Heating furnace coking prediction method and system

Abstract

The invention provides a heating furnace coking prediction method and system, and belongs to the technical field of heating furnaces. The method comprises the steps of monitoring furnace tube temperature of a target heating furnace in real time based on video monitoring information of a furnace tube of the target heating furnace; according to the furnace tube temperature, determining a current furnace tube hot area and a temperature change range value in a preset time period; according to the temperature change range value, the temperature change value delta T and the temperature change rate of the current furnace tube hot area in the preset time period are calculated; according to the video monitoring information, the temperature change value delta T and the temperature change rate, the coking condition and the cracking condition of the furnace tube are predicted. Therefore, the current state of the furnace tube hot area is obtained, the furnace tube coking condition and the furnace tube fracture condition in the next second are predicted, and the purposes of predicting furnace tube coking by means of the furnace tube temperature and achieving early warning of furnace tube fracture are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of heating furnaces, and in particular to a method and system for predicting coking of a heating furnace. Background Art

[0002] The damage of the furnace tube of the heating furnace is generally caused by coking. Coking is the phenomenon that the oil temperature inside the furnace tube exceeds a certain limit and then thermally cracks, turns into free carbon, and then accumulates on the furnace tube. The process of furnace tube damage is: coking begins inside the furnace tube, which increases the temperature of the tube wall, thereby aggravating the oxidation of the tube wall surface and the corrosion of the furnace tube. Oxidation makes the thickness of the tube wall thinner, and then the thinning of the tube wall first bulges under the dual effects of internal pressure and heat. After the furnace tube bulges, the gap between the inner wall of the furnace tube and the coke layer will increase, so that the temperature of other parts of the furnace tube will gradually increase, thereby further oxidation and thinning, and finally causing the furnace tube to rupture. It can be seen that the increase in the temperature of the furnace tube will cause the temperature of the oil inside the furnace tube to increase, which will lead to coking in the furnace tube, and the coking of the furnace tube will further increase the temperature of the tube wall, aggravate the oxidation of the tube wall surface and the corrosion of the furnace tube, and then cause the final furnace tube to rupture. Therefore, there is a certain correlation between furnace tube temperature, furnace tube coking and furnace tube rupture. Among them, only furnace tube temperature can be monitored. How to establish the relationship between furnace tube temperature, furnace tube coking and furnace tube rupture, use furnace tube temperature to predict furnace tube coking, and realize furnace tube rupture early warning is a problem that needs to be solved urgently. Summary of the invention

[0003] The purpose of the embodiments of the present invention is to provide a method and system for predicting coking in a heating furnace, so as to at least solve the above-mentioned problem that furnace tube temperature cannot be used to accurately predict furnace tube coking and warn of furnace tube rupture.

[0004] In order to achieve the above object, the present invention provides a method for predicting coking in a heating furnace in a first aspect, comprising:

[0005] Real-time monitoring of the temperature of the furnace tube of the target heating furnace based on the video monitoring information of the furnace tube of the target heating furnace;

[0006] According to the furnace tube temperature, determine the temperature change range value of the current furnace tube hot zone and the preset time period;

[0007] According to the temperature change range value, calculate the temperature change value δT and temperature change rate of the current furnace tube hot zone within the preset time period;

[0008] Based on the video monitoring information, temperature change value δT and temperature change rate, the coking and rupture of the furnace tubes are predicted.

[0009] Optionally, before real-time monitoring of the temperature of the furnace tube of the target heating furnace based on the video monitoring information of the furnace tube of the target heating furnace, the method further includes:

[0010] Determine the number and installation positions of the preset cameras according to the volume data of the furnace tube and the shooting angle of the preset cameras;

[0011] After completing the installation of each preset camera based on the determined installation position, perform camera initialization calibration, and complete the preset camera configuration when its actual shooting angle matches the expected shooting angle.

[0012] Optionally, the above-mentioned furnace tube coking condition includes whether the furnace tube is continuously coked;

[0013] The rules for determining whether the above furnace tubes continue to coke are:

[0014] When δT*>1, it is determined that the furnace tube continues to coke, otherwise, it is determined that the furnace tube does not continue to coke, wherein δT* represents the temperature change rate.

[0015] Optionally, the above-mentioned furnace tube coking condition includes a coking hazard level;

[0016] The above prediction of furnace tube coking conditions based on video monitoring information, temperature change value δT and temperature change rate includes:

[0017] Analyze and process the video monitoring information to obtain the image analysis results of the furnace tube hot zone;

[0018] The furnace tube hot zone image analysis results, temperature change value δT and temperature change rate are input into a preset database for matching to obtain a corresponding coking hazard level, wherein the preset database stores a correspondence between the furnace tube hot zone image analysis results, temperature change value δT and temperature change rate and the coking hazard level.

[0019] Optionally, the above furnace tube hot zone image analysis results include furnace tube deformation parameters and furnace tube wall thickness;

[0020] The above analysis and processing of the video monitoring information obtains the furnace tube hot zone image analysis results, including:

[0021] Decompressing video monitoring information into continuous image frames;

[0022] Compare the current furnace tube hot zone image in any image frame with the factory initial image corresponding to the current furnace tube hot zone to obtain a comparison result;

[0023] Based on the comparison results, the deformation parameters of the furnace tube are obtained;

[0024] Based on the furnace tube deformation parameters, the furnace tube wall thickness is measured.

[0025] Optionally, the above furnace tube deformation parameters include tube wall deformation position and tube wall deformation range;

[0026] The above-mentioned method of measuring the tube wall thickness of the furnace tube based on the furnace tube deformation parameters includes:

[0027] Measuring the thickness of the tube wall corresponding to the position where the tube wall is deformed to obtain a first thickness value;

[0028] Taking the deformation position of the pipe wall as the center, sequentially measuring the pipe wall thickness within the pipe wall deformation range to obtain at least one second thickness value;

[0029] The first thickness value and the second thickness value are combined to obtain the tube wall thickness of the furnace tube.

[0030] Optionally, the above furnace tube rupture condition includes a furnace tube rupture risk level;

[0031] The above prediction of furnace tube rupture based on the temperature change value δT includes:

[0032] When satisfied When the furnace tube rupture risk level is predicted to be the highest level, N represents the time length of temperature collection in N seconds, i represents the i-th second in N seconds, T fail Indicates the failure temperature of the furnace tube, T normal Indicates the normal temperature of the outer wall of the furnace tube.

[0033] Optionally, the above-mentioned heating furnace coking prediction method further includes:

[0034] When the furnace tube temperature of the target heating furnace changes, the current furnace tube hot zone is updated according to the changed furnace tube temperature;

[0035] Re-perform the coking prediction based on the updated tube hot zones.

[0036] Optionally, the above-mentioned heating furnace coking prediction method further includes:

[0037] Input the service life and use environment information of the target heating furnace into the preset risk index model to obtain the corresponding risk index data;

[0038] Obtain historical prediction results of the coking and rupture conditions of furnace tubes of the target heating furnace;

[0039] Matching the danger indicator data with the historical prediction results to obtain a matching result;

[0040] According to the matching result, it is determined whether to stop using the target heating furnace, wherein if it is determined to stop using the target heating furnace, a stop use warning signal is issued.

[0041] A second aspect of the present invention provides a heating furnace coking prediction system, comprising:

[0042] A real-time monitoring module, used for real-time monitoring of the furnace tube temperature of the target heating furnace based on the video monitoring information of the furnace tube of the target heating furnace;

[0043] The current furnace tube heat zone determination module is used to determine the current furnace tube heat zone and the temperature change range value within a preset time period according to the furnace tube temperature;

[0044] The temperature change calculation module is used to calculate the temperature change value δT and the temperature change rate of the current furnace tube hot zone within a preset time period according to the temperature change range value;

[0045] The prediction module is used to predict the coking and rupture of the furnace tubes based on the video monitoring information, the temperature change value δT and the temperature change rate.

[0046] In a third aspect of the present invention, a machine-readable storage medium is provided, on which instructions are stored, and when the instructions are executed by a processor, the processor is configured to execute the above-mentioned method for predicting coking in a heating furnace.

[0047] In a fourth aspect of the present invention, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the heating furnace coking prediction method when executing the computer program.

[0048] Through the above technical scheme, a method and system for predicting coking in a heating furnace obtains the video monitoring information of the furnace tube of the target heating furnace in real time, thereby monitoring the furnace tube temperature of the target heating furnace in real time based on the video monitoring information of the furnace tube of the target heating furnace. According to the furnace tube temperature, all sections with comprehensive temperatures higher than the preset temperature are used to obtain the temperature change range value of the current furnace tube hot zone and the preset time period. Therefore, according to the temperature change range value, the temperature change value δT and the temperature change rate of the current furnace tube hot zone in the preset time period are calculated. Combining the video monitoring information, the temperature change value δT and the temperature change rate, the state of the current furnace tube hot zone can be obtained, and then the coking condition and the rupture condition of the furnace tube in the next second can be predicted, thus achieving the purpose of predicting the coking of the furnace tube with the help of the furnace tube temperature and realizing the early warning of furnace tube rupture.

[0049] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0051] Figure 1 It is a flow chart of a method for predicting coking in a heating furnace provided by one embodiment of the present invention;

[0052] Figure 2It is a block diagram of a heating furnace coking prediction system provided by one embodiment of the present invention;

[0053] Figure 3 It is a schematic diagram of the structure of an electronic device provided by a preferred embodiment of the present invention.

[0054] Description of Reference Numerals

[0055] 10 - electronic device, 100 - processor, 101 - memory, 102 - computer program. DETAILED DESCRIPTION

[0056] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0057] Example 1

[0058] Figure 1 1 is a flow chart of a method for predicting coking in a heating furnace provided by one embodiment of the present invention. Figure 1 As shown, an embodiment of the present invention provides a method for predicting coking in a heating furnace, comprising:

[0059] S110: monitoring the temperature of the furnace tube of the target heating furnace in real time based on the video monitoring information of the furnace tube of the target heating furnace;

[0060] Specifically, the furnace tube of the target heating furnace can be monitored in real time by a preset camera to obtain video monitoring information of the furnace tube of the target heating furnace;

[0061] Among them, the preset camera can be a camera that is a coupling of an infrared camera and a thermometer.

[0062] Specifically, in the process of real-time monitoring of the target heating furnace by the preset camera, the furnace tube of the target heating furnace is photographed and recorded by the infrared camera, and the furnace tube temperature of the target heating furnace is simultaneously obtained by the infrared camera and the thermometer, thereby ensuring the accuracy of the obtained furnace tube temperature.

[0063] In the above implementation process, if the furnace tube temperatures obtained by the infrared camera and the thermometer are inconsistent and the difference between the furnace tube temperatures obtained by the two is within the preset temperature error range, the average of the temperatures measured by the two is taken as the obtained furnace tube temperature. If the furnace tube temperatures obtained by the infrared camera and the thermometer are inconsistent and the difference between the furnace tube temperatures obtained by the two is not within the preset temperature error range, it indicates that the infrared camera or the thermometer is faulty, and a test object with temperature (the temperature of the test object is adjustable) is used to test the infrared camera and the thermometer respectively to determine and replace the faulty infrared camera or thermometer, thereby further avoiding affecting the accuracy of the obtained furnace tube temperature.

[0064] S120: Determine the temperature change range value of the current furnace tube hot zone and the preset time period according to the furnace tube temperature;

[0065] Specifically, if the temperature of any section of the furnace tube is higher than a preset temperature (eg, 400° C.), it indicates that the section belongs to the current furnace tube hot zone. Thus, according to the furnace tube temperature, all sections with temperatures higher than the preset temperature are integrated to obtain the current furnace tube hot zone.

[0066] The preset time period may be 2 seconds.

[0067] Specifically, after the preset camera monitors the current furnace tube hot zone, the current furnace tube hot zone is monitored frame by frame to obtain the video monitoring information and temperature change range value of the current furnace tube hot zone within 2 seconds.

[0068] S130: Calculate the temperature change value δT and the temperature change rate of the current furnace tube hot zone within a preset time period according to the temperature change range value;

[0069] Specifically, according to the obtained temperature change range value of the current furnace tube hot zone within 2 seconds, the temperature change value δT and the temperature change rate δT* of the current furnace tube hot zone within 2 seconds are calculated.

[0070] S140: Predicting furnace tube coking and furnace tube rupture conditions based on video monitoring information, temperature change value δT and temperature change rate.

[0071] The above-mentioned coking condition of the furnace tube includes whether the furnace tube continues to coke. The judgment rule of whether the above-mentioned furnace tube continues to coke is: when δT*>1, it is judged that the furnace tube continues to coke, otherwise, it is judged that the furnace tube does not continue to coke, wherein δT* represents the temperature change rate. Therefore, by judging whether the furnace tube continues to coke in the next second through the temperature change rate, the purpose of predicting the coking condition of the furnace tube in the next second is achieved.

[0072] In addition, the above-mentioned furnace tube coking condition includes the coking danger level. The above-mentioned prediction of furnace tube coking condition based on video monitoring information, temperature change value δT and temperature change rate includes:

[0073] Analyze and process the video monitoring information to obtain the image analysis results of the furnace tube hot zone;

[0074] The furnace tube hot zone image analysis results, temperature change value δT and temperature change rate are input into a preset database for matching to obtain a corresponding coking hazard level, wherein the preset database stores a correspondence between the furnace tube hot zone image analysis results, temperature change value δT and temperature change rate and the coking hazard level.

[0075] Specifically, based on the furnace tube hot zone image analysis results obtained by analyzing the video monitoring information of the current furnace tube hot zone and the monitored calculated temperature change value δT and temperature change rate, the coking hazard level of the current target heating furnace can be determined according to the corresponding relationship stored in the preset database, thereby achieving the purpose of predicting the coking hazard.

[0076] Exemplarily, in the preset database, the highest coking hazard level is when the temperature change rate reaches 1 and / or the temperature change value δT reaches 2 and / or obvious tube wall bulging appears in the furnace tube hot zone image analysis results and the bulging volume meets the preset volume size.

[0077] In addition, the above-mentioned furnace tube rupture situation includes the furnace tube rupture risk level. The above-mentioned prediction of furnace tube rupture situation based on the temperature change value δT includes: when the When the furnace tube rupture risk level is predicted to be the highest level, N represents the time length of temperature collection in N seconds, i represents the i-th second in N seconds, T fail Indicates the failure temperature of the furnace tube, T normal Indicates the normal temperature of the outer wall of the furnace tube. Specifically, when When the conditions are met, it is predicted that the risk of furnace tube rupture is extremely high, thereby achieving the purpose of predicting the risk of furnace tube rupture by utilizing the temperature change value of the current furnace tube hot zone.

[0078] In the above implementation process, the method uses a preset camera to obtain the furnace tube temperature and furnace tube monitoring video of the target heating furnace in real time. According to the furnace tube temperature, all sections with comprehensive temperatures higher than the preset temperature are combined to obtain the current furnace tube hot zone. After the preset camera monitors the current furnace tube hot zone, the current furnace tube hot zone is monitored frame by frame to obtain the video monitoring information and temperature change range value of the current furnace tube hot zone within the preset time period. Therefore, according to the temperature change range value, the temperature change value δT and the temperature change rate of the current furnace tube hot zone within the preset time period are calculated. Combined with the video monitoring information, the temperature change value δT and the temperature change rate, the state of the current furnace tube hot zone can be obtained, and then the furnace tube coking and furnace tube rupture in the next second can be predicted, thus achieving the purpose of predicting furnace tube coking with the help of furnace tube temperature and realizing furnace tube rupture warning.

[0079] Example 2

[0080] This embodiment provides a method for predicting coking in a heating furnace, which is basically the same as the method for predicting coking in a heating furnace provided in Example 1, except that the above-mentioned furnace tube hot zone image analysis results include furnace tube deformation parameters and furnace tube wall thickness. The above-mentioned analysis and processing of the video monitoring information to obtain the furnace tube hot zone image analysis results includes:

[0081] Decompressing video monitoring information into continuous image frames;

[0082] Compare the current furnace tube hot zone image in any image frame with the factory initial image corresponding to the current furnace tube hot zone to obtain a comparison result;

[0083] Based on the comparison results, the deformation parameters of the furnace tube are obtained;

[0084] Based on the furnace tube deformation parameters, the furnace tube wall thickness is measured.

[0085] Specifically, the video monitoring information is decomposed into a plurality of continuous image frames, and the current furnace tube hot zone image is located in any image frame according to the position of the current furnace tube hot zone on the furnace tube, and the current furnace tube hot zone image in any image frame is compared with the factory initial image corresponding to the current furnace tube hot zone to determine the furnace tube deformation parameter, and the furnace tube wall thickness is measured based on the furnace tube deformation parameter. Thus, the furnace tube hot zone image analysis result is obtained by combining the furnace tube deformation parameter and the furnace tube wall thickness.

[0086] Example 3

[0087] This embodiment provides a method for predicting coking in a heating furnace, which is basically the same as the method for predicting coking in a heating furnace provided in Example 2, and the main difference is that: the above-mentioned furnace tube deformation parameters include the tube wall deformation position and the tube wall deformation range;

[0088] For example, when a bulge appears on the furnace tube due to oxidation thinning under the dual effects of internal pressure and heat, the center position of the bulge is taken as the tube wall deformation position, and the range of the furnace tube wall deformation is taken as the tube wall deformation range.

[0089] The above-mentioned method of measuring the tube wall thickness of the furnace tube based on the furnace tube deformation parameters includes:

[0090] Measuring the thickness of the tube wall corresponding to the position where the tube wall is deformed to obtain a first thickness value;

[0091] Taking the deformation position of the pipe wall as the center, sequentially measuring the pipe wall thickness within the pipe wall deformation range to obtain at least one second thickness value;

[0092] The first thickness value and the second thickness value are combined to obtain the tube wall thickness of the furnace tube.

[0093] For example, when the furnace tube is deformed to form a bulge, the center position of the bulge is first measured to obtain a first thickness value, and then the center position of the bulge is used as the reference, and the thickness of the tube wall within the deformation range of the tube wall is measured along the center position of the bulge. When the measured thickness value is inconsistent with the first thickness value, it is recorded as a second thickness value. Then, the first thickness value and all the second thickness values ​​are combined to obtain the thickness of the furnace tube wall. The first thickness value and all the second thickness values ​​can reflect the thickness change of the furnace tube wall, thereby reflecting the deformation of the furnace tube.

[0094] Example 4

[0095] This embodiment provides a method for predicting coking in a heating furnace, which is basically the same as the method for predicting coking in a heating furnace provided in Example 1, except that before the above-mentioned real-time monitoring of the furnace tube temperature of the target heating furnace based on the video monitoring information of the furnace tube of the target heating furnace, the method further includes:

[0096] Determine the number and installation positions of the preset cameras according to the volume data of the furnace tube and the shooting angle of the preset cameras;

[0097] After completing the installation of each preset camera based on the determined installation position, perform camera initialization calibration, and complete the preset camera configuration when its actual shooting angle matches the expected shooting angle.

[0098] Specifically, the method divides the field of view of the furnace tube into multiple monitoring areas for separate monitoring according to the volume data of the furnace tube and the shooting angle of the preset camera, and configures a preset camera for each monitoring area, so that targeted monitoring is carried out for each monitoring area using the corresponding camera, which not only ensures comprehensive monitoring of the furnace tube, but also improves the image recognition speed and the safety of furnace tube use.

[0099] Exemplarily, the number of the pre-set cameras configured may be 4.

[0100] Example 5

[0101] This embodiment provides a method for predicting coking in a heating furnace, which is basically the same as the method for predicting coking in a heating furnace provided in Example 1, and the main difference is that the above method for predicting coking in a heating furnace further includes:

[0102] When the furnace tube temperature of the target heating furnace changes, the current furnace tube hot zone is updated according to the changed furnace tube temperature;

[0103] Re-perform the coking prediction based on the updated tube hot zones.

[0104] Specifically, as the target heating furnace is used, the temperature of the furnace tube of the target heating furnace will continue to rise. According to the changing furnace tube temperature, the current furnace tube hot zone is updated in real time, thereby ensuring the real-time performance of the current furnace tube hot zone. At the same time, the current furnace tube hot zone is monitored frame by frame using a preset camera to ensure that the video monitoring information of the current furnace tube hot zone and the temperature change range value within a preset time period are obtained, thereby facilitating the determination of the temperature change impact of the current furnace tube hot zone.

[0105] Example 6

[0106] This embodiment provides a method for predicting coking in a heating furnace, which is basically the same as the method for predicting coking in a heating furnace provided in Example 1, and the main difference is that the above method for predicting coking in a heating furnace further includes:

[0107] Input the service life and use environment information of the target heating furnace into the preset risk index model to obtain the corresponding risk index data;

[0108] Obtain historical prediction results of the coking and rupture conditions of furnace tubes of the target heating furnace;

[0109] Matching the danger indicator data with the historical prediction results to obtain a matching result;

[0110] According to the matching result, it is determined whether to stop using the target heating furnace, wherein if it is determined to stop using the target heating furnace, a stop use warning signal is issued.

[0111] Specifically, as the service life of the target heating furnace increases, the coking speed caused by the use temperature of the furnace tubes and the furnace tube rupture caused by coking are also different for target heating furnaces of different ages. This method analyzes the service life and use environment information of the target heating furnace through a preset hazard index model to obtain the hazard index data corresponding to the current use stage of the target heating furnace. At the same time, the prediction results of the furnace tube coking and furnace tube rupture of the most recent target heating furnace are obtained, and this prediction result is used as a historical prediction result to match and judge with the hazard index data. According to the matching degree of the two, it is judged whether to stop using the target heating furnace. At the same time, when it is determined to stop using the target heating furnace, a stop use warning signal is issued to remind the user to shut down the target heating furnace. In this way, combined with the actual use scenario and service life of the target heating furnace, the furnace tube rupture of the target heating furnace is further judged in accordance with reality.

[0112] In some implementations of this embodiment, the above-mentioned heating furnace coking prediction method further includes:

[0113] Obtain multiple samples, including the service life of the heating furnace and the damage of the furnace tubes under various usage scenarios;

[0114] Establishing an initial model of risk indicators;

[0115] The initial risk indicator model is trained using multiple samples to obtain a preset risk indicator model.

[0116] Example 7

[0117] Figure 2 FIG. 1 is a block diagram of a heating furnace coking prediction system provided by one embodiment of the present invention. Figure 2 As shown, an embodiment of the present invention provides a heating furnace coking prediction system, comprising:

[0118] A real-time monitoring module, used for real-time monitoring of the furnace tube temperature of the target heating furnace based on the video monitoring information of the furnace tube of the target heating furnace;

[0119] Specifically, the furnace tubes of the target heating furnace may be monitored in real time by a preset camera coupled with an infrared camera and a thermometer to obtain video monitoring information of the furnace tubes of the target heating furnace.

[0120] The current furnace tube heat zone determination module is used to determine the current furnace tube heat zone and the temperature change range value within a preset time period according to the furnace tube temperature;

[0121] The temperature change calculation module is used to calculate the temperature change value δT and the temperature change rate of the current furnace tube hot zone within a preset time period according to the temperature change range value;

[0122] The prediction module is used to predict the coking and rupture of the furnace tubes based on the video monitoring information, the temperature change value δT and the temperature change rate.

[0123] The above-mentioned furnace tube coking condition includes whether the furnace tube continues to coke and the coking danger level. The above-mentioned furnace tube rupture condition includes the furnace tube rupture risk level.

[0124] During the above implementation process, the system uses a preset camera to obtain the furnace tube temperature and furnace tube monitoring video of the target heating furnace in real time. According to the furnace tube temperature, all sections with comprehensive temperatures higher than the preset temperature are combined to obtain the current furnace tube hot zone. When the preset camera monitors the current furnace tube hot zone, the current furnace tube hot zone is monitored frame by frame to obtain the video monitoring information and temperature change range value of the current furnace tube hot zone within the preset time period. Therefore, according to the temperature change range value, the temperature change value δT and the temperature change rate of the current furnace tube hot zone within the preset time period are calculated. Combining the video monitoring information, the temperature change value δT and the temperature change rate, the state of the current furnace tube hot zone can be obtained, and then the coking condition and the rupture condition of the furnace tube in the next second can be predicted, thus realizing the purpose of predicting the coking of the furnace tube with the help of the furnace tube temperature and realizing the early warning of the furnace tube rupture.

[0125] Example 8

[0126] An embodiment of the present invention provides a machine-readable storage medium having instructions stored thereon. When the instructions are executed by a processor, the processor is configured to execute the above-mentioned method for predicting coking in a heating furnace.

[0127] Machine-readable storage media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0128] An embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned method for predicting coking in a heating furnace is implemented.

[0129] like Figure 3 FIG. 1 is a schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, the electronic device 10 of this embodiment includes: a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, the steps in the above method embodiment are implemented. Alternatively, when the processor 100 executes the computer program 102, the functions of each module / unit in the above device embodiment are implemented.

[0130] Exemplarily, the computer program 102 may be divided into one or more modules / units, one or more modules / units are stored in the memory 101, and are executed by the processor 100 to complete the present invention. One or more modules / units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 102 in the electronic device 10. For example, the computer program 102 may be divided into a real-time monitoring module, a current furnace tube hot zone determination module, a temperature change calculation module, and a prediction module.

[0131] The electronic device 10 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 10 may include, but is not limited to, a processor 100 and a memory 101. Those skilled in the art will appreciate that Figure 3It is only an example of the electronic device 10 and does not constitute a limitation of the electronic device 10. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0132] The processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0133] The memory 101 may be an internal storage unit of the electronic device 10, such as a hard disk or memory of the electronic device 10. The memory 101 may also be an external storage device of the electronic device 10, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 10. Further, the memory 101 may also include both an internal storage unit of the electronic device 10 and an external storage device. The memory 101 is used to store computer programs and other programs and data required by the electronic device 10. The memory 101 may also be used to temporarily store data that has been output or is to be output.

[0134] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0135] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0136] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0139] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0140] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for predicting coking in a heating furnace, characterized in that: include: Real-time monitoring of the temperature of the furnace tube of the target heating furnace based on the video monitoring information of the furnace tube of the target heating furnace; According to the furnace tube temperature, determine the temperature variation range value of the current furnace tube hot zone and the preset time period; According to the temperature variation range value, calculating the temperature variation value δT and the temperature variation rate of the current furnace tube hot zone within a preset time period; The coking condition and rupture condition of the furnace tube are predicted based on the video monitoring information, the temperature change value δT and the temperature change rate.

2. The method for predicting coking in a heating furnace according to claim 1, characterized in that: Before the real-time monitoring of the furnace tube temperature of the target heating furnace based on the video monitoring information of the furnace tube of the target heating furnace, the method further includes: Determine the number and installation positions of the preset cameras according to the volume data of the furnace tube and the shooting angle of the preset cameras; After completing the installation of each preset camera based on the determined installation position, perform camera initialization calibration, and complete the preset camera configuration when its actual shooting angle matches the expected shooting angle.

3. The method for predicting coking in a heating furnace according to claim 1, characterized in that: The furnace tube coking condition includes whether the furnace tube is continuously coked; The judgment rule of whether the furnace tube continues to coke is: When δT*>1, it is determined that the furnace tube continues to coke, otherwise, it is determined that the furnace tube does not continue to coke, wherein δT* represents the temperature change rate.

4. The method for predicting coking in a heating furnace according to claim 1, characterized in that: The furnace tube coking condition includes the coking hazard level; The predicting of the coking condition of the furnace tube according to the video monitoring information, the temperature change value δT and the temperature change rate includes: Analyzing and processing the video monitoring information to obtain furnace tube hot zone image analysis results; The furnace tube hot zone image analysis result, the temperature change value δT and the temperature change rate are input into a preset database for matching to obtain a corresponding coking hazard level, wherein the preset database stores a correspondence between the furnace tube hot zone image analysis result, the temperature change value δT and the temperature change rate and the coking hazard level.

5. The method for predicting coking in a heating furnace according to claim 4, characterized in that: The furnace tube hot zone image analysis results include furnace tube deformation parameters and furnace tube wall thickness; The analyzing and processing of the video monitoring information to obtain the furnace tube hot zone image analysis result includes: Decompressing the video monitoring information into continuous image frames; Comparing the current furnace tube hot zone image in any image frame with the factory initial image corresponding to the current furnace tube hot zone to obtain a comparison result; Based on the comparison results, the furnace tube deformation parameters are obtained; Based on the furnace tube deformation parameter, the tube wall thickness of the furnace tube is measured.

6. The method for predicting coking in a heating furnace according to claim 5, characterized in that: The furnace tube deformation parameters include tube wall deformation position and tube wall deformation range; The step of measuring the thickness of the furnace tube wall based on the furnace tube deformation parameter comprises: Measuring the tube wall thickness corresponding to the tube wall deformation position to obtain a first thickness value; Taking the tube wall deformation position as the center, sequentially measuring the tube wall thickness within the tube wall deformation range to obtain at least one second thickness value; The first thickness value and the second thickness value are combined to obtain the tube wall thickness of the furnace tube.

7. The method for predicting coking in a heating furnace according to claim 1, characterized in that: The furnace tube rupture condition includes the furnace tube rupture risk level; The method of predicting the furnace tube rupture according to the temperature change value δT comprises: When satisfied When the furnace tube rupture risk level is predicted to be the highest level, N represents the time length of temperature collection in N seconds, i represents the i-th second in N seconds, T fail Indicates the failure temperature of the furnace tube, T normal Indicates the normal temperature of the outer wall of the furnace tube.

8. The method for predicting coking in a heating furnace according to claim 1, characterized in that: Also includes: When the furnace tube temperature of the target heating furnace changes, the current furnace tube hot zone is updated according to the changed furnace tube temperature; Re-perform the coking prediction based on the updated tube hot zones.

9. The method for predicting coking in a heating furnace according to claim 1, characterized in that: Also includes: Input the service life and use environment information of the target heating furnace into the preset risk index model to obtain the corresponding risk index data; Obtaining historical prediction results of furnace tube coking and furnace tube rupture conditions of the target heating furnace; Matching the risk indicator data with the historical prediction results to obtain a matching result; According to the matching result, it is determined whether to stop using the target heating furnace, wherein if it is determined that the target heating furnace is to be stopped, a stop use warning signal is issued.

10. A heating furnace coking prediction system, characterized in that: include: A real-time monitoring module, used for real-time monitoring of the furnace tube temperature of the target heating furnace based on the video monitoring information of the furnace tube of the target heating furnace; A current furnace tube hot zone determination module is used to determine the current furnace tube hot zone and the temperature variation range value within a preset time period according to the furnace tube temperature; A temperature change calculation module, used to calculate the temperature change value δT and the temperature change rate of the current furnace tube hot zone within a preset time period according to the temperature change range value; The prediction module is used to predict the coking condition and the rupture condition of the furnace tube according to the video monitoring information, the temperature change value δT and the temperature change rate.

11. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to execute the method for predicting coking in a heating furnace as described in any one of claims 1 to 9.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for predicting coking in a heating furnace as described in any one of claims 1 to 9 is implemented.

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