Coal particulate matter online monitoring system for thermal power plant

Abstract

The invention relates to the technical field of coal particulate matter monitoring, and discloses a thermal power plant coal particulate matter online monitoring system. According to the thermal power plant coal particulate matter on-line monitoring system, through the honeycomb phase array technology of the data acquisition unit, the speed, temperature, humidity and pressure data of airflow in a pipeline are accurately captured, effective monitoring of flow field characteristics under various tubular conditions is achieved, and the data processing unit utilizes the advanced noise removal and data calibration technology to monitor the flow field characteristics of the pipeline. In addition, the monitoring and early warning unit can implement threshold analysis, mark abnormal values in real time and generate a monitoring report, it is ensured that the large-screen environment monitoring and displaying unit provides reliable information feedback, and the real-time monitoring and early warning effect is achieved. By adopting a honeycomb structure and a phase array measurement mode, the large-aperture wind speed can be accurately measured, the measurement precision guarantee value is within 2%, and the resistance drop loss is less than 5%.

Description

Technical Field

[0001] The invention relates to the technical field of coal particle monitoring, and in particular to an online monitoring system for coal particles in a thermal power plant. Background Art

[0002] At present, the development trend of industry is increasingly showing large-scale, integrated and large-scale development. The development of large-scale industry is accompanied by the concentrated and large-scale discharge of three wastes. The concentrated discharge has brought many conveniences to environmental protection treatment. However, the accurate detection and monitoring of large-scale emissions has become an urgent need for enterprises and environmental protection departments. Especially for waste gas, accurate measurement of emission volume and emission factors can not only grasp the emission of pollutants and carbon dioxide of enterprises, but also have important significance in the accurate peak regulation of coal-fired power.

[0003] In actual projects, the use of large pipelines often has limitations. Large pipelines have various shapes, including round, square and rectangular. At the same time, there is a serious shortage of straight pipe sections, and the flow field distribution of gas in the pipeline is extremely uneven, resulting in generally low monitoring accuracy of coal particulate matter in thermal power plants, with a general error of more than 5% to 7%. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides an online monitoring system for coal particles in a thermal power plant. The system is equipped with a honeycomb phased array technology through a data acquisition unit to accurately capture the velocity, temperature, humidity and pressure data of the airflow in the pipeline, and realize effective monitoring of the flow field characteristics under various pipe conditions. The data processing unit utilizes advanced noise removal and data calibration technology to greatly improve the calculation accuracy of the airflow and coal particle concentration. In addition, the monitoring and early warning unit can implement threshold analysis, mark abnormal values ​​in real time and generate monitoring reports to ensure that the large-screen environmental monitoring display unit provides reliable information feedback. By adopting a honeycomb structure and phased array measurement method, large-diameter wind speed can be accurately measured, and the measurement accuracy is guaranteed to be within 2%, and the drag drop loss is less than 5%, thereby solving the above-mentioned problems.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned object, the present invention provides the following technical solutions: an online monitoring system for coal particulate matter in a thermal power plant, comprising a data acquisition unit, a data processing unit, a monitoring and early warning unit, and a display unit;

[0008] The data acquisition unit collects airflow velocity data through honeycomb phase array technology, collects temperature data in the airflow through a temperature sensor, collects humidity data in the airflow through a humidity sensor, and collects pressure data in the pipeline through a pressure sensor, and transmits the collected data to the data processing unit through a data transmission interface;

[0009] The data processing unit removes noise from the transmitted airflow velocity data, airflow temperature data, airflow humidity data, and pressure data in the pipeline, performs data calibration, and calculates the mean value of airflow volume, mean value of airflow velocity, mean value of airflow temperature, mean value of airflow humidity, pipeline pressure value, and coal particle concentration;

[0010] The monitoring and early warning unit performs threshold analysis on the calculated average value of air flow, average value of air flow velocity, average value of air flow temperature, average value of air flow humidity, pipeline pressure value and coal particle concentration, marks the values ​​exceeding the abnormality, and generates a monitoring report and sends it to the display unit;

[0011] The display unit sends the monitoring report to the environment monitoring large screen.

[0012] Preferably, the formula for removing noise from the velocity data of the airflow, the temperature data in the airflow, the humidity data in the airflow, and the pressure data in the pipeline is as follows:

[0013]

[0014] In the formula, MA(x) represents the velocity data of the airflow after denoising and smoothing, the temperature data in the airflow, the humidity data in the airflow, and the pressure data in the pipeline. t-i represents the velocity data of the original airflow at the time point ti, the temperature data in the airflow, the humidity data in the airflow, and the pressure data in the pipeline, n represents the size of the averaging window, and i represents the counting subscript.

[0015] Preferably, the formula for data calibration of the airflow velocity data, the airflow temperature data, the airflow humidity data and the pressure data in the pipeline is as follows:

[0016] Sjjz=a*x+b

[0017] In the formula, Sjjz represents the calibrated airflow velocity data value, the temperature data value in the airflow, the humidity data value in the airflow, and the pressure data value in the pipeline, a represents the calibration coefficient, x represents the uncalibrated airflow velocity data value, the temperature data value in the airflow, the humidity data value in the airflow, and the pressure data value in the pipeline directly output by the sensor, and b represents the offset, which is used to compensate for the system error.

[0018] Preferably, the calculation formula of the mean airflow volume is as follows:

[0019] Qldz=Gdjm*Qsjz

[0020] In the formula, Qldz represents the mean air flow rate, Gdjm represents the cross-sectional area of ​​the pipe, and Qsjz represents the mean air flow velocity.

[0021] Preferably, the air flow velocity mean calculation formula is as follows:

[0022]

[0023] In the formula, Qsjz represents the mean airflow velocity, N represents the number of airflow data points, Qli represents the airflow velocity data at the i-th time point, and i represents the count subscript.

[0024] Preferably, the calculation formula of the mean airflow temperature is as follows:

[0025]

[0026] In the formula, Qlwd represents the mean airflow temperature, M represents the number of airflow temperature data points, Ltwdi represents the airflow temperature data at the i-th time point, and i represents the count subscript.

[0027] Preferably, the calculation formula for the mean air flow humidity is as follows:

[0028]

[0029] In the formula, Sdpl represents the mean value of airflow humidity, K represents the number of airflow humidity data points, and Sdpm i Represents the airflow humidity data at the i-th time point, where i represents the count subscript.

[0030] Preferably, the pipeline pressure value calculation formula is as follows:

[0031]

[0032] In the formula, Gdy represents the pipeline pressure value, W represents the number of pipeline pressure data points, and Ygsj i Represents the pipeline pressure data at the i-th time point, where i represents the counting subscript.

[0033] Preferably, the calculation formula for the coal particle concentration is as follows:

[0034]

[0035] In the formula, Mknd represents the concentration of coal particles, Gk represents the number of particles per unit air flow, Zlnd represents the mass concentration of coal particles in the air flow, and Qldz represents the mean air flow.

[0036] Preferably, the monitoring report is automatically generated by ReportLab, recording abnormal monitoring data, timestamps and automatically generated charts.

[0037] Compared with the prior art, the present invention provides an online monitoring system for coal particulate matter in a thermal power plant, which has the following beneficial effects:

[0038] The present invention uses the honeycomb phased array technology of the data acquisition unit to accurately capture the velocity, temperature, humidity and pressure data of the airflow in the pipeline, and realizes effective monitoring of the flow field characteristics under various pipe conditions. The data processing unit uses advanced noise removal and data calibration technology to greatly improve the calculation accuracy of the airflow and coal particle concentration. In addition, the monitoring and early warning unit can implement threshold analysis, mark abnormal values ​​in real time and generate monitoring reports to ensure that the large-screen environmental monitoring display unit provides reliable information feedback. By adopting the honeycomb structure and phased array measurement method, large-caliber wind speed can be accurately measured, and the measurement accuracy is guaranteed to be within 2%, and the drag drop loss is less than 5%. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0040] 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.

[0041] In view of the extremely uneven distribution of gas flow in the pipeline, which results in generally low monitoring accuracy of coal particulate matter in thermal power plants, an online monitoring system for coal particulate matter in thermal power plants is proposed. Figure 1 ,The system includes a data acquisition unit, a data processing unit, a monitoring and early warning unit and a display unit;

[0042] The data acquisition unit is the core part of the online monitoring system of coal particles in thermal power plants. It uses advanced honeycomb phase array technology to accurately collect various parameters of the airflow. Specifically, the system is equipped with highly sensitive airflow velocity sensors. These sensors use the principle of honeycomb phase array to provide reliable velocity data in complex flow fields, ensuring high-precision measurements even in different pipe types (such as circular, square and rectangular) and uneven flow conditions. At the same time, temperature sensors are used to measure temperature data in the airflow. These sensors can respond quickly and have good repeatability to meet the requirements of temperature fluctuations in the working environment. Humidity sensors collect real-time data on airflow humidity by using capacitive or resistive technology, which is crucial to understanding the behavior and characteristics of coal particles, because changes in humidity will directly affect the sedimentation and suspension state of particles. The pressure data in the pipeline is collected through high-precision pressure sensors. These sensors can work reliably in high temperature and high pressure environments to ensure data accuracy;

[0043] All collected data are transmitted to the data processing unit in real time in a highly reliable manner through a high-speed data transmission interface (such as RS-485, CAN bus or wireless communication technology). This data transmission interface has the characteristics of strong anti-interference ability and high transmission rate, and can support the real-time transmission of large amounts of data, ensuring that the system responds quickly and accurately to the airflow status in the pipeline;

[0044] The data processing unit removes noise from the transmitted airflow velocity data, airflow temperature data, airflow humidity data, and pressure data in the pipeline, and performs data calibration, and calculates the mean airflow volume, mean airflow velocity, mean airflow temperature, mean airflow humidity, pipeline pressure value, and coal particle concentration, among which:

[0045] The formula for removing noise from data is as follows:

[0046]

[0047] Noise removal technology filters out external interference and random errors of the sensor itself, making the collected data more authentic and reliable. This can significantly reduce the deviation between the measured value and the true value and improve the overall reliability of the monitoring system. In the formula, MA(x) represents the velocity data, temperature data, humidity data and pressure data of the airflow after denoising and smoothing. x t-i represents the velocity data of the original airflow at time point ti, the temperature data in the airflow, the humidity data in the airflow, and the pressure data in the pipeline, n represents the size of the average window, i represents the count subscript, and the noise data may lead to wrong judgment in the subsequent analysis process. After removing the noise by moving average technology, the data processing unit can perform mean calculation and anomaly detection more accurately, reducing the occurrence of false alarms;

[0048] The formula for data calibration is as follows:

[0049] Sjjz=a*x+b

[0050] The data calibration process can eliminate the measurement deviation caused by sensor aging or environmental changes, thereby ensuring the consistency of all sensor data. This is particularly important in a multi-point acquisition system, which can ensure the comparability between different sensors. In the formula, Sjjz represents the calibrated airflow velocity data value, the temperature data value in the airflow, the humidity data value in the airflow, and the pressure data value in the pipeline, a represents the calibration coefficient, x represents the uncalibrated airflow velocity data value, the temperature data value in the airflow, the humidity data value in the airflow, and the pressure data value in the pipeline directly output by the sensor, and b represents the offset, which is used to compensate for the system error. Through regular calibration, it can be ensured that each sensor is always operating in the best working state, thereby improving the monitoring quality and credibility of the entire monitoring system;

[0051] The calculation formula of the mean air flow rate is as follows:

[0052] Qldz=Gdjm*Qsjz

[0053] The mean airflow rate directly reflects the flow conditions in the pipeline, which helps to determine whether the production process is normal or not, and timely discover potential faults or flow anomalies. In the formula, Qldz represents the mean airflow rate, Gdjm represents the cross-sectional area of ​​the pipeline, and Qsjz represents the mean airflow velocity. By tracking the changes in airflow rate, engineers can understand the combustion efficiency, and then adjust fuel consumption to optimize the energy efficiency and economy of the power plant.

[0054] The formula for calculating the mean air velocity is as follows:

[0055]

[0056] The calculation of the mean air velocity can evaluate whether the air flow distribution in the pipeline is uniform, which is crucial for the uniformity control of coal particles, thereby avoiding thermal imbalance or particle sedimentation problems caused by uneven flow velocity distribution. In the formula, Qsjz represents the mean air velocity, N represents the number of air flow data points, and Ql i represents the airflow velocity data at the i-th time point, i represents the count subscript, and the calculation of the mean airflow velocity can evaluate whether the airflow distribution in the pipeline is uniform, which is crucial for the uniformity control of coal particles, thereby avoiding thermal imbalance or particle precipitation problems caused by uneven velocity distribution;

[0057] The calculation formula for the mean airflow temperature is as follows:

[0058]

[0059] Airflow temperature is an important parameter in the combustion process. By calculating the mean, the temperature change of the combustion chamber can be monitored and the combustion strategy can be adjusted in time to improve the combustion efficiency. In the formula, Qlwd represents the mean airflow temperature, M represents the number of airflow temperature data points, and Ltwd i represents the airflow temperature data at the i-th time point, where i represents the count subscript. Persistent high temperature or temperature fluctuation can cause equipment damage. Therefore, by monitoring the mean value of the airflow temperature, equipment failure caused by overheating can be effectively prevented, which helps to improve power generation safety.

[0060] The calculation formula for the mean air flow humidity is as follows:

[0061]

[0062] Humidity levels affect the suspension and settling characteristics of coal particles. Calculating the mean humidity can help engineers better understand the behavior of the fuel and ensure the stability of the coal gasification and combustion process. In the formula, Sdpl represents the mean airflow humidity, K represents the number of airflow humidity data points, and Sdpm i represents the airflow humidity data at the i-th time point, where i represents the count subscript. Appropriate humidity control helps reduce the generation of harmful gases, so calculating the humidity mean is also crucial to meeting environmental protection requirements;

[0063] The calculation formula of pipeline pressure value is as follows:

[0064]

[0065] The pressure value in the pipeline is an important indicator for evaluating the normal operation of the system. It can help engineers grasp the load and flow status of the system in real time and detect abnormalities in time. In the formula, Gdy represents the pipeline pressure value, W represents the number of pipeline pressure data points, and Ygsj represents the pressure value of the pipeline. i Indicates the pipeline pressure data at the i-th time point, where i represents the count subscript. Over-limit high pressure or under-limit low pressure may lead to accidents. Continuously monitoring the pipeline pressure and calculating the mean value can effectively warn of possible safety hazards and ensure safe operation.

[0066] The calculation formula for coal particulate matter concentration is as follows:

[0067]

[0068] The calculation of coal particle concentration is directly related to the compliance of emission standards and environmental protection, thus helping power plants meet the requirements of environmental laws and regulations and reduce operational risks. In the formula, Mknd represents the coal particle concentration, Gk represents the number of particles per unit gas flow, Zlnd represents the mass concentration of coal particles in the air flow, and Qldz represents the mean gas flow. Accurate particle concentration measurement provides a reliable basis for the operation of dust removal equipment, enabling targeted maintenance and adjustment, improving dust removal efficiency, and reducing operating costs.

[0069] The monitoring and early warning unit plays a vital role in the online monitoring system of coal particulate matter in thermal power plants. Its core function is to conduct a comprehensive threshold analysis of the calculated mean air flow, mean air flow velocity, mean air flow temperature, mean air flow humidity, pipeline pressure value and coal particulate matter concentration. In this process, the system will set the thresholds of multiple key performance indicators based on historical data and industry standards, including normal range, warning range and abnormal range. These thresholds are dynamically adjusted according to actual working conditions to balance accuracy and flexibility.

[0070] The monitoring and early warning unit analyzes real-time data through precise algorithms and uses statistical methods to monitor various parameters. When the mean value of a parameter exceeds the set threshold, the system will mark the value as abnormal in real time and activate the early warning mechanism. This mechanism usually involves multiple alarm methods, including sound alarms, flashing indicator lights, email push and short message notifications, etc., to ensure that relevant personnel can know the abnormal situation in the first time;

[0071] While marking abnormal values, the monitoring and early warning unit can also generate a detailed monitoring report. This report will include the specific time when the abnormality occurred, the corresponding parameter values ​​and their thresholds, as well as possible cause analysis and recommended countermeasures. This information is sent to the display unit through an efficient data transmission interface, such as an environmental monitoring screen or a user terminal interface, so that managers and operators can instantly obtain the system status and deal with abnormalities in a timely manner.

[0072] Through the application of the above system, the honeycomb structure and phase array measurement method can be used to accurately measure the large-diameter wind speed, and the measurement accuracy is guaranteed to be within 2%, and the drag drop loss is less than 5%.

[0073] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An online monitoring system for coal particulate matter in a thermal power plant, characterized by: It includes a data acquisition unit, a data processing unit, a monitoring and early warning unit and a display unit; The data acquisition unit collects airflow velocity data through honeycomb phase array technology, collects temperature data in the airflow through a temperature sensor, collects humidity data in the airflow through a humidity sensor, and collects pressure data in the pipeline through a pressure sensor, and transmits the collected data to the data processing unit through a data transmission interface; The data processing unit removes noise from the transmitted airflow velocity data, airflow temperature data, airflow humidity data, and pressure data in the pipeline, performs data calibration, and calculates the mean value of airflow volume, mean value of airflow velocity, mean value of airflow temperature, mean value of airflow humidity, pipeline pressure value, and coal particle concentration; The monitoring and early warning unit performs threshold analysis on the calculated average value of air flow, average value of air flow velocity, average value of air flow temperature, average value of air flow humidity, pipeline pressure value and coal particle concentration, marks the values ​​exceeding the abnormality, and generates a monitoring report and sends it to the display unit; The display unit sends the monitoring report to the environment monitoring large screen.

2. The on-line monitoring system for coal particulates in a thermal power plant according to claim 1, characterized in that: The formula for removing noise from the velocity data of the airflow, the temperature data in the airflow, the humidity data in the airflow, and the pressure data in the pipeline is as follows: In the formula, MA(x) represents the velocity data of the airflow after denoising and smoothing, the temperature data in the airflow, the humidity data in the airflow, and the pressure data in the pipeline. t-i represents the velocity data of the original airflow at the time point ti, the temperature data in the airflow, the humidity data in the airflow, and the pressure data in the pipeline, n represents the size of the averaging window, and i represents the counting subscript.

3. The on-line monitoring system for coal particulates in a thermal power plant according to claim 2, characterized in that: The formula for data calibration of the airflow velocity data, the airflow temperature data, the airflow humidity data and the pressure data in the pipeline is as follows: Sjjz=a*x+b In the formula, Sjjz represents the calibrated airflow velocity data value, the temperature data value in the airflow, the humidity data value in the airflow, and the pressure data value in the pipeline, a represents the calibration coefficient, x represents the uncalibrated airflow velocity data value, the temperature data value in the airflow, the humidity data value in the airflow, and the pressure data value in the pipeline directly output by the sensor, and b represents the offset, which is used to compensate for the system error.

4. The on-line monitoring system for coal particulates in a thermal power plant according to claim 3, characterized in that: The calculation formula of the mean air flow rate is as follows: Qldz=Gdjm*Qsjz In the formula, Qldz represents the mean air flow rate, Gdjm represents the cross-sectional area of ​​the pipe, and Qsjz represents the mean air flow velocity.

5. The on-line monitoring system for coal particulates in a thermal power plant according to claim 4, characterized in that: The calculation formula for the mean air flow velocity is as follows: In the formula, Qsjz represents the mean airflow velocity, N represents the number of airflow data points, and Ql i represents the airflow velocity data at the i-th time point, where i represents the counting subscript.

6. The on-line monitoring system for coal particulates in a thermal power plant according to claim 5, characterized in that: The calculation formula of the mean air flow temperature is as follows: In the formula, Qlwd represents the mean airflow temperature, M represents the number of airflow temperature data points, and Ltwd i Represents the airflow temperature data at the i-th time point, where i represents the counting subscript.

7. The on-line monitoring system for coal particulates in a thermal power plant according to claim 6, characterized in that: The calculation formula of the mean air flow humidity is as follows: In the formula, Sdpl represents the mean value of airflow humidity, K represents the number of airflow humidity data points, and Sdpm i Represents the airflow humidity data at the i-th time point, where i represents the count subscript.

8. The on-line monitoring system for coal particulates in a thermal power plant according to claim 7, characterized in that: The pipeline pressure value calculation formula is as follows: In the formula, Gdy represents the pipeline pressure value, W represents the number of pipeline pressure data points, and Ygsj i Represents the pipeline pressure data at the i-th time point, where i represents the counting subscript.

9. The on-line monitoring system for coal particulate matter in a thermal power plant according to claim 8, characterized in that: The calculation formula of the coal particulate matter concentration is as follows: In the formula, Mknd represents the concentration of coal particles, Gk represents the number of particles per unit air flow, Zlnd represents the mass concentration of coal particles in the air flow, and Qldz represents the mean air flow.

10. The on-line monitoring system for coal particulates in a thermal power plant according to claim 9, characterized in that: The monitoring report is automatically generated by ReportLab, recording abnormal monitoring data, timestamps and automatically generated charts.

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