Boiler heating surface ash early warning method and device, electronic equipment and storage medium

Abstract

The application relates to a boiler heating surface ash pre-warning method and device, electronic equipment and a storage medium, wherein the method comprises the following steps: obtaining the heating surface thickness of a boiler, the interval time length from the current time to the end time of the last blowing ash, and the temperature data sequence collected at a preset frequency within the interval time length; determining the heating surface cleaning factor of the boiler based on the interval time length and the temperature data sequence, and determining the heating surface ash thickness of the boiler based on the heating surface thickness and the temperature data sequence; determining the heating surface ash index of the boiler based on the heating surface ash thickness and the heating surface cleaning factor, so as to determine the heating surface ash degree of the boiler according to the actual interval of the heating surface ash index, and giving an ash pre-warning prompt when the heating surface ash degree is greater than a preset pre-warning degree. Thus, the problems of the related art, such as the blindness of the blowing ash operation due to the lack of accurate and effective ash monitoring means in the periodic blowing ash, and the influence on the safe and economic operation of the boiler, are solved.

Description

Technical Field

[0001] This application relates to the field of boiler equipment health management technology, and in particular to a method, device, electronic equipment and storage medium for early warning of ash accumulation on boiler heating surfaces. Background Technology

[0002] In the field of thermal power generation, the efficient and stable operation of boilers is crucial to ensuring the economic benefits of power plants. With the continuous growth of electricity demand, power plants are increasingly demanding higher boiler operating efficiency and safety. Ash accumulation on boiler heating surfaces has long been a problem plaguing the industry. It not only affects heat transfer efficiency and increases energy consumption but can also trigger a series of chain reactions. Ash accumulation leading to deteriorated heat transfer causes the wall temperature of the heating surfaces to rise. Metal materials on heating surfaces exposed to high temperatures for extended periods are prone to creep, corrosion, and other damage, significantly shortening the service life of the heating surfaces, increasing equipment maintenance costs and downtime for repairs, and further reducing the overall economic benefits of the power plant.

[0003] In related technologies, periodic soot blowing is used to clean the boiler's heating surfaces. Soot blowers are activated at preset time intervals (such as per shift or a fixed number of times per day) to clean the heating surfaces using steam or other media, thus maintaining the boiler's operating status. Simultaneously, differentiated soot blowing technologies based on online monitoring can be employed. For example, infrared thermal imaging can be used to identify ash accumulation areas and thicknesses to guide zoned soot blowing, or precise soot blowing strategies can be generated by collecting operating parameters and combining them with heat transfer analysis and optimization algorithms.

[0004] However, in related technologies, the lack of accurate and effective ash accumulation monitoring methods for regular soot blowing often leads to blind operation. Excessive soot blowing can cause unnecessary energy waste and equipment wear, while untimely soot blowing can exacerbate the ash accumulation problem, affecting the safe and economical operation of the boiler. Infrared thermal imaging is susceptible to interference from the furnace radiation background, leading to ash accumulation identification errors. Furthermore, evaluation methods based on operating parameters do not respond directly and promptly to local ash accumulation conditions, limiting the accuracy of soot blowing strategies and urgently requiring improvement. Summary of the Invention

[0005] This application provides a method, device, electronic equipment, and storage medium for early warning of ash accumulation on boiler heating surfaces, in order to solve the problems in related technologies, such as the lack of accurate and effective means of monitoring ash accumulation during regular soot blowing, which often leads to blind operation, excessively frequent soot blowing causing unnecessary energy waste and equipment wear, and untimely soot blowing causing the ash accumulation problem to become more and more serious, resulting in problems affecting the safe and economical operation of the boiler.

[0006] The first aspect of this application provides a method for early warning of ash accumulation on the heated surface of a boiler, comprising the following steps: acquiring the thickness of the heated surface of the boiler, the interval between the current time and the end time of the previous soot blowing cycle, and a temperature data sequence collected at a preset frequency within the interval; determining the heating surface cleaning factor of the boiler based on the interval and the temperature data sequence, and determining the ash thickness of the heated surface of the boiler based on the heating surface thickness and the temperature data sequence; determining the ash index of the heated surface of the boiler based on the ash thickness and the heating surface cleaning factor, so as to determine the degree of ash accumulation on the heated surface of the boiler according to the actual range of the ash index, and issuing an ash accumulation early warning reminder when the degree of ash accumulation on the heated surface is greater than a preset warning level.

[0007] Through the above-mentioned technical means, the embodiments of this application can obtain the boiler's heating surface thickness, soot blowing interval, and temperature data sequence within the interval to determine the boiler's heating surface cleaning factor and heating surface soot thickness. Then, by combining the two, the heating surface soot index is calculated. Based on the range of the heating surface soot index, the degree of soot accumulation is determined and timely soot accumulation warnings are issued. This enables real-time and accurate monitoring of the boiler's heating surface soot condition and provides early warnings, allowing staff to take soot blowing measures in a timely manner. This is of great significance for improving the overall efficiency of thermal power generation and ensuring the safe and stable operation of equipment.

[0008] Optionally, in one embodiment of this application, determining the ash thickness of the boiler's heated surface based on the heated surface thickness and the temperature data sequence includes: determining the heat flux density per unit area of ​​the boiler's heated surface using the heat balance equation of the working fluid inside the tube, based on the temperature data sequence and the heated surface thickness; and determining the ash thickness of the heated surface based on the temperature data sequence, the heated surface thickness, and the heat flux density per unit area of ​​the heated surface.

[0009] Through the above-mentioned technical means, the embodiments of this application can solve the heat flux density per unit area of ​​the heated surface based on the heat balance equation of the working fluid in the tube, and then deduce the ash accumulation thickness by combining the temperature data sequence and the thickness of the heated surface. Thus, supported by thermodynamic principles, the heat balance equation is used to ensure the engineering rationality of the heat flux density calculation results, improve the reliability of the ash accumulation thickness calculation, and ensure that the ash accumulation thickness calculation results are consistent with the actual operating conditions of the boiler.

[0010] Optionally, in one embodiment of this application, the temperature data sequence includes the mean of the inner surface temperature sequence of the heated surface and the mean of the outer surface temperature sequence of the heated surface; the formula for calculating the dust thickness of the heated surface is: , in, The thickness of the ash layer on the heated area. The thickness of the heated surface, The mean value of the temperature sequence of the inner surface of the heated surface. The mean value of the temperature sequence of the outer surface of the heated surface. q The heat flux density per unit area of ​​the heated surface is... To preset the thermal conductivity of the ash accumulation layer, The preset thermal conductivity of the heated surface.

[0011] Through the above-mentioned technical means, the embodiments of this application can use the temperature difference between the inner and outer surfaces, the thermal conductivity of the heated surface and the ash layer, and the heat flux density per unit area to construct a calculation model for the ash thickness of the heated surface. The ash thickness can be solved according to the real-time temperature sequence, making the calculation results more objective and accurate, and providing a reliable quantitative basis for ash accumulation early warning.

[0012] Optionally, in one embodiment of this application, the formula for calculating the ash index of the heated area is: , in, The gray index is the area of ​​heat transfer. The cleaning factor for the heated surface, To preset the maximum ash thickness of the heated area, , These are preset weighting coefficients.

[0013] Through the above-mentioned technical means, the embodiments of this application can adopt a weighted fusion method to couple the two indicators of the cleaning factor of the heated surface and the relative proportion of ash thickness according to a preset weight coefficient to obtain the ash index of the heated surface. This can simultaneously take into account the degree of attenuation of the overall heat transfer performance of the heated surface and the degree of physical accumulation of ash, avoiding the one-sidedness of evaluation by a single indicator, making the ash accumulation degree assessment more comprehensive and scientific, and able to more accurately reflect the actual operating status of the heated surface.

[0014] Optionally, in one embodiment of this application, before determining the heating surface cleaning factor of the boiler based on the interval duration and the temperature data sequence, the method further includes: obtaining historical heating surface cleaning factors and historical temperature data sequences within multiple historical soot blowing interval durations of the boiler's heating surface; constructing and training a heating surface soot monitoring model using the historical heating surface cleaning factors and the historical temperature data sequences, so as to input the interval duration and the temperature data sequence into the heating surface soot monitoring model to output the heating surface cleaning factor.

[0015] Through the above-mentioned technical means, the embodiments of this application can construct and train a ash monitoring model for the heated surface based on the operating data of multiple sets of historical soot blowing cycles, so that the cleaning factor of the heated surface can fully reflect the actual ash accumulation evolution law of the boiler, reduce the dependence on fixed empirical parameters, improve the accuracy and adaptability of the cleaning factor output, and thus enhance the working condition adaptability and reliability of the ash accumulation index early warning.

[0016] A second aspect of this application provides a boiler heating surface ash early warning device, comprising: an acquisition module, configured to acquire the boiler heating surface thickness, the interval between the current time and the end time of the previous soot blowing cycle, and a temperature data sequence collected at a preset frequency within the interval; a determination module, configured to determine the boiler heating surface cleaning factor based on the interval and the temperature data sequence, and determine the boiler heating surface ash thickness based on the heating surface thickness and the temperature data sequence; and an early warning module, configured to determine the boiler heating surface ash index based on the heating surface ash thickness and the heating surface cleaning factor, to determine the degree of ash accumulation on the boiler heating surface according to the actual range of the heating surface ash index, and to issue an ash accumulation early warning reminder when the degree of ash accumulation on the heating surface is greater than a preset warning level.

[0017] Through the above-mentioned technical means, the embodiments of this application can obtain the boiler's heating surface thickness, soot blowing interval, and temperature data sequence within the interval to determine the boiler's heating surface cleaning factor and heating surface soot thickness. Then, by combining the two, the heating surface soot index is calculated. Based on the range of the heating surface soot index, the degree of soot accumulation is determined and timely soot accumulation warnings are issued. This enables real-time and accurate monitoring of the boiler's heating surface soot condition and provides early warnings, allowing staff to take soot blowing measures in a timely manner. This is of great significance for improving the overall efficiency of thermal power generation and ensuring the safe and stable operation of equipment.

[0018] Optionally, in one embodiment of this application, the determining module includes: a first determining unit, configured to determine the heat flux density per unit area of ​​the boiler's heating surface using the heat balance equation of the working fluid inside the tube based on the temperature data sequence and the thickness of the heating surface; and a second determining unit, configured to determine the ash thickness of the heating surface based on the temperature data sequence, the thickness of the heating surface, and the heat flux density per unit area of ​​the heating surface.

[0019] Through the above-mentioned technical means, the embodiments of this application can solve the heat flux density per unit area of ​​the heated surface based on the heat balance equation of the working fluid in the tube, and then deduce the ash accumulation thickness by combining the temperature data sequence and the thickness of the heated surface. Thus, supported by thermodynamic principles, the heat balance equation is used to ensure the engineering rationality of the heat flux density calculation results, improve the reliability of the ash accumulation thickness calculation, and ensure that the ash accumulation thickness calculation results are consistent with the actual operating conditions of the boiler.

[0020] Optionally, in one embodiment of this application, the temperature data sequence includes the mean of the inner surface temperature sequence of the heated surface and the mean of the outer surface temperature sequence of the heated surface; the formula for calculating the dust thickness of the heated surface is: , in, The thickness of the ash layer on the heated area. The thickness of the heated surface, The mean value of the temperature sequence of the inner surface of the heated surface. The mean value of the temperature sequence of the outer surface of the heated surface. q The heat flux density per unit area of ​​the heated surface is... To preset the thermal conductivity of the ash accumulation layer, The preset thermal conductivity of the heated surface.

[0021] Through the above-mentioned technical means, the embodiments of this application can use the temperature difference between the inner and outer surfaces, the thermal conductivity of the heated surface and the ash layer, and the heat flux density per unit area to construct a calculation model for the ash thickness of the heated surface. The ash thickness can be solved according to the real-time temperature sequence, making the calculation results more objective and accurate, and providing a reliable quantitative basis for ash accumulation early warning.

[0022] Optionally, in one embodiment of this application, the formula for calculating the ash index of the heated area is: , in, The gray index is the area of ​​heat transfer. The cleaning factor for the heated surface, To preset the maximum ash thickness of the heated area, , These are preset weighting coefficients.

[0023] Through the above-mentioned technical means, the embodiments of this application can adopt a weighted fusion method to couple the two indicators of the cleaning factor of the heated surface and the relative proportion of ash thickness according to a preset weight coefficient to obtain the ash index of the heated surface. This can simultaneously take into account the degree of attenuation of the overall heat transfer performance of the heated surface and the degree of physical accumulation of ash, avoiding the one-sidedness of evaluation by a single indicator, making the ash accumulation degree assessment more comprehensive and scientific, and able to more accurately reflect the actual operating status of the heated surface.

[0024] Optionally, in one embodiment of this application, it further includes: a historical data acquisition module, configured to acquire historical heating surface cleaning factors and historical temperature data sequences within multiple historical soot blowing intervals of the boiler before determining the heating surface cleaning factor of the boiler based on the interval duration and the temperature data sequence; and a construction module, configured to construct and train a heating surface soot monitoring model using the historical heating surface cleaning factors and the historical temperature data sequence before determining the heating surface cleaning factor of the boiler based on the interval duration and the temperature data sequence, so as to input the interval duration and the temperature data sequence into the heating surface soot monitoring model to output the heating surface cleaning factor.

[0025] Through the above-mentioned technical means, the embodiments of this application can construct and train a ash monitoring model for the heated surface based on the operating data of multiple sets of historical soot blowing cycles, so that the cleaning factor of the heated surface can fully reflect the actual ash accumulation evolution law of the boiler, reduce the dependence on fixed empirical parameters, improve the accuracy and adaptability of the cleaning factor output, and thus enhance the working condition adaptability and reliability of the ash accumulation index early warning.

[0026] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the boiler heating ash early warning method as described in the above embodiments.

[0027] A fourth aspect of this application provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described boiler heating ash accumulation early warning method.

[0028] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described boiler heating ash surface early warning method.

[0029] This application embodiment can acquire the boiler's heating surface thickness, soot blowing interval, and temperature data sequence within the interval to determine the boiler's heating surface cleaning factor and soot thickness. Then, it calculates the heating surface ash index based on both, and determines the degree of ash accumulation according to the range of the heating surface ash index, providing timely ash accumulation warnings. This enables real-time and accurate monitoring of the boiler's heating surface ash condition and provides early warnings, allowing operators to take timely soot blowing measures. This is of great significance for improving the overall efficiency of thermal power generation and ensuring the safe and stable operation of equipment. Therefore, it solves the problems in related technologies where the lack of accurate and effective ash accumulation monitoring methods for regular soot blowing often leads to blind operation, excessively frequent soot blowing causes unnecessary energy waste and equipment wear, while untimely soot blowing leads to increasingly serious ash accumulation problems, affecting the safe and economical operation of the boiler.

[0030] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0031] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a boiler heating surface ash early warning method according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating the variation of the cleaning factor of a boiler heating surface according to an embodiment of this application; Figure 3 This is a schematic diagram of a boiler heating surface ash early warning device according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0032] Figure label: 10-Boiler heating surface ash early warning device; 100-Acquisition module, 200-Determination module, 300-Early warning module; 401-Memory, 402-Processor, 403-Communication interface. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] The following description, with reference to the accompanying drawings, outlines a boiler heating surface ash early warning method, apparatus, electronic device, and storage medium according to embodiments of this application. In the related technologies mentioned in the background section, the lack of accurate and effective ash accumulation monitoring methods for regular soot blowing often leads to blind operation. Excessive soot blowing results in unnecessary energy waste and equipment wear, while delayed soot blowing exacerbates the ash accumulation problem, affecting the safe and economical operation of the boiler. This application provides a boiler heating surface ash early warning method. This method acquires the boiler heating surface thickness, soot blowing interval, and temperature data sequence within the interval to determine the boiler heating surface cleaning factor and heating surface ash thickness. The two are then combined to calculate the heating surface ash index. Based on the range of the heating surface ash index, the degree of ash accumulation is determined, and timely ash accumulation early warning is issued. This allows for real-time and accurate monitoring of the boiler heating surface ash condition and early warning, enabling operators to take timely soot blowing measures. This is of great significance for improving the overall efficiency of thermal power generation and ensuring the safe and stable operation of equipment. This solves the problems in related technologies, such as the lack of accurate and effective ash accumulation monitoring methods for regular soot blowing, which often leads to blind operation of soot blowing, unnecessary energy waste and equipment wear caused by excessively frequent soot blowing, and the problem of untimely soot blowing leading to more and more serious ash accumulation, thus affecting the safe and economical operation of the boiler.

[0035] Specifically, Figure 1 This is a flowchart illustrating a method for early warning of ash accumulation on the heating surface of a boiler, provided in an embodiment of this application.

[0036] like Figure 1 As shown, the boiler heating surface ash early warning method includes the following steps: In step S101, the thickness of the boiler's heating surface, the time interval between the current moment and the end of the previous soot blowing, and the temperature data sequence collected at a preset frequency within the time interval are obtained.

[0037] It is understood that, in this embodiment, the thickness of the heated surface refers to the actual physical thickness of the boiler's heated surface tube wall, which can be obtained through design drawings or actual measurement calibration. The interval duration refers to the cumulative running time from the completion of the previous soot blowing operation to the current time, and can be used to reflect the natural accumulation process of ash on the heated surface. The preset frequency can be understood as a temperature sampling frequency preset according to the boiler's operating characteristics and ash accumulation rate. For example, the preset frequency can be once per minute or once every five minutes. The preset frequency can be set by those skilled in the art according to actual conditions, and no specific limitations are made here.

[0038] For example, in this embodiment, the thickness of the heating surface can be retrieved from the boiler equipment design manual or factory parameters; this parameter is a fixed structural value. In this embodiment, the time stamp of the last soot blowing cycle's end can be recorded by the system, and the difference between this time stamp and the current system time can be calculated to obtain the soot blowing interval. In this embodiment, based on the wall temperature measuring points already deployed at the boiler site, the heating surface temperature data sequence can be automatically collected and stored at a preset frequency. Abnormal jumps in data can be filtered and removed during the process to ensure the validity of the input data. Alternatively, temperature sensors on the inner and outer surfaces of the boiler heating surface can be used to collect the heating surface temperature data sequence, such as thermocouples or distributed fiber optic temperature measuring devices. The temperature data sequence includes at least the inner surface temperature sequence and the outer surface temperature sequence of the heating surface, and may also include the flue gas temperature.

[0039] The embodiments of this application can simultaneously acquire the structural parameters of the heated surface, the ash accumulation time, and the temperature sequence reflecting the changes in heat transfer state, providing complete multidimensional data support for subsequent joint analysis of cleaning factors and ash thickness. This avoids the errors and limitations that may exist with a single data source and improves the reliability and completeness of data acquisition.

[0040] In step S102, the cleaning factor of the boiler's heating surface is determined based on the interval duration and temperature data sequence, and the ash thickness of the boiler's heating surface is determined based on the heating surface thickness and temperature data sequence.

[0041] It is understood that, in the embodiments of this application, the ash thickness on the heated surface refers to the thickness of the ash layer accumulated on the outer side of the heated surface, directly reflecting the degree of physical ash accumulation. The heated surface cleaning factor can be used to measure the influence of ash on the heat transfer efficiency, defined as the ratio of the actual heat transfer coefficient to the ideal heat transfer coefficient, expressed as: , in, This represents the cleanliness factor of the heated surface, with a value ranging from 0 to 1. The smaller the value, the greater the impact of dust accumulation on the heat transfer efficiency of the heated surface. Indicates the actual heat transfer coefficient. This represents the ideal heat transfer coefficient.

[0042] In actual implementation, the embodiments of this application can calculate the cleaning factor by combining the ash accumulation law corresponding to the soot blowing interval and the heat transfer attenuation trend reflected by the temperature data sequence through mapping relationship or trained monitoring model; for the ash accumulation thickness, the thickness of the ash layer is derived based on the principle of heat conduction by taking the thickness of the heated surface as the structural benchmark and combining the temperature difference between the inside and outside of the pipe wall reflected by the temperature data sequence.

[0043] The embodiments of this application can determine the cleaning factor and ash thickness of the heated surface separately, realize multi-dimensional quantitative characterization of the ash accumulation state, and conduct parallel evaluation of the ash accumulation state of the heated surface from two dimensions: the degree of heat transfer performance attenuation and the geometric accumulation of ash. This improves the comprehensiveness and accuracy of ash accumulation state identification and provides a reliable basis for subsequent comprehensive index calculation.

[0044] In step S103, the ash index of the boiler's heating surface is determined based on the ash thickness of the heating surface and the cleaning factor of the heating surface. The degree of ash accumulation on the boiler's heating surface is determined according to the actual range of the ash index of the heating surface, and an ash accumulation warning is issued when the degree of ash accumulation on the heating surface is greater than the preset warning level.

[0045] It is understood that in the embodiments of this application, the ash index of the heated area is used to measure the degree of ash accumulation on the heated area. The value of the ash index of the heated area is between 0 and 1, and the larger the value, the more ash accumulates. The preset warning level can be understood as a threshold value of the ash accumulation index set in advance according to the boiler operation safety requirements and economic considerations. For example, the degree of ash accumulation can be divided into four levels: normal (ash index of heated area < 0.3), light ash accumulation (0.3 ≤ ash index of heated area < 0.6), moderate ash accumulation (0.6 ≤ ash index of heated area < 0.8), and severe ash accumulation (ash index of heated area ≥ 0.8). When the ash index of the heated area reaches or exceeds the "moderate ash accumulation" level, a warning is triggered. The preset warning level can be set by those skilled in the art according to the actual situation, and no specific restrictions are made here.

[0046] For example, in this embodiment of the application, the ash index of the heated surface can be determined based on the ash thickness of the heated surface and the cleaning factor of the heated surface. Based on the actual range of the ash index of the heated surface, the degree of ash accumulation on the heated surface of the boiler can be determined. When the degree of ash accumulation exceeds the preset warning level, the system can send a ash accumulation warning message to the terminal, or issue a warning reminder to the operator through various means such as audible and visual alarms, SMS notifications, and DCS (Distributed Control System) screen prompts. At the same time, it can automatically generate a soot blowing suggestion plan.

[0047] This application embodiment can generate a heated surface ash index based on the ash thickness and heated surface cleaning factor, realize the graded and quantitative judgment of the degree of ash accumulation, and trigger active warnings based on preset warning levels. This allows operators to quickly grasp the ash status of the heated surface and adjust the soot blowing strategy in a timely manner, avoiding the problem of blind periodic soot blowing. While ensuring the cleanliness of the heated surface, it reduces energy consumption and equipment wear caused by ineffective soot blowing, and improves the safety and economy of boiler operation.

[0048] Optionally, in one embodiment of this application, before determining the heating surface cleaning factor of the boiler based on the interval duration and temperature data sequence, the method further includes: obtaining historical heating surface cleaning factors and historical temperature data sequences within multiple historical soot blowing interval durations of the boiler's heating surface; using the historical heating surface cleaning factors and historical temperature data sequences, constructing and training a heating surface soot monitoring model, so as to input the interval duration and temperature data sequence into the heating surface soot monitoring model to output the heating surface cleaning factor.

[0049] It is understood that in the embodiments of this application, the historical soot blowing interval duration refers to the time interval record between multiple past soot blowing operations; the historical heating surface cleaning factor refers to the recorded value of the heating surface cleaning factor that has been calibrated or calculated and confirmed in each soot blowing interval during the boiler's past operating cycles.

[0050] In actual implementation, embodiments of this application can input the interval duration and temperature data sequence into the heated surface ash monitoring model to obtain the heated surface cleaning factor. The heated surface ash monitoring model is trained using a deep learning model based on historical temperature data sequences and historical heated surface cleaning factor sequences within historical soot blowing intervals. The heated surface cleaning factor is used to measure the degree of influence of heated surface ash on heat transfer efficiency.

[0051] Specifically, the ash monitoring model for heated surfaces is obtained by training a deep learning model based on historical temperature data sequences and historical heated surface cleaning factor sequences within historical soot blowing intervals. For example... Figure 2 As shown, the boiler heating surface cleaning factor changes over time. Starting from the ash accumulation point, the heating surface cleaning factor gradually decreases over time (i.e., the heat transfer efficiency of the heating surface is greatly affected by ash accumulation) until it reaches the soot blowing point. The temperature data sequence includes at least the inner surface temperature sequence of the heating surface and the outer surface temperature sequence of the heating surface, and may also include the flue gas temperature.

[0052] The ash monitoring model for the heated surface can be trained as follows: acquire historical heating surface cleaning factors and historical temperature data sequences within multiple historical soot blowing intervals of the boiler heating surface; use the historical temperature data sequences within multiple historical soot blowing intervals and the corresponding historical heating surface cleaning factor sequences to train the deep learning model, wherein the deep learning model can be an ELMAN neural network model or an LSTM (Long Short-Term Memory) neural network model.

[0053] The embodiments of this application can construct and train a ash monitoring model for heated surfaces based on operating data from multiple historical soot blowing cycles. This enables the cleaning factor of the heated surfaces to fully reflect the actual ash accumulation evolution law of the boiler, reduces the dependence on fixed empirical parameters, improves the accuracy and adaptability of the cleaning factor output, and thus enhances the condition adaptability and reliability of the ash accumulation index early warning.

[0054] Optionally, in one embodiment of this application, determining the ash thickness of the boiler's heating surface based on the heating surface thickness and temperature data sequence includes: determining the heat flux density per unit area of ​​the boiler's heating surface using the heat balance equation of the working fluid inside the tube according to the temperature data sequence and the heating surface thickness; and determining the ash thickness of the heating surface based on the temperature data sequence, the heating surface thickness, and the heat flux density per unit area of ​​the heating surface.

[0055] It is understood that the heat balance equation of the working fluid inside the tube in this embodiment refers to the thermodynamic equation describing the balance between the heat absorbed by the flowing working fluid inside the tube and the heat conducted through the tube wall, and is used to reflect the convective heat transfer process on the working fluid side. Heat flux density per unit area refers to the amount of heat passing through a unit area of ​​the heated surface per unit time, and can be used to characterize the heat transfer intensity of the heated surface.

[0056] In practical implementation, embodiments of this application can determine the mean values ​​of the inner and outer surface temperature sequences of the heated surface based on the temperature data sequence. Then, based on the mean values ​​of the inner and outer surface temperature sequences and the thickness of the heated surface, and combined with boiler operating parameters (such as working fluid flow rate and specific heat capacity), the heat flux density per unit area of ​​the heated surface is determined using the heat balance equation of the working fluid inside the tube. Furthermore, embodiments of this application can determine the ash thickness of the heated surface based on the mean values ​​of the inner and outer surface temperature sequences, the thickness of the heated surface, and the heat flux density per unit area of ​​the heated surface after obtaining the heat flux density.

[0057] The embodiments of this application can solve the heat flux density per unit area of ​​the heated surface based on the heat balance equation of the working fluid inside the pipe, and then deduce the ash accumulation thickness by combining the temperature data sequence and the thickness of the heated surface. Thus, supported by thermodynamic principles, the heat flux density calculation results are guaranteed to be engineering reasonable based on the heat balance equation, which improves the reliability of the ash accumulation thickness calculation and ensures that the ash accumulation thickness calculation results are consistent with the actual operating conditions of the boiler.

[0058] Optionally, in one embodiment of this application, the temperature data sequence includes the mean of the inner surface temperature sequence of the heated surface and the mean of the outer surface temperature sequence of the heated surface; the formula for calculating the dust thickness of the heated surface is: , in, The thickness of the ash layer on the heated surface. For the thickness of the heated surface, This represents the average of the temperature sequence of the inner surface of the heated surface. This represents the average of the temperature sequence of the outer surface of the heated surface. q The heat flux density per unit area of ​​the heated surface. To preset the thermal conductivity of the ash accumulation layer, The preset thermal conductivity of the heated surface.

[0059] It is understood that the average of the temperature sequences of the inner and outer surfaces of the heated surface in this embodiment can be interpreted as the arithmetic mean of the temperature data of the inner and outer walls of the heated surface collected within the time interval, which can be used to eliminate the interference of instantaneous temperature fluctuations on the calculation results. The preset thermal conductivity of the ash layer is related to the composition and density of the ash layer, and the preset thermal conductivity of the ash layer can be set by those skilled in the art according to the actual situation, without specific limitations here. The preset thermal conductivity of the heated surface is related to the type of material of the heated surface tube wall, and the preset thermal conductivity of the heated surface can be set by those skilled in the art according to the actual situation, without specific limitations here.

[0060] In actual implementation, the following relationships exist between the mean of the inner surface temperature sequence of the heated surface, the mean of the outer surface temperature sequence of the heated surface, the thickness of the heated surface, the ash thickness of the heated surface, and the heat flux density per unit area of ​​the heated surface in the embodiments of this application: , Specifically, based on the mean of the inner surface temperature sequence of the heated surface, the mean of the outer surface temperature sequence of the heated surface, the thickness of the heated surface, and the heat flux density per unit area of ​​the heated surface, the ash thickness of the heated surface is determined according to the following formula: , in, Indicates the thickness of the ash layer on the heated surface; Indicates the thickness of the heated surface; This represents the mean value of the temperature sequence of the inner surface of the heated surface; This represents the mean value of the temperature sequence of the outer surface of the heated surface; q This represents the heat flux density per unit area of ​​the heated surface; This indicates the preset thermal conductivity of the ash layer, which is related to the composition and density of the ash layer and can be set based on expert experience. This indicates the preset thermal conductivity of the heated surface, which is related to the type of material used for the heated surface tube wall.

[0061] The embodiments of this application can utilize the temperature difference between the inner and outer surfaces, the thermal conductivity of the heated surface and the ash layer, and the heat flux density per unit area to construct a calculation model for the ash thickness of the heated surface. This model can solve for the ash thickness based on the real-time temperature sequence, making the calculation results more objective and accurate, and providing a reliable quantitative basis for ash accumulation early warning.

[0062] Optionally, in one embodiment of this application, the formula for calculating the ash index of the heated area is: , in, The ash index is the area of ​​heat transfer. As a cleaning agent for heated surfaces, To preset the maximum ash thickness of the heated area, , These are preset weighting coefficients.

[0063] It is understood that the preset maximum ash thickness of the heated surface in this embodiment refers to the upper limit of the allowable ash thickness pre-set based on the structural dimensions of the heated surface and operating experience. For example, it may be preset to 3mm based on the safe operating threshold of the boiler heated surface. When the ash thickness reaches the preset maximum ash thickness of the heated surface, it is considered that the ash accumulation has seriously affected heat transfer and safe operation. The preset maximum ash thickness of the heated surface can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here. The preset weighting coefficient refers to the weighting of the ash thickness and the cleaning factor in the ash accumulation index, satisfying the requirements of the preset weighting coefficient. + =1, for example, can be preset =0.4、 =0.6, which gives the dust accumulation thickness a slightly higher decision weight. The weight allocation can also be adjusted according to the needs on site. The preset weight coefficient can be set by those skilled in the art according to the actual situation, and no specific restrictions are made here.

[0064] In actual implementation, the embodiments of this application can first divide the real-time calculated dust accumulation thickness by a preset maximum dust accumulation thickness to obtain a normalized ratio of the dust accumulation thickness, and then use the ratio of the dust accumulation thickness to the maximum allowable thickness. Complementary cleaning factor items The ratio of dust accumulation thickness to maximum allowable thickness in a weighted linear combination. Directly reflects the geometric accumulation degree of dust, complementary cleaning factor items This reflects the degree to which ash accumulation affects heat transfer capacity. When the ash accumulation is thin but dense, the cleaning factor can effectively capture significant deterioration in heat transfer; when the ash accumulation is thick but loose, the thickness factor ensures that the amount of ash accumulation is not underestimated. Both factors work together through a weighting coefficient to calculate the ash index of the heated area. The higher the ash index, the more severe the ash accumulation on the heated area and the more significant the degradation in heat transfer performance.

[0065] The embodiments of this application can adopt a weighted fusion method, which couples the two indicators of the cleaning factor of the heated surface and the relative proportion of ash thickness according to a preset weight coefficient to obtain the ash index of the heated surface. This can simultaneously take into account the degree of attenuation of the overall heat transfer performance of the heated surface and the degree of physical accumulation of ash, avoiding the one-sidedness of evaluation by a single indicator, making the ash accumulation degree assessment more comprehensive and scientific, and able to more accurately reflect the actual operating status of the heated surface.

[0066] The boiler heating surface ash early warning method proposed in this application can acquire the boiler heating surface thickness, soot blowing interval, and temperature data sequence within the interval to determine the boiler heating surface cleaning factor and heating surface ash thickness. Then, it calculates the heating surface ash index based on both, and determines the degree of ash accumulation according to the range of the heating surface ash index, providing timely ash accumulation early warning. This enables real-time and accurate monitoring of the boiler heating surface ash condition and provides early warnings, allowing staff to take timely soot blowing measures. This is of great significance for improving the overall efficiency of thermal power generation and ensuring the safe and stable operation of equipment. Therefore, it solves the problem in related technologies where the lack of accurate and effective ash accumulation monitoring methods for regular soot blowing often leads to blind operation, excessively frequent soot blowing causes unnecessary energy waste and equipment wear, while untimely soot blowing leads to increasingly serious ash accumulation problems, affecting the safe and economical operation of the boiler.

[0067] Next, referring to the accompanying drawings, we describe the boiler heating surface ash early warning device proposed according to the embodiments of this application.

[0068] Figure 3 This is a schematic diagram of the structure of the boiler heating surface ash early warning device according to an embodiment of this application.

[0069] like Figure 3 As shown, the boiler heating surface ash early warning device 10 includes: an acquisition module 100, a determination module 200, and an early warning module 300.

[0070] The acquisition module 100 is used to acquire the thickness of the boiler's heating surface, the time interval between the current moment and the end of the previous soot blowing, and the temperature data sequence collected at a preset frequency within the time interval.

[0071] The determination module 200 is used to determine the cleaning factor of the boiler's heating surface based on the interval duration and temperature data sequence, and to determine the ash thickness of the boiler's heating surface based on the heating surface thickness and temperature data sequence.

[0072] The early warning module 300 is used to determine the ash index of the boiler's heating surface based on the ash thickness and the cleaning factor of the heating surface, so as to determine the degree of ash accumulation on the boiler's heating surface according to the actual range of the ash index, and to issue an ash accumulation early warning reminder when the degree of ash accumulation on the heating surface is greater than the preset warning level.

[0073] Optionally, in one embodiment of this application, the determining module 200 includes: a first determining unit and a second determining unit.

[0074] The first determining unit is used to determine the heat flux density per unit area of ​​the boiler's heating surface based on the temperature data sequence and the thickness of the heating surface, using the heat balance equation of the working fluid inside the tube.

[0075] The second determining unit is used to determine the ash thickness of the heated surface based on the temperature data sequence, the thickness of the heated surface, and the heat flux density per unit area of ​​the heated surface.

[0076] Optionally, in one embodiment of this application, the temperature data sequence includes the mean of the inner surface temperature sequence of the heated surface and the mean of the outer surface temperature sequence of the heated surface; the formula for calculating the dust thickness of the heated surface is: , in, The thickness of the ash layer on the heated surface. For the thickness of the heated surface, This represents the average of the temperature sequence of the inner surface of the heated surface. This represents the average of the temperature sequence of the outer surface of the heated surface. q The heat flux density per unit area of ​​the heated surface. To preset the thermal conductivity of the ash accumulation layer, The preset thermal conductivity of the heated surface.

[0077] Optionally, in one embodiment of this application, the formula for calculating the ash index of the heated area is: , in, The ash index is the area of ​​heat transfer. As a cleaning agent for heated surfaces, To preset the maximum ash thickness of the heated area, , These are preset weighting coefficients.

[0078] Optionally, in one embodiment of this application, the boiler heating surface ash early warning device 10 further includes: a historical data acquisition module and a construction module.

[0079] The historical data acquisition module is used to acquire historical heating surface cleaning factors and historical temperature data sequences within multiple historical soot blowing intervals of the boiler before determining the boiler's heating surface cleaning factor based on the interval duration and temperature data sequence.

[0080] The module is used to build and train a heating surface ash monitoring model using historical heating surface cleaning factors and historical temperature data sequences before determining the boiler heating surface cleaning factor based on the interval duration and temperature data sequence. The interval duration and temperature data sequence are input into the heating surface ash monitoring model to output the heating surface cleaning factor.

[0081] It should be noted that the foregoing explanation of the boiler heating surface ash early warning method embodiment also applies to the boiler heating surface ash early warning device of this embodiment, and will not be repeated here.

[0082] The boiler heating surface ash early warning device proposed in this application can acquire the boiler heating surface thickness, soot blowing interval, and temperature data sequence within the interval to determine the boiler heating surface cleaning factor and heating surface ash thickness. Then, it calculates the heating surface ash index based on both, and determines the degree of ash accumulation according to the range of the heating surface ash index, providing timely ash accumulation early warning. This enables real-time and accurate monitoring of the boiler heating surface ash condition and provides early warnings, allowing staff to take timely soot blowing measures. This is of great significance for improving the overall efficiency of thermal power generation and ensuring the safe and stable operation of equipment. Therefore, it solves the problem in related technologies where the lack of accurate and effective ash accumulation monitoring methods for regular soot blowing often leads to blind operation, excessively frequent soot blowing causes unnecessary energy waste and equipment wear, while untimely soot blowing leads to increasingly serious ash accumulation problems, affecting the safe and economical operation of the boiler.

[0083] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0084] When the processor 402 executes the program, it implements the boiler heating surface ash early warning method provided in the above embodiments.

[0085] Furthermore, electronic devices also include: Communication interface 403 is used for communication between memory 401 and processor 402.

[0086] The memory 401 is used to store computer programs that can run on the processor 402.

[0087] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0088] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0089] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0090] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0091] This application also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described boiler heating ash accumulation early warning method.

[0092] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-mentioned boiler heating ash accumulation early warning method.

[0093] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0094] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0095] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0096] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0097] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0098] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0099] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0100] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for early warning of ash accumulation on boiler heating surfaces, characterized in that, Includes the following steps: The boiler's heating surface thickness, the time interval between the current moment and the end of the previous soot blowing cycle, and the temperature data sequence collected at a preset frequency within the time interval are obtained. Based on the interval duration and the temperature data sequence, the cleaning factor of the boiler's heating surface is determined, and based on the heating surface thickness and the temperature data sequence, the ash thickness of the boiler's heating surface is determined. Based on the ash thickness of the heated surface and the cleaning factor of the heated surface, the ash index of the heated surface of the boiler is determined, so as to determine the degree of ash accumulation on the heated surface of the boiler according to the actual range of the ash index of the heated surface, and to issue an ash accumulation warning when the degree of ash accumulation on the heated surface is greater than the preset warning level.

2. The method according to claim 1, characterized in that, Determining the ash thickness of the boiler's heating surface based on the heating surface thickness and the temperature data sequence includes: Based on the temperature data sequence and the thickness of the heated surface, the heat flux density per unit area of ​​the heated surface of the boiler is determined using the heat balance equation of the working fluid inside the tube. The ash thickness of the heated surface is determined based on the temperature data sequence, the thickness of the heated surface, and the heat flux density per unit area of ​​the heated surface.

3. The method according to claim 2, characterized in that, The temperature data sequence includes the mean of the inner surface temperature sequence of the heated surface and the mean of the outer surface temperature sequence of the heated surface; the formula for calculating the dust thickness of the heated surface is: ， in, The thickness of the ash layer on the heated area. The thickness of the heated surface, The mean value of the temperature sequence of the inner surface of the heated surface. The mean value of the temperature sequence of the outer surface of the heated surface. q The heat flux density per unit area of ​​the heated surface is... To preset the thermal conductivity of the ash accumulation layer, The preset thermal conductivity of the heated surface.

4. The method according to claim 1, characterized in that, The formula for calculating the ash index of the heated area is: ， in, The gray index refers to the heated area. The cleaning factor for the heated surface, To preset the maximum ash thickness of the heated area, , These are the preset weighting coefficients.

5. The method according to claim 1, characterized in that, Before determining the boiler's heating surface cleaning factor based on the interval duration and the temperature data sequence, the method further includes: Obtain the historical heating surface cleaning factor and historical temperature data sequence within multiple historical soot blowing intervals of the boiler's heating surface; Using the historical heating surface cleaning factor and the historical temperature data sequence, a heating surface ash monitoring model is constructed and trained. The interval duration and the temperature data sequence are input into the heating surface ash monitoring model to output the heating surface cleaning factor.

6. A boiler heating surface ash early warning device, characterized in that, include: The acquisition module is used to acquire the thickness of the boiler's heating surface, the time interval between the current moment and the end time of the previous soot blowing, and the temperature data sequence collected at a preset frequency within the time interval. The determination module is used to determine the cleaning factor of the heating surface of the boiler based on the interval duration and the temperature data sequence, and to determine the ash thickness of the heating surface of the boiler based on the heating surface thickness and the temperature data sequence. The early warning module is used to determine the ash index of the boiler's heated surface based on the ash thickness of the heated surface and the cleaning factor of the heated surface, to determine the degree of ash accumulation on the heated surface of the boiler according to the actual range of the ash index, and to issue an ash accumulation early warning reminder when the degree of ash accumulation on the heated surface is greater than a preset warning level.

7. The apparatus according to claim 6, characterized in that, The determining module includes: The first determining unit is used to determine the heat flux density per unit area of ​​the boiler's heating surface based on the temperature data sequence and the thickness of the heating surface, using the heat balance equation of the working fluid inside the tube. The second determining unit is used to determine the ash thickness of the heated surface based on the temperature data sequence, the thickness of the heated surface, and the heat flux density per unit area of ​​the heated surface.

8. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the boiler heating surface ash early warning method as described in any one of claims 1-5.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the boiler heating surface ash early warning method as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the boiler heating surface ash early warning method as described in any one of claims 1-5.

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