CN114997509A - Early warning method and device for secondary combustion of tail flue of boiler in thermal power plant - Google Patents
Early warning method and device for secondary combustion of tail flue of boiler in thermal power plant Download PDFInfo
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- F23—COMBUSTION APPARATUS; COMBUSTION PROCESSES
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- F23L15/00—Heating of air supplied for combustion
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Abstract
The application relates to a method and a device for early warning of secondary combustion of a boiler tail flue of a thermal power plant. The specific scheme is as follows: acquiring real-time data of a boiler, and determining real-time values of the change rates of the temperatures of different areas of a tail flue of the boiler based on the real-time data; determining the current state of the boiler according to the real-time data; responding to the current state of the boiler as an operating state, and respectively inputting real-time data into a plurality of pre-trained temperature prediction models; respectively acquiring predicted temperature values output by a plurality of pre-trained temperature prediction models; determining whether the real-time data meets a preset requirement; responding to the situation that the real-time data do not meet the preset requirements, and outputting an early warning signal; responding to the current state of the boiler as a blowing-out state, and acquiring real-time values of the change rate of the temperature of different areas of a tail flue of the boiler; and responding to the situation that the real-time values of the change rates of the temperatures of different areas of the boiler tail flue do not meet the preset requirements, and outputting an early warning signal. This application can in time carry out the early warning to boiler afterbody flue postcombustion.
Description
Technical Field
The application relates to the technical field of intelligent diagnosis of equipment faults, in particular to a method and a device for early warning of secondary combustion of a boiler tail flue of a thermal power plant.
Background
In the related technology, high-temperature flue gas generated in a boiler furnace flows through a rear vertical shaft low-temperature superheater, a low-temperature reheater and an economizer, then enters a denitration device to reduce the content of NOx, then enters an air preheater flue gas bin, and primary air and secondary air are preheated in a preheater by using flue gas waste heat. And the flue gas discharged from the air preheater is discharged to a chimney through a dust remover, a draught fan and a desulfurization system. Because the low-temperature superheater, the low-temperature reheater and the economizer are horizontally arranged heating surfaces, the air preheater and the denitration catalyst module are in a crack or hole structure, and the heating surfaces of the air preheater and the denitration catalyst module face the flue gas side and the inside inevitably cause the aggregation of fly ash and other impurities carried by the flue gas. If the pulverized coal is not sufficiently combusted for a long time, the content of good fly ash combustible substances is higher, and if the soot blowing is not timely and the spontaneous combustion condition is achieved, secondary combustion of a tail flue can occur. After secondary combustion of a flue at the tail of the boiler occurs, an air preheater or an economizer in a combustion area is overheated to deform or burn down, and great harm is caused to flue equipment.
Disclosure of Invention
Therefore, the application provides an early warning method and device for secondary combustion of a tail flue of a boiler of a thermal power plant. The technical scheme of the application is as follows:
according to a first aspect of the embodiments of the present application, there is provided a method for warning secondary combustion of a tail flue of a boiler in a thermal power plant, the method including:
acquiring real-time data of a boiler, and determining real-time values of the change rates of the temperatures of different areas of a tail flue of the boiler based on the real-time data;
determining the current state of the boiler according to the real-time data; the current state of the boiler comprises an operating state and a blowing-out state;
responding to the current state of the boiler as an operating state, and respectively inputting the real-time data into a plurality of pre-trained temperature prediction models; the pre-trained temperature prediction models are respectively used for predicting the temperatures of different areas of the boiler tail flue;
respectively acquiring predicted temperature values output by the plurality of pre-trained temperature prediction models;
determining whether the real-time data meets a preset requirement or not based on the real-time data and predicted temperature values output by the plurality of pre-trained temperature prediction models respectively;
responding to the real-time data not meeting the preset requirement, and outputting an early warning signal;
responding to the fact that the current state of the boiler is a blowing-out state, and acquiring real-time values of the change rate of the temperature of different areas of the tail flue of the boiler;
and responding to the situation that the real-time values of the change rates of the temperatures of different areas of the boiler tail flue do not meet the preset requirements, and outputting an early warning signal.
According to one embodiment of the application, the real-time data comprises real-time temperature values of different areas of the boiler tail flue and main parameters of the boiler, and the temperature values of the different areas of the boiler tail flue correspond to the pre-trained temperature prediction models one by one; determining whether the real-time data meets a preset requirement based on the real-time data and predicted temperature values output by the plurality of pre-trained temperature prediction models respectively, including:
respectively determining a first difference value of each real-time temperature value and a predicted temperature value corresponding to the real-time temperature value based on real-time temperature values of different areas of the boiler tail flue and predicted temperature values output by the pre-trained temperature prediction models;
and determining that the real-time data does not meet the preset requirement in response to at least one first difference value in the plurality of first difference values being larger than a preset threshold value corresponding to the first difference value.
According to an embodiment of the application, the method further comprises:
determining a change rate real-time value of the real-time data based on the real-time data;
inputting the real-time value of the change rate of the real-time data into pre-training temperature change rate prediction models respectively corresponding to different regions of a tail flue of the boiler; the pre-training temperature change rate prediction models are respectively used for predicting the temperature change rates of different areas of the boiler tail flue;
respectively obtaining predicted values of temperature change rates of different areas of a boiler tail flue output by the pre-training temperature change rate prediction models; the pre-training temperature change rate prediction models correspond to the real-time values of the change rates of the temperatures of different areas of the boiler tail flue in a one-to-one mode;
determining whether the real-time values of the change rates of the temperatures of different areas of the boiler tail flue meet preset requirements or not based on the plurality of predicted values of the temperature change rates and the real-time values of the change rates of the temperatures of different areas of the boiler tail flue;
and responding to at least one value of the real-time values of the change rates of the temperatures of different areas of the boiler tail flue not meeting the preset requirement, and outputting an early warning signal.
According to an embodiment of the application, the determining whether the real-time values of the change rates of the temperatures of the different areas of the boiler tail flue meet preset requirements based on the plurality of predicted values of the temperature change rates and the real-time values of the change rates of the temperatures of the different areas of the boiler tail flue comprises:
determining a second difference value between the plurality of temperature change rate predicted values and the corresponding change rate real-time values based on the plurality of temperature change rate predicted values and the change rate real-time values of the temperatures of different areas of the tail flue of the boiler;
and outputting an early warning signal in response to at least one second difference value in the plurality of second difference values being larger than a preset threshold value corresponding to the second difference value.
According to an embodiment of the application, the outputting an early warning signal in response to that the real-time values of the change rates of the temperatures of different areas of the boiler tail flue do not meet the preset requirement comprises:
based on the real-time data, obtaining the duration time of the secondary air volume smaller than a first preset threshold value;
in response to the duration being less than a second preset threshold, determining whether real-time values of the change rates of the temperatures of different areas of the boiler tail flue are greater than respective corresponding third preset thresholds;
responding to at least one change rate real-time value in the change rate real-time values of the temperatures of different areas of the boiler tail flue, and outputting an early warning signal, wherein the change rate real-time value is larger than a third preset threshold corresponding to the change rate real-time value;
in response to the duration being greater than a second preset threshold, determining whether real-time values of the rate of change of the temperatures of different areas of the boiler tail flue are greater than respective corresponding fourth preset thresholds;
and responding to the fact that the real-time value of the change rate of the temperature of at least one different area of the boiler tail flue in the real-time values of the change rate of the temperature of the different areas of the boiler tail flue is larger than a fourth preset threshold corresponding to the real-time value of the change rate, and outputting an early warning signal.
According to an embodiment of the application, the third preset threshold is greater than the fourth preset threshold.
According to an embodiment of the present application, said determining a current state of said boiler from said real-time data comprises:
acquiring a secondary air quantity value based on the real-time data;
responding to the fact that the secondary air quantity value is larger than a first preset threshold value, and determining that the current state of the boiler is an operation state;
and responding to the fact that the secondary air quantity value is smaller than a first preset threshold value, and determining that the current state of the boiler is a boiler blowing-out state.
According to the second aspect of this application embodiment, provide early warning device of thermal power plant's boiler afterbody flue postcombustion, the device includes:
the first determining module is used for acquiring real-time data of the boiler and determining real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler based on the real-time data;
the judging module is used for judging the running state of the boiler according to the real-time data; the current state of the boiler comprises an operating state and a blowing-out state;
the input module is used for responding to the current state of the boiler as an operating state and inputting the real-time data into a plurality of pre-trained temperature prediction models respectively; the pre-trained temperature prediction models are respectively used for predicting the temperatures of different areas of the boiler tail flue;
the first acquisition module is used for respectively acquiring temperature values output by the plurality of pre-trained temperature prediction models;
the second determination module is used for determining whether the real-time data meet the preset requirement or not based on the real-time data and the predicted temperature values output by the plurality of pre-trained temperature prediction models respectively;
the first output module is used for responding to the fact that the real-time data do not meet the preset requirements and outputting early warning signals;
the second acquisition module is used for responding to the fact that the current state of the boiler is a blowing-out state, and acquiring real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler;
and the second output module is used for responding that the real-time values of the change rates of the temperatures of different areas of the boiler tail flue do not meet the preset requirements and outputting early warning signals.
According to a third aspect of embodiments herein, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the first aspects.
According to a fourth aspect of embodiments herein, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of the first aspects.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
the boiler is divided into two different states of an operating state and a blowing-out state to respectively perform early warning on secondary combustion of the boiler tail flue, so that real-time monitoring of the boiler tail flue is realized, and the accuracy of early warning is improved; in addition, the temperatures of different areas of the boiler tail flue are predicted by adopting a pre-trained temperature prediction model, and the predicted temperature values are compared with real-time temperature values, so that the secondary combustion of the boiler tail flue is early warned in time, and the timeliness of early warning is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application and are not to be construed as limiting the application.
Fig. 1 is a flowchart of an early warning method for secondary combustion of a tail flue of a boiler in a thermal power plant, which is provided in an embodiment of the present application;
fig. 2 is a flowchart of another warning method for secondary combustion of a tail flue of a boiler in a thermal power plant according to an embodiment of the present application;
fig. 3 is a block diagram of a structure of an early warning device for secondary combustion of a tail flue of a boiler in a thermal power plant, which is provided in an embodiment of the present application;
fig. 4 is a structural block diagram of an early warning device for secondary combustion in a tail flue of a boiler of a thermal power plant, which is provided in an embodiment of the present application;
fig. 5 is a block diagram of a computer device proposed in an embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
It should be noted that, in the related art, after passing through the rear shaft low-temperature superheater, the low-temperature reheater, and the economizer, the high-temperature flue gas generated in the boiler furnace firstly enters the denitration device to reduce the NOx content, and then enters the air preheater flue gas bin, and the flue gas waste heat is utilized in the preheater to preheat the primary air and the secondary air. And the flue gas discharged from the air preheater is discharged to a chimney through a dust remover, a draught fan and a desulfurization system. Because the low-temperature superheater, the low-temperature reheater and the economizer are horizontally arranged heating surfaces, the air preheater and the denitration catalyst module are in a crack or hole structure, and the heating surfaces of the air preheater and the denitration catalyst module face the flue gas side and the inside inevitably cause the aggregation of fly ash and other impurities carried by the flue gas. If the pulverized coal is not sufficiently combusted for a long time, the content of good fly ash combustible substances is higher, and if the soot blowing is not timely and the spontaneous combustion condition is achieved, secondary combustion of a tail flue can occur. After secondary combustion of a flue at the tail of the boiler occurs, an air preheater or an economizer in a combustion area is overheated to deform or burn down, and great harm is caused to flue equipment.
Fig. 1 is a flowchart of an early warning method for secondary combustion in a tail flue of a boiler of a thermal power plant, which is provided in an embodiment of the present application.
As shown in fig. 1, the method for pre-warning the secondary combustion of the tail flue of the boiler in the thermal power plant comprises the following steps:
and step 110, acquiring real-time data of the boiler, and determining real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler based on the real-time data.
As a possible implementation manner, after the real-time data of the boiler is obtained, the real-time data may include real-time temperature values and main boiler parameters of different areas of a boiler tail flue, and the real-time temperature values and the main boiler parameters of the different areas of the boiler tail flue may include unit load, total coal quantity, total air quantity, furnace outlet smoke temperature, feed water temperature and feed water flow; real-time temperature values of different areas of the boiler tail flue can be obtained by installing temperature sensors in different areas of the boiler tail flue and detecting the temperature through the temperature sensors. The real-time values of the change rates of the temperatures of the different areas of the boiler tail flue can be calculated according to the obtained real-time temperature values of the different areas of the boiler tail flue.
For example, the real-time values of the temperature change rates of different areas of the boiler tail flue can be calculated by the following formula:
T rate of change ＝(T 1 -T 2 )×2
Wherein, T Rate of change The real-time value of the change rate of the temperature minute of a certain area of a boiler tail flue at a certain moment, such as the real-time value of the change rate of the temperature of the flue gas at the outlet of an economizer, and the unit is ℃/min; t is 1 The temperature of a certain area of a tail flue of the boiler at a certain time, such as the temperature of flue gas at the outlet of an economizer, is measured in units of ℃; t is a unit of 2 Is the above-mentioned T 1 The temperature of the certain area of the tail flue of the boiler 30 seconds before the moment, such as the distance T 1 The temperature of the flue gas at the outlet of the economizer 30 seconds before the moment is measured in degrees centigrade.
Optionally, different regions of the boiler back flue may include any one or more of the following: the system comprises a low-temperature superheater inlet, a low-temperature reheater inlet, a low-temperature superheater outlet, a low-temperature reheater outlet, an economizer-to-denitration horizontal section flue, a denitration inlet, a denitration outlet, an air preheater inlet, an air preheater outlet and a horizontal section flue.
And step 120, determining the current state of the boiler according to the real-time data.
In the embodiment of the present application, the current state of the boiler includes an operation state and a blowing-out state.
In some embodiments of the present application, step 120 comprises:
and step 121, acquiring a secondary air quantity value based on the real-time data.
It will be appreciated that the real-time data includes a secondary air flow value.
It should be noted that, after the boiler is shut down and the fan is shut down, the smoke temperature of each area of the tail flue may slightly rise in a period of time due to heat accumulation of the heating surface, but then the smoke temperature of each area of the tail flue gradually falls. Therefore, the smoke temperature of each area of the tail flue is monitored and diagnosed in real time from two stages of boiler operation and normal ventilation of the fan, and boiler shutdown and fan shutdown. The secondary air volume is different between the running state and the blowing-out state of the boiler, so that the current state of the boiler can be judged according to the value of the secondary air volume.
And step 122, responding to the secondary air quantity value being larger than a first preset threshold value, and determining that the current state of the boiler is an operating state.
And step 123, responding to the secondary air quantity value being smaller than the first preset threshold value, and determining that the current state of the boiler is a blowing-out state.
As an example of a possible implementation, the first preset threshold value may be obtained experimentally in advance.
It can be understood that the value of the secondary air quantity of the boiler in the blowing-out state is smaller than that of the boiler in the running state.
Step 130, in response to the current state of the boiler being an operating state, inputting the real-time data into a plurality of pre-trained temperature prediction models, respectively.
In the embodiment of the application, a plurality of pre-trained temperature prediction models are respectively used for predicting the temperatures of different areas of the tail flue of the boiler.
As a possible example, a temperature prediction model may be respectively constructed for the boiler tail flue regions based on one or a mixture of multiple algorithms such as a convolutional neural network, a deep neural network, and a genetic algorithm, and the multiple temperature prediction models may be trained, so that the multiple temperature prediction models may predict the temperatures of the boiler tail flue regions corresponding to the multiple temperature prediction models according to real-time data.
Step 140, obtaining the predicted temperature values output by the plurality of pre-trained temperature prediction models respectively.
As a possible example, when the boiler is in an operating state, the real-time data is respectively input into a plurality of pre-trained temperature prediction models, and the plurality of pre-trained temperature prediction models predict and output the predicted temperature values of the boiler tail flue regions corresponding to the plurality of pre-trained temperature prediction models according to the real-time data.
And 150, determining whether the real-time data meet the preset requirements or not based on the real-time data and the predicted temperature values output by the plurality of pre-trained temperature prediction models respectively.
In some embodiments of the present application, the real-time data includes real-time temperature values of different areas of the back flue of the boiler and main parameters of the boiler, the temperature values of the different areas of the back flue of the boiler correspond to a plurality of pre-trained temperature prediction models one-to-one, and the step 150 includes:
and 151, respectively determining a first difference value between each real-time temperature value and a predicted temperature value corresponding to the real-time temperature value based on the real-time temperature values of different areas of the boiler tail flue and the predicted temperature values output by the plurality of pre-trained temperature prediction models.
As a possible example, each real-time temperature value is subtracted from the predicted temperature value corresponding to the real-time temperature value, so as to obtain a first difference value corresponding to each real-time temperature value.
Step 152, in response to at least one of the first differences being greater than a preset threshold corresponding to the first difference, determining that the real-time data does not satisfy the preset requirement.
As a possible example, in response to at least one of the first differences being greater than a preset threshold corresponding to the first difference, indicating that the real-time temperature value of the back flue region of the boiler corresponding to the first difference exceeds a safe temperature threshold, the boiler is likely to have afterburning; and responding to the fact that none of the first difference values is larger than the preset threshold corresponding to the first difference value, and the fact that the real-time temperature values of different areas of the boiler tail flue are all at the safe temperature threshold is indicated, and the boiler is free of risk of secondary combustion.
And step 160, responding to the situation that the real-time data does not meet the preset requirement, and outputting an early warning signal.
It can be understood that the real-time data does not meet the preset requirement, which indicates that the boiler is likely to have secondary combustion, and therefore, an early warning signal is output for early warning.
And 170, responding to the current state of the boiler being a blowing-out state, and acquiring real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler.
And step 180, responding to the fact that the real-time values of the change rates of the temperatures of different areas of the boiler tail flue do not meet the preset requirements, and outputting early warning signals.
The above step 180 includes:
and 181, acquiring the duration time of the secondary air volume smaller than the first preset threshold value based on the real-time data.
In response to the duration being less than the second preset threshold, it is determined whether the real-time values of the rates of change of the temperatures of the different areas of the back pass of the boiler are greater than respective third preset thresholds, step 182.
It can be understood that, generally, after the boiler is shut down and the fan is shut down, the smoke temperature of each area of the tail flue may slightly rise in a period of time due to heat accumulation of the heating surface, but then the smoke temperature of each area of the tail flue is in a slow descending trend. Therefore, an acceptable time for the smoke temperature to rise after the blower is stopped, that is, the second preset threshold value, may be preset.
As a possible example, in response to the duration being less than the second preset threshold, which indicates that the current duration is within the acceptable time threshold of the rise of the smoke temperature after the fan is stopped, it is determined whether the real-time values of the change rates of the temperatures of the different areas of the back flue of the boiler are greater than the respective third preset thresholds. The third preset threshold may be obtained by a previous experiment, and may be an acceptable temperature change rate when the current duration is within an acceptable time threshold of the rise of the smoke temperature after the fan is stopped.
And 183, responding to that at least one change rate real-time value in the change rate real-time values of the temperatures of different areas of the boiler tail flue is larger than a third preset threshold corresponding to the change rate real-time value, and outputting an early warning signal.
As a possible example, in response to that at least one of the real-time values of the change rate of the temperatures of different areas of the tail flue of the boiler is larger than a third preset threshold corresponding to the real-time value of the change rate, which indicates that the change rate of the temperatures of the areas exceeds an acceptable temperature change rate, the risk of secondary combustion exists, and an early warning signal is output to warn.
And 184, responding to the duration time being greater than the second preset threshold value, determining whether the real-time values of the change rates of the temperatures of different areas of the boiler tail flue are greater than the fourth preset threshold values respectively.
In some embodiments of the present application, the third predetermined threshold is greater than the fourth predetermined threshold.
And responding to the situation that the duration is greater than a second preset threshold, showing that the current duration exceeds an acceptable time threshold of the rise of the smoke temperature after the fan stops running, and determining whether real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler are greater than respective fourth preset thresholds. The fourth preset threshold may be a previously experimentally obtained acceptable rate of temperature change when the current duration exceeds an acceptable time threshold for the rise in smoke temperature after the blower is shut down.
And 185, responding to that the real-time value of the change rate of the temperature of different areas of the boiler tail flue in at least one real-time value of the change rate of the temperature of different areas of the boiler tail flue is larger than a fourth preset threshold corresponding to the real-time value of the change rate, and outputting an early warning signal.
As a possible example, in response to that the real-time value of the rate of change of the temperature of different areas of the boiler back flue in at least one real-time value of the rate of change of the temperature of different areas of the boiler back flue is greater than a fourth preset threshold corresponding to the real-time value of the rate of change, which indicates that the rate of change of the temperature of the area exceeds an acceptable rate of change of the temperature, a risk of secondary combustion exists, and an early warning signal is output to warn.
According to the early warning method for the secondary combustion of the boiler tail flue of the thermal power plant, the boiler is divided into two different states, namely the operation state and the blowing-out state, so that the early warning of the secondary combustion of the boiler tail flue is respectively carried out, the real-time monitoring of the boiler tail flue is realized, and the accuracy of the early warning is improved; in addition, the temperatures of different areas of the boiler tail flue are predicted by adopting a pre-trained temperature prediction model, and the predicted temperature values are compared with real-time temperature values, so that the secondary combustion of the boiler tail flue is early warned in time, and the timeliness of early warning is improved.
Fig. 2 is a flowchart of another early warning method for secondary combustion in a tail flue of a boiler of a thermal power plant according to the embodiment of the present application.
As shown in fig. 2, the method for pre-warning the secondary combustion of the tail flue of the boiler in the thermal power plant comprises the following steps:
and step 210, acquiring real-time data of the boiler, and determining real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler based on the real-time data.
In the embodiment of the present application, step 210 may be implemented by using any one of the embodiments of the present application, which is not limited in this embodiment and is not described again.
In the embodiment of the present application, step 220 may be implemented by any one of the embodiments of the present application, which is not limited in this embodiment and is not described again.
In the embodiment of the present application, step 230 may be implemented by using any one of the embodiments of the present application, which is not limited herein and is not described in detail herein.
And 240, respectively acquiring predicted temperature values output by the plurality of pre-trained temperature prediction models.
In the embodiment of the present application, step 240 may be implemented by any one of the embodiments of the present application, which is not limited in this embodiment and is not described again.
And 250, determining whether the real-time data meets the preset requirement or not based on the real-time data and the predicted temperature values output by the plurality of pre-trained temperature prediction models respectively.
In the embodiment of the present application, step 250 may be implemented by any one of the embodiments of the present application, which is not limited in this embodiment and is not described again.
And step 260, responding to the fact that the real-time data do not meet the preset requirements, and outputting early warning signals.
In the embodiment of the present application, step 260 may be implemented by any one of the embodiments of the present application, which is not limited in this embodiment and is not described again.
And 270, determining a real-time value of the change rate of the real-time data based on the real-time data, and inputting the real-time value of the change rate of the real-time data into pre-training temperature change rate prediction models respectively corresponding to different areas of the tail flue of the boiler.
In the embodiment of the application, a plurality of pre-trained temperature change rate prediction models are respectively used for predicting the temperature change rates of different areas of the tail flue of the boiler.
For example, the change rate real-time value of the real-time data can be calculated by the following formula:
T rate of change ＝(T 1 -T 2 )×2
Wherein, T Rate of change Is a real-time value of the minute change rate of the real-time data of the boiler at a certain moment, such as the temperature of the flue gas at the outlet of the economizer, and the unit is ℃/min; t is 1 Real-time data of the boiler at a certain time, such as the temperature of flue gas at the outlet of an economizer, and the unit is; t is 2 Is the above-mentioned T 1 Boiler real-time data 30 seconds before time, e.g. distance T 1 The temperature of the flue gas at the outlet of the economizer 30 seconds before the moment is measured in degrees centigrade.
As a possible example, based on one or a mixture of multiple algorithms such as a convolutional neural network, a deep neural network, and a genetic algorithm, a temperature change rate prediction model may be respectively constructed for multiple boiler tail flue regions, and the multiple temperature change rate prediction models may be trained, so that the multiple temperature change rate prediction models may predict the temperature change rate of the boiler tail flue region corresponding to each of the multiple boiler tail flue regions according to the change rate real-time value of the real-time data.
And step 280, respectively obtaining predicted values of the temperature change rates of different areas of the boiler tail flue, which are output by the plurality of pre-training temperature change rate prediction models.
In the embodiment of the application, a plurality of pre-training temperature change rate prediction models correspond to the real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler one by one.
in some embodiments of the present application, step 290 comprises:
step 291, determining a second difference value between the plurality of temperature change rate predicted values and the corresponding change rate real-time values based on the plurality of temperature change rate predicted values and the change rate real-time values of the temperatures of different areas of the boiler tail flue;
as a possible example, each real-time temperature change rate is subtracted from the pre-measured temperature change rate corresponding to the real-time temperature change rate to obtain a second difference value corresponding to each real-time temperature change rate.
In step 292, in response to at least one of the plurality of second differences being greater than a preset threshold corresponding to the second difference, outputting an early warning signal.
As a possible example, in response to at least one of the second difference values being greater than the preset threshold corresponding to the second difference value, indicating that the real-time temperature change rate of the back flue region of the boiler corresponding to the second difference value exceeds the safe temperature change rate, the boiler is likely to have secondary combustion; and responding to the fact that none of the second difference values is larger than the preset threshold corresponding to the second difference value, the real-time temperature change rates of different areas of the boiler tail flue are all at the safe temperature change rate threshold, and the boiler has no risk of secondary combustion.
It can be understood that at least one value of the real-time values of the change rates of the temperatures of different areas of the boiler tail flue does not meet the preset requirement, which indicates that the boiler is likely to have secondary combustion, and therefore, an early warning signal is output to give an early warning.
in the embodiment of the present application, step 2120 may be implemented by using any one of the embodiments of the present application, and this is not limited by the embodiment of the present application and is not described again.
And 2130, responding to the situation that the real-time values of the temperature change rates of different areas of the boiler tail flue do not meet the preset requirements, and outputting an early warning signal.
In the embodiment of the present application, step 2130 may be implemented by any one of the embodiments of the present application, which is not limited in this embodiment and is not described again.
According to the early warning method for secondary combustion of the boiler tail flue of the thermal power plant, the temperature change rates of different areas of the boiler tail flue are predicted by adopting the pre-trained temperature change rate prediction model, and the pre-measured temperature change rate is compared with the real-time temperature change rate, so that the secondary combustion of the boiler tail flue is early warned in time, and the timeliness of early warning is improved.
Fig. 3 is a block diagram of a structure of an early warning device for secondary combustion in a tail flue of a boiler of a thermal power plant, which is provided in an embodiment of the present application.
As shown in fig. 3, this early warning device of boiler afterbody flue postcombustion of thermal power plant includes:
the first determining module 301 is configured to obtain real-time data of the boiler, and determine real-time values of change rates of temperatures of different areas of a tail flue of the boiler based on the real-time data;
in some embodiments of the present application, the first determining module 301 is specifically configured to: acquiring a secondary air quantity value based on the real-time data; responding to the fact that the secondary air volume value is larger than a first preset threshold value, and determining that the current state of the boiler is an operating state; and determining the current state of the boiler to be a blowing-out state in response to the secondary air volume value being smaller than a first preset threshold value.
A judging module 302, configured to judge an operating state of the boiler according to the real-time data; the current state of the boiler comprises an operation state and a blowing-out state;
the input module 303 is configured to input real-time data into a plurality of pre-trained temperature prediction models respectively in response to that the current state of the boiler is an operating state; the pre-trained temperature prediction models are respectively used for predicting the temperatures of different areas of the boiler tail flue;
a first obtaining module 304, configured to obtain temperature values output by a plurality of pre-trained temperature prediction models, respectively;
a second determining module 305, configured to determine whether the real-time data meets a preset requirement based on the real-time data and a predicted temperature value output by each of the plurality of pre-trained temperature prediction models;
in some embodiments of the present application, the second determining module 305 is specifically configured to: respectively determining a first difference value of each real-time temperature value and a predicted temperature value corresponding to the real-time temperature value based on real-time temperature values of different areas of a boiler tail flue and predicted temperature values output by a plurality of pre-trained temperature prediction models; and determining that the real-time data does not meet the preset requirement in response to at least one first difference value in the plurality of first difference values being greater than a preset threshold value corresponding to the first difference value.
The first output module 306 is used for responding to the condition that the real-time data does not meet the preset requirement and outputting an early warning signal;
a second obtaining module 307, configured to obtain real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler in response to that the current state of the boiler is a blowing-out state;
and the second output module 308 is configured to output an early warning signal in response to that the real-time values of the change rates of the temperatures of different areas of the boiler tail flue do not meet preset requirements.
In some embodiments of the present application, the second output module 308 is specifically configured to: based on the real-time data, obtaining the duration time of the secondary air volume smaller than a first preset threshold value; in response to the duration time being less than a second preset threshold, determining whether real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler are greater than respective corresponding third preset thresholds; responding to the fact that at least one change rate real-time value in the change rate real-time values of the temperatures of different areas of the boiler tail flue is larger than a third preset threshold value corresponding to the change rate real-time value, and outputting an early warning signal; in response to the duration time being greater than a second preset threshold, determining whether real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler are greater than respective fourth preset thresholds; and responding to the fact that the real-time value of the change rate of the temperature of at least one different area of the boiler tail flue in the real-time values of the change rates of the temperatures of the different areas of the boiler tail flue is larger than a fourth preset threshold corresponding to the real-time value of the change rate, and outputting an early warning signal.
According to the early warning device of boiler afterbody flue postcombustion of thermal power plant of this application embodiment, through adopting the temperature change rate prediction model of training in advance to predict the temperature change rate of the different regions of boiler afterbody flue, compare temperature change rate and real-time temperature change rate in advance to in time carry out the early warning to boiler afterbody flue postcombustion, improved the promptness of early warning.
Fig. 4 is a block diagram of a structure of an early warning device for secondary combustion in a tail flue of a boiler of a thermal power plant, which is provided in an embodiment of the present application.
As shown in fig. 4, this early warning device of boiler afterbody flue postcombustion of thermal power plant includes:
the first determining module 401 is configured to obtain real-time data of a boiler, and determine real-time values of change rates of temperatures of different areas of a tail flue of the boiler based on the real-time data;
a judging module 402, configured to judge an operating state of the boiler according to the real-time data; the current state of the boiler comprises an operation state and a blowing-out state;
a first input module 403, configured to input real-time data into a plurality of pre-trained temperature prediction models respectively in response to that the current state of the boiler is an operating state; the pre-trained temperature prediction models are respectively used for predicting the temperatures of different areas of the boiler tail flue;
a first obtaining module 404, configured to obtain temperature values output by a plurality of pre-trained temperature prediction models, respectively;
a second determining module 405, configured to determine whether the real-time data meets a preset requirement based on the real-time data and a predicted temperature value output by each of the plurality of pre-trained temperature prediction models;
the first output module 406 is configured to output an early warning signal in response to that the real-time data does not meet the preset requirement;
a second obtaining module 407, configured to obtain real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler in response to that the current state of the boiler is a blowing-out state;
and the second output module 408 is configured to output an early warning signal in response to that the real-time values of the change rates of the temperatures of different areas of the boiler tail flue do not meet the preset requirement.
The second input module 409 is used for determining a change rate real-time value of the real-time data based on the real-time data and inputting the change rate real-time value of the real-time data into pre-training temperature change rate prediction models corresponding to different regions of the tail flue of the boiler; the pre-training temperature change rate prediction models are respectively used for predicting the temperature change rates of different areas of the boiler tail flue;
a third obtaining module 4010, configured to respectively obtain predicted values of temperature change rates of different areas of a tail flue of the boiler, where the predicted values are output by multiple pre-training temperature change rate prediction models; the pre-training temperature change rate prediction models correspond to real-time values of the change rates of the temperatures of different areas of the boiler tail flue one by one;
the third determining module 4011 is configured to determine whether the change rate real-time values of the temperatures of the different areas of the boiler tail flue meet preset requirements based on the multiple predicted values of the temperature change rate and the change rate real-time values of the temperatures of the different areas of the boiler tail flue;
and the third output module 4012 is configured to output an early warning signal in response to that at least one of the real-time values of the rate of change of the temperatures of different areas of the back flue of the boiler does not meet a preset requirement.
According to the early warning device of boiler afterbody flue postcombustion of thermal power plant of this application embodiment, through adopting the temperature change rate prediction model of training in advance to predict the temperature change rate of the different regions of boiler afterbody flue, compare temperature change rate and real-time temperature change rate in advance to in time carry out the early warning to boiler afterbody flue postcombustion, improved the promptness of early warning.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
Fig. 5 is a block diagram of a computer device provided in the embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 5, the electronic apparatus includes: one or more processors 501, memory 502, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 5 illustrates an example of a processor 501.
The memory 502, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method for pre-training model training for reading tasks in the embodiments of the present application (e.g., the first determining module 301, the determining module 302, the inputting module 303, and the first obtaining module 304 shown in fig. 3). The processor 501 executes various functional applications of the server and data processing, namely, a method for training a pre-training model for a reading task in the above method embodiments, by executing non-transitory software programs, instructions and modules stored in the memory 502.
The memory 502 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the stored data area may store data created from use of the electronic device trained according to a pre-trained model for a reading task, and the like. Further, the memory 502 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 502 may optionally include memory located remotely from the processor 501, which may be connected over a network to an electronic device for pre-training model training of reading tasks. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the method for pre-training model training of reading tasks may further comprise: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic device for pre-trained model training of reading tasks, such as a touch screen, keypad, mouse, track pad, touch pad, pointer, one or more mouse buttons, track ball, joystick, or other input device. The output devices 504 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server may be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service extensibility in a traditional physical host and VPS service ("Virtual Private Server", or "VPS" for short).
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. The early warning method for the secondary combustion of the tail flue of the boiler in the thermal power plant is characterized by comprising the following steps of:
acquiring real-time data of a boiler, and determining real-time values of the change rates of the temperatures of different areas of a tail flue of the boiler based on the real-time data;
determining the current state of the boiler according to the real-time data; the current state of the boiler comprises an operating state and a blowing-out state;
responding to the current state of the boiler as an operating state, and respectively inputting the real-time data into a plurality of pre-trained temperature prediction models; the pre-trained temperature prediction models are respectively used for predicting the temperatures of different areas of the boiler tail flue;
respectively acquiring predicted temperature values output by the plurality of pre-trained temperature prediction models;
determining whether the real-time data meets a preset requirement or not based on the real-time data and predicted temperature values output by the plurality of pre-trained temperature prediction models respectively;
responding to the real-time data not meeting the preset requirement, and outputting an early warning signal;
responding to the fact that the current state of the boiler is a blowing-out state, and acquiring real-time values of the change rate of the temperature of different areas of the tail flue of the boiler;
and responding to the situation that the real-time values of the change rates of the temperatures of different areas of the boiler tail flue do not meet the preset requirements, and outputting early warning signals.
2. The method of claim 1, wherein the real-time data comprises real-time temperature values of different areas of the boiler back flue and main boiler parameters, the temperature values of the different areas of the boiler back flue corresponding one-to-one to the plurality of pre-trained temperature prediction models; determining whether the real-time data meets a preset requirement based on the real-time data and predicted temperature values output by the plurality of pre-trained temperature prediction models respectively, including:
respectively determining a first difference value between each real-time temperature value and a predicted temperature value corresponding to the real-time temperature value on the basis of real-time temperature values of different areas of the boiler tail flue and predicted temperature values output by the pre-trained temperature prediction models;
and determining that the real-time data does not meet the preset requirement in response to at least one first difference value in the plurality of first difference values being larger than a preset threshold value corresponding to the first difference value.
3. The method of claim 1, further comprising:
determining a change rate real-time value of the real-time data based on the real-time data, and inputting the change rate real-time value of the real-time data into pre-training temperature change rate prediction models respectively corresponding to different regions of a tail flue of the boiler; the pre-training temperature change rate prediction models are respectively used for predicting the temperature change rates of different areas of the boiler tail flue;
respectively obtaining predicted values of the temperature change rates of different areas of the boiler tail flue output by the pre-training temperature change rate prediction models; the pre-training temperature change rate prediction models correspond to the real-time values of the change rates of the temperatures of different areas of the boiler tail flue in a one-to-one mode;
determining whether the real-time values of the change rates of the temperatures of the different areas of the boiler tail flue meet preset requirements or not based on the plurality of predicted values of the temperature change rates and the real-time values of the change rates of the temperatures of the different areas of the boiler tail flue;
and responding to at least one value of the real-time values of the change rates of the temperatures of different areas of the boiler tail flue not meeting the preset requirement, and outputting an early warning signal.
4. The method of claim 3, wherein determining whether the real-time values of the rates of change of the temperatures of the different zones of the boiler back pass satisfy preset requirements based on the plurality of predicted values of the rates of change of the temperatures and the real-time values of the rates of change of the temperatures of the different zones of the boiler back pass comprises:
determining a second difference value between the plurality of temperature change rate predicted values and the corresponding change rate real-time values based on the plurality of temperature change rate predicted values and the change rate real-time values of the temperatures of different areas of the tail flue of the boiler;
and outputting an early warning signal in response to at least one second difference value in the plurality of second difference values being larger than a preset threshold value corresponding to the second difference value.
5. The method of claim 1, wherein outputting a warning signal in response to the real-time values of the rate of change of the temperature of the different regions of the boiler back pass failing to meet a predetermined requirement comprises:
based on the real-time data, acquiring the duration time of the secondary air volume smaller than a first preset threshold value;
in response to the duration being less than a second preset threshold, determining whether real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler are greater than respective third preset thresholds;
responding to at least one change rate real-time value in the change rate real-time values of the temperatures of different areas of the boiler tail flue, and outputting an early warning signal, wherein the change rate real-time value is larger than a third preset threshold corresponding to the change rate real-time value;
in response to the duration being greater than a second preset threshold, determining whether real-time values of the rate of change of the temperatures of different areas of the boiler tail flue are greater than respective corresponding fourth preset thresholds;
and responding to the fact that the real-time value of the change rate of the temperature of at least one different area of the boiler tail flue in the real-time values of the change rate of the temperature of the different areas of the boiler tail flue is larger than a fourth preset threshold corresponding to the real-time value of the change rate, and outputting an early warning signal.
6. The method of claim 5, wherein the third preset threshold is greater than the fourth preset threshold.
7. The method of claim 1, wherein said determining a current state of said boiler from said real-time data comprises:
acquiring a secondary air quantity value based on the real-time data;
responding to the fact that the secondary air quantity value is larger than a first preset threshold value, and determining that the current state of the boiler is an operation state;
and responding to the fact that the secondary air quantity value is smaller than a first preset threshold value, and determining that the current state of the boiler is a blowing-out state.
8. Early warning device of boiler afterbody flue postcombustion of thermal power plant, its characterized in that, the device includes:
the first determining module is used for acquiring real-time data of the boiler and determining real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler based on the real-time data;
the judging module is used for judging the running state of the boiler according to the real-time data; the current state of the boiler comprises an operating state and a blowing-out state;
the input module is used for responding to the current state of the boiler as an operating state and inputting the real-time data into a plurality of pre-trained temperature prediction models respectively; the pre-trained temperature prediction models are respectively used for predicting the temperatures of different areas of the boiler tail flue;
the first acquisition module is used for respectively acquiring temperature values output by the plurality of pre-trained temperature prediction models;
the second determination module is used for determining whether the real-time data meet the preset requirement or not based on the real-time data and the predicted temperature values output by the plurality of pre-trained temperature prediction models respectively;
the first output module is used for responding to the situation that the real-time data does not meet the preset requirement and outputting an early warning signal;
the second acquisition module is used for responding to the fact that the current state of the boiler is a blowing-out state, and acquiring real-time values of the change rates of the temperatures of different areas of the tail flue of the boiler;
and the second output module is used for responding to the situation that the real-time values of the change rates of the temperatures of different areas of the boiler tail flue do not meet the preset requirements and outputting early warning signals.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
10. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008084217A1 (en) * | 2007-01-09 | 2008-07-17 | Marchant, Gordon | Boiler performance indicator |
| CN106710160A (en) * | 2016-12-28 | 2017-05-24 | 湖南坤宇网络科技有限公司 | Decision-making tree system-based boiler clausilium smoke temperature early-warning method |
| CN106918033A (en) * | 2017-03-21 | 2017-07-04 | 山东中实易通集团有限公司 | Three impulses leakage of boiler tubes alarm control system and method |
| CN108662602A (en) * | 2018-03-13 | 2018-10-16 | 中国神华能源股份有限公司 | Method and system for the exhaust gas temperature for controlling boiler |
| CN110068003A (en) * | 2018-01-24 | 2019-07-30 | 中国石油天然气股份有限公司 | Boiler early warning information pushing method and device |
| CN111237789A (en) * | 2020-01-09 | 2020-06-05 | 京东城市(北京)数字科技有限公司 | Boiler soot blowing method, device and computer readable storage medium |
| CN113361192A (en) * | 2021-05-31 | 2021-09-07 | 浙江大学 | Boiler internal heating surface wall temperature safety monitoring and evaluating system |
| CN114484409A (en) * | 2022-02-22 | 2022-05-13 | 北京博数智源人工智能科技有限公司 | Early warning method and device for furnace tube leakage accident of thermal power plant |
| KR20220075674A (en) * | 2020-11-30 | 2022-06-08 | 린나이코리아 주식회사 | Operating controlling method of boiler using exhaust temperature |
-
2022
- 2022-06-21 CN CN202210705265.6A patent/CN114997509A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008084217A1 (en) * | 2007-01-09 | 2008-07-17 | Marchant, Gordon | Boiler performance indicator |
| CN106710160A (en) * | 2016-12-28 | 2017-05-24 | 湖南坤宇网络科技有限公司 | Decision-making tree system-based boiler clausilium smoke temperature early-warning method |
| CN106918033A (en) * | 2017-03-21 | 2017-07-04 | 山东中实易通集团有限公司 | Three impulses leakage of boiler tubes alarm control system and method |
| CN110068003A (en) * | 2018-01-24 | 2019-07-30 | 中国石油天然气股份有限公司 | Boiler early warning information pushing method and device |
| CN108662602A (en) * | 2018-03-13 | 2018-10-16 | 中国神华能源股份有限公司 | Method and system for the exhaust gas temperature for controlling boiler |
| CN111237789A (en) * | 2020-01-09 | 2020-06-05 | 京东城市(北京)数字科技有限公司 | Boiler soot blowing method, device and computer readable storage medium |
| KR20220075674A (en) * | 2020-11-30 | 2022-06-08 | 린나이코리아 주식회사 | Operating controlling method of boiler using exhaust temperature |
| CN113361192A (en) * | 2021-05-31 | 2021-09-07 | 浙江大学 | Boiler internal heating surface wall temperature safety monitoring and evaluating system |
| CN114484409A (en) * | 2022-02-22 | 2022-05-13 | 北京博数智源人工智能科技有限公司 | Early warning method and device for furnace tube leakage accident of thermal power plant |
Non-Patent Citations (3)
| Title |
|---|
| 任永红 主编: "《循环流化床锅炉实用培训教材》", vol. 1, 30 April 2007, 中国电力出版社, pages: 176 - 177 * |
| 张俊卿: "燃油锅炉二次燃烧的预防和蒸汽灭火", 华东电力, no. 05, 24 May 1994 (1994-05-24), pages 40 - 41 * |
| 张天启 主编: "《特种作业安全技能问答》", vol. 1, 31 May 2014, 冶金工业出版社, pages: 254 * |
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