Monitoring method and device for judging coking state of hearth, equipment and storage medium

Abstract

The invention relates to the technical field of hearth coking monitoring, and discloses a monitoring method and device for judging a hearth coking state, equipment and a storage medium, and the monitoring method for judging the hearth coking state comprises the following steps: in a boiler operation combustion state, obtaining basic data of six heating surfaces; calculating operation heat transfer coefficients of the six heating surfaces; initial data of the boiler in a healthy state are collected, and an optimal heat transfer coefficient prediction model is trained; boiler operation parameters in the boiler operation combustion state are obtained, and the optimal heat transfer coefficient in the boiler operation combustion state is obtained; and comparing the operation heat transfer coefficient with the optimal heat transfer coefficient to determine whether the hearth of the boiler is coked or not. According to the method, the whole boiler is divided into six heating surfaces, whether adjustment or optimization such as coking or soot blowing needs to be carried out or not is judged through the optimal heat transfer coefficient prediction model according to the six coking surface heat transfer coefficients, boiler shutdown is not needed, the coking condition of the boiler is found in time, power generation losses are reduced, and the operation health state and the operation efficiency of a combustion hearth are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of furnace coking monitoring, and particularly to a monitoring method, device, equipment, and storage medium for judging the coking state of a furnace. Background Art

[0002] Coking is a relatively common problem in boiler operation. Generally, ash particles moving with the flue gas are cooled together with the flue gas due to the heat absorption of the heating surface of the furnace water wall. If the liquid slag particles have solidified due to temperature reduction before approaching the water wall or furnace wall, when adhering to the heating surface pipe wall, a loose ash layer will be formed, which can be easily removed by soot blowing during operation. When the temperature in the furnace is relatively high, some ash particles have reached the molten or semi-molten state. If these ash particles do not receive sufficient cooling to reach the solidification state before reaching the heating surface, they have a high bonding ability and are prone to adhering to the heating surface or furnace wall scoured by the flue gas, and even reach the molten state. The adhered molten or semi-molten ash particles and unburned coke cause the coking to continuously develop and deteriorate.

[0003] For the traditional prediction method of coking on the heating surface of a boiler, according to the boiler operation principle, the boiler operation data is encoded, classified, and modeled to establish a boiler non-coking training database in different load intervals, a boiler non-coking training model, and a boiler coking training model. Based on the boiler operation mechanism and big data model, the coking state of the boiler is predicted. The data set used by this method is trained and modeled, and this judgment mode is based on the historical data set. This prediction method is not accurate enough and cannot comprehensively display the coking state of the boiler.

[0004] In addition, there is also a method of judging by the coking thickness. By arbitrarily selecting a point on other water walls of the boiler as an observation point, the coking thickness at the position corresponding to the observation point is calculated; and so on. By adjusting the positions of the selected viewing holes and observation points, the coking thickness information at the positions corresponding to all the measured observation points is subjected to three-dimensional image processing, and the coking position and thickness of the entire boiler water wall can be obtained. Although the coking judgment of the boiler observation point is accurate, the workload is large. The furnace has a large volume and many viewing points, and multiple repeated measurements are required, consuming a large amount of manpower and material resources. And it cannot achieve a comprehensive and real-time judgment of the furnace coking state.

[0005] In summary, how to monitor the coking state inside the furnace in real time and accurately has become a problem to be solved. Summary of the Invention

[0006] The present invention provides a monitoring method for judging the coking state of a furnace. The whole boiler is divided into six heating surfaces, and the overall pollution rate of each heating surface is obtained by calculating the heat transfer coefficients of the six surfaces respectively. Finally, according to the optimal heat transfer coefficient prediction model, it is judged whether coke knocking, soot blowing or other adjustments or optimizations are needed based on the six coking surfaces. There is no need to stop the furnace, the boiler coking situation can be detected in time, power generation losses are reduced, and the operating health state and operating efficiency of the combustion furnace are improved.

[0007] The present invention provides a monitoring method for judging the coking state of a furnace, including:

[0008] Under the operating and combustion state of the boiler, basic data is obtained for each of the six heating surfaces of the boiler; wherein, the basic data includes the temperature values of the flue gas before and after passing through the water wall, the heat flow passing through the heating surface, and the area size of the heating surface.

[0009] Calculate the operating heat transfer coefficients of the six heating surfaces respectively according to the basic data;

[0010] Collect the initial data under the healthy state of the boiler, and use the initial data to train the optimal heat transfer coefficient prediction model;

[0011] Obtain the boiler operating parameters under the operating and combustion state of the boiler, and use the trained optimal heat transfer coefficient prediction model to obtain the optimal heat transfer coefficient under the operating and combustion state of the boiler;

[0012] Compare the operating heat transfer coefficient with the optimal heat transfer coefficient, and judge the decrease situation of the heat transfer coefficient of the same heating surface to determine whether the furnace of the boiler is coked.

[0013] Further, in the step of obtaining basic data for each of the six heating surfaces of the boiler under the operating and combustion state of the boiler, the six heating surfaces include the water wall, the low-temperature superheater, the dividing screen, the final-stage superheater, the low-temperature reheater, and the final-stage reheater. A temperature sensor is arranged at the starting position of the flue gas flow direction of each heating surface, and a flowmeter is installed on each heating surface.

[0014] Further, in the step of calculating the operating heat transfer coefficients of the six heating surfaces respectively according to the basic data, the calculation formula is:

[0015]

[0016] Wherein, Q is the heat flow, A is the heat transfer area, Δt is the temperature difference, and is the difference in the temperature measured by the sensor in the flue gas flow direction.

[0017] Further, the step of collecting the initial data under the healthy state of the boiler and using the initial data to train the optimal heat transfer coefficient prediction model includes:

[0018] Collect the boiler operation parameters and the optimal heat transfer coefficient under the healthy state of the boiler, and store the collection time, boiler operation parameters, and the heat transfer coefficients of six heating surfaces in the database as a sample set; wherein, the optimal heat transfer coefficient includes the heat transfer coefficients of six heating surfaces;

[0019] Use the collection time and boiler operation parameters in the database as inputs, and the optimal heat transfer coefficient as the output to train the optimal heat transfer coefficient prediction model; wherein, the optimal heat transfer coefficient prediction model is a relationship model between boiler operation parameters and the optimal heat transfer coefficient of the boiler under the un-coked state;

[0020] Further, in the step of collecting the boiler operation parameters and the optimal heat transfer coefficient under the healthy state of the boiler, and storing the collection time, boiler operation parameters, and the heat transfer coefficients of six heating surfaces in the database as a sample set, the healthy state of the boiler is the state of the first combustion operation after each boiler coke removal and soot blowing treatment;

[0021] The boiler operation parameters include combustion amount, primary air flow rate, primary air temperature, secondary air flow rate, secondary air temperature, feed water flow rate, the temperatures and pressures of six heating surfaces.

[0022] Further, in the step of comparing the operating heat transfer coefficient with the optimal heat transfer coefficient, judging the decrease of the heat transfer coefficient of the same heating surface to determine whether the furnace of the boiler is coked,

[0023] Calculate the difference between the operating heat transfer coefficient and the optimal heat transfer coefficient to determine whether the heat transfer efficiency decays and judge whether the furnace of the boiler is coked; if coking occurs in the furnace, the heat transfer efficiency of the heating surface will decrease, and the water wall and other heating surfaces will not be able to cool the flue gas or play the role of temperature control.

[0024] Further, after the step of comparing the operating heat transfer coefficient with the optimal heat transfer coefficient, judging the decrease of the heat transfer coefficient of the same heating surface to determine whether the furnace of the boiler is coked, it further includes:

[0025] Visualize the data of six heating surfaces in three dimensions to display the real-time calculation results on the front end;

[0026] If coking occurs in the furnace of the boiler, present the coking situation in the form of data and send an alarm signal to prompt the operator to perform operations such as coke removal, soot blowing, and optimization of combustion parameters.

[0027] The present invention also provides a monitoring device for judging the coking state of the furnace, including:

[0028] An acquisition module, configured to obtain basic data for each of the six heating surfaces of a boiler under the operating combustion state of the boiler; wherein the basic data includes the temperature values before and after the flue gas passes through the water wall, the heat flux passing through the heating surface, and the area size of the heating surface.

[0029] A calculation module, configured to calculate the operating heat transfer coefficients of the six heating surfaces respectively according to the basic data.

[0030] An acquisition module, configured to acquire initial data under the healthy state of the boiler, and use the initial data to train an optimal heat transfer coefficient prediction model.

[0031] A prediction module, configured to obtain the boiler operating parameters under the operating combustion state of the boiler, and use the trained optimal heat transfer coefficient prediction model to obtain the optimal heat transfer coefficient under the operating combustion state of the boiler.

[0032] A comparison module, configured to compare the operating heat transfer coefficient with the optimal heat transfer coefficient, and judge the decline situation of the heat transfer coefficient of the same heating surface, so as to determine whether the furnace of the boiler is coked.

[0033] The present invention also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned monitoring method for judging the coking state of the furnace are implemented.

[0034] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned monitoring method for judging the coking state of the furnace are implemented.

[0035] The beneficial effects of the present invention are as follows:

[0036] 1. Real-time monitoring and accuracy: By calculating the heat transfer coefficients of the six heating surfaces to obtain the overall pollution rate of each heating surface, this method can monitor the coking state of the boiler in real time, and due to the calculation based on physical values, it has high accuracy, which helps to detect the coking situation of the boiler in time and avoid the further deterioration of the problem.

[0037] 2. Setting an optimal heat transfer coefficient prediction model, the basic data set is derived from the data of the optimal state after each boiler soot blowing or coke knocking, and the optimal heat transfer coefficient is obtained according to different operating parameters, which is convenient for comparison under the same operating state. The efficiency of the equipment decays during use. The heat transfer coefficients in the optimal state after boiler soot blowing and maintenance at different time nodes are different for the same boiler operating parameters, so there is a time relationship in this database. The recurrent neural network model is good at processing time-related data and can accurately predict the optimal heat transfer coefficient under specific furnace operating parameters.

[0038] 3. Intuitively present the coking state of the boiler through 3D visualization, enabling operators to more intuitively understand the operation of the boiler, separately grasp the heat transfer coefficient of each heating surface, promptly reflect the coking state, and thus have an intuitive and quantitative understanding of the overall furnace. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic flowchart of a monitoring method for judging the coking state of a furnace in an embodiment of the present invention.

[0040] Figure 2 It is a schematic operation diagram of the process in the present invention.

[0041] Figure 3 It is a schematic structural diagram of a monitoring device for judging the coking state of a furnace in an embodiment of the present invention.

[0042] Figure 4 It is a schematic internal structure diagram of a computer device in an embodiment of the present invention.

[0043] The realization, functional characteristics, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] The present invention mainly monitors the overall coking state in a combustion boiler, divides the boiler into six heating surfaces, obtains the overall pollution rate of each heating surface by calculating the heat transfer coefficient of the six surfaces respectively, and presents it intuitively through 3D visualization. Finally, a clustering analysis model is used to judge whether coke knocking or soot blowing and other adjustments or optimizations are needed based on the six coking surfaces. By calculating physical values for online detection, this method is accurate, does not require boiler shutdown, can promptly detect boiler coking, reduce power generation losses, and improve the operation health status and operation efficiency of the combustion furnace.

[0046] As Figure 1 、 2 shown, the present invention provides a monitoring method for judging the coking state of a furnace, including:

[0047] S1. Under the combustion state of the boiler operation, obtain basic data for each of the six heating surfaces of the boiler; wherein, the basic data includes the temperature values before and after the flue gas passes through the water wall, the heat flow passing through the heating surface, and the area size of the heating surface.

[0048] Division of Heating Surfaces and Installation of Sensors: According to the operating structure and design of the boiler, and in the direction of flue gas heat transfer, the boiler is divided into six heating surfaces, namely water wall, low-temperature superheater, dividing screen, final-stage superheater, low-temperature reheater, and final-stage reheater. Each heating surface is a module, and temperature sensors are installed at the starting position of the flue gas flow direction for each module to obtain the temperature values of the flue gas before and after passing through the water wall. Flow meters are installed on the six surfaces to obtain the heat flow passing through the heating surfaces, and the areas of the six heating surfaces are obtained according to the boiler design.

[0049] S2. Calculate the operating heat transfer coefficients of the six heating surfaces respectively based on the above basic data; the calculation formula is:

[0050]

[0051] Among them, Q is the heat flow, A is the heat transfer area, Δt is the temperature difference, and is the temperature difference measured by the sensor in the flue gas flow direction. Calculate the heat transfer coefficients K of the six surfaces respectively according to the formula, which are K1, K2, K3, K4, K5, and K6.

[0052] S3. Collect the initial data under the healthy state of the boiler, and use the initial data to train the optimal heat transfer coefficient prediction model;

[0053] Step S3 specifically includes:

[0054] S31. Collect the boiler operating parameters and the optimal heat transfer coefficients under the healthy state of the boiler, and store the collection time, boiler operating parameters, and heat transfer coefficients of the six heating surfaces in the database as a sample set; among them, the optimal heat transfer coefficients include the heat transfer coefficients of the six heating surfaces; the healthy state of the boiler is the state of the first combustion operation after each boiler coke removal and soot blowing treatment;

[0055] Establish a data acquisition system: used to collect data of temperature sensors, flow meters, and other relevant parameters of boiler operation in real time (such as combustion amount, primary air volume, primary air temperature, secondary air volume, secondary air temperature, feed water volume, etc.). Only collect the boiler operating parameters and heat transfer coefficients under the healthy state of the furnace. Among them, the data of the healthy state of the boiler are the data of the first use of the boiler, and the data of the first use after each maintenance, cleaning, soot blowing, and coke removal of the furnace. This data is the optimal heat transfer coefficient under the state of no coking in the furnace. Store the collected time, corresponding boiler operating parameters, corresponding heat transfer coefficients of the six heating surfaces, etc. in the database as a sample set to prepare data for model training.

[0056] S32. Use the collection time and boiler operating parameters in the database as inputs, and the optimal heat transfer coefficient as the output to train the optimal heat transfer coefficient prediction model; among them, the optimal heat transfer coefficient prediction model is a relationship model between boiler operating parameters and the optimal heat transfer coefficient of the boiler under the non-coking state;

[0057] The training of the optimal heat transfer coefficient prediction model is carried out through the recurrent neural network algorithm. The sample set of the model is derived from the boiler operation status and heat transfer parameter database sample set in step S31. Taking time and the combustion parameters of the boiler operation as inputs and the optimal heat transfer coefficient as the output, the model is trained to obtain the relationship model between the boiler operation parameters and the optimal heat transfer coefficient of the boiler in the un-coked state. Further, the recurrent neural network model is updated and optimized regularly to improve the prediction accuracy and robustness. The implementation method is that every time when the boiler is in operation in the un-coked state after maintenance, soot blowing, and coke knocking, the combustion data and heat transfer coefficient are updated to the database. When the database is updated with data each time, the recurrent neural network model is re-trained to ensure the continuous learning and improvement of the model.

[0058] S4. Obtain the boiler operation parameters under the boiler operation combustion state, and use the trained optimal heat transfer coefficient prediction model to obtain the optimal heat transfer coefficient under the boiler operation combustion state;

[0059] Under the state of the boiler operation combustion, through the sensors of six heating surfaces, the heat transfer coefficients of the six heating surfaces are calculated in real time. The heat transfer coefficients calculated as above are K1, K2, K3, K4, K5, and K6. At the same time, according to the trained recurrent neural network model, input the current boiler operation parameters to obtain the optimal heat transfer coefficients K10, K20, K30, K40, K50, and K60 under the current situation, and compare the two heat transfer coefficients. By comparing the differences, the decrease of the heat transfer coefficient of the same heating surface can be calculated respectively to judge the coking state of the furnace. Further, the total difference of the six heating surfaces can also be calculated to obtain the overall coking situation of the boiler.

[0060] S5. Compare the operating heat transfer coefficient with the optimal heat transfer coefficient, and judge the decrease of the heat transfer coefficient of the same heating surface to determine whether the furnace of the boiler is coked.

[0061] Calculate the difference between the operating heat transfer coefficient and the optimal heat transfer coefficient to determine whether the heat transfer efficiency decays and judge whether the furnace of the boiler is coked;

[0062] If coking occurs in the furnace, the heat transfer efficiency of the heating surface will decrease, and the water wall and other heating surfaces cannot cool the flue gas or play the role of temperature control. The decay of the heat transfer efficiency is judged by the difference between the heat transfer coefficient calculated in real time under the current working condition and the optimal heat transfer coefficient under the same combustion parameters.

[0063] S6. Visualize the data of the six heating surfaces in three dimensions to display the real-time calculation results on the front end;

[0064] If coking occurs in the furnace of the boiler, the coking situation is presented in the form of data, and an alarm signal is sent to prompt the operator to perform operations such as coke knocking, soot blowing, and combustion parameter optimization.

[0065] As Figure 3 shown, the present invention also provides a monitoring device for judging the coking state of the furnace, including:

[0066] An acquisition module 1, configured to acquire basic data for each of the six heating surfaces of the boiler under the operation and combustion state of the boiler; wherein the basic data includes the temperature values before and after the flue gas passes through the water wall, the heat flux passing through the heating surface, and the area size of the heating surface;

[0067] A calculation module 2, configured to calculate the operating heat transfer coefficients of the six heating surfaces respectively according to the basic data;

[0068] An acquisition module 3, configured to acquire initial data under the healthy state of the boiler, and train the optimal heat transfer coefficient prediction model by using the initial data;

[0069] A prediction module 4, configured to acquire the boiler operation parameters under the operation and combustion state of the boiler, and adopt the trained optimal heat transfer coefficient prediction model to obtain the optimal heat transfer coefficient under the operation and combustion state of the boiler;

[0070] A comparison module 5, configured to compare the operating heat transfer coefficient with the optimal heat transfer coefficient, judge the decrease of the heat transfer coefficient of the same heating surface, so as to determine whether the furnace of the boiler is coked.

[0071] In one embodiment, in the acquisition module 1, the six heating surfaces include a water wall, a low-temperature superheater, a dividing screen, a final-stage superheater, a low-temperature reheater, and a final-stage reheater. A temperature sensor is arranged at the starting position of the flue gas flow direction of each heating surface, and a flow meter is installed on each heating surface.

[0072] In one embodiment, in the calculation module 2, the calculation formula is:

[0073]

[0074] wherein, Q is the heat flux, A is the heat transfer area, Δt is the temperature difference, and is the difference in the temperature measured by the sensor in the flue gas flow direction.

[0075] In one embodiment, the acquisition module 3 includes:

[0076] An acquisition unit, configured to acquire the boiler operation parameters and the optimal heat transfer coefficient under the healthy state of the boiler, and store the acquisition time, the boiler operation parameters, and the heat transfer coefficients of the six heating surfaces in a database as a sample set; wherein, the optimal heat transfer coefficient includes the heat transfer coefficients of the six heating surfaces;

[0077] A training unit is configured to use the acquisition time and boiler operation parameters in the database as inputs and the optimal heat transfer coefficient as an output to train an optimal heat transfer coefficient prediction model. Among them, the optimal heat transfer coefficient prediction model is a relationship model between boiler operation parameters and the optimal heat transfer coefficient of the boiler in the non-coking state.

[0078] In one embodiment, in the acquisition unit, the boiler health state is the state of the first combustion operation after each boiler coke removal and soot blowing treatment.

[0079] The boiler operation parameters include combustion amount, primary air volume, primary air temperature, secondary air volume, secondary air temperature, feed water volume, and the temperature and pressure of six heating surfaces.

[0080] In one embodiment, in the comparison module 5, calculate the difference between the operating heat transfer coefficient and the optimal heat transfer coefficient to determine whether the heat transfer efficiency decays and to judge whether the furnace of the boiler is coked. If coking occurs in the furnace, the heat transfer efficiency of the heating surface will decrease, and the water-cooled wall and other heating surfaces will not be able to cool the flue gas or play a role in temperature control.

[0081] In one embodiment, it further includes:

[0082] A display module that visualizes the data of six heating surfaces in three dimensions to display the results of real-time calculations on the front end.

[0083] If coking occurs in the furnace of the boiler, present the coking situation in the form of data and send an alarm signal to prompt the operator to perform operations such as coke removal, soot blowing, and optimization of combustion parameters.

[0084] Each of the above modules and units is configured to correspondingly execute each step in the monitoring method for judging the coking state of the furnace. The specific implementation manner refers to the method embodiments described above and will not be elaborated here.

[0085] As Figure 4 shown, the present invention also provides a computer device, which can be a server, and its internal structure can be as Figure 4 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store all the data required for the process of the monitoring method for judging the coking state of the furnace. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes the monitoring method for judging the coking state of the furnace.

[0086] Those skilled in the art can understand that Figure 4 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied.

[0087] An embodiment of this application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned monitoring method for judging the coking state of the furnace is implemented.

[0088] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above embodiments of the methods. Among them, any reference to a memory, storage, database, or other medium provided in this application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0089] It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, device, article or method. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, device, article or method including that element.

[0090] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A monitoring method for judging the coking state of a furnace, characterized in that, Including: Under the operating and combustion state of the boiler, basic data is obtained for each of the six heating surfaces of the boiler; wherein, the basic data includes the temperature values before and after the flue gas passes through the water wall, the heat flux passing through the heating surface, and the area size of the heating surface. Calculate the operating heat transfer coefficients of the six heating surfaces respectively according to the basic data. Collect the initial data under the healthy state of the boiler, and use the initial data to train the optimal heat transfer coefficient prediction model. Obtain the boiler operating parameters under the operating and combustion state of the boiler, and use the trained optimal heat transfer coefficient prediction model to obtain the optimal heat transfer coefficient under the operating and combustion state of the boiler. Compare the operating heat transfer coefficient with the optimal heat transfer coefficient, and judge the decrease of the heat transfer coefficient of the same heating surface to determine whether the furnace of the boiler is coked.

2. The monitoring method for judging the coking state of the furnace chamber according to claim 1, characterized in that In the step of obtaining basic data for each of the six heating surfaces of the boiler under the operating and combustion state of the boiler, the six heating surfaces include the water wall, the low-temperature superheater, the dividing screen, the final-stage superheater, the low-temperature reheater, and the final-stage reheater. A temperature sensor is provided at the starting position of the flue gas flow direction of each heating surface, and a flowmeter is installed on each heating surface.

3. The monitoring method for judging the coking state of the furnace hearth according to claim 1, characterized in that, In the step of calculating the operating heat transfer coefficients of the six heating surfaces respectively according to the basic data, the calculation formula is: Wherein, Q is the heat flux, A is the heat transfer area, Δt is the temperature difference, and is the difference in temperature measured by the sensor in the flue gas flow direction.

4. The monitoring method for judging the coking state of the furnace chamber according to claim 1, characterized in that The step of collecting the initial data under the healthy state of the boiler and using the initial data to train the optimal heat transfer coefficient prediction model includes: Collect the boiler operating parameters and the optimal heat transfer coefficient under the healthy state of the boiler, and store the collection time, the boiler operating parameters, and the heat transfer coefficients of the six heating surfaces in the database as a sample set; wherein, the optimal heat transfer coefficient includes the heat transfer coefficients of the six heating surfaces. Use the collection time and the boiler operating parameters in the database as inputs, and the optimal heat transfer coefficient as the output to train the optimal heat transfer coefficient prediction model; wherein, the optimal heat transfer coefficient prediction model is a relationship model between the boiler operating parameters and the optimal heat transfer coefficient of the boiler in the non-coked state.

5. The monitoring method for judging the coking state of the furnace hearth according to claim 4, characterized in that In the step of collecting the boiler operating parameters and the optimal heat transfer coefficient under the healthy state of the boiler, and storing the collection time, the boiler operating parameters, and the heat transfer coefficients of the six heating surfaces in the database as a sample set, the healthy state of the boiler is the state of the first combustion operation after each boiler coke removal and ash cleaning treatment. The boiler operating parameters include the combustion amount, the primary air volume, the primary air temperature, the secondary air volume, the secondary air temperature, the feed water volume, the temperatures and pressures of the six heating surfaces.

6. The monitoring method for judging the coking state of the furnace chamber according to claim 1, characterized in that, In the step of comparing the operating heat transfer coefficient with the optimal heat transfer coefficient, judging the decrease of the heat transfer coefficient of the same heating surface to determine whether the furnace of the boiler is coked, Calculate the difference between the operating heat transfer coefficient and the optimal heat transfer coefficient to determine whether the heat transfer efficiency decays and judge whether the furnace of the boiler is coked; if coking occurs in the furnace, the heat transfer efficiency of the heating surface will decrease, and the water wall and other heating surfaces will not be able to cool the flue gas or play the role of temperature control.

7. The monitoring method for judging the coking state of the furnace chamber according to claim 1, wherein, After the step of comparing the operating heat transfer coefficient with the optimal heat transfer coefficient to determine the decrease in the heat transfer coefficient of the same heating surface and thereby determine whether the furnace of the boiler is fouled, the method further includes: Performing three-dimensional visualization on the data of six heating surfaces to display the results of real-time calculation at the front end; If fouling occurs in the furnace of the boiler, presenting the fouling situation in a data form and sending out an alarm signal to prompt the operating personnel to perform operations such as slagging removal, soot blowing, and combustion parameter optimization.

8. A monitoring device for judging the coking state of a furnace, characterized in that, Including: An acquisition module for acquiring basic data for each of the six heating surfaces of the boiler under the operating combustion state of the boiler; Wherein the basic data includes the temperature values of the flue gas before and after passing through the water wall, the heat flux passing through the heating surface, and the area size of the heating surface; A calculation module for calculating the operating heat transfer coefficients of the six heating surfaces respectively according to the basic data; A collection module for collecting initial data in the healthy state of the boiler and training an optimal heat transfer coefficient prediction model using the initial data; A prediction module for obtaining the boiler operation parameters under the operating combustion state of the boiler and using the trained optimal heat transfer coefficient prediction model to obtain the optimal heat transfer coefficient under the operating combustion state of the boiler; A comparison module for comparing the operating heat transfer coefficient with the optimal heat transfer coefficient to judge the decrease in the heat transfer coefficient of the same heating surface and thereby determine whether the furnace of the boiler is fouled.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it realizes the steps of the monitoring method for judging the fouling state of the furnace according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it realizes the steps of the monitoring method for judging the fouling state of the furnace according to any one of claims 1 to 7.

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