A rapid detection method and system for clean coal ash content

Abstract

This invention relates to the field of clean coal ash content detection technology, and particularly to a rapid detection method and system for clean coal ash content. The method includes: preparing a test sample of the coal sample to be tested into a predetermined shape; emitting a pulsed laser to the detection area of ​​the test sample to excite plasma and acquiring the original characteristic spectrum of the plasma emission; emitting a probe ray to the same detection area and measuring the physical background parameters after the probe ray interacts with the sample, which reflect the current physical state of the test sample; extracting elemental spectral feature data related to ash content based on the original characteristic spectrum; retrieving a pre-stored correction function based on the physical background parameters to perform real-time correction on the elemental spectral feature data, obtaining corrected elemental spectral feature data; and inputting the corrected elemental spectral feature data into a preset ash content calculation model to output the ash content value of the coal sample. This invention improves detection accuracy through multi-source information collaborative processing, meeting the industrial application requirements for rapid and accurate online detection of clean coal ash content.

Description

Technical Field

[0001] This invention relates to the technical field of clean coal ash content detection, and in particular to a rapid method and system for detecting clean coal ash content. Background Technology

[0002] Laser-induced breakdown spectroscopy (LAS) is widely used in the field of rapid detection of ash content in clean coal due to its advantages such as fast detection speed, no need for complex pretreatment, and the ability to perform online detection. The core principle of this technology is to excite the surface of the coal sample to generate plasma by pulsed laser, collect the characteristic spectrum of plasma emission, and calculate the ash content of the coal sample based on the characteristic peak information of inorganic elements related to ash in the spectrum.

[0003] However, in actual continuous industrial production environments, the physical states of the coal sample, such as surface morphology, bulk density, and moisture content, are prone to real-time changes due to factors such as sampling method, transport vibration, and ambient humidity. This directly alters the interaction efficiency between the pulsed laser and the coal sample, leading to instability in the plasma excitation state and inevitably causing deviations in the acquired original characteristic spectra. Existing methods for detecting ash content in clean coal using laser-induced breakdown spectroscopy (LAS) mostly only perform data processing such as noise reduction and baseline correction on the original characteristic spectra themselves, without considering interference compensation based on the real-time physical state of the coal sample detection area. This results in deviations in the ash content detection results due to fluctuations in the physical state of the coal sample. Summary of the Invention

[0004] This invention provides a rapid detection method and system for clean coal ash content, which can effectively solve the problems in the background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A rapid method for detecting the ash content of clean coal includes: Prepare the coal sample to be tested into a test sample with a preset shape; A pulsed laser is emitted toward the detection area of ​​the test sample to excite plasma, and the original characteristic spectrum of the plasma emission is collected; A probe ray is emitted toward the same detection area, and the physical background parameters after the probe ray interacts with the sample are measured to reflect the current physical state of the test sample. Based on the original characteristic spectrum, extract elemental spectral feature data related to ash content; Based on the physical background parameters, a pre-stored correction function is retrieved to perform real-time correction on the elemental spectrum feature data, resulting in corrected elemental spectrum feature data. The corrected elemental spectrum feature data is input into the preset ash content calculation model, and the ash content value of the coal sample to be tested is output.

[0006] Furthermore, the coal sample to be tested is prepared into a test sample of a predetermined shape, including: crushing and mixing the coal sample to be tested, and then pressing it into a coal cake with a predetermined thickness and flatness as the test sample.

[0007] Furthermore, the detection ray is X-ray; the physical background parameters are obtained using transmission measurement or scattering measurement methods.

[0008] Furthermore, when using the transmission measurement method, the attenuation intensity of the X-rays after penetrating the detection area is measured, and the attenuation intensity or the equivalent mass thickness calculated from it is used as the physical background parameter; When using the scattering measurement method, the scattering intensity of the X-rays at a specific angle in the detection area is measured, and this scattering intensity is used as the physical background parameter.

[0009] Further, based on the original characteristic spectrum, elemental spectral feature data related to ash content are extracted, including: The original characteristic spectra are preprocessed by background subtraction and intensity normalization; Identify and extract the intensity information of at least one characteristic spectral line corresponding to a specific mineral element in ash, wherein the specific mineral element includes one or more of silicon, aluminum, iron, and calcium; The intensity information of the at least one characteristic spectral line or its combination ratio is used as the elemental spectral feature data.

[0010] Furthermore, the method for constructing the ash content calculation model includes: Multiple standard coal samples with known ash content values ​​are obtained. For each standard coal sample, the steps of extracting elemental spectral feature data, obtaining physical background parameters and correction are performed to obtain its corresponding corrected elemental spectral feature data. Using the corrected elemental spectrum characteristic data as input and the known ash content value as output, a quantitative mapping relationship between the two is established through linear regression analysis. This quantitative mapping relationship is the ash content calculation model.

[0011] Furthermore, the ash content calculation model is configured with a set of weighting coefficients, and the ash content value of the output coal sample is obtained by linearly weighting and summing the features in the corrected elemental spectrum feature data with the corresponding weighting coefficients.

[0012] Further, the intensity information of the at least one characteristic spectral line or its combination ratio is used as the elemental spectral feature data, including: Based on the mineral composition characteristics of coal ash, the ratio of the spectral intensities of two or more elements with stable ash content and strong correlation with ash value is calculated, and this ratio is used as elemental spectral characteristic data.

[0013] Furthermore, elemental spectral feature data related to ash content are extracted based on the original feature spectra, including: The original characteristic spectra are preprocessed by background subtraction and intensity normalization; The background subtraction employs a polynomial fitting algorithm to subtract the background fitting curve from the original feature spectrum; The intensity normalization adopts the peak normalization method, which uses a fixed spectral line in the original characteristic spectrum that is independent of ash content as a reference spectral line, and converts the intensity of all spectral lines into a ratio relative to the peak value of the reference spectral line.

[0014] On the other hand, the present invention also provides a rapid detection system for clean coal ash content, comprising: The sample preparation module is used to prepare the coal sample to be tested into a test sample with a preset shape. A pulsed laser emitting module is used to emit pulsed lasers into the detection area of ​​the test sample to excite plasma; A spectral acquisition module is used to acquire the raw characteristic spectrum of the plasma emission; A detection ray emitting module is used to emit detection rays toward the same detection area; The physical measurement module is used to measure the physical background parameters after the detection rays interact with the sample. The physical background parameters are used to reflect the current physical state of the test sample. The elemental spectrum extraction module is used to extract elemental spectral feature data related to ash content based on the original feature spectrum; The data correction module is used to call a pre-stored correction function based on the physical background parameters to perform real-time correction on the elemental spectrum feature data, so as to obtain the corrected elemental spectrum feature data. The ash content calculation module is used to input the corrected elemental spectrum feature data into the preset ash content calculation model and output the ash content value of the coal sample to be tested.

[0015] The technical solution of this invention achieves the following technical effects: By synergistically combining laser-induced breakdown spectroscopy detection and probe ray physical state characterization, along with real-time correction of elemental spectral characteristic data and ash content calculation model operations, the detection deviation problem caused by real-time changes in the physical state of coal samples in existing technologies can be effectively overcome; by simultaneously implementing pulsed laser excitation and probe ray detection in the same detection area, the real-time physical state of the coal sample corresponding to the laser action site can be accurately obtained, achieving precise matching between physical state characterization and spectral excitation site, establishing a correlation between the physical state of the coal sample and the laser action efficiency, and overcoming the limitations of existing technologies that only process spectral data. The previous method addresses the shortcomings of not compensating for interference based on actual physical conditions. It corrects elemental spectral characteristic data by calling pre-stored correction functions based on real-time physical background parameters. This adaptively compensates for spectral signal deviations according to real-time changes in the physical state of the coal sample, eliminating the influence of fluctuations in coal sample surface morphology, bulk density, and moisture content on plasma excitation states. The corrected elemental spectral characteristic data is then input into a preset ash content calculation model, forming a complete detection chain of physical state characterization, spectral correction, and ash content calculation. This improves the stability and accuracy of ash content detection results, ensuring that the results are not affected by real-time changes in the physical state of the coal sample, thus meeting the detection needs of continuous industrial production.

[0016] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a logic flowchart of the rapid detection method for clean coal ash content in an embodiment of the present invention; Figure 2 This is a structural block diagram of the rapid detection system for clean coal ash content in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] like Figure 1 As shown, the present invention provides a rapid detection method for the ash content of clean coal, which specifically includes the following steps: Step S1: Prepare the coal sample to be tested into a test sample with a preset shape; Step S2: Emit pulsed laser to the detection area of ​​the test sample to excite plasma, and collect the original characteristic spectrum of plasma emission; Step S3: Emit probe rays to the same detection area and measure the physical background parameters after the probe rays interact with the sample, which are used to reflect the current physical state of the test sample; Step S4: Based on the original characteristic spectrum, extract elemental spectral feature data related to ash content; Step S5: Based on the physical background parameters, retrieve the pre-stored correction function to perform real-time correction on the elemental spectrum feature data in order to eliminate or reduce the influence of changes in the physical state of the coal sample on the ash content correlation in the first feature parameter, and obtain the corrected elemental spectrum feature data. Step S6: Input the corrected elemental spectrum feature data into the preset ash content calculation model and output the ash content value of the coal sample to be tested.

[0022] In this embodiment, by combining laser-induced breakdown spectroscopy detection and probe ray physical state characterization, along with real-time correction of elemental spectral feature data and ash content calculation model calculation, the detection deviation problem caused by real-time changes in the physical state of coal samples in existing technologies can be effectively overcome. By simultaneously implementing pulsed laser excitation and probe ray detection in the same detection area, the real-time physical state of the coal sample corresponding to the laser action site can be accurately obtained, achieving precise matching between physical state characterization and spectral excitation site, establishing a correlation between the physical state of the coal sample and the laser action efficiency, and overcoming the shortcomings of existing technologies that only process spectral data without considering interference compensation based on actual physical state. By calling a pre-stored correction function based on real-time physical background parameters to correct the elemental spectral feature data, spectral signal deviation can be adaptively compensated according to real-time changes in the physical state of the coal sample, eliminating the influence of fluctuations in coal sample surface morphology, bulk density, and moisture content on the plasma excitation state. The corrected elemental spectral feature data is input into a preset ash content calculation model, forming a complete detection chain of physical state characterization, spectral correction, and ash content calculation, improving the stability and accuracy of ash content detection results, ensuring that the detection results are not affected by real-time changes in the physical state of the coal sample, and adapting to the detection needs of continuous industrial production.

[0023] In some embodiments of the present invention, in order to achieve stable online application of laser-induced breakdown spectroscopy, it is necessary to control the interference of the physical state of the sample on the detection results. This embodiment achieves coal sample shaping from detectable to suitable for detection through graded crushing, homogenization mixing, and precise pressing, as specifically implemented as follows: The coal sample to be tested is first subjected to graded crushing. The first stage uses a jaw crusher to crush the lumpy coal sample into particles with a diameter of no more than 10 mm. This upper limit of particle size can avoid equipment jamming due to excessive feed size during subsequent impact crushing, while reducing the impact wear of large particles on the impact crushing blades and ensuring the continuity of the crushing process. Large impurities mixed in the coal sample are removed to avoid impurities affecting the subsequent molding and testing accuracy. The second stage employs an impact crusher to further crush the coal sample after the first stage to a powder with a particle size of 0.1-0.5 mm. This particle size range allows the coal particles to form a uniform packing structure during subsequent pressing. Particles smaller than 0.1 mm are prone to agglomeration, increasing the probability of uneven pore distribution within the coal cake. Particles larger than 0.5 mm are prone to causing a decrease in the surface smoothness of the pressed coal cake. The device speed is controlled at 2000-2500 r / min and the crushing time is 3-5 min. This combination of speed and time allows the coal particles to achieve the target particle size distribution while reducing the accumulation of mechanical heat during crushing and lowering the possibility of oxidation of ash-related mineral elements in the coal sample. After crushing, the powdered coal sample is fed into a two-way mixing device. The mixing device speed is set to 300-400 r / min and the mixing time is 8-10 min. During the mixing process, a low-frequency vibration with a frequency of 50-60 Hz is applied simultaneously, and the vibration amplitude is controlled at 0.5-1 mm to ensure that different components in the coal sample are fully mixed and to avoid the enrichment or deficiency of ash-related elements in local areas. After mixing, the coal sample is fed into a pre-set positioning mold. The mold is equipped with thickness adjustment shims; the appropriate shim thickness is selected based on the testing requirements to control the coal cake thickness at 2-3 mm. This thickness ensures that the pulsed laser can act on the stable layer inside the coal cake, while allowing the detection beam to penetrate and obtain a recognizable signal intensity. The positioning mold containing the coal sample is then fed into a hydraulic pressing device using a layered pressurization method. The first stage applies a pressure of 5-8 MPa and holds it for 2-3 minutes to expel air from the gaps inside the coal sample, preventing the formation of closed pores inside the coal cake. The second stage applies a pressure of 12-15 MPa and holds it for 4-5 minutes to further pulverize the coal sample particles. To form a dense and uniform internal structure, pressures below 12 MPa can easily lead to a loose coal cake structure, while pressures above 15 MPa can easily cause brittle breakage of the coal sample particles, altering the original particle packing state and ensuring a dense coal cake structure with uniform porosity. During the pressing process, the mold temperature is controlled at 25±2℃ to avoid excessively high temperatures causing rapid evaporation of moisture in the coal sample, forming pores, or excessively low temperatures causing the coal cake to crack easily after forming. After pressing, an ejector demolding method is used, with the demolding speed controlled at 5-10 mm / s to avoid scratches or damage to the coal cake surface during demolding. Finally, a coal cake with a surface flatness error of no more than 0.1 mm and a thickness error of no more than 0.05 mm is obtained as a test sample.

[0024] In this embodiment, graded crushing controls the uniformity of coal sample particle size, avoiding uneven energy absorption during laser action caused by large particles, and preventing excessive agglomeration of fine powder to form localized dense areas; bidirectional stirring combined with low-frequency vibration ensures uniformity of coal sample composition, ensuring consistent distribution of ash-related mineral elements in all areas of the coal cake, avoiding spectral characteristic deviations caused by local element enrichment or deficiency; layered pressurization and constant temperature control ensure uniform porosity, surface flatness, and accurate thickness of the coal cake, guaranteeing a unified physical state of the sites for subsequent laser excitation and detection ray measurement, and reducing detection deviations caused by uneven coal cake formation.

[0025] In some embodiments of the present invention, step S2 involves exciting a coal sample with a pulsed laser to generate stable plasma and acquiring its emitted original characteristic spectrum. In existing laser excitation and spectral acquisition methods, the laser emission parameters are fixed and do not match the physical state of the coal cake prepared in step S1. Positioning deviations in the detection area can easily lead to inconsistencies between the laser action site and the measurement site of the probe ray in step S3. Furthermore, the asynchronous spectral acquisition and laser excitation can easily introduce environmental stray light interference, resulting in a low signal-to-noise ratio and distorted characteristic peak signals in the acquired original characteristic spectrum, failing to accurately reflect the true spectral information of the ash-related elements in the coal sample. Therefore, to achieve precise adaptation between laser excitation and spectral acquisition, the specific implementation is as follows: Step S21: Locate the detection area of ​​the coal cake test sample prepared in step S1. Use an industrial CCD vision positioning module to acquire images of the coal cake surface. Use an edge recognition algorithm to lock the detection area in the center of the coal cake. This area avoids possible damage and uneven thickness at the edges of the coal cake to ensure that the physical state of the detection area is uniform. After positioning, the vision positioning module sends a synchronous trigger signal to the laser emitting device and the spectral acquisition device to realize the linkage control of positioning, excitation and acquisition.

[0026] Step S22: A pulsed laser is selected as the laser emitting device. Its parameters are set to match the physical state of the coal cake prepared in step S1. The laser wavelength is selected to effectively penetrate the slight floating dust on the surface of the coal cake and reduce the volatilization of elements caused by the thermal effect on the surface of the coal sample. The laser pulse width is controlled within the range that can fully excite the plasma without aggravating the ablation of the coal sample surface, so as to avoid ablation damage to the physical state of the detection area. The laser pulse energy is adjusted to the range that can generate stable plasma in the coal cake detection area, so as to avoid excessive energy causing excessive ablation of the coal sample or insufficient energy to excite the characteristic spectra of ash-related inorganic elements. The laser repetition frequency is matched with the integration time of the spectral acquisition device to ensure that a complete plasma emission spectrum can be acquired after each laser excitation.

[0027] Step S23: The laser emitting device focuses the laser beam onto the positioned detection area through a focusing lens. The focal length of the focusing lens is set to match the size of the detection area, so that the size of the laser spot matches the detection area, ensuring that the laser energy is concentrated on the positioning area and improving the efficiency of the laser interaction with the coal sample. The laser emission angle is perpendicular to the surface of the coal cake, and the angle deviation is controlled within a very small range to avoid the laser energy reflection or uneven depth of action caused by the angle deviation, which would affect the stability of plasma excitation.

[0028] Step S24: The characteristic spectrum of plasma emission is acquired by a fiber optic spectrometer. The horizontal distance and incident angle between the incident fiber port of the fiber optic spectrometer and the laser excitation site are set to a state that can effectively receive the characteristic spectrum of plasma emission and avoid damage to the spectrometer caused by direct laser incident on the fiber optic. The spectral acquisition range of the spectrometer covers the characteristic spectral wavelengths of ash-related inorganic elements such as silicon, aluminum, iron, and calcium. The spectral integration time is matched with the laser repetition frequency to ensure that the plasma emission spectrum generated by each laser excitation can be completely acquired. The resolution of the spectrometer is set to be able to clearly identify the characteristic peaks of different elements at a horizontal level to avoid spectral information distortion caused by overlapping characteristic peaks.

[0029] Step S25: During the acquisition process, the spectrometer and the laser emitting device work synchronously. After each laser pulse excitation, the spectrometer delays for a certain period of time before starting to acquire data. The delay time can avoid the strong background interference of the laser pulse itself and the initial plasma, thereby improving the signal-to-noise ratio of the original characteristic spectrum. After each spectral acquisition is completed, the spectrometer automatically stores the original spectral data and sends a feedback signal to the laser emitting device to trigger the next laser excitation and spectral acquisition. The number of spectral acquisitions for a single test sample is set to multiple, and the average value of the spectral data acquired multiple times is taken as the original characteristic spectrum to reduce the random error of a single excitation and acquisition.

[0030] In this embodiment, the application of the industrial CCD vision positioning module ensures that the laser excitation site is completely consistent with the detection ray measurement site in step S3; the parameter settings of the pulsed laser are adapted to the physical state of the coal cake prepared in step S1, which can generate stable plasma and ensure that ash-related elements are fully excited; the control of laser focusing and emission angle improves the interaction efficiency between the laser and the coal sample and reduces plasma excitation fluctuations; the parameter and acquisition mode settings of the spectral acquisition device can effectively receive the plasma characteristic spectrum and avoid interference signals; the synchronous control of positioning, excitation and acquisition reduces the time deviation of each link and ensures that the acquired original characteristic spectrum corresponds accurately with the physical state of the detection area.

[0031] In some embodiments of the present invention, step S3 involves emitting X-rays into the same detection area locked in step S2 to measure the physical background parameters after the detection rays interact with the coal cake, thus accurately reflecting the current physical state of the test sample. If the X-ray emission and laser excitation in step S2 are not synchronized, the detection site and the laser interaction site may shift. Furthermore, using only a single measurement method cannot adapt to coal cakes with different densities and thicknesses prepared in step S1, resulting in a mismatch between the measured physical background parameters and the actual physical state of the detection area. This fails to effectively compensate for the interference of changes in the physical state of the coal sample on the spectral signal. To achieve precise adaptation between X-ray detection, laser excitation, and the detection area, the specific implementation is as follows: Step S31: The industrial CCD vision positioning module linked to step S2 acquires the coordinate information of the locked detection area and synchronously transmits this coordinate information to the X-ray emitting device and the X-ray detection device. This achieves precise spatial alignment between the X-ray emitting point, the X-ray receiving point, and the laser excitation point in step S2, ensuring that the X-rays act on the same detection area and avoiding the disconnect between the physical background parameters and the physical state of the laser-acting area caused by point offset. After positioning is completed, the X-ray emitting device receives the synchronous trigger feedback signal from the spectral acquisition device in step S2, ensuring the timing coordination of X-ray emission, laser excitation, and spectral acquisition, and avoiding the physical background parameters failing to reflect the real-time physical state of the detection area at the moment of laser excitation due to timing deviation.

[0032] Step S32: Select an X-ray emitting device suitable for detecting the physical state of coal cake. The emission parameters are set to match the thickness and density of the coal cake prepared in step S1. The X-ray tube voltage is adjusted to a range that can penetrate the coal cake and generate a recognizable signal, avoiding excessive voltage that could cause changes in the internal composition of the coal cake, or insufficient voltage that could not penetrate the coal cake to obtain an effective signal. The X-ray tube current is controlled to a range that can stably emit X-rays without damaging the detection area, ensuring stable X-ray intensity and improving the repeatability of physical background parameter measurements.

[0033] Step S33: Adopt a bidirectional adaptation design of transmission measurement method and scattering measurement method. Select the corresponding measurement method according to the actual physical state of the coal cake prepared in step S1. The two measurement methods share the same X-ray emitting device and detection area positioning system, without the need for additional adjustment of the detection position.

[0034] Step S34: When using the transmission measurement method, the X-ray detection device is set on the side of the coal cake away from the X-ray emitting device, so that the X-rays penetrate the detection area locked in step S2 and are received by the X-ray detection device. The X-ray detection device captures the attenuation intensity after the X-rays penetrate in real time and records the attenuation intensity data; or the attenuation intensity is converted into equivalent mass thickness through a preset conversion formula. The equivalent mass thickness is directly related to the coal cake's bulk density and thickness, and can more intuitively reflect the physical state of the detection area. The attenuation intensity or equivalent mass thickness is used as a physical background parameter.

[0035] Step S35: When using the scattering measurement method, adjust the installation angle of the X-ray detection device to align it with the scattering direction of the detection area, and capture the scattering intensity of X-rays at a specific angle in the detection area. The scattering intensity is directly related to the surface morphology and particle size distribution of the coal cake, and this scattering intensity is used as a physical background parameter. During the measurement process, fix the receiving angle of the X-ray detection device to avoid deviations in the scattering intensity measurement caused by angle changes.

[0036] Step S36: Transmit the measured physical background parameters such as attenuation intensity, equivalent mass thickness or scattering intensity to the data processing module in real time, synchronously associate them with the spectral acquisition data of the corresponding detection area, mark the detection area coordinates and time sequence information corresponding to the data, and ensure that the physical background parameters correspond accurately with the elemental spectral feature data extracted in step S4.

[0037] In this embodiment, the visual positioning module and synchronous triggering mechanism in step S2 ensure that X-rays act on the same detection area and are time-coordinated, so that the physical background parameters can truly reflect the real-time physical state of the detection area at the moment of laser excitation; the X-ray emission parameters are adapted to the physical state of the coal cake in step S1, so as to obtain an effective action signal without damaging the coal cake or changing its physical state; the bidirectional adaptation of the transmission measurement method and the scattering measurement method can select the adaptation method according to the actual thickness and density of the coal cake, so as to ensure that the physical background parameters can comprehensively and truly reflect the physical state of the coal cake; the real-time correlation between the physical background parameters and the spectral data ensures that during the correction process in step S5, each set of elemental spectral feature data can correspond to the physical state parameters at the moment of detection, making the correction more targeted.

[0038] In some embodiments of the present invention, step S4 is to extract elemental spectral feature data related to ash content based on the original feature spectrum acquired in step S2; this embodiment achieves the extraction of elemental spectral feature data by combining the acquisition characteristics of the original feature spectrum in step S2 with the spectral characteristics of specific mineral elements in the ash, as specifically implemented as follows: Step S41: Perform background subtraction and intensity normalization preprocessing on the original feature spectrum acquired in step S2. The preprocessing method is adapted to the interference characteristics of the spectrum acquisition in step S2. Background subtraction adopts a polynomial fitting algorithm to generate a background fitting curve based on the background distribution trend of the original feature spectrum. Subtract the background fitting curve signal from the original feature spectrum signal to remove background interference caused by ambient stray light and continuous plasma radiation, and avoid the background noise residue affecting subsequent spectral line identification and intensity extraction. Intensity normalization adopts peak normalization method. Use a fixed spectral line in the original feature spectrum that is independent of ash content as a reference spectral line. Convert all spectral line intensities into ratios relative to the peak values ​​of the reference spectral line to eliminate the spectral line intensity fluctuations caused by the small fluctuations in laser pulse energy and the spectral acquisition integration time deviation in step S2. Step S42: Preset a characteristic spectral library of specific mineral elements in ash. The characteristic spectral library contains characteristic spectral wavelength information of silicon, aluminum, iron and calcium. The spectral wavelength is set to match the acquisition range and resolution of the spectrometer in step S2 to ensure that the characteristic spectral lines can be clearly identified. At the same time, spectral lines that overlap with organic matter and interfering elements in the coal sample are removed to avoid identification errors caused by spectral line overlap. Step S43: Based on the preset feature spectral line library, spectral line identification is performed on the preprocessed spectral signal. The spectral line peak detection algorithm is used to lock the feature spectral lines corresponding to each specific mineral element. During the identification process, a spectral line signal-to-noise ratio threshold is set, and only spectral lines with a signal-to-noise ratio higher than the threshold are retained as valid feature spectral lines. False peaks and weak peaks caused by noise interference are eliminated to ensure that the identified feature spectral lines are all valid spectral lines of ash-related mineral elements. After the identification is completed, the intensity information of each valid feature spectral line is extracted. The intensity information is the peak intensity or the integral intensity of the spectral line. The integral intensity of the spectral line is the integral value of the spectral signal within a certain wavelength range near the peak of the feature spectral line, which is adapted to the intensity distribution characteristics of the spectral line. Step S44: Based on the mineral composition characteristics of the ash in the coal sample, select the corresponding type of elemental spectral feature data. You can select the effective characteristic spectral line intensity information of a single specific mineral element as the elemental spectral feature data, or select the combination ratio of the effective characteristic spectral line intensities of multiple specific mineral elements as the elemental spectral feature data. The selection of the combination ratio is based on the content ratio of each mineral element in the ash. Priority should be given to selecting the spectral line intensities of two or more elements with stable ash content and strong correlation with ash value for ratio calculation, so as to avoid the influence of local component fluctuations of the coal sample on the intensity of the spectral line of a single element.

[0039] In this embodiment, the preprocessing method is adapted to the acquisition interference characteristics of the original feature spectrum in step S2, which can effectively remove background noise and intensity fluctuation interference; the preset feature spectral library is adapted to the spectrometer acquisition parameters, and the effective spectral lines are identified by combining the spectral line signal-to-noise ratio threshold, which can avoid identification errors caused by spectral line overlap and false peak interference; the selection of elemental spectral feature data is adapted to the mineral composition characteristics of coal sample ash, and the selection of single intensity or combination ratio has a clear basis, which can improve the correlation between elemental spectral feature data and ash value, and ensure that the corrected elemental spectral feature data can accurately reflect the actual situation of coal sample ash.

[0040] In some embodiments of the present invention, step S5, based on the physical background parameters measured in step S3, performs real-time correction on the elemental spectrum feature data extracted in step S4 using a pre-stored correction function, thereby eliminating or reducing the influence of changes in the physical state of the coal sample on the correlation between elemental spectrum feature data and ash content; the specific implementation is as follows: Step S51: Establish a classification correction function library. The correction function library is classified according to the type of physical background parameters in Step S3, corresponding to the attenuation intensity obtained by transmission measurement, equivalent mass thickness, and scattering intensity obtained by scattering measurement, and corresponding correction functions are pre-stored for each. At the same time, according to the type of elemental spectral feature data in Step S4, the correction functions corresponding to each type of physical background parameter are further subdivided to adapt to the spectral intensity data of a single specific mineral element and the spectral intensity combination ratio data of multiple specific mineral elements. The establishment of correction functions is based on the deviation law between the physical background parameters and elemental spectral feature data of standard coal samples under different physical states, ensuring that each type of correction function can eliminate the interference caused by the change of specific physical state. Step S52: The elemental spectrum feature data extracted in step S4, the physical background parameters measured in step S3, and the corresponding detection area coordinates and time sequence information are synchronously transmitted to the data processing module to complete the accurate association of the data and ensure that each set of elemental spectrum feature data can match the physical background parameters corresponding to its detection moment, so as to avoid data misalignment and correction deviation. The association is based on the synchronous triggering mechanism of step S2 and step S3 and the consistency of the detection area of ​​step S4 and step S3. Step S53: The data processing module identifies the type of the current physical background parameter and the corresponding elemental spectrum feature data type. Based on the identification result, it retrieves the corresponding correction function from the classification correction function library. During the retrieval process, the specific value of the physical background parameter is read synchronously to determine the physical state fluctuation range corresponding to the value. The corresponding correction parameter in the correction function is matched to ensure that the correction function is adapted to the actual physical state of the current coal sample. Step S54: Input the elemental spectrum feature data extracted in step S4 into the retrieved correction function, and perform real-time correction calculation in combination with the specific values ​​of the physical background parameters. During the correction calculation, the correction amplitude is dynamically adjusted according to the degree of fluctuation of the physical state of the coal sample reflected by the physical background parameters. The greater the deviation of the physical background parameters from the standard range, the greater the correction amplitude is adjusted accordingly to ensure that the deviation of elemental spectrum feature data caused by the change in laser efficiency and the instability of plasma excitation under this physical state can be effectively offset. Step S55: After the correction operation is completed, output the corrected elemental spectrum feature data, and simultaneously label the physical background parameter type, elemental spectrum feature data type, and correction function information corresponding to the data.

[0041] In this embodiment, the establishment of the classification correction function library adapts to different types of physical background parameters in step S3 and different types of elemental spectral feature data in step S4, ensuring the pertinence of the correction function; data association ensures the spatiotemporal correspondence between elemental spectral feature data and physical background parameters during the correction process, avoiding correction deviations caused by data misalignment; the precise retrieval of correction functions and the dynamic adjustment of correction amplitude can specifically eliminate the interference caused by changes in different physical states, and reduce the impact of fluctuations in the physical state of coal samples on the correlation between elemental spectral feature data and ash content.

[0042] In some embodiments of the present invention, step S6 uses a preset ash content calculation model to perform quantitative calculations on the corrected elemental spectrum feature data obtained in step S5, and outputs the ash content value of the coal sample to be tested. To ensure that the construction process of the ash content calculation model is accurately adapted to the detection process and data types of steps S1 to S5, this embodiment combines the type characteristics of the elemental spectrum feature data in step S4, the characteristics of the corrected data in step S5, and the requirements of actual industrial testing scenarios to achieve accurate construction of the ash content calculation model and reliable output of the ash content value. The specific implementation is as follows: Step S61: Prepare multiple standard coal samples with known ash content. The ash content of the standard coal samples should cover the common range of ash content of the clean coal to be tested in actual industrial production. The ash content range is selected based on the fluctuation range of clean coal ash content in actual industrial production. The ash content of each standard coal sample is determined using an industrial analysis method (GB / T 212-2008) and serves as the baseline output data for model construction. At the same time, the standard coal samples need to be prepared in different physical states, corresponding to different thicknesses, densities, and surface morphologies of the coal cake in step S1. The fluctuation range of physical states should cover the possible thickness deviations, density differences, and surface morphology fluctuations of coal samples in actual continuous industrial production. Each standard coal sample in each physical state is prepared according to the molding process in step S1 to ensure that the physical state of the standard coal samples is consistent with that of the coal samples to be tested. The number of standard coal samples should be no less than 30, and the standard coal samples with different ash contents and different physical states should be evenly distributed to avoid model deviation due to insufficient sample size or uneven distribution.

[0043] Step S62: For each standard coal sample, sequentially execute all detection procedures from steps S1 to S5. Specifically, according to the forming process in step S1, the standard coal sample is crushed, mixed, and pressed to prepare a coal cake with the same specifications as the coal sample to be tested. Following the parameter settings and operation procedures in step S2, the detection area of ​​the standard coal sample coal cake is located, pulsed laser excitation is performed, and plasma emission spectroscopy is acquired to obtain the original characteristic spectrum of the standard coal sample. Following the detection method and operation procedures in step S3, X-rays are emitted into the same detection area to measure and obtain the corresponding physical background parameters of the standard coal sample, including attenuation intensity, equivalent mass thickness, or scattering intensity. Following the extraction process in step S4, the original feature spectra are preprocessed and spectral lines are identified to extract the elemental spectral feature data corresponding to the standard coal sample. Following the correction process in step S5, the corresponding correction function is invoked based on the physical background parameters to perform real-time correction on the elemental spectral feature data, obtaining the corrected elemental spectral feature data corresponding to each standard coal sample. The detection process and parameter settings for each standard coal sample are completely consistent with those for the coal sample to be tested, ensuring that the acquisition conditions and data characteristics of the corrected elemental spectral feature data of the standard coal sample and the corrected elemental spectral feature data of the coal sample to be tested are completely unified, eliminating model construction deviations caused by process differences and parameter differences.

[0044] Step S63: Organize the corrected elemental spectrum characteristic data and corresponding known ash values ​​of all standard coal samples, removing outliers. Outliers refer to corrected elemental spectrum characteristic data and corresponding ash values ​​that deviate from the normal data distribution range or are caused by detection operation errors, ensuring the reliability of the data used for model construction. Use the organized corrected elemental spectrum characteristic data as model input variables and the corresponding known ash values ​​as model output variables. Establish a quantitative mapping relationship between input and output variables using multiple linear regression analysis; this quantitative mapping relationship constitutes the ash calculation model. During the linear regression analysis, determine the correlation coefficient between each corrected elemental spectrum characteristic data and the known ash value to identify specific minerals. The correlation between the characteristic data corresponding to an element and ash content is determined by the strength of the correlation coefficient. The larger the absolute value of the correlation coefficient, the stronger the correlation between the characteristic data and ash content. Based on this, the input corrected elemental spectrum characteristic data is weighted accordingly. The stronger the correlation, the larger the weight coefficient is assigned to the characteristic data, and the weaker the correlation, the smaller the weight coefficient is assigned to the characteristic data. This ensures that the quantitative mapping relationship can accurately reflect the inherent correlation between the corrected elemental spectrum characteristic data and ash content. During the linear regression analysis, the goodness of fit of the model is calculated simultaneously. The goodness of fit must meet the requirement of not less than 0.95. If the goodness of fit does not meet this requirement, the number of standard coal samples needs to be increased, the outlier data removal criteria need to be adjusted, and the linear regression analysis needs to be re-executed until the goodness of fit meets the requirement.

[0045] Step S64: Configure the ash content calculation model with a set of fixed weight coefficients. The specific values ​​of the weight coefficients are directly determined by the results of the multiple linear regression analysis in step S63. The number of weight coefficients is consistent with the number of feature terms in the corrected elemental spectral feature data. Each corrected elemental spectral feature data corresponds to a unique weight coefficient, and there is no reuse or mismatch of weight coefficients. The weight coefficients correspond one-to-one with the types of corrected elemental spectral feature data, that is, they are respectively adapted to the spectral intensity data of a single specific mineral element and the spectral intensity combination ratio data of multiple specific mineral elements in step S4. Different types of corrected elemental spectral feature data correspond to different sets of weight coefficients. The weight coefficients are stored in the model database of the data processing module, and an association index between the weight coefficients and the data types is established.

[0046] Step S65: The corrected elemental spectrum feature data and corresponding type information output in step S5 are synchronously transmitted to the ash content calculation model through the data interface. The model calls the association index in the data processing module, and quickly identifies and retrieves the corresponding weight coefficient group according to the type information of the corrected elemental spectrum feature data. During the retrieval process, the matching consistency between the data type and the weight coefficient group is checked synchronously. If the type and weight coefficient group do not match, the model pauses the operation and feeds back a matching abnormality signal to avoid ash content value calculation deviation caused by weight coefficient mismatch.

[0047] Step S66: The ash content calculation model performs a linear weighted summation operation on the corrected elemental spectral feature data and the corresponding weight coefficients according to the quantitative mapping relationship established in step S63 using multiple linear regression. The calculation formula strictly follows the quantitative relationship expression obtained from the multiple linear regression analysis, that is, the ash content value is equal to the sum of the products of each corrected elemental spectral feature data and its corresponding weight coefficient. Four significant figures are retained after the decimal point during the calculation process to avoid precision loss. The calculation trajectory of each data is recorded synchronously during the calculation process, including the corrected elemental spectral feature data, the corresponding weight coefficients, the product results of individual items, and the sum result.

[0048] Step S67: After the calculation is completed, the model performs accuracy calibration on the result of the linear weighted summation. The calibration is based on the fitting error range of the standard coal sample in step S63. The calculation result is compared with the fitting error range. If the calculation result is within the fitting error range, it is determined to be a valid result, and the model outputs the result as the ash value of the coal sample to be tested. If the calculation result exceeds the fitting error range, it is determined to be an invalid result, the model feeds back an abnormal calculation signal, and triggers the re-detection calculation process from step S2 to step S6. At the same time, the model synchronously marks the correlation information corresponding to the output ash value, including the specific value of the corrected elemental spectrum feature data, data type, adapted weight coefficient group, physical background parameter type, and calculation trajectory, so as to realize the traceability correlation between the ash value and all previous detection data and model parameters.

[0049] In this embodiment, the preparation of standard coal samples follows national standards to determine ash content values, covering the actual ash content range and the fluctuation range of physical states. The sample size meets statistical significance requirements, ensuring that the ash content calculation model can adapt to actual industrial testing scenarios. The standard coal samples sequentially and completely execute steps S1 to S5, ensuring that the quantitative mapping relationship established by the model can fully match the testing process and data characteristics of the coal samples to be tested, eliminating model bias caused by process differences. Multiple linear regression analysis, combined with correlation coefficients, determines weight allocation while controlling the goodness of fit, ensuring that the quantitative mapping relationship accurately reflects the intrinsic correlation between the corrected elemental spectrum characteristic data and the ash content value. Weight coefficients correspond one-to-one with data types, and an association index is established to avoid weight mismatch. Precision control, trajectory recording, and result calibration during the calculation process further ensure the accuracy of ash content value calculation.

[0050] Based on the same inventive concept as the rapid detection method for clean coal ash in the foregoing embodiments, the present invention also provides a rapid detection system for clean coal ash, such as... Figure 2 As shown, the system includes: The sample preparation module is used to prepare the coal sample to be tested into a test sample with a preset shape. A pulsed laser emission module is used to emit pulsed lasers into the detection area of ​​the test sample to excite plasma; The spectral acquisition module is used to acquire the raw characteristic spectra of plasma emission; The detection ray emitting module is used to emit detection rays into the same detection area; The physical measurement module is used to measure the physical background parameters after the probe rays interact with the sample. The physical background parameters reflect the current physical state of the test sample. The elemental spectrum extraction module is used to extract elemental spectral feature data related to ash content based on the original feature spectra; The data correction module is used to call a pre-stored correction function based on physical background parameters to perform real-time correction on the elemental spectrum feature data, and obtain the corrected elemental spectrum feature data. The ash content calculation module is used to input the corrected elemental spectrum characteristic data into the preset ash content calculation model and output the ash content value of the coal sample to be tested.

[0051] The system described above in this invention can effectively realize a rapid detection method for the ash content of clean coal, and the technical effects it can achieve are as described in the above embodiments, which will not be repeated here.

[0052] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A rapid detection method for ash content in clean coal, characterized in that, include: Prepare the coal sample to be tested into a test sample with a preset shape; A pulsed laser is emitted toward the detection area of ​​the test sample to excite plasma, and the original characteristic spectrum of the plasma emission is collected; A probe ray is emitted toward the same detection area, and the physical background parameters after the probe ray interacts with the sample are measured to reflect the current physical state of the test sample. Based on the original characteristic spectrum, extract elemental spectral feature data related to ash content; Based on the physical background parameters, a pre-stored correction function is retrieved to perform real-time correction on the elemental spectrum feature data, resulting in corrected elemental spectrum feature data. The corrected elemental spectrum feature data is input into the preset ash content calculation model, and the ash content value of the coal sample to be tested is output.

2. The rapid detection method for ash content in clean coal according to claim 1, characterized in that, Preparing the coal sample to be tested into a test sample of a predetermined shape includes: crushing and mixing the coal sample to be tested, and then pressing it into a coal cake with a predetermined thickness and flatness as the test sample.

3. The rapid detection method for clean coal ash content according to claim 1, characterized in that, The detection ray is X-ray; the physical background parameters are obtained by transmission measurement or scattering measurement.

4. The rapid detection method for clean coal ash content according to claim 3, characterized in that, When using the transmission measurement method, the attenuation intensity of the X-rays after penetrating the detection area is measured, and the attenuation intensity or the equivalent mass thickness calculated therefrom is used as the physical background parameter. When using the scattering measurement method, the scattering intensity of the X-rays at a specific angle in the detection area is measured, and this scattering intensity is used as the physical background parameter.

5. The rapid detection method for ash content in clean coal according to claim 1, characterized in that, Based on the original characteristic spectrum, elemental spectral feature data related to ash content are extracted, including: The original characteristic spectra are preprocessed by background subtraction and intensity normalization; Identify and extract the intensity information of at least one characteristic spectral line corresponding to a specific mineral element in ash, wherein the specific mineral element includes one or more of silicon, aluminum, iron, and calcium; The intensity information of the at least one characteristic spectral line or its combination ratio is used as the elemental spectral feature data.

6. The rapid detection method for ash content in clean coal according to claim 1, characterized in that, The method for constructing the ash content calculation model includes: Multiple standard coal samples with known ash content values ​​are obtained. For each standard coal sample, the steps of extracting elemental spectral feature data, obtaining physical background parameters and correction are performed to obtain its corresponding corrected elemental spectral feature data. Using the corrected elemental spectrum characteristic data as input and the known ash content value as output, a quantitative mapping relationship between the two is established through linear regression analysis. This quantitative mapping relationship is the ash content calculation model.

7. The rapid detection method for clean coal ash content according to claim 6, characterized in that, The ash content calculation model is configured with a set of weighting coefficients, and the output ash content value of the coal sample is obtained by linearly weighting and summing the features in the corrected elemental spectrum feature data with the corresponding weighting coefficients.

8. The rapid detection method for ash content in clean coal according to claim 5, characterized in that, Using the intensity information of the at least one characteristic spectral line or its combination ratio as the elemental spectral feature data, including: Based on the mineral composition characteristics of coal ash, the ratio of the spectral intensities of two or more elements with stable ash content and strong correlation with ash value is calculated, and this ratio is used as elemental spectral characteristic data.

9. The rapid detection method for ash content in clean coal according to claim 1, characterized in that, Based on the original characteristic spectra, elemental spectral feature data related to ash content are extracted, including: The original characteristic spectra are preprocessed by background subtraction and intensity normalization; The background subtraction employs a polynomial fitting algorithm to subtract the background fitting curve from the original feature spectrum; The intensity normalization adopts the peak normalization method, which uses a fixed spectral line in the original characteristic spectrum that is independent of ash content as a reference spectral line, and converts the intensity of all spectral lines into a ratio relative to the peak value of the reference spectral line.

10. A rapid detection system for clean coal ash content, characterized in that, include: The sample preparation module is used to prepare the coal sample to be tested into a test sample with a preset shape. A pulsed laser emitting module is used to emit pulsed lasers into the detection area of ​​the test sample to excite plasma; A spectral acquisition module is used to acquire the raw characteristic spectrum of the plasma emission; A detection ray emitting module is used to emit detection rays toward the same detection area; The physical measurement module is used to measure the physical background parameters after the detection rays interact with the sample. The physical background parameters are used to reflect the current physical state of the test sample. The elemental spectrum extraction module is used to extract elemental spectral feature data related to ash content based on the original feature spectrum; The data correction module is used to call a pre-stored correction function based on the physical background parameters to perform real-time correction on the elemental spectrum feature data, so as to obtain the corrected elemental spectrum feature data. The ash content calculation module is used to input the corrected elemental spectrum feature data into the preset ash content calculation model and output the ash content value of the coal sample to be tested.

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