Coal quality on-line detection method

Abstract

The invention discloses a coal quality online detection method, which comprises a sample acquisition module, a sensor integration module, an online detection module, a data processing and analysis module, an automatic control module, a self-adaptive calibration and maintenance module and a result output and feedback module. According to the coal quality on-line detection method disclosed by the invention, accurate measurement of coal quality parameters can be realized by virtue of a high-precision sensor and an advanced data processing algorithm and a coal quality on-line detection technology; compared with a traditional laboratory detection method, the method has the advantages of being smaller in error, more reliable in result and high in automation and intelligence, and automatic control and data analysis in the detection process can be achieved; therefore, manual intervention and personal errors are reduced, the detection efficiency and the working efficiency are improved, the manual maintenance and operation cost is reduced through the automatic and intelligent design, and meanwhile the stability and the reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of coal detection, and in particular to an online coal quality detection method. Background Art

[0002] At present, coal is an important fossil energy in my country and plays an important role in ensuring economic construction. The annual coal consumption has been steadily increasing. Coal quality is a very concerned issue in the process of coal mining and use. It is directly related to the economic benefits of enterprises and the use of coal. Unstable coal quality has become one of the main problems affecting the safe and economic operation of coal-fired units in thermal power plants. At present, most of the coal quality analysis in power plants adopts offline laboratory analysis, which requires sampling, reduction, sample preparation, and testing. This process generally takes a long time. The delay in obtaining coal quality analysis reports by boiler operators may be longer. Therefore, a large amount of raw coal is burned in the absence of coal quality data. In the case of large changes in coal types, if necessary operation adjustments and corresponding measures are not made in time, serious consequences are likely to occur. In addition, when the quality of coal in power plants changes or fluctuates, there is often no fast and effective coal quality testing method, which makes it impossible to grasp the situation of incoming coal in time and cannot correctly determine whether the coal is suitable for boiler combustion. The harm caused by blind combustion is great.

[0003] Traditional coal quality testing usually needs to be carried out in the laboratory, including sampling, sample preparation, analysis and other steps. The entire testing process is time-consuming and cannot meet the real-time demand for coal quality parameters on the coal production line. Laboratory testing not only requires professional equipment and reagents, but also requires professional technicians to operate and maintain, resulting in high testing costs. In the analysis process, it mainly relies on manual labor. Due to the uneven manual experience, there is a possibility of errors in the test results, or quality problems are detected in a certain spot check, and the coal quality cannot be guaranteed. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides an online coal quality detection method, the purpose of which is to solve the problems that the entire detection process is time-consuming, cannot meet the real-time demand for coal quality parameters on the coal production line, and still has deficiencies in detection accuracy, stability and adaptability.

[0005] In order to achieve the above-mentioned purpose of the invention, the technical solution adopted by the present invention is: an online coal quality detection method, including a sample collection module, a sensor integration module, an online detection module, a data processing and analysis module, an automatic control module, an adaptive calibration and maintenance module, and result output and feedback.

[0006] Furthermore, the sample collection module includes using a sampler to collect coal samples in real time on the conveyor belt, using a sample splitter to split large samples into small portions for subsequent analysis, placing the samples in a drying box for drying to prevent moisture from affecting the test results, and the samples must also be properly stored to avoid moisture, oxidation, etc. At the same time, each collected sample is marked, including the sampling time, location, sampling method, etc., to ensure the traceability of the sample.

[0007] Furthermore, the sensor integration module includes an infrared spectrometer, a microwave moisture sensor, and a thermogravimetric analyzer to collect data of coal samples in real time in a variety of contact modes, thereby providing comprehensive data support for coal quality analysis.

[0008] Furthermore, the online detection module includes sending the pretreated sample into the online detection system, which can measure the main physical and chemical indicators of the coal sample in real time, by selecting a high-resolution, high-sensitivity infrared spectrometer, collecting the spectral information of the coal sample in a non-contact manner, using the characteristic absorption peaks of different chemical components in the infrared spectrum to identify the coal quality components, and using a microwave moisture sensor to use the principle of interaction between microwaves and matter to quickly determine the moisture content in the coal through the reflection characteristics of the coal sample; heating the coal sample under a controlled atmosphere through a thermogravimetric analyzer, while measuring the change in sample mass with temperature, and calculating the volatile matter content of the coal by analyzing the thermogravimetric curve.

[0009] Furthermore, the online detection module includes transmitting data acquired by sensors and analytical instruments to a data processing center, converting the collected raw data into an analyzable format, converting from analog signals to digital signals, and combining data from different sensors to provide more comprehensive coal quality information, extracting key characteristics of coal composition, moisture content, and ash content from the data, applying statistical model algorithms to analyze the data, identifying trends in the data to evaluate changes in coal quality, detecting and identifying anomalies in the data, and generating a detailed coal quality report.

[0010] Furthermore, the automation control module includes real-time transmission of sensor data and intelligent algorithm processing results to the central control unit, and displays coal quality change trends and early warning information through a visual interface to help managers understand the coal quality status in real time and make corresponding adjustments.

[0011] Furthermore, the adaptive calibration and maintenance module includes regular calibration and verification of sensors to ensure the accuracy of detection data; at the same time, by monitoring the working status of sensors and data analysis results, potential faults and abnormal conditions are timely discovered and reported, providing strong support for system maintenance and ensuring the long-term stable operation and high-precision detection capabilities of the detection system.

[0012] Furthermore, the result output and feedback includes outputting the test results through a remote communication system for reference by operators. If the test results exceed the preset quality standard range, the system can automatically issue an alarm and provide optimization suggestions or adjust relevant parameters in the coal production process, facilitating multi-party collaborative operations and decision support.

[0013] The beneficial effects of the present invention are:

[0014] The present invention provides an online coal quality detection method. With the help of high-precision sensors and advanced data processing algorithms, the online coal quality detection technology can realize accurate measurement of coal quality parameters. Compared with traditional laboratory detection methods, it has smaller errors, more reliable results, and has a high degree of automation and intelligence, and can realize automatic control and data analysis of the detection process. This not only reduces manual intervention and human errors, but also improves detection efficiency and work efficiency, and can simultaneously detect multiple components and parameters in coal (such as ash content, moisture, volatile matter, calorific value, etc.); this comprehensive detection helps to understand the quality status of coal more comprehensively. Compared with traditional laboratory detection methods, the operation of the online coal quality detection system is simpler. ; Operators only need simple training to get started, which reduces the requirements for professional skills and has relatively low maintenance costs; the automated and intelligent design reduces the cost of manual maintenance and operation, while improving the stability and reliability of the system. By real-time monitoring of coal quality parameters, online coal quality detection technology helps to promptly discover problems in the production process and take measures to resolve them, thereby avoiding production interruptions and waste; this helps to improve production efficiency and reduce production costs. The large amount of real-time data provided by online coal quality detection technology provides strong support for production decisions; through data analysis and mining, companies can more accurately grasp market trends and customer needs, and formulate more scientific production plans and sales strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention is a workflow diagram of an online coal quality detection method. DETAILED DESCRIPTION

[0016] The specific implementation of the present invention is further described below in conjunction with the accompanying drawings, wherein the same components are represented by the same reference numerals.

[0017] It should be noted that the words "front", "rear", "left", "right", "up" and "down" used in the following description refer to directions in the drawings, and the words "inside" and "outside" refer to directions toward or away from the geometric center of a specific component, respectively.

[0018] In order to make the contents of the present invention more clearly understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0019] like Figure 1 As shown, a method for online detection of coal quality includes a sample collection module, a sensor integration module, an online detection module, a data processing and analysis module, an automatic control module, an adaptive calibration and maintenance module, and result output and feedback.

[0020] The sample collection module includes using a sampler to collect coal samples in real time on the conveyor belt, using a sample splitter to split large samples into small portions for subsequent analysis, and placing the samples in a drying oven for drying to prevent moisture from affecting the test results. The samples must also be properly stored to avoid moisture, oxidation, etc. At the same time, each collected sample is marked, including the sampling time, location, sampling method, etc., to ensure the traceability of the sample.

[0021] Through the above scheme, the separated coal samples are screened by a vibrating screen, and the mixer evenly mixes the screened coal samples to ensure the uniformity and representativeness of the samples.

[0022] The sensor integration module includes an infrared spectrometer, a microwave moisture sensor, and a thermogravimetric analyzer, which collect data from coal samples in real time using multiple contact methods to provide comprehensive data support for coal quality analysis.

[0023] The online detection module includes sending the pretreated samples into the online detection system, which can measure the main physical and chemical indicators of the coal samples in real time. By selecting a high-resolution and high-sensitivity infrared spectrometer, the spectral information of the coal samples is collected in a non-contact manner, and the characteristic absorption peaks of different chemical components in the infrared spectrum are used to identify the coal quality components. The microwave moisture sensor uses the principle of interaction between microwaves and matter to quickly determine the moisture content in the coal through the reflection characteristics of the coal sample; the coal sample is heated under a controlled atmosphere by a thermogravimetric analyzer, and the change in sample mass with temperature is measured at the same time, and the volatile matter content of the coal is calculated by analyzing the thermogravimetric curve.

[0024] Through the above scheme, the spectrometer is equipped with an automatic calibration system to ensure spectral stability under long-term operation, and the sensor has a built-in temperature compensation mechanism to eliminate the influence of temperature changes on the measurement results. By optimizing the heating rate and atmosphere control, the resolution and repeatability of volatile detection are improved.

[0025] The online detection module includes transmitting the data obtained by sensors and analytical instruments to the data processing center, converting the collected raw data into an analyzable format, converting from analog signals to digital signals, and combining data from different sensors to provide more comprehensive coal quality information, extracting key characteristics of coal composition, moisture content, and ash content from the data, applying statistical model algorithms to analyze the data, identifying trends in the data to evaluate changes in coal quality, detecting and identifying anomalies in the data, and generating detailed coal quality reports.

[0026] Through the above scheme, online coal quality detection technology helps to timely discover problems in the production process and take measures to solve them, thereby avoiding production interruptions and waste.

[0027] The automated control module transmits sensor data and intelligent algorithm processing results to the central control unit in real time, and displays coal quality change trends and early warning information through a visual interface, helping managers to understand coal quality conditions in real time and make corresponding adjustments.

[0028] The adaptive calibration and maintenance module includes regular calibration and verification of sensors to ensure the accuracy of detection data. At the same time, by monitoring the working status of sensors and data analysis results, potential faults and abnormal conditions can be discovered and reported in a timely manner, providing strong support for system maintenance and ensuring the long-term stable operation and high-precision detection capabilities of the detection system.

[0029] Through the above scheme, by real-time monitoring of coal quality parameters, online coal quality detection technology helps to promptly discover problems in the production process and take measures to solve them, thereby avoiding production interruptions and waste; this helps to improve production efficiency and reduce production costs.

[0030] Result output and feedback include outputting the test results through a remote communication system for operators’ reference. If the test results exceed the preset quality standard range, the system can automatically issue an alarm and provide optimization suggestions or adjust relevant parameters in the coal production process, facilitating multi-party collaborative operations and decision-making support.

[0031] The workflow of the patent of the present invention first starts and continuously monitors the operating status of the coal production line. The system determines whether coal quality testing is needed based on the production cycle. When it is determined that testing is needed, the system automatically selects the coal sample collection point and collects coal samples. The collected coal samples are crushed and screened. The processed coal samples are transported to the multi-sensor integrated detection module through the conveying system, and then tested by multiple sensors such as infrared spectrometer, microwave moisture detector, thermogravimetric analyzer, etc. The collected multi-source data are integrated and processed in the data fusion and processing unit. The processed coal quality test results are output in real time and displayed to the operator in the form of charts through the user interface.

[0032] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for online detection of coal quality, It is characterized in that It includes sample collection module, sensor integration module, online detection module, data processing and analysis module, automatic control module, adaptive calibration and maintenance module, and result output and feedback.

2. A method for online detection of coal quality according to claim 1, Features: The sample collection module includes using a sampler to collect coal samples in real time on the conveyor belt, using a sample splitter to split large samples into small portions for subsequent analysis, and placing the samples in a drying box for drying to prevent moisture from affecting the test results. The samples must also be properly stored to avoid being affected by moisture, oxidation, etc. At the same time, each collected sample is marked, including the sampling time, location, sampling method, etc., to ensure the traceability of the sample.

3. A method for online detection of coal quality according to claim 1, Features: The sensor integration module includes an infrared spectrometer, a microwave moisture sensor, and a thermogravimetric analyzer, which collect data of coal samples in real time in a variety of contact modes, providing comprehensive data support for coal quality analysis.

4. A method for online detection of coal quality according to claim 1, Features: The online detection module includes sending the pretreated sample into the online detection system, which can measure the main physical and chemical indicators of the coal sample in real time, by selecting a high-resolution and high-sensitivity infrared spectrometer, collecting the spectral information of the coal sample in a non-contact manner, using the characteristic absorption peaks of different chemical components in the infrared spectrum to identify the coal quality components, using a microwave moisture sensor to use the principle of interaction between microwaves and matter to quickly determine the moisture content in the coal based on the reflection characteristics in the coal sample; heating the coal sample under a controlled atmosphere through a thermogravimetric analyzer, while measuring the change in sample mass with temperature, and calculating the volatile matter content of the coal by analyzing the thermogravimetric curve.

5. A method for online detection of coal quality according to claim 1, Features: The online detection module includes transmitting data acquired by sensors and analytical instruments to a data processing center, converting the collected raw data into an analyzable format, converting from analog signals to digital signals, and combining data from different sensors to provide more comprehensive coal quality information, extracting key characteristics of coal composition, moisture content, and ash content from the data, applying statistical model algorithms to analyze the data, identifying trends in the data to evaluate changes in coal quality, detecting and identifying abnormalities in the data, and generating a detailed coal quality report.

6. A method for online detection of coal quality according to claim 1, Features: The automation control module includes real-time transmission of sensor data and intelligent algorithm processing results to the central control unit, and displays coal quality change trends and early warning information through a visual interface to help managers understand the coal quality status in real time and make corresponding adjustments.

7. A method for online detection of coal quality according to claim 1, Features: The adaptive calibration and maintenance module includes regular calibration and verification of sensors to ensure the accuracy of detection data; at the same time, by monitoring the working status of sensors and data analysis results, potential faults and abnormal conditions are discovered and reported in a timely manner, providing strong support for system maintenance and ensuring the long-term stable operation and high-precision detection capabilities of the detection system.

8. A method for online detection of coal quality according to claim 1, Features: The result output and feedback include outputting the test results through a remote communication system for reference by operators. If the test results exceed the preset quality standard range, the system can automatically issue an alarm and provide optimization suggestions or adjust relevant parameters in the coal production process to facilitate multi-party collaborative operations and decision-making support.

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