Coal quality rapid detection method based on thermal gravimetric curve

Abstract

The application provides a coal quality rapid detection method based on a thermal gravimetric curve, and is characterized in that the method comprises the following steps: step 1, establishing a thermal gravimetric-coal quality database; step 1-1, selecting standard coals with different coal quality components, and processing each standard coal; step 1-1a, taking the powder of the standard coal as a coal sample to perform a thermal gravimetric experiment, and obtaining a thermal gravimetric curve; step 1-1b, obtaining thermal gravimetric characteristic parameters of the corresponding coal sample from the thermal gravimetric curve; step 1-2, calculating a mapping relationship between the thermal gravimetric characteristic parameters and the coal quality components according to the thermal gravimetric characteristic parameters of the standard coals with different coal quality components obtained in step 1-1, and establishing the thermal gravimetric-coal quality database; step 2, obtaining thermal gravimetric characteristic parameters of a coal sample to be detected in the manner of steps 1-1a and 1-1b; and step 3, calculating coal quality components of the coal sample to be detected according to the thermal gravimetric characteristic parameters of the coal sample to be detected and the corresponding mapping relationship in the thermal gravimetric-coal quality database.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of coal quality detection, and particularly relates to a coal quality rapid detection method based on a thermal gravimetric curve. BACKGROUND

[0002] The detection methods of coal quality are generally chemical methods and physical methods. At present, the coal quality detection of coal-fired power plants usually adopts a chemical method to analyze the required coal quality. In the past, the sampling, sample preparation, and testing steps in the laboratory need to be specifically completed, and it takes a long time to obtain the coal quality information. Therefore, the thermal efficiency of the boiler cannot be analyzed in real time.

[0003] At present, there are methods for analyzing the combustion characteristics of coal quality by using thermal gravimetric analysis, such as traditional industrial analysis and a thermal gravimetric analyzer. The coal quality loses weight through heating, combustion, and pyrolysis, and the weight loss data is analyzed to obtain a certain component parameter of the coal quality. However, in the prior art, the industrial analysis method needs to use a series of instruments such as a muffle furnace, an oven, an analytical balance, and a dryer. The experimental process is complicated, and each time the component parameter needs to be obtained by repeatedly sampling, weighing, and calculating after weighing and testing. Although the thermal gravimetric analyzer can realize integrated measurement of multiple component parameters, the heating conditions of these component parameters are different. For example, the moisture content, ash content, and volatile matter content need to be measured separately under different heating conditions, and the measurement efficiency is low.

[0004] In addition, the existing technology uses Raman and photoluminescence spectroscopy to detect coal quality. However, the spectrum has many limiting factors, and the requirements for the environment and the sample are higher than those of thermal gravimetric analysis. The measured system is easily polluted by the outside world, which greatly affects the analysis results. Moreover, the instrument for the spectroscopy method is also expensive. SUMMARY

[0005] The present application is made to solve the above problems, and aims to provide a coal quality rapid detection method based on a thermal gravimetric curve, which can simply and quickly obtain the moisture content, volatile matter content, ash content, and fixed carbon content of the coal quality based on the thermal gravimetric spectrum data under a predetermined working condition, and is particularly suitable for efficiently and accurately obtaining coal quality information under a high temperature rise rate (the thermal gravimetric temperature rise rate reaches 800℃ / min or more).

[0006] To achieve the above-mentioned purpose, the present application adopts the following scheme:

[0007] The present application provides a coal quality rapid detection method based on a thermal gravimetric curve, characterized by comprising the following steps:

[0008] Step 1: establishing a thermal gravimetric- coal quality database;

[0009] Step 1-1, select a plurality of standard coals with different coal components, and for each standard coal, the following treatment is performed:

[0010] Step 1-1a, perform a thermal gravimetric experiment on the powder as a coal sample to obtain a thermal gravimetric curve;

[0011] Step 1-1b, obtain the thermal gravimetric characteristic parameters of the corresponding coal sample from the thermal gravimetric curve, specifically:

[0012] (1) first-order differential processing is performed on the thermal gravimetric curve, and then twice smoothing processing is performed to obtain a first-order differential curve;

[0013] (2) first-order differential processing is performed on the first-order differential curve to obtain a second-order differential curve;

[0014] (3) the demarcation line for dividing the characteristic peaks and characteristic regions is determined based on the second-order differential curve, the first-order differential curve is divided into characteristic peaks and characteristic regions based on the demarcation line, and then area calculation is performed, and the areas of the characteristic peaks and characteristic regions of the first-order differential curve are taken as the thermal gravimetric characteristic parameters;

[0015] Step 1-2, according to the thermal gravimetric characteristic parameters of the standard coals with different coal components obtained in step 1-1, the mapping relationship between the thermal gravimetric characteristic parameters and the coal components is calculated, and a thermal gravimetric-coal quality database is constructed; the coal components at least include moisture, volatile matter, ash content, and fixed carbon;

[0016] Step 2: the coal sample to be tested with unknown coal components is subjected to a thermal gravimetric experiment in the manner of step 1-1a and step 1-1b, and the thermal gravimetric curve and the thermal gravimetric characteristic parameters of the coal sample to be tested are obtained;

[0017] Step 3: according to the thermal gravimetric characteristic parameters of the coal sample to be tested and the corresponding mapping relationship in the thermal gravimetric-coal quality database, the coal components of the coal sample to be tested are calculated.

[0018] Preferably, the coal quality rapid detection method based on the thermal gravimetric curve provided by the application can also have the following characteristics: in step 1-1a, the coal sample is subjected to a thermal gravimetric experiment at a temperature rising rate of 1000℃-6000℃ / min to obtain the thermal gravimetric curve.

[0019] Preferably, the coal quality rapid detection method based on the thermal gravimetric curve provided by the application can also have the following characteristics: in step 1-1b(1), the window point number of the twice smoothing processing is between 65 and 70.

[0020] Preferably, the coal quality rapid detection method based on the thermal gravimetric curve provided by the present application can further have the following features: in step 1-1b(3), three points a1, b1, c1 are taken from the intersection of the 0-value reference line of the longitudinal axis of the second-order differential curve and the second-order differential curve, the corresponding three points a2, b2, c2 are obtained from the first-order differential curve according to the horizontal coordinate values t of the three points, the horizontal coordinate values of the three points are taken as three boundary lines L1-L3 of the A characteristic peak, the B characteristic peak and the C characteristic region in turn, the longitudinal coordinate of the intersection d of the 0-value reference line of the longitudinal axis of the first-order differential curve and the end of the first-order differential curve is taken as a fourth boundary line L4, and L3 and L4 enclose the C region; the total area formed by the A characteristic peak, the B characteristic peak and the whole A, B and C is subjected to peak area integration; the absolute peak area values of the A peak and the B peak are taken as the thermal gravimetric characteristic parameters x1 and x2, and the thermal gravimetric characteristic parameter x3 = 100-total absolute peak area value.

[0021] Preferably, the coal quality rapid detection method based on the thermal gravimetric curve provided by the present application can further have the following features: in the thermal gravimetric- coal quality database, the mapping relationship between the thermal gravimetric characteristic parameters and the coal quality components is as follows:

[0022] Moisture Mad = 1.66746 * x1-0.11167 * x1 2 +0.01137 * x1 3 -3.81E -4 *x1 4 ;E -4 10 -4 , and the same as below;

[0023] Volatile matter Vad = 0.52266 * x2+0.07022 * x2 2 -0.00297 * x2 3 -4.04E -5 *x2 4 ;

[0024] Ash content Aad = 0.69668 * x3-3.38E -6 *x3 2 +4.10E -4 *x3 3 -6.24E -6 *x3 4 ;

[0025] Fixed carbon Fcad = 100-Mad-Aad-Vad.

[0026] Preferably, the coal quality rapid detection method based on the thermal gravimetric curve provided by the present application can further have the following characteristics: in step 1-1b(3), the three points a1, b1 and c1 are selected from the intersection of the 0-value reference line of the second derivative curve and the second derivative curve according to the approximate positions of the first two main characteristic peaks A and B on the first derivative curve; the A peak is approximately in the range of t=2-25s, the B peak is in the range of t=25-70s, and the remaining range is basically the C region.

[0027] Preferably, the coal quality rapid detection method based on the thermal gravimetric curve provided by the present application can further have the following characteristics: in step 1-1b(1), the window point number of the two times of smoothing processing is between 100-120.

[0028] Preferably, the coal quality rapid detection method based on the thermal gravimetric curve provided by the present application can further have the following characteristics: in step 1-1b(3), the six points a1, b1, c1, d1, e1 and f1 are selected from the intersection of the 0-value reference line of the second derivative curve and the second derivative curve; the six selected points should satisfy the constraint condition: a1 is in the range of t=2-25s, b1 is in the range of t=25-70s, c1 is in the range of t=70-105s, d1 is in the range of t=105-140, e1 is in the range of t=140-170, and f1 is in the range of t=170-200, and the interval between each point is preferably kept between t=25-35s; and in the fitting control, before iteration, the positions of c1-f1 points are not fixed, and the iteration is performed until convergence; if the fitting degree R 2 >0.98 and still satisfies the constraint condition, the fitting is completed; if it does not satisfy, the adjustment is made within the constraint condition until the fitting degree R 2 >0.98 and still satisfies the constraint condition, the fitting is completed; if it does not satisfy, the adjustment is made within the constraint condition until the fitting degree R

[0029] Preferably, the coal quality rapid detection method based on the thermal gravimetric curve provided by the present application can further have the following characteristics: in the thermal gravimetric-coal quality database, the mapping relationship between the thermal gravimetric characteristic parameters and the coal quality components is as follows:

[0030] Moisture Mad = 2.09922 * x1-0.05641x1 2 +4.88989E-4*x1 3 -0.0643;

[0031] Volatile matter Vad = 1.48033 x 2 - 0.02023 x 2 2 +1.38228E -4 *x2 3 ;

[0032] Ash content Aad = 0.73469*x3 + 0.003*x3 2 +6.84111E -5 *x3 3 ;

[0033] Fixed carbon Fcad = 100 - Mad - Aad - Vad.

[0034] Furthermore, the present invention also provides another rapid coal quality detection method based on thermogravimetric curves, characterized by comprising the following steps:

[0035] Step 1: Conduct a thermogravimetric experiment on the coal sample to be tested under high heating rate conditions and obtain the thermogravimetric curve;

[0036] Step II: Obtain the corresponding thermogravimetric characteristic parameters of the coal sample from the thermogravimetric curve, specifically:

[0037] (1) Perform first-order differential processing on the thermogravimetric curve, and then perform two smoothing processes to obtain the first-order differential curve;

[0038] (2) Perform a first-order differential operation on the first-order differential curve to obtain the second-order differential curve;

[0039] (3) Determine the boundary line for dividing each characteristic peak and characteristic region based on the second-order differential curve, divide the first-order differential curve into characteristic peaks and characteristic regions based on the boundary line, and then calculate the area. Use the area of ​​these characteristic peaks and characteristic regions of the first-order differential curve as the thermogravimetric characteristic parameter.

[0040] Step III: Obtain the corresponding mapping relationship from the established thermogravimetric-coal quality database based on the thermogravimetric characteristic parameters of the coal sample to be tested, and then calculate the coal quality composition of the coal sample to be tested based on the thermogravimetric characteristic parameters and the mapping relationship.

[0041] In addition, in the rapid coal quality detection method based on thermogravimetric curves provided by this invention: the standard coals are all raw ore coals. During the database establishment process, the coal quality composition of the standard coals is obtained by industrial analysis in accordance with GB / T212-2008, and the same benchmark is used for different standard coals. The benchmark is an air-dry basis.

[0042] The role and effect of invention

[0043] The rapid coal quality detection method based on thermogravimetric analysis (TGA) curves provided by this invention first obtains thermogravimetric characteristic parameters from the TGA curves of standard coals with different coal compositions, calculates the mapping relationship between the TGA characteristic parameters and coal composition, and constructs a TGA-coal quality correlation database. Then, a TGA experiment is performed on the coal sample to be tested to obtain its TGA curve. The TGA characteristic parameters of the coal sample are obtained from the TGA curve and matched with the TGA-coal quality correlation database to obtain the mapping relationship. Based on the mapping relationship, the moisture, volatile matter, ash content, and fixed carbon of the coal sample are calculated, achieving rapid and accurate measurement of coal composition. This invention's detection method is simple to operate, low in cost, and saves time and effort. A single TGA experiment can simultaneously obtain the moisture, volatile matter, ash content, and fixed carbon of the coal sample. It has low requirements for the testing environment and sample, is not easily affected by external contamination, and has a wide range of applications, well adapting to the complexity of various coal samples in power plants, mining, and other production processes.

[0044] In particular, for thermogravimetric curves obtained at ultra-high heating rates, such as heating rates of 1000–6000 °C / min, the detection method of this invention can also accurately obtain coal quality parameters. This not only significantly increases the upper limit of heating rate for coal quality analysis and detection, but also greatly shortens the detection time. It is also beneficial for real-time analysis of boiler thermal efficiency and is very suitable for rapid detection of large batches of coal samples. Attached Figure Description

[0045] Figure 1 This is a weight loss curve of a portion of standard coal involved in Embodiment 1 of the present invention;

[0046] Figure 2 This is a diagram of first-order and second-order differential curves and characteristic peaks and characteristic regions according to Embodiment 1 of the present invention. The upper curve in the diagram is the second-order differential curve, and the lower curve is the first-order differential curve.

[0047] Figure 3 This is a mapping diagram of the thermogravimetric characteristic parameter x3 and the ash content of the coal sample involved in Embodiment 1 of the present invention;

[0048] Figure 4 This is a diagram of first-order and second-order differential curves and characteristic peaks and characteristic regions involved in Embodiment 2 of the present invention. The upper curve in the diagram is the second-order differential curve, and the lower curve is the first-order differential curve.

[0049] Figure 5 This is a peak fitting diagram of standard coal involved in Embodiment 2 of the present invention;

[0050] Figure 6 This is a mapping diagram of the thermogravimetric characteristic parameter x1 and the moisture content of the coal sample involved in Embodiment 2 of the present invention. Detailed Implementation

[0051] The principle and method of rapid coal quality detection based on comprehensive photoluminescence spectral analysis, as described in this invention, will be explained in detail below with reference to the accompanying drawings.

[0052] Photothermal rapid heating technology is a technique that achieves rapid heating based on concentrated light irradiation. Photothermal rapid heating thermogravimetric analysis (TGA) utilizes this technology to perform thermogravimetric experiments on samples, obtaining the thermogravimetric curves of the sample under concentrated light irradiation heating. The following examples all use photothermal rapid heating technology to obtain thermogravimetric curves at ultra-high heating rates. The photothermal rapid heating technology used can be the heating system described in "CN113867445A - A Rapid Heating System Based on Concentrated Light Heating and Thermogravimetric Analysis Method".

[0053] It should be noted that the present invention is not limited to photothermal heating; other heating methods can also be used to rapidly raise the temperature and obtain the thermogravimetric curve.

[0054] The main components of coal in industrial analysis (coal quality composition) include moisture (M), volatile matter (V), fixed carbon (Fc), and ash (A). The moisture content of coal is determined by weighing a certain amount of general analytical test coal sample, placing it in a drying oven at (105–110) degrees Celsius, drying it in a dry nitrogen stream until the mass is constant, and then calculating the mass fraction of the moisture based on the mass loss of the coal sample. The volatile matter content of coal is the mass loss after heating the coal sample under specified conditions in the absence of air and correcting for moisture content. The ash content of coal is the residue obtained after the complete combustion of the coal sample under certain conditions. Ash is not an inherent substance in coal but is the residue after the complete combustion of minerals. Volatile matter Fcad = 100% - (Mad + Vad + Aad), where ad represents the air-dried basis.

[0055] This invention has found that during the thermogravimetric analysis of coal samples, the weight loss rates of different components of the coal sample are significantly different, which is specifically reflected in the characteristics of the weight loss curve. Therefore, it proposes to obtain the thermogravimetric characteristic parameters by analyzing the weight loss curve and to establish the mapping relationship between the thermogravimetric characteristic parameters and each component.

[0056] The following specific embodiments will be used to describe in detail the method for rapid coal quality detection using the technical solution of the present invention.

[0057] <Example 1>

[0058] The rapid coal quality detection method based on thermogravimetric curves provided in this embodiment includes the following steps:

[0059] 1) Establish a photothermal-coal quality database under concentrated irradiation rapid heating: Select various coal samples with different coal ranks and different ash contents, grind standard coal into powder as coal samples, and conduct photothermal rapid heating thermogravimetric experiments on the coal samples respectively.

[0060] In this embodiment, the standard coal selected for database establishment is unmixed coal, covering various coals with different ranks and ash contents. Unmixed raw coal is preferred, and as many coal types as possible are selected to ensure the comprehensiveness and broad applicability of the database. During database establishment, the coal quality composition of the standard coal was obtained through industrial analysis according to GB / T212-2008, and the same standard basis was used for different standard coals; all standards were based on an air-dry basis.

[0061] 1a) The thermogravimetric curves obtained through rapid photothermal heating thermogravimetric experiments need to be preprocessed. Preprocessing includes baseline removal, that is, a blank thermogravimetric experiment with the same method and benchmark as the formal experiment needs to be performed before the formal experiment to obtain a blank baseline. The expansion of gas in a high-temperature atmosphere will generate a certain buoyancy on the object, resulting in weight loss. Therefore, a blank experiment is performed before the formal experiment.

[0062] 1b) Data analysis of the thermogravimetric curves, including differential smoothing and peak analysis. The specific steps of differential smoothing are as follows:

[0063] The thermogravimetric curve is processed using the first-order derivative. The resulting differential curve needs to be smoothed further. The smoothing window number is between 65 and 70 points. If it is too large, the differential curve will be distorted during the smoothing process, causing changes in relevant characteristic parameters. If it is too small, the differential curve characteristics will be unclear, making it difficult to select characteristic parameters. Since the differential spectrum still has many spikes after the first smoothing, a second smoothing process is performed on the smoothed curve. The smoothing window number is also between 65 and 70 points. At this point, the differential curve is basically free of spikes, yielding the first-order differential curve. Then, the first-order differential curve is processed again using the first-order derivative to obtain the second-order differential curve, which does not require further smoothing.

[0064] 1c) Peak analysis mainly involves calculating the peak area of ​​the characteristic peaks of the first-order differential curve. Figure 3 It is clear that feature peak A, feature peak B, and the remaining portion are referred to here as feature region C. The steps for obtaining each feature peak and feature region are as follows:

[0065] Draw the zero-value reference line of the vertical axis of the second-order differential curve in step 1b). Take three points a1, b1, and c1 from the intersection points of the second-order differential curve. Take the x-coordinate values ​​of the above three points to correspond to the first-order differential curves a2, b2, and c2 respectively. Use the x-coordinate values ​​of these three points as the boundary lines of characteristic peak A, characteristic peak B, and characteristic region C. Then take the zero-value reference line of the vertical axis of the first-order differential curve and intersect the end of the first-order differential curve at point d. The area enclosed by the x-coordinate values ​​of points c and d is region C.

[0066] Perform peak area integration on characteristic peak A, characteristic peak B, and the entire region formed by A, B, and C respectively. Use the absolute peak area values ​​of characteristic peak A and characteristic peak B as thermogravimetric characteristic parameters x1 and x2, and then calculate the thermogravimetric characteristic parameter x3 = 100 - the absolute peak area value of the entire region.

[0067] The mapping relationships between the thermogravimetric characteristic parameters x1, x2, and x3 and the moisture (M), volatile matter (V), and ash (A) components of the coal sample under industrial analysis were calculated to establish a database relating the thermogravimetric characteristic parameters to coal composition under rapid heating under concentrated irradiation. A mapping curve can be plotted with the thermogravimetric characteristic parameters as the x-axis and the coal sample's industrial analysis components as the y-axis.

[0068] The mapping relationship between thermogravimetric characteristic parameters and coal quality industrial analysis parameters was obtained through the above calculations and analysis, and a thermogravimetric-coal quality database under rapid heating by concentrated irradiation was established.

[0069] 2) Perform the same rapid heating thermogravimetric experiment on the unknown coal as in step 1) and on the benchmark, and perform the same preprocessing and data analysis on the obtained thermogravimetric curve of the unknown coal as in step 1) to obtain the thermogravimetric characteristic parameters of the coal sample to be tested.

[0070] 3) Match the thermogravimetric characteristic parameters of the coal sample to be tested obtained in step 2) with the established thermogravimetric-coal quality database under rapid photothermal heating. Based on the mapping relationship between the thermogravimetric characteristic parameters and the coal quality components, obtain the coal quality components such as M, V, A, and Fc of the coal sample to be tested.

[0071] Specifically, in this embodiment:

[0072] First, 26 standard coals of different ranks and ash contents were taken, ground to 74-105 μm, and numbered 1-26, and industrial analysis was performed on each. The results were obtained on an air-dried basis.

[0073] Then, rapid photothermal heating thermogravimetric analysis was conducted on these 26 types of coal. Each type of coal underwent at least three experiments, and the data from the two most reproducible experiments were used for subsequent analysis. The weight loss spectra of some standard coals were also analyzed. Figure 1 As shown. Experimental conditions for the rapid heating thermogravimetric experiment: heating rate of 1000℃ / min, final temperature of 900℃, constant temperature for 2.5min, air atmosphere, coal weight of 5-6mg.

[0074] Subsequently, the obtained weightlessness spectrum was preprocessed and analyzed. The preprocessed weightlessness curve and first-order differential curve are shown below. Figure 2 As shown, the thermogravimetric characteristic parameters were obtained by analyzing the above method;

[0075] Next, the mapping relationship between the thermogravimetric characteristic parameters and the industrial analytical composition parameters of the coal sample was calculated, and a photothermal-coal quality database under concentrated irradiation rapid heating was established. Taking the mapping relationship between parameter x3 and coal ash content (A) as an example, the thermogravimetric characteristic parameter x3 was used as the abscissa, and the industrial analytical composition of the coal sample ash content (A) was used as the ordinate, and the mapping relationship curve between the two was plotted as follows. Figure 3 As shown.

[0076] The following is the mathematical model of the mapping relationship:

[0077] Mad (moisture): y=1.66746*x1-0.11167*x1 2 +0.01137*x1 3 -3.81E -4 *x1 4 ;

[0078] Vad (volatile matter): y = 0.52266*x² + 0.07022*x² 2 -0.00297*x2 3 -4.04E -5 *x2 4 ;

[0079] Aad (ash content): y = 0.69668 * x³ - 3.38E -6 *x3 2 +4.10E -4 *x3 3 -6.24E -6 *x3 4 ;

[0080] Fcad (fixed carbon) = 100 - Mad - Aad - Vad.

[0081] Following the above process, a rapid photothermal heating thermogravimetric experiment was conducted on the raw coal to be tested using the same method and benchmark. The thermogravimetric characteristic parameters of the raw coal to be tested were obtained according to the above processing and analysis methods, as shown in Table 1.

[0082] Table 1

[0083] Coal sample Parameter 1 Parameter 2 Parameter 3 Raw coal 1 to be tested 11.65 24.02 8.08 Raw coal 2 to be tested 2.62 26.62 28.28 Raw coal 3 to be tested 1.58 6.76 27.74 Raw coal 4 to be tested 1.32 11.67 22.53

[0084] The calculated thermogravimetric characteristic parameters of the raw coal to be tested were input into the photothermal-coal quality correlation database. The content of the above-mentioned coal quality components finally measured in this application example is shown in Table 2.

[0085] Table 2

[0086] Coal sample Parameter 1 Parameter 2 Parameter 3 Raw coal 1 to be tested 15.11 25.33 5.82 Raw coal 2 to be tested 3.81 27.89 24.96 Raw coal 3 to be tested 2.38 5.83 24.39 Raw coal 4 to be tested 1.92 12.08 19.24

[0087] The comparison and differences between the coal composition parameters obtained from industrial analysis and those obtained are shown in Table 3:

[0088] Table 3

[0089] Coal sample Moisture Difference Volatile matter Difference Ash content Difference Coal sample 1 to be tested 14.34 -0.77 24.91 -0.42 6.85 1.03 Coal sample 2 to be tested 3.01 -0.80 28.27 0.38 25.64 0.68 Coal sample 3 to be tested 1.83 -0.55 7.53 1.70 23.14 -1.25 Coal sample 4 to be tested 2.59 0.67 12.7 0.62 18.12 -1.12

[0090] Observing the differences, the errors of each parameter of the four types of raw coal to be tested are basically within 2%, which is in line with expectations and confirms that the test results of this scheme are accurate and reliable.

[0091] <Example 2>

[0092] The difference between Example 2 and Example 1 lies only in the analysis and processing of the thermogravimetric curves to obtain the thermogravimetric characteristic parameters and the resulting mapping relationship. The differences will be described below, and identical content will not be repeated.

[0093] 1b) Data analysis of the thermogravimetric curves, including differential smoothing and peak analysis. The specific steps of differential smoothing are as follows:

[0094] The weightlessness spectrum was processed by first-order differential, and then smoothed twice, with the number of window points for each smoothing process being between 100 and 120.

[0095] Gaussian curve fitting was used, and the number of peaks was 6; peak selection: as follows Figure 4 As shown, draw a second-order differential curve, take the reference line Y = 0, and take a1, b1, c1, d1, e1, f1 as the peak centers of peak A, peak B, peak C, peak D, peak E, and peak F from the intersection points of the two.

[0096] In the second-order differential curve, a1 and b1 vertically correspond to a2 and b2 in the first-order differential curve. a2 and b2 are the apexes of peaks A and B, respectively, determined using the same method as in Example 1. As for points c, d, e, and f, which are in region C, adjustments need to be made based on the actual fitting results. Initially, points c1 to f1 are selected, corresponding to the x-coordinates of c2 to f2. The x-coordinates of c2 to f2 are used as the centers of peaks C to F. c1 to f1 are taken as the intersection points of the second-order differential curve and the zero-value reference. Points are selected according to constraints (where c1 ranges from t = 70 to 105, d1 from t = 105 to 140, e1 from t = 140 to 170, and f1 from t = 170 to 200, with the distance between points kept as close as possible to t = 25 to 35). Then, in the fitting control, before iteration, the positions of points c1 to f1 are not fixed. Iteration continues until convergence. If R... 2 If R > 0.98 and still meets the constraints, the fitting is complete. If not, adjustments are made within the constraints until R < 0.98. 2 The value is greater than 0.98, and the constraints are still met.

[0097] The thermogravimetric characteristic parameters were selected as the areas of peak A and peak B, and the total peak areas of A to F, IA, IB, and S. IA and IB were used as thermogravimetric characteristic parameters x1 and x2, and 100-S was used as thermogravimetric characteristic parameter x3. These parameters were matched with the moisture, volatile matter, and ash content from industrial analysis to calculate the mapping relationship and establish a thermogravimetric-coal quality database.

[0098] like Figure 5 As shown in Example 2, 10 types of standard coal were selected, and the thermogravimetric characteristic parameters and industrial analysis composition parameters obtained according to the standard processing and analysis procedures are as follows:

[0099]

[0100]

[0101] Taking the mapping relationship between parameter 1 and coal sample moisture as an example, with the thermogravimetric characteristic parameter x3 as the abscissa and the moisture content of the coal sample as the ordinate, a mapping relationship curve is plotted. This mapping relationship is as follows: Figure 6 As shown.

[0102] The following is the mathematical model of the mapping relationship:

[0103] Mad (moisture): y = 2.09922*x1 - 0.05641x1 2 +4.88989E -4 *x1 3 -0.0643;

[0104] Vad (volatile matter): y = 1.48033*x² - 0.02023*x² 2 +1.38228E -4 *x2 3 ;

[0105] Aad (ash content): y = 0.73469*x³ + 0.003*x³ 2 +6.84111E -5 *x3 3 ;

[0106] Fcad (fixed carbon) = 100 - Mad - Aad - Vad.

[0107] The above is merely an illustrative example of the technical solution of this invention. The rapid coal quality detection method based on thermogravimetric curves involved in this invention is not limited to the structure described above, but is defined by the scope of the claims. Any modifications, additions, or equivalent substitutions made by those skilled in the art based on this invention are within the scope of protection claimed in the claims of this invention.

Claims

1. A rapid coal quality detection method based on thermogravimetric curves, characterized in that, Includes the following steps: Step 1: Establish a thermogravimetric analysis (TGA) database for coal quality; Step 1-1: Select standard coals with different coal quality compositions, and perform the following processing on each standard coal: Step 1-1a: Perform a thermogravimetric analysis on the powder as a coal sample and obtain the thermogravimetric curve; Step 1-1b: Obtain the corresponding thermal weight loss characteristic parameters of the coal sample from the thermal weight loss curve, specifically: (1) Perform first-order differential processing on the thermogravimetric curve, and then perform two smoothing processes to obtain the first-order differential curve; (2) Perform a first-order differential operation on the first-order differential curve to obtain the second-order differential curve; (3) Determine the dividing line for each characteristic peak and characteristic region based on the second-order differential curve, divide the first-order differential curve into characteristic peaks and characteristic regions based on the dividing line, and then calculate the area. Use the area of ​​these characteristic peaks and characteristic regions of the first-order differential curve as the thermogravimetric characteristic parameter. Step 1-2: Based on the thermogravimetric characteristic parameters of standard coal with different coal quality components obtained in Step 1-1, calculate the mapping relationship between the thermogravimetric characteristic parameters and the coal quality components, and construct a thermogravimetric-coal quality database. The coal composition includes at least: moisture, volatile matter, ash, and fixed carbon; Step 2: Perform thermogravimetric analysis on the coal sample with unknown coal composition according to steps 1-1a and 1-1b, and obtain the thermogravimetric curve and thermogravimetric characteristic parameters of the coal sample. Step 3: Calculate the coal composition of the coal sample based on the thermogravimetric characteristic parameters of the coal sample and the corresponding mapping relationship in the thermogravimetric-coal quality database.

2. The rapid coal quality detection method based on thermogravimetric curves according to claim 1, characterized in that: in, In step 1-1a, the coal sample is subjected to a thermogravimetric experiment at a heating rate of 1000℃~6000℃ / min to obtain the thermogravimetric curve.

3. The rapid coal quality detection method based on thermogravimetric curves according to claim 1, characterized in that: in, In step 1-1b (1), the number of window points for both smoothing processes is between 65 and 70.

4. The rapid coal quality detection method based on thermogravimetric curves according to claim 3, characterized in that: in, In step 1-1b(3), three points a1, b1, and c1 are taken from the intersection of the zero-value reference line of the vertical axis of the second-order differential curve and the second-order differential curve. Based on the abscissa value t of these three points, the corresponding three points a2, b2, and c2 are obtained from the first-order differential curve as boundary points. The abscissa values ​​of the three boundary points are successively used as the three boundary lines L1~3 of the A characteristic peak, B characteristic peak, and C characteristic region. Then, the ordinate of the intersection point d of the zero-value reference line of the vertical axis of the first-order differential curve and the end of the first-order differential curve is taken as the fourth boundary line L4. L3 and L4 enclose the C region. The peak area integral is performed on the A characteristic peak, B characteristic peak, and the entire A, B, and C region. The absolute peak area values ​​of the A peak and B peak are used as the thermogravimetric characteristic parameters. x 1 and x 2. Thermogravimetric characteristics x 3 = 100 - Total absolute peak area value of the region.

5. The rapid coal quality detection method based on thermogravimetric curves according to claim 4, Its features are: In the thermogravimetric analysis (TGA)-coal quality database, the mapping relationship between TGA characteristic parameters and coal composition is as follows: Moisture content Mad = 1.66746 × x 1-0.11167× x 1 2 +0.01137× x 1 3 -3.81E -4 × x 1 4 ; Volatile matter Vad = 0.52266 × x 2+0.07022× x 2 2 -0.00297× x 2 3 -4.04E -5 × x 2 4 ; Ash content Aad = 0.69668 × x 3-3.38E -6 × x 3 2 +4.10E -4 × x 3 3 -6.24E -6 × x 3 4 ; Fixed carbon Fcad = 100 - Mad - Aad - Vad.

6. The rapid coal quality detection method based on thermogravimetric curves according to claim 4, characterized in that: in, In step 1-1b (3), based on the approximate positions of the first two main characteristic peaks A and B on the first-order differential curve, three points a1, b1, and c1 are taken from the intersection of the 0-value reference line of the vertical axis of the second-order differential curve and the second-order differential curve; peak A is in the range of t=2~25s, peak B is in the range of t=25~70s, and the remaining range is basically region C.

7. The rapid coal quality detection method based on thermogravimetric curves according to claim 1, characterized in that: in, In step 1-1b(1), the number of window points for both smoothing processes is between 100 and 120.

8. The rapid coal quality detection method based on thermogravimetric curves according to claim 7, characterized in that: in, In step 1-1b(3), six points a1, b1, c1, d1, e1, and f1 are roughly selected from the intersection of the reference line for the 0 value of the vertical axis of the second-order differential curve and the second-order differential curve. The six selected points should meet the following constraints: a1 is in the range of t=2~25s, b1 is in the range of t=25~70s, c1 is in the range of t=70~105s, d1 is in the range of t=105~140, e1 is in the range of t=140~170, and f1 is in the range of t=170~200, and the distance between each point should be kept between t=25~35s as much as possible; and in the fitting control, before iteration, the positions of points c1~f1 are not fixed. Iteration continues until convergence. If the fitting degree R 2 If the R-value is greater than 0.98 and the constraints are still met, the fitting is complete. If not, adjustments are made within the constraints until the fit is satisfactory. 2 The value is greater than 0.98, and the constraint conditions are still met, thus determining the coordinates of six points: a1, b1, c1, d1, e1, and f1. Based on the x-coordinate values ​​of these six points, the corresponding six points a2, b2, c2, d2, e2, and f2 are obtained from the first-order differential curve as the center points of characteristic peaks A to F, and the boundaries of each characteristic peak are determined accordingly. The characteristic peaks are then fitted. Finally, the area IA of characteristic peak A and the area IB of characteristic peak B are used as thermogravimetric characteristic parameters. x 1. x 2. Thermogravimetric characteristics x 3 = 100 - A ~ F total peak area S.

9. The rapid coal quality detection method based on thermogravimetric curves according to claim 8, characterized in that: in, In the thermogravimetric analysis (TGA)-coal quality database, the mapping relationship between TGA characteristic parameters and coal composition is as follows: Moisture content Mad = 2.09922 × x 1-0.05641× x 1 2 +4.88989E-4× x 1 3 -0.0643; Volatile matter Vad = 1.48033 × x 2-0.02023× x 2 2 +1.38228E -4 × x 2 3 ; Ash content Aad = 0.73469 × x 3+0.003× x 3 2 +6.84111E -5 × x 3 3 ; Fixed carbon Fcad = 100 - Mad - Aad - Vad.

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