Boiler operation optimization method and system based on coal quality-fly ash online

Abstract

The invention relates to a boiler operation optimization method and system based on coal quality-fly ash online. The method comprises the following steps: constructing a boiler wall temperature safety early warning model based on coal feeding quantity input of fly ash carbon content, coal quality and unit load requirements; the boiler wall temperature is predicted through the safety early warning model, and the predicted wall temperature is output; judging whether the boiler wall temperature has an overtemperature risk or not according to the predicted wall temperature, and generating a judgment result; the coal feeding amount and the air supply mode are adjusted according to the judgment result so as to reduce the wall temperature of the boiler, or the air supply mode is adjusted according to the judgment result so as to reduce the NOX content, the wall temperature of the boiler in operation can be predicted, whether the temperature of the boiler exceeds the temperature or not can be further judged according to the prediction result, and combustion of the boiler is optimized according to the judgment result; and the wall temperature of the boiler is prevented from being overheated in the boiler operation process, the content of NOX is prevented from being too high, and the safety and reliability of the boiler are improved.

Description

Technical Field

[0001] The invention relates to the technical field of boiler combustion control, and in particular to a method and system for optimizing boiler operation based on coal quality-fly ash online. Background Art

[0002] From the perspective of my country's energy structure, thermal power is still the main power generation method. With the rapid development and progress of the information age, the demand for electricity in society is also increasing. Therefore, the installed capacity and power generation of power plants are constantly increasing, but this makes the various parameters of the units (such as coal feed, power generation, boiler wall temperature, etc.) extremely high. If thermal power units operate under high parameter conditions for a long time, the stability and safety of the units will be subject to certain risks.

[0003] As one of the most important parameters for the stability and safety of the unit, online monitoring of the boiler wall temperature during the boiler combustion process is extremely important. Effective monitoring of the wall temperature can better prevent the problem of boiler overheating and tube bursting, and further optimize the combustion of the boiler unit without overheating. On the one hand, the quality of the coal entering the furnace has a significant impact on the wall temperature. On the other hand, the existing scheme cannot monitor and predict the combustion conditions of the boiler in real time. Therefore, when the parameters such as the quality of the coal entering the furnace and the carbon content of the fly ash cannot be effectively monitored and obtained, the combustion of the boiler may be seriously delayed, and the coal feeding amount does not match the actual load. At this time, the operator frequently feeds coal, which is very likely to cause the boiler wall temperature to overheat.

[0004] Therefore, it is urgent for those skilled in the art to provide a method and system for optimizing boiler operation based on coal quality-fly ash online to avoid overheating of the boiler wall temperature and further optimize the combustion of the boiler unit. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the defects in the prior art, thereby providing a boiler operation optimization method and system based on coal quality-fly ash online.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for optimizing boiler operation based on coal quality-fly ash online includes the following steps:

[0008] Construct a boiler wall temperature safety warning model based on the fly ash carbon content, coal quality and coal feed quantity input required by the unit load;

[0009] Predicting the boiler wall temperature using the safety warning model and outputting the predicted wall temperature;

[0010] Judging whether there is an over-temperature risk in the boiler wall temperature according to the predicted wall temperature, and generating a judgment result;

[0011] According to the judgment result, the coal feeding amount and the air supply mode are adjusted to reduce the boiler wall temperature, or, according to the judgment result, the air supply mode is adjusted to reduce NO X content.

[0012] Preferably, the construction of a boiler wall temperature safety warning model based on the fly ash carbon content, coal quality and coal feed quantity input according to the unit load requirement comprises the following steps:

[0013] Construct the coal feed rate, air volume, wall temperature, NO X Dynamically associated database of content;

[0014] The security early warning model is constructed based on the dynamic association database.

[0015] Preferably, the fly ash carbon content, coal quality, coal feed rate, air volume, wall temperature, NO X The dynamic association database specifically includes the following steps:

[0016] Establish a historical database of boiler combustion data, including the coal feed rate required for different unit loads, wall temperature and NO under different coal qualities and fly ash carbon content conditions. X Content data;

[0017] Carry out CFD simulation on the wall temperature characteristics under different working conditions and build a CFD simulation database of wall temperature under different working conditions;

[0018] Based on the historical database and the CFD simulation database, the dynamic association database is established through coupling.

[0019] Preferably, the coupling and establishing of the dynamic association database based on the historical database and the CFD simulation database comprises the following steps:

[0020] Standardizing the data in the historical database and the data in the CFD simulation database;

[0021] Removing abnormal data from the historical database and the CFD simulation database;

[0022] The data in the CFD simulation database are mapped to the corresponding working conditions in the historical database, so as to couple the CFD simulation database and the historical database into the dynamic association database.

[0023] Preferably, the method of predicting the boiler wall temperature by using the safety warning model and outputting the predicted wall temperature comprises the following steps:

[0024] The real-time monitoring parameters are input into the safety warning model so that the safety warning model outputs the predicted wall temperature. The real-time monitoring parameters include the coal feed rate required by the actual load, the coal quality results of online monitoring, and the carbon content of fly ash of online monitoring.

[0025] Preferably, adjusting the coal feeding amount and the air supply mode according to the judgment result to reduce the boiler wall temperature specifically includes the following steps:

[0026] When the judgment result is that there is an over-temperature risk, the coal supply is reduced, the primary air volume supplied to the boiler is reduced, and the secondary air volume supplied to the boiler is increased;

[0027] Determine again whether there is a risk of overheating of the boiler wall temperature.

[0028] Preferably, the air supply mode is adjusted according to the judgment result to reduce NO X The content specifically comprises the following steps:

[0029] When the judgment result is that there is no over-temperature risk, the primary air volume supplied to the boiler is increased, and the secondary air volume supplied to the boiler is reduced.

[0030] A system based on the above-mentioned coal quality-fly ash online boiler operation optimization method comprises:

[0031] A model building module, used to build a boiler wall temperature safety warning model based on the input of coal feed amount, coal quality and fly ash carbon content; the safety warning model is used to predict the boiler wall temperature and output the predicted wall temperature;

[0032] A judgment module, used to judge whether there is an over-temperature risk of the boiler wall temperature according to the predicted wall temperature, and generate a judgment result;

[0033] A control module is used to adjust the coal supply and air supply mode according to the judgment result to reduce the boiler wall temperature, or to adjust the air supply mode according to the judgment result to reduce NO X content.

[0034] Preferably, the model building module comprises:

[0035] The historical database construction unit is used to collect boiler combustion data and store them accordingly to generate a historical database. The boiler combustion data includes the wall temperature and NO under different load requirements, different coal qualities, and different fly ash carbon content conditions. X Content data;

[0036] A CFD simulation database construction unit is used to perform CFD simulation on the wall temperature characteristics under different working conditions and store them accordingly to generate a CFD simulation database of the wall temperature under different working conditions;

[0037] The dynamic association database construction unit is used to couple the historical database and the CFD simulation database to generate the dynamic association database.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The above technical solution provides a method for optimizing boiler operation based on coal quality-fly ash online. Through the constructed safety warning model, the wall temperature of the boiler in operation can be predicted in combination with the carbon content of fly ash, coal quality and the coal feeding amount required by the unit load. It can further determine whether the boiler is overheated based on the prediction results, and optimize the combustion of the boiler based on the judgment results to avoid overheating of the boiler wall temperature during boiler operation, and avoid NO X The content is too high, which improves the safety and reliability of the boiler. At the same time, the system based on the above-mentioned online monitoring boiler operation optimization method can predict the wall temperature of the running boiler, further determine whether the boiler is overheated according to the prediction result, and optimize the combustion of the boiler according to the judgment result. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 The present invention provides a schematic diagram of one example of the steps of the method.

[0042] Figure 2 for Figure 1 Schematic diagram of the specific steps of step S1.

[0043] Figure 3 for Figure 2 Schematic diagram of the simulation results of the temperature field of boiler primary air inlet A, primary air inlet B, primary air inlet D and sofa1 burnout air inlet in step S1.1.2.

[0044] Figure 4 for Figure 1 Schematic diagram of the process for one example of the method. DETAILED DESCRIPTION

[0045] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0047] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] See also Figures 1 to 4 The embodiment of the present invention provides a method for optimizing boiler operation based on coal quality-fly ash online, comprising the following steps:

[0049] S1: Construct a boiler wall temperature safety warning model based on the fly ash carbon content, coal quality and coal feed input required by the unit load.

[0050] S2: Use the safety warning model to predict the boiler wall temperature and output the predicted wall temperature.

[0051] S3: Determine whether there is an over-temperature risk of the boiler wall temperature based on the predicted wall temperature, and generate a judgment result.

[0052] S4: According to the judgment result, adjust the coal supply and air supply mode to reduce the boiler wall temperature, or adjust the air supply mode according to the judgment result to reduce NO X content.

[0053] Through the safety warning model constructed, combined with the fly ash carbon content, coal quality and the coal feed required by the unit load, the amount of coal actually participating in the combustion process in the coal supplied to the boiler is evaluated, and then the wall temperature of the boiler in operation can be predicted. Further, it can be determined whether the boiler is overheated based on the prediction results, and the combustion of the boiler can be optimized based on the judgment results to avoid overheating of the boiler wall temperature during boiler operation, which can avoid NO X The content is too high, which improves the safety and reliability of the boiler.

[0054] It can be known that the carbon content of fly ash, coal quality and the coal feed required by the unit load can all be obtained through online monitoring. The monitoring results are input into the safety warning model, and the safety warning model can output the predicted wall temperature of the boiler, and then optimize the combustion of the boiler according to the predicted wall temperature.

[0055] Specifically, step S1 includes:

[0056] S1.1: Construct the coal feed rate, air volume, wall temperature, NO X Dynamically associated database of content;

[0057] S1.2: Build a security warning model based on dynamic association database.

[0058] The safety early warning model is constructed by dynamically associating the database, so that the carbon content of fly ash, coal quality, coal feed rate, air volume, wall temperature, NO X The contents are interrelated, and the boiler wall temperature can be predicted based on the coal quality, fly ash carbon content and unit load requirements of the input safety warning model, and the boiler unit can be further controlled to optimize combustion, that is, the air volume is adjusted according to the prediction results to control NO X It is worth noting that the "air volume" here includes the primary air volume, secondary air volume and burnout air volume.

[0059] Specifically, step S1.1 includes:

[0060] S1.1.1: Establish a historical database of boiler combustion data. The boiler combustion data includes the coal feed rate required by different unit loads, the wall temperature under different coal qualities and different fly ash carbon content conditions, and NO X Content data.

[0061] S1.1.2: Carry out CFD simulation on the wall temperature characteristics under different working conditions and build a CFD simulation database of wall temperature under different working conditions.

[0062] S1.1.3: Based on the historical database and CFD simulation database, a dynamic correlation database is established by coupling.

[0063] The historical database of boiler combustion data established in step S1.1.1 is specifically to collect and organize the actual operation data of the boiler under different combustion times, different load requirements, different coal qualities, and different fly ash carbon content. The actual operation data includes wall temperature, NO X content, etc., to form the corresponding parameters at different combustion times (coal feed required by load - coal quality - fly ash carbon content - wall temperature - NO X The data in the historical database can be used as a subsequent reference to analyze the performance of filtration under different operating conditions.

[0064] In step S1.1.2, the distribution of boiler wall temperature under different working conditions can be simulated by CFD simulation (using existing CFD simulation software for simulation), and CFD simulation data can be obtained to form a CFD simulation database.

[0065] It can be known that the historical database reflects the data collected and sorted during the actual operation process, reflecting the actual wall temperature and NO of the boiler under different historical conditions. X content, has a high reference value, but may be limited by the specific operating conditions and measurement conditions at the time, resulting in limited measurement data; the CFD simulation database, based on CFD simulation, can predict the temperature distribution of the boiler under different assumptions and conditions, so that the data of the simulation database supplements the operating conditions that may not be covered in the data of the historical database, and can carefully simulate the temperature distribution that is difficult to measure under actual conditions, provide a more comprehensive wall temperature distribution, and supplement the deficiencies of actual data (historical database); at the same time, it makes the dynamic correlation database established based on the coupling of the historical database and the CFD simulation database more complete, thereby improving the accuracy of the wall temperature prediction of the safety early warning model.

[0066] Furthermore, the historical database can also verify the accuracy of the CFD simulation database. By comparing the data in the CFD simulation database with the data in the historical database, the CFD simulation data can be verified and optimized, and the safety warning model can be further verified and optimized, so that the dynamic correlation database established based on the coupling of the historical database and the CFD simulation database is more complete, comprehensive and detailed, and the safety warning model constructed based on the dynamic correlation database can further improve the accuracy of wall temperature prediction, so that the prediction results of the safety warning model are more reliable.

[0067] In this embodiment, step S1.1.3 specifically includes two steps: data preprocessing and data fusion. Data preprocessing is mainly to ensure the consistency and compatibility of data and to prepare for the data fusion step. Data fusion is to integrate the historical database and the CFD simulation database into a comprehensive dynamically associated database.

[0068] Specifically, the data preprocessing steps include:

[0069] Data standardization: standardize the data in the historical database and the CFD simulation database, such as unit conversion, normalization, etc.; data cleaning: remove abnormal data (such as actual values, abnormal values, etc.) in the historical database and CFD simulation database to ensure data quality; data matching: ensure the correspondence between the data in the historical database and the CFD simulation database in terms of time, space and working conditions.

[0070] Furthermore, the data fusion step includes:

[0071] Mapping and alignment: Map the data of the CFD simulation database to the corresponding working conditions in the historical database to couple the CFD simulation database and the historical database into a dynamically associated database. For example, for the same load, coal quality and fly ash carbon content data in the historical database, find the corresponding CFD simulation data. Data merging: Merge the CFD simulation database and the historical database to form a database containing wall temperature, NO under different working conditions. X Dynamically associated database of content, etc.

[0072] Specifically, in this embodiment, S2 specifically includes: inputting real-time monitoring parameters into the safety warning model so that the safety warning model outputs a predicted wall temperature. The real-time monitoring parameters include the coal feed rate required by the unit load, the online monitored coal quality, and the online monitored fly ash carbon content.

[0073] Specifically, in this embodiment, step S4 specifically includes:

[0074] S4.1.1: When the judgment result shows that there is an over-temperature risk, reduce the coal supply, reduce the primary air volume supplied to the boiler, and increase the secondary air volume supplied to the boiler;

[0075] S4.1.2: Determine again whether the boiler wall temperature has an over-temperature risk. It can be known that if there is still an over-temperature risk, repeat S4.1.1 until there is no over-temperature risk and then proceed to the next step. The next step can be to reduce NO X Adjust the air supply mode for the destination (such as executing S4.1.3) or maintain the current operating conditions.

[0076] Specifically, step S4 also includes:

[0077] S4.1.3: When it is determined that there is no risk of over-temperature, increase the primary air volume supplied to the boiler and reduce the secondary air volume supplied to the boiler.

[0078] For the convenience of explanation, a specific example is given according to the above method.

[0079] First, historical data samples are collected and sorted based on the operation of power plant boilers under different operating conditions for one quarter. The collected and sorted data include the following historical data:

[0080] 1. Different load requirements (such as 330MW, 500MW, 660MW);

[0081] 2. Different coal qualities (such as low volatile coal, high volatile coal, etc.);

[0082] 3. Different air volume settings (such as primary air volume, secondary air volume, burnout air, etc.);

[0083] 4. Different fly ash carbon content (such as 5%, 10%, 15%)

[0084] 5. Boiler wall temperature data;

[0085] 6. NO x Content data.

[0086] After data collection and organization, a historical database containing tens of thousands of records is formed. Taking some samples as an example, the data collection and organization are as follows in Table 1:

[0087] Table 1

[0088]

[0089] Secondly, the CFD simulation software was used to simulate the boiler wall temperature under the above different working conditions and generate a CFD simulation database. Figure 3 , for 660MW load, low volatile coal, primary air volume 50000Nm 3 / h, secondary air volume 80000Nm 3 / h, and the fly ash carbon content is 15%, the simulation results of the temperature field of the boiler's primary air inlet A, primary air inlet B, primary air inlet D and sofa1 burnout air inlet are as follows Figure 3 It is worth noting that Figure 3 The thermal diagram generated by the CFD simulation software cannot be well represented using black and white drawings, so the instructions Figure 3 Use color graphics.

[0090] Then, based on the coupling of the historical database and the CFD simulation database, dynamic correlation data is constructed, and a safety warning model is constructed based on the dynamic correlation database. The input end of the safety warning model is used to obtain the parameters in the actual operation process, that is, to obtain the coal feed required by the actual load, the coal quality results of online monitoring, and the carbon content of fly ash of online monitoring. The output end of the safety warning model is used to output the predicted wall temperature.

[0091] Under the condition that other conditions remain unchanged, high-load operation of the boiler usually means higher fuel input and stronger combustion intensity, which will cause more heat to be generated inside the boiler. If the heat transfer capacity or cooling capacity of the boiler is not enough to cope with the increased heat load, the boiler wall temperature will rise, increasing the risk of overheating. Therefore, under the condition that other conditions remain unchanged, the boiler is more likely to overheat under high-load conditions. The load of the boiler unit usually needs to be adjusted according to the grid demand, process requirements or operation strategy, and there are many load conditions.

[0092] In the actual operation application, two operating conditions with boiler loads of 500MW and 660MW were selected for testing.

[0093] When the boiler load is 500MW, the online data provided by real-time monitoring, the predicted wall temperature based on the monitored real-time line data, and the monitored NO X When the content is as shown in Table 2:

[0094] Table 2

[0095]

[0096] As shown in Table 2 above, the unit load is known to be 500MW. After the coal quality type, actual coal feed rate and carbon content of fly ash monitored online in Table 2 are input into the safety warning model, the predicted wall temperature output by the safety warning model is 680℃, NO X The content is 220ppm.

[0097] Since the judgment standard for wall temperature overtemperature is usually determined based on design parameters such as boiler design specifications, material properties and operation safety requirements, the boiler overtemperature range selected in this example is 700-740℃.

[0098] According to the prediction results and the over-temperature range of the boiler in the cost-effectiveness, the system determines that there is no risk of over-temperature at the wall temperature at present, so NO X The content and combustion efficiency are adjusted for optimization purposes.

[0099] The specific optimization adjustment method is: on the premise of ensuring that unburned carbon does not increase and the pulverized coal can be fully burned, increase the valve opening of the primary air port and reduce the valve opening of the secondary air port to improve the combustion efficiency. At the same time, increase the valve opening of the sofa1 burnout air port to ensure sufficient air supply to the upper part of the combustion chamber of the boiler, reduce the local high temperature area, and reduce NO X The actual operating parameters after adjustment are shown in Table 3:

[0100] Table 3

[0101]

[0102]

[0103] From Table 2 and Table 3 above, it can be seen that after adjustment, the boiler wall temperature dropped from the predicted 680℃ to 665℃, and NO x Reducing the content from 220ppm to 210ppm can reduce the wall temperature to a certain extent and reduce NO x content.

[0104] Similarly, when the boiler load is 660MW, the online data provided by real-time monitoring is as shown in Table 4:

[0105] Table 4

[0106]

[0107] It can be seen from Table 4 above that the unit load is known to be 660MW. After the online monitored coal quality type, actual coal feed rate, and online monitored fly ash carbon content in Table 4 are input into the safety warning model, the predicted wall temperature output by the safety warning model is 750℃.

[0108] Since the judgment standard for wall temperature overtemperature is usually determined based on design parameters such as boiler design specifications, material properties and operation safety requirements, the boiler overtemperature range selected in this example is 700-740℃.

[0109] According to the prediction results and the over-temperature range of the boiler in this example, the system determines that there is a risk of wall temperature over-temperature at present, so adjustments are made with the reduction of boiler wall temperature as the optimization goal.

[0110] The specific optimization and adjustment method is: reduce the valve opening of the primary air inlet, increase the valve opening of the secondary air inlet, and then reduce the combustion temperature and the wall temperature of the boiler until there is no risk of overheating.

[0111] It is known that primary air is air mixed with fuel for combustion. It is usually used to provide oxygen required for initial combustion and control the combustion speed and temperature of the fuel. Adjusting the primary air can affect the completeness of combustion and flame temperature. Increasing the primary air can usually improve combustion efficiency, but excessive primary air may cause the flame temperature to be too high, increasing the risk of wall temperature overheating. Secondary air is air added after the primary air and fuel are mixed and burned. It is usually used to further provide oxygen to complete the combustion, which is conducive to mixing and dispersing the flame. Adjusting the secondary air can optimize the combustion process, reduce the flame temperature and NO X When the primary air is increased, the combustion efficiency and initial combustion temperature can be improved, which is conducive to the full combustion of the fuel. However, excessive primary air may cause local overheating of the boiler and increase the risk of overheating of the wall temperature. When the secondary air is reduced, the combustion temperature can be reduced, local overheating can be reduced, thereby controlling the wall temperature and reducing the risk of overheating of the wall temperature. X It is mainly generated at high temperatures. The higher the combustion temperature, the higher the NOX The larger the amount generated.

[0112] In actual operation, the boiler load, fuel characteristics and current combustion conditions can be comprehensively considered for dynamic adjustment. At high load, based on online monitoring parameters and safety warning models, the wall temperature is monitored to prevent overheating, and the ratio of primary air, secondary air and burnout air is adjusted dynamically according to actual needs. At low load, the focus is on improving combustion efficiency, reducing carbon emissions, and reducing NO X Emission, appropriately reduce primary air, increase secondary air and burnout air to ensure complete combustion.

[0113] This embodiment also provides a system based on the above-mentioned coal quality-fly ash online boiler operation optimization method, which is not shown in the figure. The system includes:

[0114] A model building module is used to build a boiler wall temperature safety warning model based on the input of coal feed amount, coal quality and fly ash carbon content; the safety warning model is used to predict the boiler wall temperature and output the predicted wall temperature;

[0115] A judgment module is used to judge whether there is an over-temperature risk of the boiler wall temperature according to the predicted wall temperature, and generate a judgment result;

[0116] The control module is used to adjust the coal supply and air supply mode according to the judgment result to reduce the boiler wall temperature, or to adjust the air supply mode according to the judgment result to reduce NO X content.

[0117] Also includes:

[0118] The historical database construction unit is used to collect boiler combustion data and store them accordingly to generate a historical database. The boiler combustion data includes the wall temperature and NO under different load requirements, different coal qualities, and different fly ash carbon content conditions. X Content data;

[0119] A CFD simulation database construction unit is used to perform CFD simulation on the wall temperature characteristics under different working conditions and store them accordingly to generate a CFD simulation database of the wall temperature under different working conditions;

[0120] The dynamic association database construction unit is used to couple the historical database and the CFD simulation database to generate a dynamic association database.

[0121] The above-mentioned embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present invention shall fall within the scope of protection required by the present invention.

Claims

1. A method for optimizing boiler operation based on coal quality-fly ash online, characterized in that: The following steps are involved: Construct a boiler wall temperature safety warning model based on the fly ash carbon content, coal quality and coal feed input required by the unit load; Predicting the boiler wall temperature using the safety warning model and outputting the predicted wall temperature; Judging whether there is an over-temperature risk in the boiler wall temperature according to the predicted wall temperature, and generating a judgment result; According to the judgment result, the coal feeding amount and the air supply mode are adjusted to reduce the boiler wall temperature, or, according to the judgment result, the air supply mode is adjusted to reduce NO X content.

2. The method for optimizing boiler operation based on coal quality-fly ash online according to claim 1, characterized in that: The construction of a boiler wall temperature safety warning model based on the fly ash carbon content, coal quality and coal feed quantity input according to the unit load requirement comprises the following steps: Construct the coal feed rate, air volume, wall temperature, NO X Dynamically associated database of content; The security early warning model is constructed based on the dynamic association database.

3. The method for optimizing boiler operation based on coal quality-fly ash online according to claim 2 is characterized in that: The fly ash carbon content, coal quality, coal feed rate, air volume, wall temperature, NO X The dynamic association database specifically includes the following steps: Establish a historical database of boiler combustion data, including the coal feed rate required for different unit loads, wall temperature and NO under different coal qualities and fly ash carbon content conditions. X Content data; Carry out CFD simulation on the wall temperature characteristics under different working conditions and build a CFD simulation database of wall temperature under different working conditions; Based on the historical database and the CFD simulation database, the dynamic association database is established through coupling.

4. The method for optimizing boiler operation based on coal quality-fly ash online according to claim 3 is characterized in that: The method of coupling and establishing the dynamic association database based on the historical database and the CFD simulation database comprises the following steps: Standardizing the data in the historical database and the data in the CFD simulation database; Removing abnormal data from the historical database and the CFD simulation database; The data in the CFD simulation database are mapped to the corresponding working conditions in the historical database, so as to couple the CFD simulation database and the historical database into the dynamic association database.

5. The method for optimizing boiler operation based on coal quality-fly ash online according to claim 1, characterized in that: The method of using the safety warning model to predict the boiler wall temperature and outputting the predicted wall temperature comprises the following steps: The real-time monitoring parameters are input into the safety warning model so that the safety warning model outputs the predicted wall temperature. The real-time monitoring parameters include the coal feed rate required by the actual load, the coal quality results of online monitoring, and the carbon content of fly ash of online monitoring.

6. The method for optimizing boiler operation based on coal quality-fly ash online according to claim 1, characterized in that: The step of adjusting the coal feeding amount and the air supply mode according to the judgment result to reduce the boiler wall temperature specifically includes the following steps: When the judgment result is that there is an over-temperature risk, the coal supply is reduced, the primary air volume supplied to the boiler is reduced, and the secondary air volume supplied to the boiler is increased; Determine again whether there is a risk of overheating of the boiler wall temperature.

7. The method for optimizing boiler operation based on coal quality-fly ash online according to claim 1 or 6, characterized in that: The air supply mode is adjusted according to the judgment result to reduce NO X The content specifically comprises the following steps: When the judgment result is that there is no over-temperature risk, the primary air volume supplied to the boiler is increased, and the secondary air volume supplied to the boiler is reduced.

8. A system based on the coal quality-fly ash online boiler operation optimization method according to any one of claims 1 to 7, characterized in that: include: Model building module, used to build a boiler wall temperature safety warning model based on coal feed, coal quality and fly ash carbon content input; The safety warning model is used to predict the boiler wall temperature and output the predicted wall temperature; A judgment module, used to judge whether there is an over-temperature risk of the boiler wall temperature according to the predicted wall temperature, and generate a judgment result; A control module is used to adjust the coal supply and air supply mode according to the judgment result to reduce the boiler wall temperature, or to adjust the air supply mode according to the judgment result to reduce NO X content.

9. The system according to claim 8, characterized in that The model building module includes: The historical database construction unit is used to collect boiler combustion data and store them accordingly to generate a historical database. The boiler combustion data includes the wall temperature and NO under different load requirements, different coal qualities, and different fly ash carbon content conditions. X Content data; A CFD simulation database construction unit is used to perform CFD simulation on the wall temperature characteristics under different working conditions and store them accordingly to generate a CFD simulation database of the wall temperature under different working conditions; The dynamic association database construction unit is used to couple the historical database and the CFD simulation database to generate the dynamic association database.

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