A coal sample moisture and wettability detection method, device, equipment and storage medium

Abstract

This application discloses a method, apparatus, equipment, and storage medium for detecting moisture and wettability in coal samples. The method includes the following steps: S1, analyzing the standard true moisture value of each coal sample in multiple groups; S2, collecting near-infrared spectral data for each coal sample; S3, preprocessing the near-infrared spectral data and extracting key characteristic parameters of the absorption peaks in the moisture-sensitive band; S4, fusing the key characteristic parameters extracted from the moisture-sensitive band with the original spectral absorbance data as input variables for a PLS model, and simultaneously optimizing the latent variable number prediction of the PLS model to obtain an initial moisture prediction value; S5, obtaining the corresponding dynamic spectral characteristic parameters of the coal sample through a time-resolved coal sample wettability experiment; S6, obtaining the true moisture prediction value of the coal sample based on the initial moisture prediction value, standard true moisture value, and dynamic spectral characteristic parameters. This application improves the accuracy, stability, and applicability of moisture detection in coal.

Description

Technical Field

[0001] This application relates to the field of industrial testing technology, and in particular to a method, apparatus, equipment, and storage medium for detecting moisture and wettability in coal samples. Background Technology

[0002] Moisture content and wettability are key parameters affecting coal quality, processing, and utilization efficiency. The moisture content and its form in coal directly affect its calorific value, transportation costs, and combustion efficiency. Meanwhile, coal wettability is typically characterized by the solid-liquid contact angle: a smaller contact angle indicates stronger hydrophilicity and better wettability, which is significant in coal mining and processing. Strongly wettable coal particles are easily wetted by water, reducing flotation recovery rates and causing significant loss of clean coal with tailings, but it is beneficial for improving dust suppression efficiency. Therefore, accurate detection of coal sample moisture and wettability has significant engineering application value.

[0003] Existing methods for detecting moisture in coal mainly include drying, Karl Fischer, electrical methods, and microwave methods. Drying is a standard method with high measurement accuracy, but it suffers from drawbacks such as long detection cycles, sample damage, and inability to perform online detection. While electrical and microwave methods offer some rapid detection capabilities, they are easily affected by factors such as coal type, conductivity, and bulk density, resulting in limited stability and universality. Traditional wettability testing relies heavily on contact angle measuring instruments, which are difficult to apply directly to porous, powdery bulk coal samples, are cumbersome to operate, and cannot meet the needs of rapid, continuous detection in industrial settings.

[0004] Near-infrared spectroscopy has gained widespread attention in coal quality analysis due to its advantages such as fast detection speed, no need for sample pretreatment, non-destructive testing, and online detection. The OH bond in water molecules exhibits significant absorption characteristics in the near-infrared band, especially around 1940 nm, corresponding to the combined frequency of OH stretching and bending vibrations. This area shows sharp peaks and high absorption intensity, making it a sensitive band for moisture detection. However, existing research and applications mostly rely on static spectral absorbance or simple characteristic peak intensities to establish quantitative moisture models. In practical applications, it has been found that even coal samples with high actual moisture content do not show significant differences in their corresponding near-infrared moisture absorption characteristics, resulting in insufficient model generalization ability and limited quantitative accuracy.

[0005] The root cause of the above problems lies in the fact that moisture in coal does not exist in a single form, but rather includes intrinsic moisture (pore water, bound water) and extrinsic moisture (surface free water). Differences in coal wettability directly affect the distribution of different forms of moisture in the pore structure and surface of the coal. Different forms of moisture exhibit significant differences in their response mechanisms and volatilization dynamics to near-infrared light. Simply relying on the static spectral intensity at a single moment is insufficient to comprehensively characterize the true moisture state in coal, nor can it reflect the coal's wettability characteristics. Therefore, a new method is needed that comprehensively considers the differences in moisture forms and utilizes dynamic spectral characteristics to achieve accurate detection of moisture and wettability in coal samples. Summary of the Invention

[0006] This application provides a method for detecting moisture and wettability in coal samples, which solves the technical problem that the existing technology has low accuracy and stability in detecting moisture and wettability in coal samples.

[0007] This application is achieved through the following solution: A method for detecting moisture and wettability in coal samples, comprising the following steps: S1. Test the standard true moisture content of each coal sample in multiple groups of coal samples, wherein the moisture content of each coal sample is between 5% and 40%; S2. Near-infrared spectral data of each coal sample were collected using a spectrometer; S3. After calibration and preprocessing of the near-infrared spectral data, extract the key characteristic parameters of the absorption peaks in the moisture-sensitive band of the near-infrared spectral data. The key characteristic parameters include the absorption peak intensity and the absorption peak area. S4. The key feature parameters extracted from the moisture-sensitive band are fused with the original spectral absorbance data and used as input variables for the partial least squares prediction model. At the same time, the number of latent variables in the partial least squares prediction model is optimized through cross-validation. Finally, an accurate prediction model adapted to small samples and high moisture content coal is constructed to predict the initial moisture value. S5. The dynamic spectral characteristic parameters of the corresponding coal sample are obtained through a time-resolved coal sample wetting experiment. The dynamic spectral characteristic parameters include the instantaneous increase in absorbance and the rate of change of absorbance. S6. Based on the initial predicted moisture value, standard true moisture value, instantaneous increase in absorbance, and rate of change of absorbance of the corresponding coal sample, obtain the predicted true moisture value of the coal sample.

[0008] Further, in step S2, before scanning, the spectral scanning range is set to 1100~2400nm, the integration time is set to 20~25ms, the averaging times are set to 15~25, and the smoothness is set to 15~25. During scanning, under room temperature conditions, after the near-infrared spectrometer is preheated for 10min, an equal amount of coal samples are placed in a petri dish. During the scanning process, 8~10 spectra of each coal sample are collected by translation and the average value is taken as the near-infrared spectral data of each coal sample.

[0009] Furthermore, step S3 specifically includes the following steps: S31. Near-infrared spectral data noise is removed and baseline drift is corrected through smoothing and baseline correction preprocessing methods; S32. Extract the key characteristic parameters of the absorption peak at the moisture-sensitive band, including the absorption peak intensity and the absorption peak area.

[0010] Furthermore, step S5 specifically includes the following steps: S51. By collecting near-infrared spectral data of each coal sample at different time points under water wetting conditions, the dynamic spectral response information of moisture in the coal sample is obtained, the change law of absorbance in the moisture-sensitive band (mainly around 1940 nm) with time is analyzed, and dynamic spectral characteristic parameters that can characterize the wetting behavior and internal permeability of coal samples are constructed.

[0011] Furthermore, step S51 specifically includes the following steps: S511. After adding water to each coal sample and immediately measuring the near-infrared spectral data of the moisture-sensitive band, the sample is sealed. The near-infrared spectral data of the moisture-sensitive band is collected again after different time intervals. The coal sample particles used are in powder form. S512. The instantaneous increase in absorbance of the coal sample is obtained by calculating the difference between the instantaneous spectral absorbance after adding water and the baseline absorbance before adding water. This instantaneous increase in absorbance directly reflects the instantaneous adsorption capacity of the coal sample surface for water. The instantaneous increase in absorbance is positively correlated with the wetting rate and initial wettability of the coal sample surface. The rate of absorbance change is calculated by the ratio of the absorbance difference at different time points to the time interval, which characterizes the rate of water penetration into the coal sample. The rate of absorbance change is positively correlated with the water penetration capacity of the pores inside the coal sample and the overall wettability.

[0012] Further, step S6 specifically includes: S61. Using the extracted spectral dynamic characteristic parameters as compensation variables, establish a moisture compensation formula based on multiple linear regression (MLR) to obtain the corrected true moisture prediction value M: M=M PLS +α×ΔA0+β×v+γ; Where M is the predicted true moisture content of the coal sample after wettability correction; M PLS ΔA0 represents the initial moisture prediction value; v represents the instantaneous increase in absorbance; v represents the stable absorbance change rate after a set time following the addition of water to the coal sample; α and β are the moisture compensation weighting coefficients for the instantaneous increase in absorbance and the rate of absorbance change, respectively; and γ is the constant term error compensation coefficient.

[0013] Further, in step S61, the steps for obtaining the constant term compensation coefficient γ, compensation weight coefficient α, and β include: selecting a standard true moisture value M. TRUE The initial moisture prediction value M was calculated from the calibrated coal samples. PLS The instantaneous increase in absorbance ΔA0 and the rate of change in absorbance v are used to minimize the sum of squared residuals Σ(M) of the entire calibration set. i -M TRUE_i ) 2 Let i be the objective function, which is obtained by fitting using the least squares method, where i is the sample number in the calibration set.

[0014] Furthermore, a coal sample moisture and wettability detection device includes: The testing module is used to test the standard true moisture content of each coal sample in multiple groups of coal samples, where the moisture content of each coal sample ranges from 5% to 40%. The near-infrared spectral data acquisition module uses a spectrometer to acquire near-infrared spectral data for each type of coal sample. The key feature parameter extraction module is used to extract key feature parameters of the absorption peak characteristics in the moisture-sensitive band of the near-infrared spectral data after calibration and preprocessing. The key feature parameters include the absorption peak intensity and the absorption peak area. The initial moisture prediction module is used to fuse key feature parameters extracted from the moisture-sensitive band with the original spectral absorbance data as input variables for the partial least squares prediction model. At the same time, it optimizes the number of latent variables in the partial least squares prediction model and finally constructs an accurate prediction model adapted to small samples and high moisture content coal to predict the initial moisture value. The spectral dynamic feature parameter extraction module obtains the spectral dynamic feature parameters of the corresponding coal sample through a time-resolved coal sample wetting experiment. The spectral dynamic feature parameters include the instantaneous increase in absorbance and the rate of change of absorbance. The coal sample true moisture prediction module obtains the predicted true moisture value of the coal sample based on the initial predicted moisture value, the standard true moisture value, the instantaneous increase in absorbance, and the rate of change in absorbance.

[0015] This application also provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned method for detecting the moisture and wettability of a coal sample.

[0016] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for detecting moisture and wettability in coal samples.

[0017] This application also provides a computer program product, including a computer program or computer-executable instructions, which, when executed by a processor, implement the method for detecting moisture and wettability of coal samples.

[0018] Compared with the prior art, this application can produce the following beneficial effects: The purpose of this invention is to propose a method for detecting moisture and wettability in coal samples based on near-infrared spectral characteristic parameters. By introducing the time dimension and spectral dynamic response characteristics, a characteristic parameter system that can reflect the wetting behavior and inherent moisture change characteristics of coal samples is constructed, thereby improving the accuracy, stability and applicability of moisture detection in coal.

[0019] Specifically, the advantages of this invention are as follows: 1. Spectral acquisition achieves second-level response, significantly improving efficiency compared to the traditional two-step drying method (approximately 2 hours); 2. The microscopic mechanism of the response characteristics of coal wettability to spectral moisture was analyzed. The obtained dynamic parameters of wettability (ΔA0 and v) were used to reverse correct the differences in moisture absorbance of coal samples with different wettability. 3. A set of characteristic parameters based on near-infrared reflectance spectroscopy is proposed to clarify the quantitative judgment method of coal sample wettability, which makes up for the shortcomings of traditional detection methods that cannot be adapted to bulk coal samples and are cumbersome to operate. At the same time, a correlation mechanism between spectral dynamic parameters and wettability is established to realize rapid detection of wettability.

[0020] 4) The dynamic spectral characteristic parameters are used as the basis for moisture detection. The moisture prediction model is corrected by the wettability parameter to improve the accuracy of moisture detection in high moisture content coal samples. It is compatible with different coal types such as bituminous coal, anthracite, and lignite. No sample pretreatment is required, it is non-destructive and can be detected online.

[0021] In addition to the purposes, features, and advantages described above, this application has other purposes, features, and advantages. A further detailed description of this application will be provided below with reference to the figures. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a schematic diagram illustrating the composition and operating principle of an infrared spectroscopy measurement system. Figure 2 This is a schematic flowchart of a preferred embodiment of a method for detecting moisture and wettability in coal samples according to this application. Figure 3 This is a schematic diagram of the near-infrared reflectance spectra of eight coal samples with different moisture contents according to a preferred embodiment of this application; Figure 4 This is a schematic diagram of the PLS modeling result after baseline correction preprocessing according to a preferred embodiment of this application; Figure 5 This is a schematic diagram comparing the changes in standard moisture content and characteristic peaks of coal samples; Figure 6 This is a schematic diagram of the near-infrared reflectance spectrum of coal sample No. 2 over time; Figure 7 This is a schematic diagram of the near-infrared reflectance spectrum of coal sample No. 3 over time; Figure 8 This is a schematic diagram of the near-infrared reflectance spectrum of coal sample No. 8 over time; Figure 9 This is a schematic diagram of a module of a coal sample moisture and wettability detection device according to a preferred embodiment of this application; Figure 10 This is a schematic block diagram of an electronic device according to a preferred embodiment of this application; Figure 11 This is a schematic diagram of the internal structure of a computer device according to a preferred embodiment of this application. Detailed Implementation

[0024] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0025] It should be noted that the executing entity in this embodiment can be a computing service system with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a coal sample moisture and wettability detection device capable of performing the above functions. The following description uses a coal sample moisture and wettability detection device as an example to illustrate this embodiment and the subsequent embodiments.

[0026] This application first establishes, as follows: Figure 1 The measurement system shown uses near-infrared spectroscopy to collect light in the range of 780 nm to 2500 nm, primarily reflecting the combination and overtone absorption of vibrations of groups such as CH, NH, OH, CO, and SH in molecules. Therefore, near-infrared spectroscopy can obtain moisture content information through the absorption of OH groups. Among the absorption wavelengths of OH bonds, there are strong absorption peaks in the near-infrared diffuse reflectance spectrum at 1440 nm and 1940 nm. Measurements at 1440 nm show good accuracy for moisture content measurements of 0% to 10%, while measurements at 1940 nm show higher accuracy for moisture content above 10%.

[0027] like Figure 2 As shown, to address the aforementioned technical problems, a preferred embodiment of this application provides a method for detecting the moisture and wettability of coal samples, comprising the following steps: S1. Analyze the standard true moisture content of each coal sample in multiple groups (e.g., 8 groups), where the moisture content of each coal sample is between 5% and 40%. S2. Near-infrared spectral data of each coal sample were collected using a spectrometer (such as the NIRQuest+ spectrometer); S3. After calibration and preprocessing of the near-infrared spectral data, extract the key characteristic parameters of the absorption peak at the moisture-sensitive band (1940nm) in the near-infrared spectral data. The key characteristic parameters include the absorption peak intensity and the absorption peak area. S4. The key feature parameters extracted at the moisture-sensitive band (1940nm) are fused with the original spectral absorbance data and used as input variables for the partial least squares (PLS) prediction model. At the same time, the number of latent variables in the partial least squares prediction model is optimized through cross-validation. Finally, an accurate prediction model adapted to small samples and high moisture content coal is constructed to predict the initial moisture value. S5. The dynamic spectral characteristic parameters of the corresponding coal sample are obtained through a time-resolved coal sample wetting experiment. The dynamic spectral characteristic parameters include the instantaneous increase in absorbance and the rate of change of absorbance. S6. Based on the initial predicted moisture value, standard true moisture value, instantaneous increase in absorbance, and rate of change of absorbance of the corresponding coal sample, obtain the predicted true moisture value of the coal sample.

[0028] To address the shortcomings of existing technologies, this embodiment aims to propose a method for detecting coal sample moisture and wettability based on near-infrared spectral characteristic parameters. By introducing the time dimension and spectral dynamic response characteristics, a characteristic parameter system that can reflect the wetting behavior and inherent moisture change characteristics of coal samples is constructed, thereby improving the accuracy, stability, and applicability of coal moisture detection.

[0029] Specifically, in step S2, before scanning, the spectral scanning range is set to 1100~2400nm, the integration time is set to 20~25ms, the averaging times are set to 15~25, and the smoothness is set to 15~25. During scanning, under room temperature conditions, after the near-infrared spectrometer is preheated for 10 minutes, an equal amount of coal samples are placed in a petri dish. During the scanning process, 8~10 spectra of each coal sample are collected by translation and the average value is taken as the near-infrared spectral data of each coal sample to ensure the uniformity of the diffuse reflectance spectrum of the sample scanning.

[0030] Preferably, step S3 specifically includes the following steps: S31. Near-infrared spectral data noise is removed and baseline drift is corrected through smoothing and baseline correction preprocessing methods; S32. Extract the key characteristic parameters of the absorption peak at the moisture-sensitive band (e.g., 1940 nm), the key characteristic parameters including the absorption peak intensity and the absorption peak area.

[0031] Near-infrared reflectance spectra of eight coal samples with different moisture contents, as shown below. Figure 3 As shown, coal samples No. 3, 4, 5, and 8 exhibit absorption characteristics near a wavelength of 1940 nm. The actual moisture content of these four coal samples is all above 30%, confirming that near-infrared spectroscopy can reflect spectral information related to moisture. To address the prediction needs of small-sample, high-moisture-content coal, this application proposes an improved method based on absorption peak feature enhancement combined with a PLS model. After removing spectral noise and correcting baseline drift through smoothing and baseline correction preprocessing methods, key feature parameters of the absorption peak near a wavelength of 1940 nm are extracted. These key feature parameters include absorption peak intensity and absorption peak area. These extracted key feature parameters are fused with the original spectral absorbance data as input variables for a partial least squares (PLS) prediction model. Simultaneously, the number of latent variables in the PLS model is optimized, ultimately constructing an accurate prediction model suitable for small-sample, high-moisture-content coal. The results are shown below. Figure 4 As shown.

[0032] Preferably, step S5 specifically includes the following steps: S51. By collecting near-infrared spectral data of each coal sample at different time points under water wetting conditions, the dynamic spectral response information of moisture in the coal sample is obtained, the change law of absorbance in the moisture-sensitive band (mainly around 1940 nm) with time is analyzed, and dynamic spectral characteristic parameters that can characterize the wetting behavior and internal permeability of coal samples are constructed.

[0033] Specifically, step S51 includes the following steps: S511. After adding water to each coal sample, immediately measure the near-infrared spectral data of the moisture-sensitive band (1940nm), seal the sample, and collect the near-infrared spectral data of the moisture-sensitive band again after different time intervals. The coal sample particles used are in powder form. S512. The instantaneous increase in absorbance ΔA0 of the coal sample is obtained by calculating the difference between the instantaneous spectral absorbance after adding water and the baseline absorbance before adding water. This directly reflects the instantaneous adsorption capacity of the coal sample surface for water. The instantaneous increase in absorbance ΔA0 is positively correlated with the wetting rate and initial wettability of the coal sample surface. The rate of change in absorbance v is calculated by the ratio of the absorbance difference at different time points to the time interval, which represents the rate of water penetration into the coal sample. The rate of change in absorbance v is positively correlated with the water penetration capacity of the pores inside the coal sample and the overall wettability.

[0034] To further improve prediction accuracy, such as Figure 5 The results showed that the spectral characteristics of different coal samples still differed significantly. Comparison with the actual moisture values ​​determined by standard methods revealed that although the measured moisture content of the coal samples was high, the absorption characteristics near the wavelength of 1940 nm were weak or insignificant. This anomaly of high moisture content and weak absorption indicates that the near-infrared spectral response to moisture depends not only on the moisture content of the coal sample but also highly on the form of moisture within the coal body (i.e., the distribution ratio of internal to external moisture) and its microscopic binding state. The spreading and adhesion of moisture on the surface of coal particles and its penetration into the pores are fundamentally controlled by the wettability of the coal. Due to the differences in wettability among different coal samples, their adsorption strength and water film morphology vary, resulting in completely different spectral characteristics in the near-infrared band. To further reveal this microscopic mechanism and verify the influence of wettability on the dynamic response of moisture spectra, this invention selected coal sample No. 2, which showed a good match between actual moisture content and spectral characteristics, and coal samples No. 3 and No. 8, which showed significant differences, and designed a time-resolved coal sample wettability experiment. The coal sample used is in powder form with a particle size of approximately 0.1 mm. This method is also applicable to granular and lumpy coal samples, but these require pre-treatment by grinding to obtain powder of the appropriate particle size. After adding water to the coal sample and immediately measuring the reflectance spectrum, the sample is sealed, and spectral data are collected again after 30 and 60 minutes. Figures 6-8 As shown, the instantaneous spectral response after adding water reveals significant differences in absorbance changes around 1940 nm among different coal samples.

[0035] The instantaneous increase in absorbance ΔA0 is defined as the difference between the instantaneous spectral absorbance of the coal sample after water addition and the baseline absorbance before water addition. This parameter directly reflects the instantaneous adsorption capacity of the coal sample surface for water. The larger ΔA0 is, the faster the coal sample surface wets and the stronger the initial wettability. The absorbance change rate v is calculated by the ratio of the absorbance difference at different time points to the time interval. It characterizes the rate at which water penetrates into the coal sample. The larger the v value and the longer the duration, the stronger the water penetration capacity of the pores inside the coal sample and the better the overall wettability.

[0036] Table 1 Spectral dynamic characteristic parameters

[0037] Based on the dynamic parameter data in Table 1, the wettability of the three typical coal samples was specifically judged: Coal sample No. 8 had the largest ΔA0, with a rapid initial increase in absorbance and reaching adsorption saturation in a short time, indicating that it had the strongest wettability. Water could quickly spread on the surface and fill the surface pores, so its instantaneous spectral absorption characteristics were the most significant. Coal sample No. 3 had the second largest ΔA0, with absorbance increasing slowly over time and not reaching saturation within 24 hours, indicating that its wettability was moderate. Water could gradually penetrate into the internal pores, but the surface wetting rate and internal penetration rate were both lower than those of coal sample No. 8. Coal sample No. 2 had a ΔA0 of 0, with no significant change in absorbance at each time point and no clear adsorption saturation trend, indicating that it had the weakest wettability. Water could hardly spread on the surface and penetrate into the interior. Therefore, even if the actual moisture content was high, its near-infrared spectral moisture absorption characteristics were not obvious. To verify the accuracy of the above wettability judgment method, this embodiment uses a traditional contact angle measuring instrument to conduct comparative tests on coal samples No. 2, No. 3, and No. 8. The test results are completely consistent with the judgment results based on spectral dynamic parameters.

[0038] Preferably, step S6 specifically includes: S61. Using the extracted spectral dynamic characteristic parameters as compensation variables, establish a moisture compensation formula based on multiple linear regression (MLR) to obtain the corrected true moisture prediction value M: M=M PLS +α×ΔA0+β×v+γ; Where M is the predicted true moisture content of the coal sample after wettability correction; M PLS The initial moisture prediction value is obtained by using the absorption peak intensity and peak area characteristics near 1940 nm as input features; ΔA0 is the instantaneous increase in absorbance; v is the stable absorbance change rate after adding water to the coal sample at a set time (selecting the stable absorbance change rate after 30 min or 60 min after adding water); α and β are the moisture compensation weighting coefficients for the instantaneous increase in absorbance and the rate of change in absorbance, respectively, and γ is the constant term error compensation coefficient.

[0039] Because the weaker the wettability of the coal sample (i.e., the smaller ΔA0 and v), the more difficult it is for moisture to spread and penetrate on the surface of the coal particles, resulting in a weaker near-infrared absorption characteristic near a wavelength of 1940 nm. In this case, if only the traditional static PLS model (predicted value M) is relied upon... PLS This can lead to a severe underestimation of the actual moisture content. Therefore, this embodiment uses the extracted spectral dynamic characteristic parameters (ΔA0 and v) as compensation variables to establish a moisture compensation formula based on multiple linear regression (MLR) to obtain the corrected predicted actual moisture value M. By introducing this compensation formula, the wettability parameter can further correct the moisture prediction model, achieving synergistic linkage with moisture detection and improving the accuracy of coal sample moisture detection.

[0040] In step S61, the steps for obtaining the constant term compensation coefficient γ, compensation weight coefficient α, and β include: selecting a standard true moisture value M. TRUE The initial moisture prediction value M was calculated from the calibrated coal samples. PLS The instantaneous increase in absorbance ΔA0 and the rate of change in absorbance v are used to minimize the sum of squared residuals Σ(M) of the entire calibration set. i -M TRUE_i ) 2 Let i be the objective function, which is obtained by fitting using the least squares method, where i is the sample number in the calibration set.

[0041] The specific calculation steps include: First, a calibration set is constructed by selecting n coal samples with known standard true moisture values ​​to form the calibration set. Let M be the standard true moisture value of the i-th coal sample. TRUE_i , where i = 1, 2, ..., n; Next, for each coal sample in the calibration set, the initial moisture prediction value M is calculated using a pre-established partial least squares prediction model. PLS_i Simultaneously, the instantaneous increase in absorbance ΔA of the coal sample was extracted through time-resolved wetting experiments. 0_i and the rate of change of absorbance v _i ; Then, the deviation E_ between the standard true moisture value and the initial moisture prediction value of each coal sample in the calibration set is calculated. i ; Finally, a multiple linear regression matrix is ​​constructed, and the coefficients are solved using the least squares method.

[0042] In summary, this application acquires near-infrared reflectance spectra of coal samples at different time points under water-wetting conditions to obtain dynamic spectral response information of moisture in the coal samples, analyzes the change law of absorbance in the moisture-sensitive band (mainly around 1940 nm) over time, and constructs spectral characteristic parameters that can characterize the wetting behavior and internal permeability of coal samples, thereby achieving effective characterization of moisture and wettability. Specifically, this includes: 1. A moisture detection method based on time-dimension-resolved near-infrared spectroscopy: In the near-infrared band range of the absorption band of moisture-sensitive OH bonds, near-infrared spectra of the same sample are collected multiple times at different time points to form time-series near-infrared spectral data for moisture detection. 2. After smoothing and baseline correction preprocessing, the intensity and area of ​​the absorption peak near 1940nm are extracted and fused with the original spectral absorbance data as input to the PLS prediction model. The optimal number of latent variables is determined through cross-validation to achieve accurate prediction of coal sample moisture. 3. For powdery bulk coal samples with a particle size of about 0.1 mm, a time-resolved wetting experiment was designed. Near-infrared spectra were collected immediately after water addition, at 30 min intervals, and at 60 min intervals. The instantaneous increase in absorbance (ΔA0) and the rate of change of absorbance (v) were defined. Based on the adsorption saturation trend, the wetting was divided into three levels: strong (contact angle <30°), medium (30°~60°), and weak (>60°). The error was controlled within 5%. 4. Using dynamic spectral characteristic parameters as the basis for moisture detection, and correcting the moisture prediction model through wettability parameters, the accuracy of moisture detection in high-moisture coal samples is improved. It is compatible with different coal types such as bituminous coal, anthracite, and lignite, and requires no sample pretreatment, is non-destructive, and can be detected online.

[0043] like Figure 9 As shown, a preferred embodiment of this example also provides a coal sample moisture and wettability detection device, comprising: The testing module is used to test the standard true moisture content of each coal sample in multiple groups of coal samples, where the moisture content of each coal sample ranges from 5% to 40%. The near-infrared spectral data acquisition module uses a spectrometer to acquire near-infrared spectral data for each type of coal sample. The key feature parameter extraction module is used to extract key feature parameters of the absorption peak characteristics in the moisture-sensitive band of the near-infrared spectral data after calibration and preprocessing. The key feature parameters include the absorption peak intensity and the absorption peak area. The initial moisture prediction module is used to fuse key feature parameters extracted from the moisture-sensitive band with the original spectral absorbance data as input variables for the partial least squares prediction model. At the same time, it optimizes the number of latent variables in the partial least squares prediction model and finally constructs an accurate prediction model adapted to small samples and high moisture content coal to predict the initial moisture value. The spectral dynamic feature parameter extraction module obtains the spectral dynamic feature parameters of the corresponding coal sample through a time-resolved coal sample wetting experiment. The spectral dynamic feature parameters include the instantaneous increase in absorbance and the rate of change of absorbance. The coal sample true moisture prediction module obtains the predicted true moisture value of the coal sample based on the initial predicted moisture value, the standard true moisture value, the instantaneous increase in absorbance, and the rate of change in absorbance.

[0044] This embodiment provides a coal sample moisture and wettability detection device, which employs a coal sample moisture and wettability detection method described in the above embodiments, solving the technical problem of low accuracy and stability in the detection of coal sample moisture and wettability in existing technologies. Compared with the prior art, the beneficial effects of the coal sample moisture and wettability detection device provided in this embodiment are the same as those of the coal sample moisture and wettability detection method described in the above embodiments, and other technical features in the coal sample moisture and wettability detection device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0045] like Figure 10 As shown, a preferred embodiment of this example also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a coal sample moisture and wettability detection method described in the above embodiment.

[0046] This embodiment also provides an electronic device that uses a coal sample moisture and wettability detection method from the above embodiments to solve the technical problem of low accuracy and stability in the detection of coal sample moisture and wettability in the prior art. Compared with the prior art, the beneficial effects of the electronic device provided in this embodiment are the same as those of the coal sample moisture and wettability detection method provided in the above embodiments, and other technical features of the electronic device are the same as those disclosed in the method of the above embodiments, which will not be repeated here.

[0047] like Figure 11 As shown, a preferred embodiment of this application also provides a computer device, which may be a terminal or a liveness detection server, and its internal structure diagram may be as follows. Figure 11As shown. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with other external computer devices via a network connection. When the computer program is executed by the processor, it implements the steps of the aforementioned method for detecting the moisture and wettability of coal samples.

[0048] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the solution of this embodiment, and does not constitute a limitation on the computer device to which the solution of this embodiment is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0049] The computer device provided in this embodiment adopts a coal sample moisture and wettability detection method in the above embodiment, which solves the technical problem of low accuracy and stability of coal sample moisture and wettability detection in the prior art. Compared with the prior art, the beneficial effects of the computer device provided in this embodiment are the same as the beneficial effects of the coal sample moisture and wettability detection method provided in the above embodiment. In addition, other technical features in the electronic device are the same as the features disclosed in the method of the above embodiment, and will not be repeated here.

[0050] A preferred embodiment of this application also provides a storage medium, the storage medium including a stored program, which, when the program is executed, controls the device where the storage medium is located to perform the steps of a coal sample moisture and wettability detection method described in the above embodiments.

[0051] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0052] If the functions described in this embodiment are implemented as software functional units and sold or used as independent products, they can be stored in one or more computing device-readable storage media. Based on this understanding, the parts of this embodiment that contribute to the prior art or the technical solution can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computing device (which may be a personal computer, server, mobile computing device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this embodiment. The aforementioned storage media include: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0053] Those skilled in the art will understand that the embodiments of this example can be provided as methods, systems, or computer program products. Therefore, this example can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this example can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code. The solutions in this example can be implemented using various computer languages, such as the object-oriented programming language C++ and the embedded programming language C.

[0054] This embodiment is described with reference to flowchart illustrations and / or block diagrams of the method, apparatus (system), and computer program product according to this embodiment. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0057] This embodiment also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the coal sample moisture and wettability detection method described above.

[0058] The computer program product provided in this embodiment solves the technical problem of low accuracy and stability in the detection of coal sample moisture and wettability in existing technologies. Compared with the prior art, the beneficial effects of the computer program product provided in this embodiment are the same as those of the coal sample moisture and wettability detection method provided in the above embodiments, and will not be repeated here.

[0059] Obviously, those skilled in the art can make various modifications and variations to this embodiment without departing from the spirit and scope of this embodiment. Therefore, if these modifications and variations of this embodiment fall within the scope of the claims of this embodiment and their equivalents, this embodiment is also intended to include these modifications and variations.

Claims

1. A method for detecting moisture and wettability in coal samples, characterized in that, Including the following steps: S1. Test the standard true moisture content of each coal sample in multiple groups of coal samples, wherein the moisture content of each coal sample is between 5% and 40%; S2. Near-infrared spectral data of each coal sample were collected using a spectrometer. S3. After calibration and preprocessing of the near-infrared spectral data, extract the key characteristic parameters of the absorption peaks in the moisture-sensitive band of the near-infrared spectral data. The key characteristic parameters include the absorption peak intensity and the absorption peak area. S4. The key feature parameters extracted from the moisture-sensitive band are fused with the original spectral absorbance data and used as input variables for the partial least squares prediction model. At the same time, the number of latent variables in the partial least squares prediction model is optimized through cross-validation. Finally, an accurate prediction model adapted to small samples and high moisture content coal is constructed to predict the initial moisture value. S5. The dynamic spectral characteristic parameters of the corresponding coal sample are obtained through a time-resolved coal sample wetting experiment. The dynamic spectral characteristic parameters include the instantaneous increase in absorbance and the rate of change of absorbance. S6. Based on the initial predicted moisture value, standard true moisture value, instantaneous increase in absorbance, and rate of change in absorbance of the corresponding coal sample, the predicted true moisture value of the coal sample is obtained, specifically including: S61. Using the extracted spectral dynamic characteristic parameters as compensation variables, establish a moisture compensation formula based on multiple linear regression to obtain the corrected true moisture prediction value M: M=M PLS +α×ΔA0+β×v+γ; Where M is the predicted true moisture content of the coal sample after wettability correction; M PLS ΔA0 represents the initial moisture prediction value; v represents the instantaneous increase in absorbance; v represents the stable absorbance change rate after a set time following the addition of water to the coal sample; α and β are the moisture compensation weighting coefficients for the instantaneous increase in absorbance and the rate of absorbance change, respectively; and γ is the constant term error compensation coefficient.

2. The method for detecting moisture and wettability of coal samples according to claim 1, characterized in that, In step S2, before scanning, the spectral scanning range is set to 1100~2400nm, the integration time is set to 20~25ms, the averaging times are set to 15~25, and the smoothness is set to 15~25. During scanning, under room temperature conditions, after the near-infrared spectrometer is preheated for 10min, an equal amount of coal samples are placed in a petri dish. During the scanning process, 8~10 spectra of each coal sample are collected by translation and the average value is taken as the near-infrared spectral data of each coal sample.

3. The method for detecting moisture and wettability of coal samples according to claim 1, characterized in that, Step S3 specifically includes the following steps: S31. Near-infrared spectral data noise is removed and baseline drift is corrected through smoothing and baseline correction preprocessing methods; S32. Extract the key characteristic parameters of the absorption peak at the moisture-sensitive band, including the absorption peak intensity and the absorption peak area.

4. The method for detecting moisture and wettability of coal samples according to claim 1, characterized in that, Step S5 specifically includes the following steps: S51. By collecting near-infrared spectral data of each coal sample at different time points under water wetting conditions, the dynamic spectral response information of moisture in the coal sample is obtained, the change law of absorbance in the moisture-sensitive band with time is analyzed, and dynamic spectral characteristic parameters that can characterize the wetting behavior and internal permeability of the coal sample are constructed.

5. The method for detecting moisture and wettability of coal samples according to claim 4, characterized in that, Step S51 specifically includes the following steps: S511. After adding water to each coal sample and immediately measuring the near-infrared spectral data of the moisture-sensitive band, the sample is sealed. The near-infrared spectral data of the moisture-sensitive band is collected again after different time intervals. The coal sample particles used are in powder form. S512. The instantaneous increase in absorbance of the coal sample is obtained by calculating the difference between the instantaneous spectral absorbance after adding water and the baseline absorbance before adding water. This instantaneous increase in absorbance directly reflects the instantaneous adsorption capacity of the coal sample surface for water. The instantaneous increase in absorbance is positively correlated with the wetting rate and initial wettability of the coal sample surface. The rate of absorbance change is calculated by the ratio of the absorbance difference at different time points to the time interval, which characterizes the rate of water penetration into the coal sample. The rate of absorbance change is positively correlated with the water penetration capacity of the pores inside the coal sample and the overall wettability.

6. The method for detecting moisture and wettability of coal samples according to claim 1, characterized in that, In step S61, the steps for obtaining the constant term compensation coefficient γ, compensation weight coefficient α, and β include: selecting a standard true moisture value M. TRUE The initial moisture prediction value M was calculated from the calibrated coal samples. PLS The instantaneous increase in absorbance ΔA0 and the rate of change in absorbance v are used to minimize the sum of squared residuals Σ(M) of the entire calibration set. i -M TRUE_i ) 2 Let i be the objective function, which is obtained by fitting using the least squares method, where i is the sample number in the calibration set.

7. A device for detecting the moisture and wettability of coal samples, used to implement the method as described in any one of claims 1 to 6, characterized in that, include: The testing module is used to test the standard true moisture content of each coal sample in multiple groups of coal samples, where the moisture content of each coal sample ranges from 5% to 40%. The near-infrared spectral data acquisition module uses a spectrometer to acquire near-infrared spectral data for each type of coal sample. The key feature parameter extraction module is used to extract key feature parameters of the absorption peak characteristics in the moisture-sensitive band of the near-infrared spectral data after calibration and preprocessing. The key feature parameters include the absorption peak intensity and the absorption peak area. The initial moisture prediction module is used to fuse key feature parameters extracted from the moisture-sensitive band with the original spectral absorbance data as input variables for the partial least squares prediction model. At the same time, it optimizes the number of latent variables in the partial least squares prediction model and finally constructs an accurate prediction model adapted to small samples and high moisture content coal to predict the initial moisture value. The spectral dynamic feature parameter extraction module obtains the spectral dynamic feature parameters of the corresponding coal sample through a time-resolved coal sample wetting experiment. The spectral dynamic feature parameters include the instantaneous increase in absorbance and the rate of change of absorbance. The coal sample true moisture prediction module obtains the predicted true moisture value of the coal sample based on the initial predicted moisture value, the standard true moisture value, the instantaneous increase in absorbance, and the rate of change of absorbance.

8. An electronic device, the electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it implements a method for detecting the moisture and wettability of a coal sample as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a method for detecting the moisture and wettability of a coal sample as described in any one of claims 1 to 6.

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