Online solid mineral detection system based on belt sampling

Abstract

The invention discloses an on-line solid mineral detection system based on belt sampling, and relates to the technical field of mineral detection.The on-line solid mineral detection system comprises a collecting module, a crushing module, a division module and an on-line detection module, all sampled products are divided after being crushed, and the accuracy of a test result can be improved by 10% or above; after division, the sample is shaped for multiple times, all the samples pass through the online detection module, and the use efficiency of detection rays and the accuracy of spectrum collection in the detection process can be improved through shaping treatment; besides, the online detection system detects the mineral quality in a transmission mode, the detection range is larger, transmission detection is equivalent to detection of information of all coal, and compared with traditional surface detection, the amount of substances represented by the detection result is increased by more than 30 times.

Description

Technical Field

[0001] The invention relates to the technical field of mineral detection, and in particular to an online solid mineral detection system based on belt sampling. Background Art

[0002] The quality of bulk solid minerals needs to be determined during the mining, transportation and trading process. It is usually necessary to sample, prepare samples and test solid minerals at certain time intervals or quality intervals to determine the quality of solid minerals. The representativeness of sample collection directly affects the final test results. For bulk solid minerals, it is necessary to obtain the quality information of minerals in real time during the washing and processing process, and adjust the washing and processing parameters in real time according to the quality information of minerals. The traditional sampling, sample preparation and testing methods take from tens of minutes to several hours to obtain the quality of minerals, which cannot meet the requirements of obtaining mineral quality information in real time. Conventional online detection equipment needs to process the samples in a certain way and obtain samples with a specified particle range before quality analysis can be performed. Summary of the invention

[0003] The purpose of the present invention is to provide an online solid mineral detection system based on belt sampling to solve the problems existing in the above-mentioned prior art. Based on the belt sampling device, the online solid mineral detection system is used to obtain the quality information of solid minerals in real time to guide the real-time adjustment of washing and processing process parameters.

[0004] To achieve the above object, the present invention provides the following solution: The present invention provides an online solid mineral detection system based on belt sampling, comprising: A collection module, the collection module is used to extract solid minerals from the coal conveyor belt; and A crushing module, wherein the transport module transports the solid minerals collected at the collection module to the crushing module, and the crushing module is used to crush the collected solid minerals; and A reduction module, which is used to reduce the crushed solid minerals, and the reduced materials are shaped and sent to the online detection module; and An online detection module includes a plurality of independent spectral analysis devices, and the spectral analysis devices are used to detect organic and inorganic substances in solid minerals.

[0005] Preferably, the collection module includes a host control machine and a belt sampler, and the collection frequency of the host control machine is set according to actual working conditions; the belt sampler is used to collect coal samples from the coal conveyor belt.

[0006] Preferably, the transfer module comprises a slide pipe and a transport belt, and the transport belt can be flexibly disassembled and assembled according to on-site working conditions.

[0007] Preferably, the crushing module comprises a primary crushing device and a secondary crushing device, the primary crushing device adopts a hammer crusher or a double-roll crusher; the secondary crushing device adopts a horizontal hammer crusher or a vertical hammer crusher.

[0008] Preferably, the reduction module adopts a belt-type reducer or a rotary reducer, and the reduction module is divided into a primary reduction and a secondary reduction. The primary reduction reduces the sample crushed by the primary crushing device; a part of the material after the primary reduction is sent to the secondary crushing device through the transfer module, and the remaining material is sent to the waste material multi-bucket after passing through the waste material belt and sent back to the coal conveying belt; a part of the material after the secondary reduction is sent to the online detection module through the shaping mechanism, and the remaining material is sent to the waste material multi-bucket after passing through the waste material belt and sent back to the coal conveying belt.

[0009] Preferably, the shaping mechanism includes a shaping belt, and the retained sample after secondary reduction enters the shaping belt. A baffle is arranged above the middle part of the shaping belt, and the baffle is vertically installed on the top of the shaping belt. The baffle contacts the coal flow on the shaping belt and shapes the coal flow by extrusion.

[0010] Preferably, the baffle is made of soft material, the distance between the baffle and the surface of the shaping belt is 3-10 cm, and the closer the baffle is to the online detection module, the lower the height of the baffle from the surface of the shaping belt.

[0011] Preferably, it also includes a waste material return module, which includes a waste material return belt and a waste material multi-bucket elevator. The crushed waste, the reduced waste and the solid minerals after online detection are all sent to the waste material belt and then sent to the waste material multi-bucket elevator via the waste material belt. The waste material multi-bucket elevator returns all the waste materials to the coal conveyor belt.

[0012] Preferably, the spectral analysis equipment adopts a dual-spectrum coal quality analysis method combining near-infrared spectroscopy and X-ray fluorescence spectroscopy.

[0013] Preferably, the dual-spectrum coal quality analysis method combining near-infrared spectroscopy and X-ray fluorescence spectroscopy is as follows: When testing specific solid mineral indicators, a standard curve must first be established, and samples with known accurate coal quality indicators are sent to near-infrared spectroscopy and X-ray fluorescence spectroscopy equipment for testing to obtain spectral curves corresponding to each coal quality indicator in each sample with known accurate coal quality indicators, thereby obtaining a standard curve; the near-infrared spectrum and X-ray fluorescence spectrum of each sample with known accurate coal quality indicators are a continuous curve with a front, and each element or group in the sample with known accurate coal quality indicators corresponds to one or more characteristic fronts; the data of the near-infrared spectrum and X-ray fluorescence spectrum of the sample with known accurate coal quality indicators are processed, the input end is the position and height of the characteristic peaks of each element or group in the near-infrared spectrum and X-ray fluorescence spectrum of the sample with known accurate coal quality indicators, and the output end is one or more coal quality indicators of the sample with known accurate coal quality indicators; a calibration model is formulated using the coal quality indicator data of the sample with known accurate coal quality indicators; During actual online detection of solid minerals, the near-infrared spectrum and X-ray fluorescence spectrum of the tested sample are input into the near-infrared spectrum and X-ray fluorescence spectrum equipment after testing by the equipment, and finally one or more coal quality results of the sample are output from the near-infrared spectrum and X-ray fluorescence spectrum equipment; if the coal quality index of the tested sample is within the coal quality index range of the calibration model, the test result is accurate; if it exceeds the coal quality index range of the calibration model, the test result is biased, and it is necessary to expand the sampling range of samples with known accurate coal quality indexes, and form a calibration model again to improve the accuracy of the test result.

[0014] Compared with the prior art, the present invention has achieved the following beneficial technical effects: In the online solid mineral detection system based on belt sampling of the present invention, all sampled samples are crushed and then reduced, which can improve the accuracy of the test results by more than 10%; after reduction, the samples are reshaped multiple times and all pass through the online detection module. The shaping process can improve the efficiency of the use of detection rays and the accuracy of the collected spectrum during the detection process; in addition, the online detection system detects the quality of minerals in a transmission manner, and the detection range is larger. Transmission detection is equivalent to detecting all the information of coal. Compared with traditional surface detection, the amount of substance represented by the detection result is increased by more than 30 times. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1This is the workflow diagram of the online solid mineral detection system based on belt sampling. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] The purpose of the present invention is to provide an online solid mineral detection system based on belt sampling to solve the problems existing in the above-mentioned prior art. Based on the belt sampling device, the online solid mineral detection system is used to obtain the quality information of solid minerals in real time to guide the real-time adjustment of washing and processing process parameters.

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1 As shown, the present invention provides an online solid mineral detection system based on a belt sampling device, comprising: Collection module: The collection module includes the upper control machine and the belt sampler. The upper control machine can set the collection weight and collection frequency according to the actual working conditions; the belt sampler takes coal samples from the coal conveyor belt, generally sampling once every 90 seconds or more, and the sampling weight varies from 20 to 50 kg. The amount of coal collected is related to the particle size of the coal, the belt conveying volume, etc. Generally, the particle size of the raw coal belt sample ranges from 50 to 300 mm, and the particle size of the clean coal belt sample is about 50 mm. The upper control machine provides an operating platform, and the operator determines the sampling frequency according to the actual working conditions. The sampling weight cannot be set by the upper operating machine. The sampling weight is determined when the equipment is designed and cannot be adjusted.

[0021] Transfer module: The transfer module includes a chute and a transport belt, which are used to transport solid minerals collected from the belt sampler. The transport belt can be flexibly disassembled and assembled according to the on-site working conditions; the belt transfer method is adopted, which can also play a caching role.

[0022] Crushing module: The crushing module includes a primary crushing device and a secondary crushing device. The primary crushing device generally adopts a double-roll crusher to crush large coal into about 50mm. When the raw coal particle size is not large (not exceeding 150mm), a hammer crusher can also be used; the secondary crushing device generally adopts a horizontal hammer crusher. According to the subsequent test requirements, the material is crushed to 13mm or 6mm. A vertical hammer crusher can also be used; Reduction module: The reduction module adopts a belt-type reducer or a rotary reducer, which is divided into primary reduction and secondary reduction. The primary reduction reduces the sample after primary crushing. After the reduction, part of the material is transferred to the secondary crushing, and part is sent back to the coal conveyor belt through the discard belt, discard multi-bucket elevator and other equipment; after the secondary reduction, the material is shaped and sent to the online detection equipment, and the rest of the material is sent to the discard multi-bucket elevator after the discard belt. For example: the input material of the primary reduction is 100, 30 of the output enters the secondary reduction, and 70 of the output enters the discard; the ratio of 30 to 70 can be adjusted; according to the sampling weight, the final required sample weight is determined. The retained samples after secondary reduction are generally put into a small-sized shaping belt with a width of 10-30cm and a belt speed of 0.2-1m / s. In the middle of the shaping belt, in front of the online detection module, there are 1-4 baffles, which are vertically installed on the top of the belt. The baffles are used to contact the coal flow on the belt and shape the coal flow by squeezing. The baffles are generally made of soft materials, and the distance between the baffles and the belt surface varies from 3 to 10cm. The closer to the online detection position, the lower the height of the baffles.

[0023] Online monitoring module: The online detection equipment is composed of two or more independent spectral analysis devices, which detect organic and inorganic substances respectively; common equipment that uses spectroscopy or laser to detect coal quality includes prompt gamma neutron activation analysis (PGNAA), laser-induced breakdown spectroscopy (LIBS), near-infrared spectroscopy (NIRS) and X-ray fluorescence spectrometry (XRF). Separate equipment that uses these methods to detect coal quality has mature products on the market.

[0024] Abandoned material return module: It consists of an abandoned material belt and an abandoned material multi-bucket. The coal after the first-stage reduction, second-stage reduction and online detection is sent to the abandoned material belt, and then sent to the abandoned material multi-bucket through the abandoned material belt. The abandoned material multi-bucket returns all the abandoned materials to the sampling belt. The discharge port of the abandoned material belt must be downstream of the sampling head.

[0025] Online solid mineral detection method based on belt sampling device: Step 1: Set the sampling frequency in the upper control according to the on-site working conditions. The upper control machine controls the belt sampler to take coal samples from the coal conveyor (or other solid mineral) belt. Generally, the sampling is done every 90 seconds or more. The sampling weight varies from 20 to 50 kg. The amount of coal collected is related to the particle size of the coal, the conveying capacity of the belt, etc. Generally, the particle size of the raw coal belt sample varies from 50 to 300 mm, and the particle size of the clean coal belt sample is about 50 mm. Step 2: The coal sample collected in step 1 is sent to the primary crushing equipment through a chute and a conveyor belt; Step 3: Use roller crushing to crush the large coal into about 50mm. When the raw coal particle size is not large (no more than 150mm), a hammer crusher can also be used; Step 4: The coal sample crushed in step 3 is transported to the first-level reduction unit through a transfer belt. The length and running speed of the transfer belt are related to the material processing volume. Generally, the speed of the transfer belt is between 0.2-0.5m / s, and the width and length are related to the required reduction time. Generally, the width is 200-500mm, so as to achieve material buffering and leave enough reduction time for the reduction unit; Step 5: Perform primary reduction. The materials after reduction are divided into two parts. One part is transferred to the secondary crushing, and the other part is sent back to the coal conveyor belt through the discarded material belt, discarded material multi-bucket elevator and other equipment; Step 6: Perform secondary crushing, using a horizontal hammer crusher or a vertical hammer crusher to crush the material to 13mm or 6mm, and transfer the secondary crushed material to the secondary reduction device through a belt; Step 7: Use a belt-type reducer or a rotary reducer. After the materials are reduced and shaped, they are sent to the online testing equipment. The remaining materials are sent to the waste material multi-bucket after the waste material belt. Step 8: Perform online detection. The online detection module is composed of multiple independent spectral analysis devices to detect organic and inorganic substances respectively. Using a variety of coal samples, according to the detection spectra of organic and inorganic substances, the relationship between various components in coal and the spectra is established, and the content of ash, volatile matter, calorific value, sulfur, etc. in coal is calculated. For example, near-infrared spectroscopy can be used to analyze the organic components in coal, and XRF (X-ray diffraction spectroscopy) can be used to analyze the inorganic components in coal. Combine the organic and inorganic components to obtain the accurate content of ash, volatile matter, calorific value, sulfur, etc. in coal. Using multiple devices to detect spectra in combination can eliminate the defects of single device detection, and use the advantages of multiple device spectra in organic or inorganic detection. Select the accurate part of the spectrum, and use multiple devices in combination, and the final result will be more accurate. Compared with a single spectrum detection device, the accuracy of this device can be improved by more than 20%, and compared with manual methods, the detection accuracy is consistent with manual methods. The calculation method is to use a neural network model, input the intensity of various spectral lines, the manual test results of the sample, and the software performs correlation calculations on the spectral lines and the test results, and cyclically corrects them. When the simulation calculation results meet the requirements set in advance (for example, the deviation between the ash calculation results and the actual test results is within 0.2%), the calculation process is completed, and the neural network model in the calculation process is saved as the final model; Step 9: The waste material is returned. The coal after the first-level reduction, the second-level reduction and the online detection is sent to the waste material belt, and then sent to the waste material multi-bucket elevator. The waste material multi-bucket elevator returns all the waste materials to the sampling belt to complete the solid mineral detection.

[0026] In the online solid mineral detection system based on belt sampling of the present invention, the spectrum analysis equipment of the online detection module adopts a dual spectrum coal quality analysis method combining NIRS and XRF. NIRS can efficiently and stably detect organic groups in coal, and XRF has a high measurement accuracy for ash-forming inorganic components and sulfur elements in coal. Therefore, the two are used together, with complementary advantages, to realize the analysis of coal.

[0027] When XRF and NIRS are used to test specific coal quality indicators, a standard curve must be established first. The method is as follows: Use coal samples with known accurate components, such as standard coal samples, or obtain samples with accurate coal quality components through manual testing. Send a batch of samples with known accurate coal quality indicators to the XRF and NIRS equipment for testing to obtain the spectral curve corresponding to each sample. The number of the above "batch" is usually more than 100, which is related to the number of coal quality indicators and the numerical distribution of specific indicators. A good standard sample library is preferably more than 1,000 samples. The coal quality indicators of each sample usually include moisture, ash, volatile matter, calorific value, total sulfur, etc. The XRF or NIRS of each sample is usually a continuous curve with a front. Each element or group (carbon-hydrogen bond, carbon-oxygen bond, etc.) in the coal sample corresponds to one or more characteristic fronts (i.e., the highest front in a certain area). Use a machine learning algorithm model (one or more of a linear model, a support vector machine model, a decision tree model, a convolutional neural network, a recurrent neural network, etc.) to process the above data. The input end is the position and height of the characteristic peaks of XRF and NIRS, and the output end is one or more coal quality indicators of the sample. The model is trained through the data of the standard sample (using a neural network model, such as a support vector machine, a genetic model, etc., inputting coal quality indicators, ray spectra, setting the model calculation step, convergence rate, convergence conditions, etc., a specific mathematical model can be established; the establishment process is that the computer automatically calculates and obtains according to the algorithm of the selected model, which is a mature conventional technology and will not be described in detail here), and finally the trained model is obtained. In actual testing, the XRF and NIRS spectra of the tested sample are input into the model after the equipment is tested, and finally the model outputs one or more coal quality indicator results of the sample (the standard sample refers to a sample with specific coal quality indicators. Through XRF and NIRS testing, the sample spectrum can be obtained, and multiple groups of characteristic peaks in the spectrum can be matched with coal quality indicators; while obtaining accuracy, specific material information is also known, which reflects the accuracy of specific mineral detection results). If the coal quality index of the tested sample is within the coal quality index range of the sample used for training, the test result is relatively accurate. If it exceeds the index range of the training sample, the test result may have a certain deviation. It is necessary to expand the training range of the training sample and train the model again, and the test result will be more accurate.

[0028] The online solid mineral detection system based on belt sampling in the present invention has the following characteristics: (1) All samples are crushed before being reduced. Crushing first and then reducing. The samples are reduced within a uniform particle size range, which makes the samples more representative. The traditional method is to reduce the particles first and then crush them. When reducing the particles, the particle size is very large, and the deviation of the reduction is large. Adjusting the order of reduction and crushing can increase the accuracy of the test results by more than 10%.

[0029] (2) After the samples are reduced, they are shaped several times and all pass through the online mineral detection system. Shaping: The thickness, width and bulk density of the coal flow are kept in a stable state through the shaping blocks on the belt. The efficiency of the detection rays in the detection process can be increased by more than 20%, and the accuracy of the collected spectrum can be increased by more than 10%.

[0030] (3) The online detection system detects mineral quality in a transmission manner. Compared with the traditional laser-induced detection technology that only detects a very thin layer of elements on the surface of the material, the detection range is larger. Transmission detection is equivalent to detecting all the information of the coal. Compared with traditional surface detection, the amount of material represented by the detection result has increased by more than 30 times. Surface detection only represents the material of about 1mm on the surface, while the transmission method represents the material of 30-50mm.

[0031] (4) Different functions are analyzed using different software. Spectrum analysis and qualitative software: high-energy rays penetrate minerals, record the emission capacity of each frequency band, record the attenuation of each frequency band after passing through the coal flow, record the reflected spectrum of each frequency band, and determine the main spectrum range of elements and compounds. Quantitative software for material content: software analysis and screening of the most representative spectra of elements and compounds to determine the composition and proportion of elements and compounds. Standard material modeling software: using standard minerals, a standard spectrometer database for quantitative analysis is established, and the content of elements and compounds is determined by comparative analysis with the standard spectrometer database. Standard minerals include common ores in coal, a single ore or a combination of multiple ores. When modeling, the specific elemental composition, molecular structure, and proportion of each mineral of the ore are known. Standard minerals also include the main types of coal involved in the location where the equipment is used, including local coal types with different ash levels and moisture levels.

[0032] It should be noted that it is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any figure mark in the claims should not be regarded as limiting the claims involved.

[0033] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. An online solid mineral detection system based on belt sampling, characterized in that: include: A collection module, the collection module is used to extract solid minerals from the coal conveyor belt; as well as A crushing module, wherein the transport module transports the solid minerals collected by the collection module to the crushing module, and the crushing module is used to crush the collected solid minerals; as well as A reduction module, which is used to reduce the crushed solid minerals. After the reduction, the materials are shaped and sent to the online detection module; as well as An online detection module includes a plurality of independent spectral analysis devices, and the spectral analysis devices are used to detect organic and inorganic substances in solid minerals.

2. The online solid mineral detection system based on belt sampling according to claim 1 is characterized in that: The collection module includes a host control machine and a belt sampler. The collection frequency of the host control machine is set according to actual working conditions; the belt sampler is used to collect coal samples from the coal conveying belt.

3. The online solid mineral detection system based on belt sampling according to claim 1 is characterized in that: The transfer module includes a slide pipe and a transport belt, and the transport belt can be flexibly disassembled and assembled according to on-site working conditions.

4. The online solid mineral detection system based on belt sampling according to claim 1 is characterized in that: The crushing module comprises a primary crushing device and a secondary crushing device. The primary crushing device adopts a hammer crusher or a double-roll crusher; the secondary crushing device adopts a horizontal hammer crusher or a vertical hammer crusher.

5. The online solid mineral detection system based on belt sampling according to claim 4 is characterized in that: The reduction module adopts a belt-type reducer or a rotary reducer, and the reduction module is divided into a primary reduction and a secondary reduction. The primary reduction reduces the sample crushed by the primary crushing device; a part of the material after the primary reduction is sent to the secondary crushing device through the transfer module, and the rest of the material is sent to the waste material multi-bucket after the waste material belt and sent back to the coal conveying belt; After the secondary shrinkage, part of the material is sent to the online detection module through the shaping mechanism, and the rest of the material is sent to the discarded material multi-bucket after passing through the discarded material belt and then sent back to the coal conveying belt.

6. The on-line solid mineral detection system based on belt sampling according to claim 5 is characterized in that: The shaping mechanism includes a shaping belt, and the retained sample after secondary reduction enters the shaping belt. A baffle is arranged above the middle part of the shaping belt, and the baffle is vertically installed on the top of the shaping belt. The baffle contacts the coal flow on the shaping belt and shapes the coal flow by extrusion.

7. The on-line solid mineral detection system based on belt sampling according to claim 6 is characterized in that: The baffle is made of soft material, and the distance between the baffle and the surface of the shaping belt is 3-10 cm, and the closer the baffle is to the online detection module, the lower the height of the baffle from the surface of the shaping belt.

8. The online solid mineral detection system based on belt sampling according to claim 1 is characterized in that: It also includes a waste material return module, which includes a waste material return belt and a waste material multi-bucket elevator. Crushed waste, reduced waste and solid minerals after online detection are all sent to the waste material belt, and then sent to the waste material multi-bucket elevator via the waste material belt. The waste material multi-bucket elevator returns all waste materials to the coal conveyor belt.

9. The on-line solid mineral detection system based on belt sampling according to claim 1 is characterized in that: The spectral analysis equipment adopts a dual-spectrum coal quality analysis method combining near-infrared spectroscopy and X-ray fluorescence spectroscopy to perform spectral analysis.

10. The on-line solid mineral detection system based on belt sampling according to claim 9 is characterized in that: The double-spectrum coal quality analysis method combining near-infrared spectroscopy and X-ray fluorescence spectroscopy is specifically as follows: When testing specific solid mineral indicators, a standard curve must first be established, and samples with known accurate coal quality indicators are sent to near-infrared spectroscopy and X-ray fluorescence spectroscopy equipment for testing to obtain spectral curves corresponding to each coal quality indicator in each sample with known accurate coal quality indicators, thereby obtaining a standard curve; the near-infrared spectrum and X-ray fluorescence spectrum of each sample with known accurate coal quality indicators are a continuous curve with a front, and each element or group in the sample with known accurate coal quality indicators corresponds to one or more characteristic fronts; the data of the near-infrared spectrum and X-ray fluorescence spectrum of the sample with known accurate coal quality indicators are processed, the input end is the position and height of the characteristic peaks of each element or group in the near-infrared spectrum and X-ray fluorescence spectrum of the sample with known accurate coal quality indicators, and the output end is one or more coal quality indicators of the sample with known accurate coal quality indicators; a calibration model is formulated using the coal quality indicator data of the sample with known accurate coal quality indicators; During actual online detection of solid minerals, the near-infrared spectrum and X-ray fluorescence spectrum of the tested sample are input into the near-infrared spectrum and X-ray fluorescence spectrum equipment after testing by the equipment, and finally one or more coal quality index results of the tested sample are output from the near-infrared spectrum and X-ray fluorescence spectrum equipment; if the coal quality index of the tested sample is within the coal quality index range of the calibration model, the test result is accurate; if it exceeds the coal quality index range of the calibration model, the test result is biased, and it is necessary to expand the sampling range of samples with known accurate coal quality indexes, and form a calibration model again to improve the accuracy of the test result.

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