler coal based on flame spectral intensity.
  • Coal has a wide range of applications in industrial production, and is called "industrial grain", and coal is divided into different types of coal. Coal-fired boilers have great differences in the process of burning different types of coal. Such as the calorific value of coal burning, the ignition distance, the temperature distribution characteristics in the furnace, and the coking characteristics.
  • methods for identifying coal types include: gamma photon scanning using a ash rapid measuring instrument or coal type detection using a laser induced plasma spectroscopy system for calibrating coal.
  • the cost of ash rapid measurement instrument is high, there is a certain risk in using ⁇ -ray source, and the identification process is complicated. It is impossible to obtain the information of burning coal in the furnace in real time.
  • the cost of laser-induced plasma spectroscopy system is high.
  • the production of standard coal samples is time-consuming and difficult to update in real time.
  • the atomic characteristic line information of each element is large, and the comparison is difficult, and its practicality is limited.
  • the technical problem to be solved by the present invention is to provide a method and a system for online identification of boiler coals based on flame spectral intensity, which can realize more convenient and safer real-time acquisition of information on coal combustion in a boiler, and at the same time, reduce coal types.
  • the cost of identification is to provide a method and a system for online identification of boiler coals based on flame spectral intensity, which can realize more convenient and safer real-time acquisition of information on coal combustion in a boiler, and at the same time, reduce coal types.
  • a method for online identification of boiler coal based on flame spectral intensity comprising: obtaining spectral data information within a predetermined wavelength range of a flame in a furnace;
  • the calculated coal type corresponding to the specific constant is determined, and the identification result of the coal type is obtained.
  • the predetermined wavelength range is a wavelength range corresponding to a characteristic spectrum of a sodium element.
  • the wavelength range of the lithium element characteristic spectrum is 660nm ⁇ 680nm
  • the wavelength range corresponding to the potassium element spectrum is 760nm ⁇ 780nm.
  • the line spectral intensity at the peak of the line optical term and the continuous spectral intensity at the corresponding wavelength of the peak are obtained by linear equal-scale interpolation.
  • the preset spectral intensity is: less than or equal to the spectral intensity saturation upper threshold, and greater than or equal to the spectral intensity threshold.
  • the method further comprises: after calculating a specific constant of the coal species, selecting a median value of the specific constant from a specific constant within a preset window range.
  • a system for online identification of boiler coal based on flame spectral intensity comprising: a first acquisition unit, a second acquisition unit, a filtration unit, a calculation unit, and a determination unit, wherein:
  • the first acquiring unit is configured to acquire spectral data information in a preset wavelength range of a flame in the furnace;
  • the second acquiring unit connected to the first acquiring unit is configured to acquire a line spectral intensity at a peak of a spectral line of the spectral data information, and obtain a continuous spectral intensity at a wavelength corresponding to the peak;
  • the filtering unit connected to the second obtaining unit is configured to filter the spectral intensity of the line and the continuous spectral intensity
  • the calculating unit connected to the filtering unit is configured to calculate a specific constant of the coal type by using a line spectral intensity and a continuous spectral intensity obtained by filtering to satisfy a preset spectral intensity;
  • the determining unit connected to the calculating unit is configured to determine the calculated coal type corresponding to the specific constant according to a preset correspondence between the predetermined coal type and the specific constant, and obtain the identification result of the coal type.
  • the predetermined wavelength range is a wavelength range corresponding to a characteristic spectrum of a sodium element. 580nm ⁇ 600nm, the wavelength range of the characteristic spectrum of lithium element is 660nm ⁇ 680nm, and the wavelength range corresponding to the characteristic spectrum of potassium element is 760nm ⁇ 780nm.
  • the second obtaining unit specifically acquires a line spectral intensity at a peak of the line spectrum and a continuous spectral intensity at a peak corresponding to the peak by linear proportional interpolation.
  • the preset spectral intensity is: less than or equal to the spectral intensity saturation upper threshold, and greater than or equal to the spectral intensity threshold.
  • the system further comprises a median selection unit connected between the calculation unit and the comparison unit, the median selection unit is configured to select a median value of the specific constant among specific constants within a preset window range .
  • the method and system for online identification of boiler coal based on flame spectral intensity disclosed in the present application, the method for obtaining a line at the peak of the line optical term in a predetermined wavelength range of the flame in the furnace
  • the spectral intensity and the continuous spectral intensity at the corresponding wavelength of the peak are filtered, and the spectral intensity and the continuous spectral intensity of the predetermined spectral intensity obtained by the filtering are used to calculate a specific constant of the coal type, and finally according to the preset coal type Correspondence with specific constants determines the coal type currently burning in the boiler.
  • the above identification method described in the present application eliminates the influence of temperature, pulverized coal concentration, air coefficient, wind speed, etc., and the introduction of interference between the measuring instrument and the environment. Therefore, the process is safe and reliable, the identification result is single, and the reproduction is high. Sexuality, at the same time, greatly reduces the complexity of system maintenance and verification, improves environmental adaptability and stability, reduces system environmental requirements and application costs, and achieves real-time recognition capability in seconds.
  • FIG. 1 is a flow chart of a method for online identification of boiler coal based on flame spectral intensity according to a first embodiment of the present application
  • FIG. 2 is a flame emission spectrum diagram disclosed in the present application.
  • FIG. 3 is a flow chart of a method for online identification of boiler coal based on flame spectral intensity disclosed in Embodiment 2 of the present application; 4 is a schematic structural diagram of a system for online identification of a boiler coal based on flame spectral intensity according to a third embodiment of the present application;
  • FIG. 5 is a schematic structural diagram of a system for online identification of boiler coal based on flame spectral intensity disclosed in Embodiment 4 of the present application.
  • the technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. example. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without the creative work are all within the scope of the present invention.
  • FIG. 1 is a flow chart of a method for online identification of boiler coal based on flame spectral intensity disclosed in Embodiment 1 of the present application.
  • the method includes:
  • S101 Obtain information of the optical language data in a preset wavelength range of the flame in the furnace.
  • the flame optical data information in the preset wavelength range is obtained by using a fiber optic spectroscopy, such as the flame emission spectrum shown in FIG. 2.
  • the flame emission spectrum is divided into two parts: continuous spectrum and discrete spectrum.
  • the continuous spectrum is a general radiation spectrum emitted by a cloud of carbon black particles rich in hydrocarbons in a flame.
  • the discrete spectrum is generated by an isolated atom or molecule.
  • the spectrum ie, the line spectrum
  • the spectrum is a linear spectrum that is confined to a narrow range of wavelengths, superimposed on a continuous spectrum.
  • the predetermined wavelength range is 580 nm to 600 nm corresponding to the characteristic spectrum of the sodium element, 660 nm to 680 nm corresponding to the characteristic spectrum of the lithium element, and the wavelength range of 760 nm to 780 nm corresponding to the characteristic spectrum of the potassium element.
  • S102 Obtain a spectral intensity of a line at a peak of a line optical term in the spectral data information and a continuous spectral intensity at a wavelength corresponding to the peak.
  • a linear proportional difference is obtained by using a continuous spectral intensity value that is closest to the peak wavelength point of the line spectrum and is not affected by the line spectrum, and obtains a continuous spectral intensity at a wavelength corresponding to the peak value of the line optical language in the optical language data information.
  • Obtaining the optical data data by taking the maximum value in the vicinity of the peak wavelength point The peak intensity of the midline spectrum.
  • the line spectral intensity at the peak of the line optical language and the continuous optical intensity at the peak corresponding wavelength are obtained, that is, the peaks of the line spectra of the sodium element, the lithium element, and the potassium element are respectively obtained.
  • the line spectral intensity, and the continuous spectral intensity at the wavelength corresponding to the peak of the line spectrum of the sodium element, the lithium element, and the potassium element are respectively.
  • the sample data saturated with the measured value is eliminated, wherein the upper limit of the spectral intensity saturation corresponds to a specific value of the optical language parameter, and at the same time, the reasonable spectral intensity of the threshold is set to be too weak.
  • the sample data, wherein the line spectral intensity threshold value is an empirical value artificially set according to the data situation, and finally obtains a real-time effective sample data set, that is, a line spectral intensity satisfying the spectral intensity and a continuous spectral intensity.
  • S104 Calculating a specific constant of the coal type by using a line spectrum intensity and a continuous spectrum intensity satisfying the spectral intensity obtained by the filtering.
  • the preset spectral intensity referred to in this step is: less than or equal to the spectral intensity saturation upper threshold, and greater than or equal to the spectral intensity threshold. That is to say, the specific spectral constant of the coal type is calculated by using the spectral intensity of the line obtained by the filtering in step S103 and the continuous spectral intensity.
  • the spectral intensity of the acquired Na, K, and Li lines and the continuous spectral intensity data at the wavelength corresponding to the peak value of the line are substituted into equations (9) and (10) to obtain a specific constant of the coal.
  • the continuous radiation of the flame is in accordance with the characteristics of the gray body radiation, and can be expressed by the principle formula of Plank's law and Kirchhoff's law, and the expression of the measured value of the spectral radiation intensity. as follows: Where, the actual measured value of the spectral intensity is the theoretical radiation intensity of the black body, which is the wavelength-dependent optical device influence coefficient, which is the other common influence coefficient of the measurement process, the emissivity, the wavelength, the absolute temperature, and the Planck constant. For the Boltzmann constant, c is the speed of light.
  • ⁇ is the actual measured value of the line spectral intensity, which is the theoretical spectral radiation intensity of the line spectrum, the wavelength-dependent optical device influence coefficient, and the transmittance, which is the statistical weight of the number of atoms in the energy level, g.
  • the weight of the number of ground state atoms is the energy transition probability, N.
  • the number of atoms in the ground state of the element is the volume fraction of the pulverized coal particles, and D is the concentration of the element released in the pulverized coal particles, which is a conversion factor of the element content and the number of ground state atoms.
  • ⁇ ⁇ _ is a constant independent of the measurement environment and combustion conditions.
  • the different alkali metal (Na, K, Li) concentration values of different coal species correspond to different constant results, only the line spectral radiance of the element is continuous with the same wavelength.
  • the spectral radiation intensity can calculate the specific constants that are not affected by the measurement environment and the combustion conditions corresponding to the relationship between different elemental components in real time, and realize online identification of coal types.
  • S105 Determine the coal type corresponding to the calculated specific constant according to the correspondence between the preset coal type and the specific constant.
  • the central eigenvalue and the matching range domain value are determined corresponding to different coal types, and the matching degree is determined according to the deviation between the measured value and the central value, and the output comparison is concluded.
  • the coal type identification result is outputted by text or number to the corresponding coal type identifier in the sample database.
  • the method for online identification of a boiler coal type based on flame spectral intensity disclosed in the first embodiment of the present application is performed by obtaining a line spectral intensity at a peak of a line spectrum in a predetermined wavelength range of a flame in a furnace and a continuous spectral intensity at a peak corresponding wavelength. Filtering treatment, and using the spectral intensity of the line obtained by filtering to meet the preset spectral intensity and the continuous spectral intensity, the specific constant of the coal type is calculated, and finally the current boiler combustion is determined according to the correspondence between the preset coal type and the specific constant.
  • Coal type The method eliminates the influence of temperature, pulverized coal concentration, air coefficient, wind speed, etc., and the introduction of interference between the measuring instrument and the environment.
  • Embodiment 2 the process is safe and reliable, the identification result is single, and the reproducibility is high, and at the same time, greatly reduced
  • the complexity of the system maintenance and verification improves the environmental adaptability and stability, reduces the system environment requirements and application costs, and realizes the real-time recognition capability of the second level.
  • the present application provides another method for online identification of boiler coal based on flame spectral intensity, which is provided by the second embodiment of the present application.
  • the solution further includes: after calculating a specific constant of the coal species, selecting a median value of the specific constant among specific constants within a predetermined window range.
  • FIG. 3 is a flow chart of a method for online identification of boiler coal based on flame spectral intensity disclosed in Embodiment 2 of the present application.
  • the method includes:
  • S301 Obtain information of the optical language data in a preset wavelength range of the flame in the furnace.
  • S302 Acquire a spectral intensity of a line at a peak of a line optical term in the spectral data information and a continuous spectral intensity at a wavelength corresponding to the peak.
  • S305 Select a median of a specific constant among specific constants in a preset window range.
  • a specific constant of the coal species is obtained, and the median value is continuously taken within a certain window range, that is, the specific constant of the coal obtained in a certain period of time to reduce the sample dispersion.
  • the method for online identification of a boiler coal type based on flame spectral intensity disclosed in Embodiment 2 of the present application is performed by obtaining a line spectral intensity at a peak of a line spectrum in a predetermined wavelength range of a flame in a furnace and a continuous spectral intensity at a peak corresponding wavelength. Filtering and using the spectral intensity and the continuous spectral intensity of the predetermined spectral intensity obtained by filtering, calculating the specific constant of the coal species and taking the median value, and finally determining according to the correspondence between the preset coal species and the specific constant.
  • the coal type currently burned by the boiler is performed by obtaining a line spectral intensity at a peak of a line spectrum in a predetermined wavelength range of a flame in a furnace and a continuous spectral intensity at a peak corresponding wavelength. Filtering and using the spectral intensity and the continuous spectral intensity of the predetermined spectral intensity obtained by filtering, calculating the specific constant of the coal species and taking the median value, and
  • the method eliminates the influence of temperature, pulverized coal concentration, air coefficient, wind speed, etc., and the introduction of interference between the measuring instrument and the environment. Therefore, the process is safe and reliable, the identification result is single, and the reproducibility is high, and at the same time, the method is greatly reduced.
  • the complexity of the system maintenance and verification improves the environmental adaptability and stability, reduces the system environment requirements and application costs, and realizes the real-time recognition capability of the second level.
  • FIG. 4 is the present application A schematic diagram of a system for identifying a boiler coal type on-line based on flame spectral intensity disclosed in Embodiment 3.
  • the system includes: a first obtaining unit 401, a second obtaining unit 402, a filtering unit 403, a calculating unit 404, and a determining unit 405, wherein:
  • the first obtaining unit 401 is configured to obtain spectral data information within a preset wavelength range of the flame in the furnace.
  • the predetermined wavelength range refers to a wavelength range of 580 nm to 600 nm corresponding to the characteristic element of the sodium element, a wavelength range of 660 nm to 680 nm corresponding to the characteristic spectrum of the lithium element, and a wavelength range of 760 nm to 780 nm corresponding to the characteristic spectrum of the potassium element.
  • the second obtaining unit 402 connected to the first obtaining unit 401 is configured to acquire a line spectral intensity at a peak of the line optical term of the optical language data information, and obtain a continuous spectral intensity at a peak corresponding wavelength.
  • a linear proportional difference is obtained for a continuous spectral intensity value that is closest to the peak wavelength point of the line spectrum and is not affected by the line spectrum, and the continuous spectral intensity at the wavelength corresponding to the peak of the line spectrum of the optical data information is obtained.
  • the peak intensity of the line spectrum of the optical data information is obtained by taking the maximum value in the vicinity of the peak wavelength point.
  • a filtering unit 403 connected to the second obtaining unit 402 is configured to filter the line spectral intensity and the continuous spectral intensity.
  • the sample data of the saturation of the measured value is eliminated according to the saturation limit of the spectral intensity of the optical fiber, wherein the upper limit of the spectral intensity corresponds to a specific value of the optical language parameter, and at the same time, a reasonable line spectral intensity threshold value is set.
  • the line spectral intensity threshold value is an empirical value artificially set according to the data situation, and finally obtains a real-time effective sample data set, that is, a line spectral intensity satisfying the spectral intensity and a continuous spectral intensity.
  • the functions of the first obtaining unit 401, the second obtaining unit 402, and the filtering unit 403 can be implemented by a fiber optic instrument.
  • a calculation unit 404 connected to the filter unit 403 is configured to calculate a specific constant of the coal type by using the spectral intensity of the line obtained by the filtering to satisfy the preset spectral intensity and the continuous spectral intensity.
  • the preset spectral intensity is: less than or equal to the spectral upper limit of the spectral intensity saturation, and greater than or equal to the spectral intensity threshold.
  • the spectral intensity of the acquired Na, K, and Li lines is connected to the wavelength corresponding to the peak value of the line optical term.
  • the continuous spectral intensity data is substituted into the formulas (9) and (10) given in the first embodiment to obtain a specific constant of the coal species.
  • the determining unit 405 connected to the calculating unit 404 is configured to determine the coal type corresponding to the calculated specific constant according to the correspondence between the preset coal type and the specific constant.
  • the central eigenvalue and the matching range domain value are determined corresponding to different coal types, and the matching degree is determined according to the deviation between the measured value and the central value, and the output comparison is concluded.
  • the coal type identification result is outputted by text or number to the corresponding coal type identifier in the sample database.
  • the system for identifying the boiler coal type on-line based on the flame spectral intensity disclosed in the third embodiment of the present application the line at the peak of the line spectrum in the preset wavelength range of the flame in the furnace obtained by the first acquisition unit and the second acquisition unit by the filtering unit.
  • the spectral intensity and the continuous spectral intensity at the corresponding wavelength of the peak are filtered, and the calculated spectral constant and the continuous spectral intensity obtained by the calculation unit satisfying the preset spectral intensity are used to calculate a specific constant of the coal type, and finally the unit is determined according to the pre-determination
  • the correspondence between the coal type and the specific constant is determined to determine the coal type currently burned by the boiler.
  • the system eliminates the effects of temperature, pulverized coal concentration, air coefficient, wind speed, etc., as well as the introduction of interference between the measuring instrument and the environment. Therefore, the process is safe and reliable, the identification result is single, and the reproducibility is high. At the same time, the system is greatly reduced.
  • the complexity of system maintenance and verification improves environmental adaptability and stability, reduces system environmental requirements and application costs, and realizes real-time recognition capability in seconds.
  • the method for simultaneously identifying the coal type of the boiler according to the second embodiment of the present application is provided on the basis of the third embodiment.
  • the present application provides a system for online identification of the boiler coal based on the flame spectral intensity, which is provided in comparison with the third embodiment.
  • the solution further includes: a median selection unit connected between the calculation unit and the comparison unit, configured to select a median value of the specific constant among the specific constants in the preset window range.
  • FIG. 5 is a schematic structural diagram of a system for identifying boiler coal on-line based on flame spectral intensity disclosed in Embodiment 4 of the present application.
  • the system includes: a first obtaining unit 501, a second obtaining unit 502, a filtering unit 503, a calculating unit 504, a median selecting unit 505, and a determining unit 506, wherein:
  • the first obtaining unit 501 is configured to acquire spectral data information within a preset wavelength range of the flame in the furnace.
  • the predetermined wavelength range refers to a wavelength range of 580 nm to 600 nm corresponding to the characteristic element of the sodium element, a wavelength range of 660 nm to 680 nm corresponding to the characteristic spectrum of the lithium element, and a wavelength range of 760 nm to 780 nm corresponding to the characteristic spectrum of the potassium element.
  • the second obtaining unit 502 connected to the first acquiring unit 501 is configured to acquire a line spectral intensity at a peak of the line optical term of the optical language data information, and obtain a continuous spectral intensity at a peak corresponding wavelength.
  • the linear spectral equal-interpolation method is used to obtain the spectral intensity of the line at the peak of the line optical information and the continuous spectral intensity at the peak corresponding wavelength.
  • a filtering unit 503 connected to the second obtaining unit 502 is configured to filter the line spectral intensity and the continuous spectral intensity.
  • the sample data of the saturation of the measured value is eliminated according to the saturation limit of the spectral intensity of the optical fiber, wherein the upper limit of the spectral intensity corresponds to a specific value of the optical language parameter, and at the same time, a reasonable line spectral intensity threshold value is set.
  • the line spectral intensity threshold value is an empirical value artificially set according to the data situation, and finally obtains a real-time effective sample data set, that is, a line spectral intensity satisfying the spectral intensity and a continuous spectral intensity.
  • the functions of the first obtaining unit 501, the second obtaining unit 502, and the filtering unit 503 can be implemented by a fiber optic instrument.
  • a calculation unit 504 connected to the filter unit 503 is configured to calculate a specific constant of the coal type by using the spectral intensity of the line obtained by the filtering to satisfy the preset spectral intensity and the continuous spectral intensity.
  • the preset spectral intensity is: less than or equal to the spectral upper limit of the spectral intensity saturation, and greater than or equal to the spectral intensity threshold.
  • the obtained spectral intensity of the Na, K, and Li lines and the continuous spectral intensity data at the wavelength corresponding to the peak value of the line are substituted into the formulas (9) and (10) given in the first embodiment to obtain a specific constant of the coal. .
  • a median selection unit 505 coupled to the computing unit 504 is configured to select a median of a particular constant among a particular constant within a predetermined window range.
  • a specific constant of the coal species is obtained, and the median value is continuously taken within a certain window range, that is, the specific constant of the coal obtained in a certain period of time to reduce the sample dispersion.
  • the determining unit 506 connected to the median selecting unit 505 is configured to determine the coal type corresponding to the calculated specific constant according to the correspondence between the preset coal type and the specific constant, and obtain the identification result of the coal type.
  • the central eigenvalue and the matching range domain value are determined corresponding to different coal types, and the matching degree is determined according to the deviation between the measured value and the central value, and the comparison result is output.
  • the coal type identification result is outputted by text or number to the corresponding coal type identifier in the sample database.
  • the system for identifying the boiler coal type on-line based on the flame spectral intensity disclosed in the fourth embodiment of the present application the line at the peak of the line spectrum in the preset wavelength range of the flame in the furnace obtained by the first acquisition unit and the second acquisition unit by the filtering unit.
  • the spectral intensity and the continuous spectral intensity at the corresponding wavelength of the peak are filtered, and the calculation unit uses the line spectral intensity and the continuous spectral intensity satisfying the preset spectral intensity to calculate a specific constant of the coal type and selects the unit by the median value.
  • the median value is selected, and finally the unit determines the coal type of the current boiler combustion according to the correspondence between the preset coal type and the specific constant.
  • the system eliminates the effects of temperature, pulverized coal concentration, air coefficient, wind speed, etc., as well as the introduction of interference between the measuring instrument and the environment. Therefore, the process is safe and reliable, the identification result is single, and the reproducibility is high. At the same time, the system is greatly reduced.
  • the complexity of the system maintenance and verification improves the environmental adaptability and stability, reduces the system environment requirements and application costs, and realizes the real-time recognition capability of the second level. It should also be noted that, in this context, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying such entities or operations. There is any such actual relationship or order between them. Moreover, the term "includes”,
  • RAM random access memory
  • ROM read only memory
  • electrically programmable ROM electrically erasable programmable ROM
  • registers hard disk, removable disk, CD-ROM, or any other form of storage known in the art. In the medium.

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Abstract

一种基于火焰光谱强度在线辨识锅炉煤种的方法及系统，该方法包括：对获取的炉膛中火焰的预设波长范围内线光谱峰值处的线光谱强度以及峰值对应波长处的连续光谱强度进行过滤处理，利用过滤得到的满足预设光谱强度的线光谱强度以及连续光谱强度，计算得到煤种的特定常数，依据预设的煤种和特定常数的对应关系，确定计算得到的特定常数对应的煤种。该方法消除了温度、煤粉浓度、空气系数、风速等的影响以及测量仪器与环境的引入干扰。

Description

一种基于火焰光谱强度在线辨识锅炉煤种的方法及系统 技术领域 本发明涉及煤质检测技术领域，特别是涉及一种基于火焰光谱强度在线辨 识锅炉煤种的方法及系统。 背景技术 煤炭在工业生产中有着很广泛的应用， 被称为 "工业的粮食"， 而煤炭又 分为不同的煤种， 燃煤锅炉在燃烧不同煤种的过程中会存在很大的特性差异， 如燃煤的发热量、 着火距离、 炉内温度分布特性、 结焦特性等。 由于不同的煤 种特性将影响到炉内配风、 过量空气系数、 减温水分布等控制输出的合理性， 从而影响机组的燃烧控制水平， 并进一步影响机组的运行经济性， 因此， 对各 燃烧器燃用煤种的及时准确辨识可以解决变煤种情况下的燃烧控制难题，为燃 烧优化控制提供实时数据与自动调节依据。
现有技术中， 对煤种的识别方法有： 使用灰分快速测定仪器使用 γ光子扫 描或者使用激光诱导等离子光谱系统对定标煤进行煤种检测等方法。 然而，灰 分快速测定仪器成本较高， 使用 γ射线源有一定的风险， 而且辨识过程操作较 复杂， 不能在线实时获取炉内燃烧煤种信息； 同时， 激光诱导等离子光谱系统 的成本较高， 定标煤样品制作费时， 不易实时更新， 而且， 各元素原子特征谱 线信息量大， 比对难度较大， 其实用性受限制。 发明内容 本发明要解决的技术问题是提供一种基于火焰光谱强度在线辨识锅炉煤 种的方法及系统，能够实现更便捷、更安全地实时获取锅炉内燃烧煤种的信息， 同时， 降低煤种辨识的成本。
为实现上述目的， 本申请提供如下技术方案：
一种基于火焰光谱强度在线辨识锅炉煤种的方法， 该方法包括： 获取炉膛 中火焰的预设波长范围内的光谱数据信息；
获取所述光谱数据信息中线光谱峰值处的线光谱强度 ,并获取所述峰值对 应波长处的连续光谱强度；
对所述线光谱强度以及所述连续光谱强度进行过滤处理；
利用过滤得到的满足预设光谱强度的线光谱强度以及连续光谱强度，计算 得出煤种的特定常数；
依据预设的煤种和特定常数的对应关系 ,确定计算得到的所述特定常数对 应的煤种， 得到煤种的辨识结果。
优选的， 所述预设波长范围为钠元素特征光谱对应的波长范围
580nm~600nm、 锂元素特征光谱对应的波长范围 660nm~680nm和钾元素特征 光谱对应的波长范围 760nm~780nm。
优选的， 所述线光语峰值处的线光谱强度、所述峰值对应波长处的连续光 谱强度是通过线性等比例插值的方式获取的。
优选的，所述预设光谱强度为： 小于或等于光谱强度饱和上限阔值， 同时， 大于或等于光谱强度门槛阔值。
优选的， 该方法还包括： 计算出煤种的特定常数后， 在预设窗口范围内的 特定常数中选取所述特定常数的中值。
一种基于火焰光谱强度在线辨识锅炉煤种的系统， 该系统包括： 第一获取 单元、 第二获取单元、 过滤单元、 计算单元以及确定单元， 其中：
所述第一获取单元，用于获取炉膛中火焰的预设波长范围内的光谱数据信 息；
与所述第一获取单元相连的所述第二获取单元，用于获取所述光谱数据信 息中线光谱峰值处的线光谱强度， 并获取所述峰值对应波长处的连续光谱强 度；
与所述第二获取单元相连的所述过滤单元，用于对所述线光谱强度以及所 述连续光谱强度进行过滤处理；
与所述过滤单元相连的所述计算单元，用于利用过滤得到的满足预设光谱 强度的线光谱强度以及连续光谱强度， 计算得出煤种的特定常数；
与所述计算单元相连的所述确定单元，用于依据预设的煤种和特定常数的 对应关系， 确定计算得到的所述特定常数对应的煤种， 得到煤种的辨识结果。
优选的， 所述预设波长范围为钠元素特征光谱对应的波长范围 580nm~600nm、 锂元素特征光谱对应的波长范围 660nm~680nm和钾元素特征 光谱对应的波长范围 760nm~780nm。
优选的，所述第二获取单元具体是通过线性等比例插值的方式获取所述线 光谱的峰值处的线光谱强度以及所述峰值对应波长处的连续光谱强度。
优选的，所述预设光谱强度为： 小于或等于光谱强度饱和上限阔值， 同时， 大于或等于光谱强度门槛阔值。
优选的 ,该系统还包括连接于所述计算单元与对比单元之间的中值选取单 元，所述中值选取单元用于在预设窗口范围内的特定常数中选取所述特定常数 的中值。
从上述的技术方案可以看出，本申请公开的基于火焰光谱强度在线辨识锅 炉煤种的方法及系统，所述方法通过对获取炉膛中火焰的预设波长范围内的线 光语峰值处的线光谱强度以及峰值对应波长处的连续光谱强度进行过滤处理， 并利用过滤得到的满足预设光谱强度的线光谱强度以及连续光谱强度，计算得 出煤种的特定常数， 最后依据预设的煤种和特定常数的对应关系， 确定当前锅 炉燃烧的煤种。 本申请所述的上述辨识方法， 消除了温度、 煤粉浓度、 空气系 数、 风速等的影响以及测量仪器与环境的引入干扰， 因此， 其过程安全可靠， 辨识结果单一， 具有很高的复现性， 同时， 大大降低了系统的维护量与校验的 复杂性， 提高了环境适应性与稳定性， 降低系统环境要求与应用成本， 并实现 秒级的实时辨识能力。 附图说明 为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施 例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地， 下面描述 中的附图仅仅是本发明的实施例，对于本领域普通技术人员来讲， 在不付出创 造性劳动的前提下， 还可以根据提供的附图获得其他的附图。
图 1为本申请实施例一公开的一种基于火焰光谱强度在线辨识锅炉煤种的 方法流程图；
图 2为本申请公开的火焰发射光谱图；
图 3为本申请实施例二公开的一种基于火焰光谱强度在线辨识锅炉煤种的 方法流程图； 图 4为本申请实施例三公开的一种基于火焰光谱强度在线辨识锅炉煤种的 系统的结构示意图；
图 5为本申请实施例四公开的一种基于火焰光谱强度在线辨识锅炉煤种的 系统的结构示意图。 具体实施方式 下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清 楚、 完整地描述， 显然， 所描述的实施例仅仅是本发明一部分实施例， 而不是 全部的实施例。基于本发明中的实施例， 本领域普通技术人员在没有做出创造 性劳动前提下所获得的所有其他实施例， 都属于本发明保护的范围。
为了解决现有技术中， 对煤种辨识的成本高、 操作复杂、 实时性差或者存 在风险的诸多问题， 为燃烧优化控制提供实时数据与自动调节依据， 本申请提 供如下技术方案：
实施例一
如图 1所示，图 1为本申请实施例一公开的一种基于火焰光谱强度在线辨识 锅炉煤种的方法流程图。 该方法包括：
S101 : 获取炉膛中火焰的预设波长范围内的光语数据信息。
本步骤中， 釆用光纤光语仪获取预设波长范围内的火焰光语数据信息，如 图 2所示的火焰发射光谱图。 火焰发射光谱分为连续光谱与离散光谱两部分， 连续光谱是由火焰中含丰富碳氢化合物的碳黑颗粒云发出的一般辐射光谱，离 散光谱则由孤立的原子或分子激发产生， 其中， 原子光谱（即线光谱）是局限 在很窄波长范围内的线形光谱， 叠加在连续光谱之上。
其中， 所指的预设波长范围为钠元素特征光谱对应的波长范围 580nm~600nm、 锂元素特征光谱对应的波长范围 660nm~680nm和钾元素特征 光谱对应的波长范围 760nm~780nm。
S 102：获取光谱数据信息中线光语峰值处的线光谱强度以及峰值对应波长 处的连续光谱强度。
具体的，釆用对线光谱峰值波长点前后最近且未受线光谱影响的连续光谱 强度值进行线性等比例差值，获取光语数据信息中线光语峰值对应波长处的连 续光谱强度，在线光语峰值波长点附近范围内取最大值的方式获取光语数据信 息中线光谱峰值强度。
本步骤中，根据获取的光语数据信息，得到线光语峰值处的线光谱强度以 及峰值对应波长处的连续光语强度， 即分别获取钠元素、锂元素以及钾元素的 线光谱的峰值处的线光谱强度， 并分别获取钠元素、锂元素以及钾元素的线光 谱的峰值对应波长处的连续光谱强度。
S103: 对线光谱强度以及连续光谱强度进行过滤处理。
根据光纤光语仪光谱强度饱和上限剔除测量值饱和的样本数据， 其中， 光 谱强度饱和上限对应于光语仪参数的特定的值， 同时，设置合理的线光谱强度 门槛域值滤除强度过弱的样本数据， 其中， 线光谱强度门槛域值则是根据数据 情况人为设置的经验值， 最终获得实时的有效样本数据集， 即满足光谱强度的 线光谱强度以及连续光谱强度。
S104: 利用过滤得到的满足光谱强度的线光谱强度以及连续光谱强度，计 算得出煤种的特定常数。
本步骤中所指的预设光谱强度为： 小于或等于光谱强度饱和上限阔值， 同 时， 大于或等于光谱强度门槛阔值。 也就是说， 利用步骤 S103过滤后得到的 线光谱强度以及连续光谱强度， 计算得出煤种的特定常数。
具体的， 将获取的 Na、 K、 Li线光谱强度与其线光语峰值对应波长处连 续光谱强度数据代入算式（9 ) 与 （10 ), 获得该煤种的特定常数。
由于不同煤种在形成时期的地质环境和成煤植物类型不同，其所含的各种 元素的浓度和比例是不完全相同的，且在同一煤种中是相对恒定的； 碱金属元 素 （包括 Li 、 Na、 K )在燃煤火焰中原子发射光谱的共振谱线最为明显， 尤 其是 Na、 K元素在几乎所有煤种光谱中都清晰可见， 其在成煤植物中的富集 程度较高， 是良好的标识元素； 均勾研磨的煤粉在锅炉炉膛内燃烧时， 火焰中 这三种碱金属元素原子发射光谱的共振谱线强度与其在燃煤中的含量存在一 定的对应关系。
1 )火焰的连续辐射光语符合灰体辐射特征，可以釆用普朗克定律（Plank's law )与基尔霍夫定律（Kirchhoff's law )的原理公式进行表达， 其光谱辐射强 度测量值的表达式如下：
Figure imgf000008_0001
式中， 为光谱强度实际测量值， 为黑体理论辐射强度， 为波长相 关的光学器件影响系数， 为测量过程其他通用影响系数， 为发射率， 为 波长， 为绝对温度， 为普朗克常数， 为玻尔兹曼常数， c为光速。
2 )火焰中的原子离散辐射光谱可釆用玻尔理论（Bohr's theory )与玻尔兹 曼公式（ Boltzmann formula ) 的原理公式进行表达， 同时根据普朗克定律
( Plank's law ) , 频率为 ν的原子共振线能级 Α的活化能 = ½ = / ， 其线光 谱辐射强度测量值的表达式如下：
Jt = SyslIl = kCgkAh N。e— = SlVsl ( 2 )
Figure imgf000008_0002
式中， ^为线光谱强度实际测量值， 为线光谱理论辐射强度， 为波 长相关的光学器件影响系数， 为透射率， 为处于能级 的原子数量统计权 重， g。为基态原子数量统计权重， 为能量跃迁概率， N。为元素基态原子数， 为煤粉颗粒的体积份数， D为煤粉颗粒中释放的元素浓度， 为元素含量与 基态原子数的换算系数。
3 )原子线光谱辐射强度测量值与该波长下的连续光谱辐射强度测量值的 比值表达式如下：
Figure imgf000008_0003
4 )根据比尔 -朗伯特定律 (Beer-Lambert law), 光通过光程为 ， 消光系数 为 的透光介质时， 原始强度为 的辐射光的透射强度为 = /L exp(-^J) , 根据基尔霍夫定律（Kirchhoff's law )，在热平衡状态下， 物体的发射率等于吸 收率， 因此火焰灰体辐射的发射率 的表达式如下： exp(— ） (4)
对于元素线光谱在光程方向 上的透射率 的表达式如下:
)dx = fo (Qxp(-KAx))dx = -^(1- Qxp(-KAL))
K 根据已有研究得出的煤粉火焰消光系数经验公式可知， Κλ = Ιλα , g和" 均为经验常数， 因此（3 ) 中光谱辐射强度测量值的比值表达式进一步转化如 下：
Ji _ (1 - exp(-^ ) gkAhA4 D _ Tmfv gkAhA4 ^ _ g„ )m ^ _^ ( 6 ) K (l-Qxp(-K L) S g0c gfv S g0c ^0cg
式中， μ = ^ _ ， 为和测量环境与燃烧工况无关的常数。
5)根据阿伦尼乌斯公式（ Arrhenius equation ) , 可得煤粉颗粒内的元素 浓度 C与其燃烧时释放出的元素浓度 D间的关系为 C = D/(Aexpf-Ea/RTJ)，其中 ^ 为指前因子， £。为表观活化能， R为摩尔气体常量。 将（6 )代入并取对数后 可得表达式如下： ln = ln(^)-lnC^)-^ ( 7 )
根据试验数据分析后可知， Ea—Na =l.2Ea_K =2AEa_Ll , 因此代入（7 )后可得 煤粉颗粒中 Na与 K元素的浓度关系如下：
Figure imgf000009_0001
令常数 δΝα—κ
Figure imgf000009_0002
, 则 Na与 Κ元素的辐射强度关系为： ln(-^) - 1.21n( -^) = ln ^ - 1.21nQ + δΝα_κ ( 9 )
J λ-Να J λ-Κ 同理， 令常数^ ^ ^nC^AJ^^H^A) , 则 Na与 Li元素的辐射强度关 系为：
) _ 2.41η ) = ln ^ - 2.41n L! + SNa_Ll ( 10 )
J λ-Να J λ-Π 因此， 不同煤种所含的不同的碱金属（Na、 K、 Li )浓度值就对应了不同 的常数结果，仅由元素的线光谱辐射强度与同波长的连续光谱辐射强度， 即可 实时计算出对应于不同元素组分关系的、不受测量环境与燃烧工况影响的特定 常数， 实现对煤种的在线辨识。
S 105 :依据预设的煤种和特定常数的对应关系，确定计算得到的特定常数 对应的煤种。
在煤种和特定常数的对应关系样本数据库中对应不同煤种确定中心特征 值与匹配范围域值，根据实测值与中心值的偏差大小确定匹配度， 输出比对结 论。 煤种辨识结果以文字或编号方式输出样本数据库中的对应煤种标识。
本申请实施例一公开的基于火焰光谱强度在线辨识锅炉煤种的方法，通过 对获取炉膛中火焰的预设波长范围内的线光谱峰值处的线光谱强度以及峰值 对应波长处的连续光谱强度进行过滤处理，并利用过滤得到的满足预设光谱强 度的线光谱强度以及连续光谱强度，计算得出煤种的特定常数， 最后依据预设 的煤种和特定常数的对应关系 ,确定当前锅炉燃烧的煤种。该方法消除了温度、 煤粉浓度、 空气系数、 风速等的影响以及测量仪器与环境的引入干扰， 因此， 其过程安全可靠， 辨识结果单一， 具有^^高的复现性， 同时， 大大降低了系统 的维护量与校验的复杂性，提高了环境适应性与稳定性， 降低系统环境要求与 应用成本， 并实现秒级的实时辨识能力。 实施例二
在本申请实施例一的基础上，本申请提供了另一种基于火焰光谱强度在线 辨识锅炉煤种的方法，相较于实施例一提供的方案， 本申请实施例二所提供的 方案， 还包括： 计算出煤种的特定常数后， 在预定窗口范围内的特定常数中选 取所述特定常数的中值。
如图 3所示，图 3为本申请实施例二公开的一种基于火焰光谱强度在线辨识 锅炉煤种的方法流程图。 该方法包括：
S301 : 获取炉膛中火焰的预设波长范围内的光语数据信息。
S302:获取光谱数据信息中线光语峰值处的线光谱强度以及峰值对应波长 处的连续光谱强度。
S303 : 对线光谱强度以及连续光谱强度进行过滤处理。
S304: 利用过滤得到的满足预设光谱强度的线光谱强度以及连续光谱强 度， 计算得出煤种的特定常数。
S305: 在预设窗口范围内的特定常数中选取特定常数的中值。
获得煤种的特定常数， 并连续在一定窗口范围内， 即在一定时间段内获得 的煤种特定常数中取中值以降低样本离散度。
S306:依据预设的煤种和特定常数的对应关系，确定选取的特定常数对应 的煤种， 得到煤种的辨识结果。
本申请实施例二公开的基于火焰光谱强度在线辨识锅炉煤种的方法，通过 对获取炉膛中火焰的预设波长范围内的线光谱峰值处的线光谱强度以及峰值 对应波长处的连续光谱强度进行过滤处理，并利用过滤得到的满足预设光谱强 度的线光谱强度以及连续光谱强度，计算得出煤种的特定常数并取中值， 最后 依据预设的煤种和特定常数的对应关系，确定当前锅炉燃烧的煤种。该方法消 除了温度、 煤粉浓度、 空气系数、 风速等的影响以及测量仪器与环境的引入干 扰， 因此， 其过程安全可靠， 辨识结果单一， 具有很高的复现性， 同时， 大大 降低了系统的维护量与校验的复杂性，提高了环境适应性与稳定性， 降低系统 环境要求与应用成本， 并实现秒级的实时辨识能力。 实施例三
为了在降低成本、 保证安全的前提下， 实现对煤种实时、 准确地辨识， 并 能实现本发明实施例一所公开的基于火焰光谱强度在线辨识锅炉煤种的方法， 本实施例公开如下基于火焰光谱强度在线辨识锅炉煤种的系统， 图 4为本申请 实施例三公开的一种基于火焰光谱强度在线辨识锅炉煤种的系统的结构示意 图。
该系统包括： 第一获取单元 401、 第二获取单元 402、 过滤单元 403、 计 算单元 404以及确定单元 405 , 其中：
第一获取单元 401 , 用于获取炉膛中火焰的预设波长范围内的光谱数据信 息。
其中，预设波长范围是指钠元素特征光语对应的波长范围 580nm~600nm、 锂元素特征光谱对应的波长范围 660nm~680nm和钾元素特征光谱对应的波长 范围 760nm~780nm。
与第一获取单元 401相连的第二获取单元 402, 用于获取光语数据信息中 线光语峰值处的线光谱强度， 并获取峰值对应波长处的连续光谱强度。
具体的，釆用对线光谱峰值波长点前后最近且未受线光谱影响的连续光谱 强度值进行线性等比例差值，获取光语数据信息中线光谱峰值对应波长处的连 续光谱强度，在线光语峰值波长点附近范围内取最大值的方式获取光语数据信 息中线光谱峰值强度。
与第二获取单元 402相连的过滤单元 403 , 用于对线光谱强度以及连续光 谱强度进行过滤处理。
具体的， 根据光纤光语仪光谱强度饱和上限剔除测量值饱和的样本数据， 其中， 光谱强度饱和上限对应于光语仪参数的特定的值， 同时， 设置合理的线 光谱强度门槛域值滤除强度过弱的样本数据， 其中， 线光谱强度门槛域值则是 根据数据情况人为设置的经验值， 最终获得实时的有效样本数据集， 即满足光 谱强度的线光谱强度以及连续光谱强度。
需要说明的是， 本申请中， 第一获取单元 401、 第二获取单元 402以及过 滤单元 403的功能可以通过光纤光语仪实现。
与过滤单元 403相连的计算单元 404, 用于利用过滤得到的满足预设光谱 强度的线光谱强度以及连续光谱强度， 计算得出煤种的特定常数。
其中， 预设光谱强度为： 小于或等于光谱强度饱和上限阔值， 同时， 大于 或等于光谱强度门槛阔值。
具体的， 将获取的 Na、 K、 Li线光谱强度与其线光语峰值对应波长处连 续光谱强度数据代入实施例一中给出的算式（9 )与（10 ), 获得该煤种的特定 常数。
与计算单元 404相连的确定单元 405 , 用于依据预设的煤种和特定常数的 对应关系， 确定计算得到的特定常数对应的煤种。
在煤种和特定常数的对应关系样本数据库中对应不同煤种确定中心特征 值与匹配范围域值，根据实测值与中心值的偏差大小确定匹配度， 输出比对结 论。 煤种辨识结果以文字或编号方式输出样本数据库中的对应煤种标识。
本申请实施例三公开的基于火焰光谱强度在线辨识锅炉煤种的系统，通过 过滤单元对第一获取单元、第二获取单元获取的炉膛中火焰的预设波长范围内 的线光谱峰值处的线光谱强度以及峰值对应波长处的连续光谱强度进行过滤 处理，由计算单元利用过滤得到的满足预设光谱强度的线光谱强度以及连续光 谱强度，计算得出煤种的特定常数， 最后确定单元依据预设的煤种和特定常数 的对应关系， 确定当前锅炉燃烧的煤种。 该系统消除了温度、 煤粉浓度、 空气 系数、风速等的影响以及测量仪器与环境的引入干扰， 因此，其过程安全可靠， 辨识结果单一， 具有很高的复现性， 同时， 大大降低了系统的维护量与校验的 复杂性， 提高了环境适应性与稳定性， 降低系统环境要求与应用成本， 并实现 秒级的实时辨识能力。 实施例四
在实施例三的基础上，同时实现本申请实施例二所述的线辨识锅炉煤种的 方法， 本申请提供了基于火焰光谱强度在线辨识锅炉煤种的系统，相较于实施 例三所提供的方案，还包括：连接于计算单元与对比单元之间的中值选取单元， 用于在预设窗口范围内的特定常数中选取特定常数的中值。
如图 5所示，图 5为本申请实施例四公开的一种基于火焰光谱强度在线辨 识锅炉煤种的系统的结构示意图。 该系统包括： 第一获取单元 501、 第二获取 单元 502、过滤单元 503、计算单元 504、中值选取单元 505以及确定单元 506 , 其中：
第一获取单元 501 , 用于获取炉膛中火焰的预设波长范围内的光谱数据信 息。 其中，预设波长范围是指钠元素特征光语对应的波长范围 580nm~600nm、 锂元素特征光谱对应的波长范围 660nm~680nm和钾元素特征光谱对应的波长 范围 760nm~780nm。
与第一获取单元 501相连的第二获取单元 502, 用于获取光语数据信息中 线光语峰值处的线光谱强度， 并获取峰值对应波长处的连续光谱强度。
具体的，釆用线性等比例插值的方式获取光谱数据信息中线光语峰值处的 线光谱强度以及峰值对应波长处的连续光谱强度。
与第二获取单元 502相连的过滤单元 503 , 用于对线光谱强度以及连续光 谱强度进行过滤处理。
具体的， 根据光纤光语仪光谱强度饱和上限剔除测量值饱和的样本数据， 其中， 光谱强度饱和上限对应于光语仪参数的特定的值， 同时， 设置合理的线 光谱强度门槛域值滤除强度过弱的样本数据， 其中， 线光谱强度门槛域值则是 根据数据情况人为设置的经验值， 最终获得实时的有效样本数据集， 即满足光 谱强度的线光谱强度以及连续光谱强度。
需要说明的是， 本申请中， 第一获取单元 501、 第二获取单元 502以及过 滤单元 503的功能可以通过光纤光语仪实现。
与过滤单元 503相连的计算单元 504, 用于利用过滤得到的满足预设光谱 强度的线光谱强度以及连续光谱强度， 计算得出煤种的特定常数。
其中， 预设光谱强度为： 小于或等于光谱强度饱和上限阔值， 同时， 大于 或等于光谱强度门槛阔值。
具体的， 将获取的 Na、 K、 Li线光谱强度与其线光语峰值对应波长处连 续光谱强度数据代入实施例一中给出的算式（9 )与（10 ), 获得该煤种的特定 常数。
与计算单元 504相连的中值选取单元 505 , 用于在预设窗口范围内的特定 常数中选取特定常数的中值。
获得煤种的特定常数， 并连续在一定窗口范围内， 即在一定时间段内获得 的煤种特定常数中取中值以降低样本离散度。
与中值选取单元 505相连的确定单元 506 , 用于依据预设的煤种和特定常 数的对应关系， 确定计算得到的特定常数对应的煤种， 得到煤种的辨识结果。 在煤种和特定常数的对应关系样本数据库中对应不同煤种确定中心特征 值与匹配范围域值，根据实测值与中心值的偏差大小确定匹配度， 输出比对结 论。 煤种辨识结果以文字或编号方式输出样本数据库中的对应煤种标识。
本申请实施例四公开的基于火焰光谱强度在线辨识锅炉煤种的系统，通过 过滤单元对第一获取单元、第二获取单元获取的炉膛中火焰的预设波长范围内 的线光谱峰值处的线光谱强度以及峰值对应波长处的连续光谱强度进行过滤 处理，由计算单元利用过滤得到的满足预设光谱强度的线光谱强度以及连续光 谱强度，计算得出煤种的特定常数并由中值选取单元选取中值， 最后确定单元 依据预设的煤种和特定常数的对应关系，确定当前锅炉燃烧的煤种。该系统消 除了温度、 煤粉浓度、 空气系数、 风速等的影响以及测量仪器与环境的引入干 扰， 因此， 其过程安全可靠， 辨识结果单一， 具有很高的复现性， 同时， 大大 降低了系统的维护量与校验的复杂性，提高了环境适应性与稳定性， 降低系统 环境要求与应用成本， 并实现秒级的实时辨识能力。 还需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来 将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这 些实体或操作之间存在任何这种实际的关系或者顺序。 而且， 术语 "包括"、
"包含"或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列 要素的过程、 方法、 物品或者设备不仅包括那些要素， 而且还包括没有明确列 出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。 在没有更多限制的情况下， 由语句 "包括一个 ... ... " 限定的要素， 并不排除在 包括所述要素的过程、 方法、 物品或者设备中还存在另外的相同要素。
结合本文中所公开的实施例描述的方法或算法的步骤可以直接用硬件、处 理器执行的软件模块， 或者二者的结合来实施。软件模块可以置于随机存储器
( RAM ),内存、只读存储器（ROM )、电可编程 ROM、电可擦除可编程 ROM、 寄存器、 硬盘、 可移动磁盘、 CD-ROM、 或技术领域内所公知的任意其它形式 的存储介质中。
对所公开的实施例的上述说明，使本领域专业技术人员能够实现或使用本 发明。 对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见 的， 本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下，在 其它实施例中实现。 因此， 本发明将不会被限制于本文所示的这些实施例， 而 是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。

Claims

权 利 要 求
1、 一种基于火焰光谱强度在线辨识锅炉煤种的方法， 其特征在于， 该方 法包括：
获取炉膛中火焰的预设波长范围内的光谱数据信息；
获取所述光谱数据信息中线光谱峰值处的线光谱强度，并获取所述峰值对 应波长处的连续光谱强度；
对所述线光谱强度以及所述连续光谱强度进行过滤处理；
利用过滤得到的满足预设光谱强度的线光谱强度以及连续光谱强度，计算 得出煤种的特定常数；
依据预设的煤种和特定常数的对应关系 ,确定计算得到的所述特定常数对 应的煤种， 得到煤种的辨识结果。
2、 根据权利要求 1所述的方法， 其特征在于， 所述预设波长范围为钠元 素特征光谱对应的波长范围 580nm~600nm、 锂元素特征光谱对应的波长范围 660nm~680nm和钾元素特征光谱对应的波长范围 760nm~780nm。
3、 根据权利要求 1所述的方法， 其特征在于， 所述线光谱峰值处的线光 谱强度、所述峰值对应波长处的连续光谱强度是通过线性等比例插值的方式获 取的。
4、 根据权利要求 1所述的方法， 其特征在于， 所述预设光谱强度为： 小 于或等于光谱强度饱和上限阔值， 同时， 大于或等于光谱强度门槛阔值。
5、 根据权利要求 1所述的方法， 其特征在于， 该方法还包括： 计算出煤 种的特定常数后， 在预设窗口范围内的特定常数中选取所述特定常数的中值。
6、 一种基于火焰光谱强度在线辨识锅炉煤种的系统， 其特征在于， 该系 统包括： 第一获取单元、 第二获取单元、 过滤单元、 计算单元以及确定单元， 其中：
所述第一获取单元，用于获取炉膛中火焰的预设波长范围内的光谱数据信 息；
与所述第一获取单元相连的所述第二获取单元，用于获取所述光谱数据信 息中线光谱峰值处的线光谱强度， 并获取所述峰值对应波长处的连续光谱强 度； 与所述第二获取单元相连的所述过滤单元，用于对所述线光谱强度以及所 述连续光谱强度进行过滤处理；
与所述过滤单元相连的所述计算单元，用于利用过滤得到的满足预设光谱 强度的线光谱强度以及连续光谱强度， 计算得出煤种的特定常数；
与所述计算单元相连的所述确定单元，用于依据预设的煤种和特定常数的 对应关系， 确定计算得到的所述特定常数对应的煤种， 得到煤种的辨识结果。
7、 根据权利要求 6所述的系统， 其特征在于， 所述预设波长范围为钠元 素特征光谱对应的波长范围 580nm~600nm、 锂元素特征光谱对应的波长范围 660nm~680nm和钾元素特征光谱对应的波长范围 760nm~780nm。
8、 根据权利要求 6所述的系统， 其特征在于， 所述第二获取单元具体是 通过线性等比例插值的方式获取所述线光谱的峰值处的线光谱强度以及所述 峰值对应波长处的连续光谱强度。
9、 根据权利要求 6所述的系统， 其特征在于， 所述预设光谱强度为： 小 于或等于光谱强度饱和上限阔值， 同时， 大于或等于光谱强度门槛阔值。
10、根据权利要求 6所述的系统， 其特征在于， 该系统还包括连接于所述 计算单元与对比单元之间的中值选取单元，所述中值选取单元用于在预设窗口 范围内的特定常数中选取所述特定常数的中值。
PCT/CN2014/081828 2014-07-08 2014-07-08 一种基于火焰光谱强度在线辨识锅炉煤种的方法及系统 WO2016004577A1 (zh)

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