Boiler carbon monitoring and four-dimensional combustion optimization method, system, equipment and medium

Abstract

The invention relates to a boiler carbon monitoring and four-dimensional combustion optimization method, system, equipment and medium. CO2 / CO online detection devices are arranged at a hearth and a flue of a boiler, and a CO2 online detection device is arranged at a chimney at the tail of the boiler; the method comprises the steps of obtaining historical operation data of a boiler; the historical operation data comprises data detected by the CO2 / CO online detection device and the CO2 online detection device; performing mechanism modeling processing and data driving processing on the historical operation data to obtain a carbon monitoring and intelligent four-dimensional combustion optimization model; acquiring to-be-processed operation data of the boiler in a combustion state; analyzing the to-be-processed operation data by using the carbon monitoring and intelligent four-dimensional combustion optimization model to obtain an optimization strategy; and controlling the boiler to perform optimized combustion based on the optimization strategy. By the adoption of the method, efficient combustion, low nitrogen and carbon emission, low-corrosion operation and intelligent operation of the boiler can be guaranteed.

Description

Background Art

[0002] Boilers are core equipment for thermal power generation, cogeneration and industrial heating units, and play an important role in ensuring the safe, efficient and clean use of energy such as coal. Currently, pulverized coal boilers face the following severe challenges:

[0003] (1) Challenges brought by low-nitrogen combustion transformation: The "Boiler Air Pollutant Emission Standard" has set strict requirements for the emission limits of coal-fired boilers, requiring boiler systems to achieve low-nitrogen emissions through technical upgrades and transformations. In order to reduce nitrogen oxide (NOx) emissions, boiler systems generally adopt low-nitrogen combustion technology. This technology mainly reduces the local high-temperature peak during the combustion process by means of staged combustion, thereby reducing the generation of NOx. However, with the strengthening of air staging, the local reducing atmosphere inside the boiler furnace increases, and the incomplete combustion of fuel gradually becomes prominent. Incompletely burned fuel will lead to an increase in the concentration of reducing gases such as carbon monoxide (CO) and hydrogen sulfide (H2S), which not only reduces the efficiency of the boiler, but also accelerates the corrosion of the boiler heating surface. There is a contradiction between the balance of efficient combustion, low nitrogen emissions, and high-temperature corrosion. In response to this problem, the "Air Pollution Prevention and Control Law of the People's Republic of China" clearly pointed out that thermal power boilers should increase the application of low-nitrogen combustion technology, and at the same time strengthen the maintenance and monitoring of boiler equipment to ensure the efficient operation of the boiler and reduce pollutant emissions. How to effectively balance the contradictions between efficient combustion, low nitrogen emissions, and high-temperature corrosion during the actual operation of the boiler is a key problem in boiler operation optimization.

[0004] (2) Carbon dioxide emissions and combustion optimization: The main fuel for combustion systems such as thermal power boilers is carbon-containing fuels such as pulverized coal, which will produce a large amount of carbon dioxide (CO2) after combustion. As a major carbon emitter, the thermal power industry is promoting the application of energy-saving and emission reduction technologies through various means. The "Comprehensive Work Plan for Energy Conservation and Emission Reduction" encourages coal-fired power plants to promote and apply high-efficiency energy-saving and combustion technologies. As the global climate change problem becomes increasingly serious, how to reduce carbon dioxide emissions while ensuring boiler efficiency has become a major technical challenge. The use of advanced carbon monitoring technology to monitor carbon dioxide emissions during boiler operation in real time and to adjust combustion conditions in a timely manner in combination with combustion optimization technology will help achieve a dynamic balance between boiler efficiency and carbon emissions. Based on carbon monitoring, the boiler system optimizes the operation of the boiler system in four dimensions of time and space. Through precise air distribution and combustion parameter control, the thermal efficiency is improved and the generation of carbon dioxide is minimized while ensuring complete combustion of the fuel.

[0005] (3) Intelligent combustion and energy management: Boiler systems are gradually required to move towards digitalization and intelligence. The construction of intelligent combustion technology and intelligent energy management systems has become an important trend for future development. China's "Intelligent Manufacturing 2025" plan clearly points out that it is necessary to increase investment in the intelligent transformation of energy systems and improve the digitalization and intelligence level of boiler systems through independent innovation and technology introduction. The Chinese government has clearly pointed out in the "Energy Development Plan" that it will promote the intelligent transformation of energy systems and accelerate the intelligent upgrade of traditional coal-fired power generation units. Intelligent combustion technology collects various data on boiler operation in real time, and optimizes the combustion process based on the mechanism model with the help of big data analysis and artificial intelligence algorithms. It can effectively improve boiler efficiency, reduce the generation of pollutants, and effectively extend the service life of boiler equipment. Build an intelligent energy management platform to achieve comprehensive monitoring and optimization of the boiler combustion process, ensure efficient use of energy and optimization of emissions, etc.

[0006] Therefore, based on the national policy support in the field of energy conservation and emission reduction, boiler technology in the future will gradually achieve the development goals of intelligence, low carbonization, and operation optimization, and develop boiler carbon monitoring and intelligent four-dimensional combustion optimization methods and technologies, which will help to efficiently utilize energy and support the dual carbon goals.

[0007] In the existing technology, in the process of low-nitrogen combustion transformation, carbon emission control and the development of intelligent combustion technology, how to ensure efficient combustion, low nitrogen and carbon emissions, low corrosion operation and intelligent operation of boilers has become a problem that the industry urgently needs to solve. Summary of the invention

[0008] In order to solve the above problems, the present application provides a boiler carbon monitoring and four-dimensional combustion optimization method, system, equipment and medium.

[0009] In the first aspect, in order to solve the above technical problems, the present application provides a method for boiler carbon monitoring and four-dimensional combustion optimization, wherein a CO2 / CO online detection device is provided at the furnace and flue of the boiler, and a CO2 online detection device is provided at the tail chimney of the boiler; the method comprises:

[0010] Obtaining historical operation data of the boiler; wherein the historical operation data includes data detected by the CO2 / CO online detection device and the CO2 online detection device;

[0011] Carry out mechanism modeling and data-driven processing on historical operation data to obtain carbon monitoring and intelligent four-dimensional combustion optimization models;

[0012] Obtaining the operation data to be processed of the boiler in the combustion state;

[0013] Use carbon monitoring and intelligent four-dimensional combustion optimization model to analyze the processing operation data and obtain optimization strategies;

[0014] The boiler is controlled based on the optimization strategy to optimize combustion.

[0015] In a second aspect, the present application also provides a boiler carbon monitoring and four-dimensional combustion optimization system, comprising:

[0016] The first acquisition module is used to acquire historical operation data of the boiler; wherein the historical operation data includes data detected by the CO2 / CO online detection device and the CO2 online detection device;

[0017] Model building module, used to perform mechanism modeling and data-driven processing on historical operation data to obtain carbon monitoring and intelligent four-dimensional combustion optimization models;

[0018] A second acquisition module is used to acquire the to-be-processed operation data of the boiler in the combustion state;

[0019] Strategy module, used to analyze the processing operation data using carbon monitoring and intelligent four-dimensional combustion optimization model to obtain optimization strategy;

[0020] The optimized combustion module is used to control the boiler for optimized combustion based on the optimization strategy.

[0021] In a third aspect, the present application also provides a computing device, including a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps of a boiler carbon monitoring and four-dimensional combustion optimization method provided in the present application.

[0022] In a fourth aspect, the present application also provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed on a terminal device, the terminal device executes the steps of a boiler carbon monitoring and four-dimensional combustion optimization method.

[0023] The beneficial effect of the present application is: by carrying out mechanism modeling and data-driven processing on the historical operation data of the boiler, a carbon monitoring and intelligent four-dimensional combustion optimization model is obtained. The pending operation data of the boiler in the combustion state is obtained to realize real-time monitoring of the operation status of each area of ​​the boiler, and the carbon monitoring and intelligent four-dimensional combustion optimization model are used to analyze the pending operation data, so as to understand the combustion characteristics of the boiler when it is in the combustion state at this time, so as to obtain an operation optimization strategy for balancing the contradictions between high boiler efficiency, low nitrogen and carbon emissions, and low corrosion for the combustion characteristics. Then, based on the optimization strategy, the boiler is controlled to optimize combustion, which can ensure efficient combustion, low nitrogen and carbon emissions, and low corrosion operation of the boiler, thereby completing the intelligent optimization management of boiler combustion. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1A schematic flow chart of a boiler carbon monitoring and four-dimensional combustion optimization method shown as an exemplary embodiment of the present application;

[0025] Figure 2 This is a schematic structural diagram of a hardware system for boiler combustion optimization used in a boiler combustion optimization method in an exemplary embodiment of the present application;

[0026] Figure 3 A schematic structural diagram of a boiler carbon monitoring and four-dimensional combustion optimization system is shown as an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0027] The following examples are provided to further explain and supplement the present application and do not constitute any limitation to the present application.

[0028] The following describes a boiler carbon monitoring and four-dimensional combustion optimization method, system, equipment and medium according to an embodiment of the present application in conjunction with the accompanying drawings.

[0029] A boiler carbon monitoring and four-dimensional combustion optimization method is disclosed in an embodiment of the present application. The method is applied to a terminal device. In the present application scheme, the terminal device is used as the execution subject to illustrate the present application scheme. The terminal device is used to execute the steps of a boiler carbon monitoring and four-dimensional combustion optimization method.

[0030] See also Figure 1 , Figure 1 A boiler carbon monitoring and four-dimensional combustion optimization method is shown as an exemplary embodiment of the present application. Figure 1 As shown, the present application provides a method for boiler carbon monitoring and four-dimensional combustion optimization, wherein a CO2 / CO online detection device is provided at the furnace and flue of the boiler, and a CO2 online detection device is provided at the tail chimney of the boiler; the method comprises:

[0031] Step S11, obtaining historical operation data of the boiler; wherein the historical operation data includes data detected by the CO2 / CO online detection device and the CO2 online detection device;

[0032] Step S12, performing mechanism modeling and data-driven processing on the historical operation data to obtain a carbon monitoring and intelligent four-dimensional combustion optimization model;

[0033] Step S13, obtaining the operation data to be processed of the boiler in the combustion state;

[0034] Step S14, using carbon monitoring and intelligent four-dimensional combustion optimization model to analyze the operation data to be processed to obtain an optimization strategy;

[0035] Step S15, controlling the boiler to perform optimized combustion based on the optimization strategy.

[0036] A method for carbon monitoring and four-dimensional combustion optimization of a boiler in this embodiment obtains a carbon monitoring and intelligent four-dimensional combustion optimization model by performing mechanism modeling and data-driven processing on the historical operation data of the boiler. The pending operation data of the boiler in the combustion state is obtained to realize real-time monitoring of the operation status of each area of ​​the boiler, and the carbon monitoring and intelligent four-dimensional combustion optimization model is used to analyze the pending operation data, so as to understand the combustion characteristics of the boiler when it is in the combustion state at this time, so as to obtain an optimization strategy for the combustion characteristics to balance the contradiction between high boiler efficiency, low nitrogen and carbon emissions, and low corrosion operation. Then, based on the optimization strategy, the boiler is controlled to optimize combustion, which can ensure efficient combustion, low nitrogen and carbon emissions, and low corrosion operation of the boiler, thereby completing the intelligent optimization management of boiler combustion.

[0037] In this embodiment, the historical operation data is the boiler operation parameters of at least one historical month under multiple typical working conditions divided based on the load parameters of the boiler and the difference in coal quality. Among them, the implementation method of dividing multiple typical working conditions based on the load parameters of the boiler and the difference in coal quality is: taking the load parameters of 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% as one working condition, and obtaining multiple load working conditions, and dividing the working conditions of a preset number of combustion coals with coal quality differences according to the air-dried basis volatile matter, and taking the air-dried basis volatile matter difference of 5% as the basis for division when dividing the working conditions, and obtaining multiple coal quality working conditions. Then, one working condition is randomly selected from each of the load working condition and the coal quality working condition to form a total working condition, and each total working condition is taken as a typical working condition of this embodiment to obtain multiple typical working conditions.

[0038] Mechanism modeling refers to making reasonable assumptions based on the application scenarios of the modeling object and the purpose of the model; then, mathematical equations are established based on the inherent mechanism of the system, and the number of process variables and the number of independent equations are compared to perform degree of freedom analysis to ensure that the model has a solution; finally, the model is simplified and verified.

[0039] Data-driven processing refers to starting from the initial data or observations, using heuristic rules to find and establish relationships between internal features, thereby discovering some theorems or laws. It also usually refers to natural language processing methods based on large-scale statistical data.

[0040] In this embodiment, when the historical operation data is subjected to mechanism modeling and data-driven processing, the historical operation data is also numerically simulated to obtain the CO2, CO, and H2S concentration fields under typical operating conditions. Therefore, the carbon monitoring and intelligent four-dimensional combustion optimization model can monitor the real-time CO2, CO, and H2S conditions of the boiler in real time, thereby improving the degree of intelligent optimization management of boiler combustion.

[0041] See also Figure 2 , Figure 2 FIG. 1 is a schematic diagram of a hardware system for boiler combustion optimization in an exemplary embodiment of the present application, as shown in FIG. Figure 2 As shown, the hardware system includes a boiler, a chimney, an extractive CO2 / CO online detection device, an SCR denitration device, an extractive CO2 online detection device, a DCS (Distributed Control System, power plant distributed control system) control system, and a carbon monitoring and intelligent four-dimensional combustion optimization platform. The boiler is connected to the chimney through a pipeline, the SCR denitration device is arranged in the flue between the boiler and the chimney, the extractive CO2 / CO online detection device is arranged in the furnace and flue of the boiler, and the extractive CO2 online detection device is arranged at the connection between the chimney and the flue, that is, the device is arranged at the chimney at the rear of the boiler. The boiler is connected to the DCS control system in communication, and the DCS control system, the extractive CO2 / CO online detection device and the extractive CO2 online detection device are all connected to the carbon monitoring and intelligent four-dimensional combustion optimization platform. The carbon monitoring and intelligent four-dimensional combustion optimization platform reads the historical operation data of the boiler through the extractive CO2 / CO online detection device and the extractive CO2 online detection device, and constructs a carbon monitoring and intelligent four-dimensional combustion optimization model based on the historical operation data. Among them, the carbon monitoring and intelligent four-dimensional combustion optimization model includes a carbon generation and emission model, a combustion state model and a four-dimensional optimized combustion model. The carbon generation and emission model is stored in the carbon monitoring and analysis system of the carbon monitoring and intelligent four-dimensional combustion optimization platform, and the combustion state model and the four-dimensional optimized combustion model are stored in the four-dimensional combustion analysis and optimization system of the carbon monitoring and intelligent four-dimensional combustion optimization platform. Then, the obtained carbon monitoring and intelligent four-dimensional combustion optimization model is used to analyze the real-time processing operation data of the boiler, and the optimization strategy obtained by the analysis is transmitted to the boiler through the DCS system to control the boiler to optimize combustion based on the optimization strategy.

[0042] Among them, the furnace of the boiler is equipped with 2 sets of extractive CO2 / CO online detection devices (single host two measuring points, a total of 4 measuring points), the flue is equipped with 1 set of extractive CO2 / CO online detection device (single host two measuring points, a total of 2 measuring points), and the connection between the chimney and the flue is equipped with 1 set of extractive CO2 online detection device (single host single measuring point, a total of 1 measuring point). The carbon monitoring and intelligent four-dimensional combustion optimization platform is an external platform built in the boiler electronics room. The data collected by the aforementioned measuring points are the operating data of the boiler. After the operating data is collected, it is transmitted to the carbon monitoring and intelligent four-dimensional combustion optimization platform for model construction or model analysis. In this embodiment, the number of measuring points installed in the furnace is at least 4, the number of measuring points installed in the flue is at least 2, and the number of measuring points installed at the connection between the chimney and the flue is at least 1. When the method of this application is implemented, the more measuring points, the better, but the above requirements should be met, and the installation of measuring points should be considered in combination with the characteristics of the boiler. The four-dimensional combustion analysis and optimization system reads operating data from the power plant DCS system.

[0043] Optionally, the historical operation data includes coal feeding parameters, flue gas parameters, air distribution parameters, fly ash and slag carbon content, CO2 detection parameters, and CO detection parameters; the historical operation data is subjected to mechanism modeling processing and data-driven processing to obtain a carbon monitoring and intelligent four-dimensional combustion optimization model, including:

[0044] Carry out mechanism modeling processing on CO2 detection parameters and CO detection parameters to obtain carbon generation and emission model;

[0045] The coal feeding parameters, flue gas parameters, carbon content of fly ash and slag, CO2 detection parameters and CO detection parameters are modeled and processed to obtain the combustion state model;

[0046] The coal feeding parameters, flue gas parameters, air distribution parameters, carbon content of fly ash and slag, CO detection parameters and carbon generation and emission models are processed by mechanism modeling and data-driven processing to obtain a four-dimensional optimized combustion model.

[0047] Based on the carbon generation and emission model, the combustion state model and the four-dimensional optimized combustion model, a carbon monitoring and intelligent four-dimensional combustion optimization model is obtained.

[0048] In this embodiment, targeted modeling is performed based on the boiler's historical coal feeding parameters, flue gas parameters, air distribution parameters, fly ash and slag carbon content, CO2 detection parameters, and CO detection parameters to obtain a carbon generation and emission model, a combustion state model, and a four-dimensional optimized combustion model. Based on the carbon generation and emission model, the combustion state model, and the four-dimensional optimized combustion model, a carbon monitoring and intelligent four-dimensional combustion optimization model is obtained that can perform real-time monitoring and optimization analysis of the boiler, thereby realizing intelligent optimization management of boiler combustion.

[0049] Optionally, the CO2 detection parameters include furnace CO2 concentration, flue CO2 concentration, and chimney CO2 concentration, and the CO detection parameters include furnace CO concentration and flue CO concentration;

[0050] The CO2 detection parameters and CO detection parameters are processed by mechanism modeling to obtain the carbon generation and emission model, including:

[0051] Carry out mechanism modeling for furnace CO2 concentration, flue CO2 concentration, furnace CO concentration and flue CO concentration to obtain a carbon generation model;

[0052] Carry out mechanism modeling on the chimney CO2 concentration to obtain the carbon emission model;

[0053] The carbon generation model and the carbon emission model are integrated to obtain the carbon generation and emission model.

[0054] In this embodiment, the furnace CO2 concentration, flue CO2 concentration, furnace CO concentration and flue CO concentration are subjected to mechanism modeling, so that the CO2 concentration field of the boiler furnace and flue can be understood, so that the CO2 concentration field of the boiler furnace and flue can be monitored in real time according to the obtained carbon generation model. The chimney CO2 concentration is subjected to mechanism modeling, so that the CO2 concentration field of the boiler chimney can be understood, so that the CO2 concentration field of the boiler chimney can be monitored in real time according to the obtained carbon emission model. In this way, the carbon generation and emission model obtained by integrating the carbon generation model and the carbon emission model can simultaneously monitor the CO2 concentration field of the boiler furnace and flue and the CO2 concentration field of the boiler chimney in real time, realizing the intelligent management of carbon monitoring in the boiler.

[0055] Optionally, the coal feeding parameters include coal feeding amount, coal feeding temperature, coal quality industrial analysis / sulfur content / calorific value, and the flue gas parameters include tail oxygen content, exhaust gas temperature, and nitrogen oxide concentration;

[0056] The coal feeding parameters, flue gas parameters, carbon content of fly ash and slag, CO2 detection parameters and CO detection parameters are modeled and processed to obtain the combustion state model, including:

[0057] The boiler efficiency is obtained by performing reverse balance calculation on the coal feed rate, coal feed temperature, coal quality industrial analysis / sulfur content / calorific value, tail oxygen content, flue gas temperature, fly ash and slag carbon content and flue CO concentration;

[0058] The boiler efficiency, nitrogen oxide concentration, carbon generation and emission model and furnace CO concentration are modeled and processed to obtain the combustion state model.

[0059] In this embodiment, the coal feeding amount, coal feeding temperature, industrial analysis of coal quality / sulfur content / calorific value, tail oxygen content, exhaust gas temperature, fly ash and slag carbon content and flue CO concentration are counter-balanced to obtain boiler efficiency. The boiler efficiency, nitrogen oxide concentration, carbon generation and emission model and furnace CO concentration are modeled and processed by mechanism, so that the relationship between boiler efficiency and nitrogen oxide concentration, carbon generation and emission model and furnace CO concentration can be understood. Based on the relationship, the boiler efficiency can be obtained, so that the obtained combustion state model can calculate the boiler efficiency in real time, so as to facilitate intelligent management of boiler combustion.

[0060] Optionally, the air distribution parameters include total air volume, burnout air damper opening, and main combustion zone damper opening;

[0061] The coal feeding parameters, flue gas parameters, air distribution parameters, fly ash and slag carbon content, CO detection parameters and carbon generation and emission models are processed by mechanism modeling and data-driven processing to obtain a four-dimensional optimized combustion model, including:

[0062] The boiler efficiency and tail oxygen content are modeled and processed to obtain the boiler efficiency optimization model;

[0063] The mechanism modeling of nitrogen oxide concentration, total air volume and burnout air damper opening was carried out to obtain the optimization model of nitrogen oxide generation;

[0064] Carry out mechanism modeling on carbon generation and emission model, boiler efficiency, carbon content of fly ash and slag, and tail oxygen content to obtain the carbon generation and emission optimization model;

[0065] The carbon generation model, furnace CO concentration and main combustion zone air door opening are processed by data-driven processing and mechanism modeling processing to obtain a high-temperature corrosion optimization model.

[0066] The boiler efficiency optimization model, nitrogen oxide generation optimization model, carbon generation and emission optimization model and high temperature corrosion optimization model are integrated to obtain a four-dimensional combustion optimization model.

[0067] In this embodiment, based on the coal feeding amount, coal feeding temperature, industrial analysis of coal quality / sulfur content / calorific value, tail oxygen content, exhaust gas temperature, nitrogen oxide concentration, total air volume, burnout air damper opening and main combustion zone damper opening, fly ash and slag carbon content, furnace CO2 concentration, flue CO2 concentration, chimney CO2 concentration, furnace CO concentration and flue CO concentration, targeted modeling is performed to obtain a boiler efficiency optimization model, a nitrogen oxide generation optimization model, a carbon generation and emission optimization model and a high-temperature corrosion optimization model, and based on the four optimization models, a four-dimensional combustion optimization model capable of performing real-time combustion optimization analysis of the boiler is obtained, thereby realizing intelligent optimization management of boiler combustion.

[0068] In this embodiment, high temperature corrosion is reflected by the furnace CO concentration, so the high temperature corrosion optimization model outputs the furnace CO optimization concentration.

[0069] The nested processing of data-driven processing and mechanism modeling processing is a data-driven nested mechanism method, which refers to the boiler operating parameters under multiple typical operating conditions divided based on the boiler load parameters and coal quality differences for at least one month in history. The boiler operating parameters under different operating conditions are used to construct a nonlinear mapping relationship between CO2 / CO concentration and furnace damper opening using big data methods including neural networks and deep learning, and then the influence of single damper opening on CO2 / CO is constrained.

[0070] Optionally, the carbon monitoring and intelligent four-dimensional combustion optimization model includes a carbon generation and emission model, a combustion state model and a four-dimensional optimized combustion model, and the four-dimensional optimized combustion model includes a boiler efficiency optimization model, a nitrogen oxide generation optimization model, a carbon generation and emission optimization model and a high-temperature corrosion optimization model;

[0071] Carbon monitoring and intelligent four-dimensional combustion optimization model are used to analyze the processing operation data to obtain optimization strategies, including:

[0072] Input the pending operation data into the carbon monitoring and intelligent four-dimensional combustion optimization model, and use the combustion state model to process the pending operation data to obtain the real-time boiler efficiency;

[0073] The real-time boiler efficiency and the pending operation data are processed using the boiler efficiency optimization model to obtain the optimized boiler efficiency;

[0074] The operation data to be processed is processed using the nitrogen oxide generation optimization model to obtain the optimized concentration of nitrogen oxides;

[0075] The carbon generation and emission model is used to process the pending operation data to obtain the CO2 concentration;

[0076] The CO2 concentration, real-time boiler efficiency and pending operation data are processed using the carbon generation and emission optimization model to obtain the optimized chimney CO2 concentration;

[0077] The carbon generation model is used to process the operation data to be processed to obtain the CO2 generation concentration;

[0078] The high-temperature corrosion optimization model is used to process the CO2 generation concentration and the operation data to be processed to obtain the optimized CO concentration in the furnace;

[0079] Based on the optimized furnace CO concentration, the optimized chimney CO2 concentration, the optimized boiler efficiency and the optimized nitrogen oxide concentration, the optimization strategies for the boiler's tail oxygen content, total air volume, burnout air damper opening and main combustion zone damper opening are obtained.

[0080] In this embodiment, the data types of the operation data to be processed are the same as those in the historical operation data. The operation data to be processed are input into the carbon monitoring and intelligent four-dimensional combustion optimization model. Based on the construction relationship of the historical operation data used when constructing each sub-model in the carbon monitoring and intelligent four-dimensional combustion optimization model, each sub-model is used to predict the historical operation data to obtain the optimized boiler efficiency, the optimized concentration of nitrogen oxides, the optimized concentration of chimney CO2, and the optimized concentration of furnace CO. The optimization parameters of these four dimensions correspond to the optimal combustion state that the boiler can currently achieve, and when in this optimal combustion state, the boiler can ensure efficient combustion, low nitrogen and carbon emissions, and low corrosion operation. Therefore, based on the four optimization parameters, an optimization strategy for the boiler's tail oxygen content, total air volume, burnout air damper opening and main combustion zone damper opening is obtained, which is convenient for the subsequent control of the boiler for optimized combustion based on the optimization strategy, so that the boiler efficiency of the boiler can reach the optimized boiler efficiency, the nitrogen oxide concentration can reach the optimized nitrogen oxide concentration, the chimney CO2 concentration can reach the optimized chimney CO2 concentration, and the furnace CO concentration can reach the optimized furnace CO concentration, thereby ensuring efficient combustion, low nitrogen and carbon emissions, and low corrosion operation of the boiler, thereby completing the intelligent optimization management of boiler combustion.

[0081] In this embodiment, after the operation data to be processed is input into the carbon monitoring and intelligent four-dimensional combustion optimization model, various heat losses of the boiler can also be calculated in the model. Among them, various heat losses include heat loss of flue gas, heat loss of incomplete combustion of gas, heat loss of incomplete combustion of solid, heat loss of heat dissipation and physical heat loss of ash. The model calculates and displays various heat losses of the boiler in real time, which is convenient for the staff to understand the use of the boiler at any time, and can respond to abnormal combustion caused by abnormal heat loss in time, further improving the intelligent optimization management of boiler combustion.

[0082] Optionally, based on the optimized furnace CO concentration, the optimized chimney CO2 concentration, the optimized boiler efficiency and the optimized nitrogen oxide concentration, an optimization strategy for the tail oxygen content, the total air volume, the burnout air damper opening and the main combustion zone damper opening of the boiler is obtained, including:

[0083] Perform counter-balance calculation on the operation data to be processed to obtain the real-time boiler efficiency;

[0084] When the difference between the optimized boiler efficiency and the real-time boiler efficiency is greater than the first preset value, or the ratio of the optimized chimney CO2 concentration to the to-be-processed chimney CO2 concentration in the to-be-processed operation data is less than the second preset value, the optimization strategy for the tail oxygen content and the total air volume of the boiler is to adjust the tail oxygen content and the total air volume until the boiler efficiency of the boiler reaches the optimized boiler efficiency, or until the chimney CO2 concentration of the boiler reaches the chimney CO2 optimization concentration; otherwise, the optimization strategy for the tail oxygen content and the total air volume of the boiler is not to adjust the tail oxygen content and the total air volume;

[0085] When the ratio of the optimized concentration of nitrogen oxides to the concentration of nitrogen oxides to be processed in the operation data to be processed is greater than a third preset value, the optimization strategy for the opening of the burnout air damper of the boiler is to increase the opening of the burnout air damper until the nitrogen oxide concentration of the boiler reaches the optimized concentration of nitrogen oxides; otherwise, the optimization strategy for the opening of the burnout air damper of the boiler is to maintain the opening of the burnout air damper;

[0086] When the ratio of the optimized furnace CO concentration to the to-be-processed furnace CO concentration in the to-be-processed operating data is greater than the fourth preset value, the optimization strategy for the damper opening in the main combustion zone of the boiler is to increase the damper opening in the main combustion zone until the furnace CO concentration of the boiler reaches the optimized furnace CO concentration; otherwise, the optimization strategy for the damper opening in the main combustion zone of the boiler is to maintain the damper opening in the main combustion zone.

[0087] In this embodiment, based on the difference between the optimized boiler efficiency and the real-time boiler efficiency obtained by the counter-balance calculation, and the ratio between the optimized chimney CO2 concentration and the to-be-treated chimney CO2 concentration in the to-be-treated operating data, and the ratio between the optimized nitrogen oxide concentration and the to-be-treated nitrogen oxide concentration in the to-be-treated operating data, and the ratio between the optimized furnace CO concentration and the to-be-treated furnace CO concentration in the to-be-treated operating data, an optimization strategy for the boiler's tail oxygen amount, total air volume, burnout air damper opening and main combustion zone damper opening is obtained, so as to facilitate the subsequent control of the boiler for optimized combustion based on the optimization strategy, so that the boiler efficiency of the boiler can reach the optimized boiler efficiency, the nitrogen oxide concentration can reach the optimized nitrogen oxide concentration, the chimney CO2 concentration can reach the optimized chimney CO2 concentration, and the furnace CO concentration can reach the optimized furnace CO concentration, thereby balancing the contradictions between high boiler efficiency, low nitrogen and carbon emissions, and low corrosion operation, and realizing intelligent optimization management of boiler combustion.

[0088] In this embodiment, the first preset value is 0.1%, the second preset value is 99.5%, the third preset value is 105%, and the fourth preset value is 102%. Under this preset value setting, the contradiction between high boiler efficiency, low nitrogen and carbon emissions, and low corrosion operation is best balanced.

[0089] In an exemplary embodiment, taking the operating condition of a coal-fired boiler with a load of 600MW as an example, the real-time boiler efficiency obtained based on the operating data at this time is 91.87%, and the concentration of nitrogen oxides to be treated before denitrification is 285mg / m 3 At this time, the CO concentration in the furnace to be treated is 89664ppm, indicating that high-temperature corrosion is more serious. After implementing the method of the present application, the optimized boiler efficiency is 92.24%, and the optimized concentration of nitrogen oxides before denitrification is 263mg / m 3, the optimized furnace CO concentration is 43852ppm, which greatly reduces high-temperature corrosion, and the chimney CO2 concentration is reduced by 0.57% compared with before optimization. That is, at this time, the difference between the optimized boiler efficiency and the real-time boiler efficiency is 0.37%, which is greater than the first preset value; the ratio of the optimized chimney CO2 concentration to the chimney CO2 concentration to be treated is 99.43%, which is less than the second preset value; the ratio of the optimized nitrogen oxide concentration to the nitrogen oxide concentration to be treated is 92.28%, which is less than the third preset value; the ratio of the optimized furnace CO concentration to the furnace CO concentration to be treated is 48.91%, which is less than the fourth preset value. Then the corresponding optimization strategy is obtained, and then the boiler is controlled to optimize combustion based on the optimization strategy, so that the coal-fired boiler can realize intelligent combustion operation, thereby ensuring efficient combustion, low nitrogen and carbon emissions, and low corrosion operation of the boiler, thereby completing the intelligent optimization management of boiler combustion.

[0090] See also Figure 3 , Figure 3 A boiler carbon monitoring and four-dimensional combustion optimization system is shown as an exemplary embodiment of the present application. Figure 3 As shown, the present application provides a boiler carbon monitoring and four-dimensional combustion optimization system, comprising:

[0091] The first acquisition module is used to acquire historical operation data of the boiler; wherein the historical operation data includes data detected by the CO2 / CO online detection device and the CO2 online detection device;

[0092] Model building module, used to perform mechanism modeling and data-driven processing on historical operation data to obtain carbon monitoring and intelligent four-dimensional combustion optimization models;

[0093] A second acquisition module is used to acquire the to-be-processed operation data of the boiler in the combustion state;

[0094] Strategy module, used to analyze the processing operation data using carbon monitoring and intelligent four-dimensional combustion optimization model to obtain optimization strategy;

[0095] The optimized combustion module is used to control the boiler for optimized combustion based on the optimization strategy.

[0096] A boiler carbon monitoring and four-dimensional combustion optimization system of this embodiment performs mechanism modeling and data-driven processing on the historical operation data of the boiler acquired by the first acquisition module through the model building module to obtain a carbon monitoring and intelligent four-dimensional combustion optimization model. The second acquisition module is used to obtain the pending operation data of the boiler in the combustion state to achieve real-time monitoring of the operation status of each area of ​​the boiler, and the carbon monitoring and intelligent four-dimensional combustion optimization model is used in the strategy module to analyze the pending operation data, so as to understand the combustion characteristics of the boiler when it is in the combustion state at this time, so as to obtain an operation optimization strategy that balances the contradictions between high boiler efficiency, low nitrogen and carbon emissions, and low corrosion according to the combustion characteristics. Then, the optimized combustion module is used to control the boiler for optimized combustion based on the optimization strategy, which can ensure efficient combustion, low nitrogen and carbon emissions, and low corrosion operation of the boiler, thereby completing the intelligent optimization management of boiler combustion.

[0097] Optionally, the historical operation data includes coal feeding parameters, flue gas parameters, air distribution parameters, carbon content of fly ash and slag, CO2 detection parameters, and CO detection parameters; the model building module is specifically used for:

[0098] Carry out mechanism modeling processing on CO2 detection parameters and CO detection parameters to obtain carbon generation and emission model;

[0099] The coal feeding parameters, flue gas parameters, carbon content of fly ash and slag, CO2 detection parameters and CO detection parameters are modeled and processed to obtain the combustion state model;

[0100] The coal feeding parameters, flue gas parameters, air distribution parameters, carbon content of fly ash and slag, CO detection parameters and carbon generation and emission models are processed by mechanism modeling and data-driven processing to obtain a four-dimensional optimized combustion model.

[0101] Based on the carbon generation and emission model, the combustion state model and the four-dimensional optimized combustion model, a carbon monitoring and intelligent four-dimensional combustion optimization model is obtained.

[0102] Optionally, the CO2 detection parameters include furnace CO2 concentration, flue CO2 concentration, and chimney CO2 concentration, and the CO detection parameters include furnace CO concentration and flue CO concentration;

[0103] Model building modules, specifically for:

[0104] Carry out mechanism modeling for furnace CO2 concentration, flue CO2 concentration, furnace CO concentration and flue CO concentration to obtain a carbon generation model;

[0105] Carry out mechanism modeling on the chimney CO2 concentration to obtain the carbon emission model;

[0106] The carbon generation model and the carbon emission model are integrated to obtain the carbon generation and emission model.

[0107] Optionally, the coal feeding parameters include coal feeding amount, coal feeding temperature, coal quality industrial analysis / sulfur content / calorific value, and the flue gas parameters include tail oxygen content, exhaust gas temperature, and nitrogen oxide concentration;

[0108] Model building modules, specifically for:

[0109] The boiler efficiency is obtained by performing reverse balance calculation on the coal feed rate, coal feed temperature, coal quality industrial analysis / sulfur content / calorific value, tail oxygen content, flue gas temperature, fly ash and slag carbon content and flue CO concentration;

[0110] The boiler efficiency, nitrogen oxide concentration, carbon generation and emission model and furnace CO concentration are modeled and processed to obtain the combustion state model.

[0111] Optionally, the air distribution parameters include total air volume, burnout air damper opening, and main combustion zone damper opening;

[0112] Model building modules, specifically for:

[0113] The boiler efficiency and tail oxygen content are modeled and processed to obtain the boiler efficiency optimization model;

[0114] The mechanism modeling of nitrogen oxide concentration, total air volume and burnout air damper opening was carried out to obtain the optimization model of nitrogen oxide generation;

[0115] Carry out mechanism modeling on carbon generation and emission model, boiler efficiency, carbon content of fly ash and slag, and tail oxygen content to obtain the carbon generation and emission optimization model;

[0116] The carbon generation model, furnace CO concentration and main combustion zone air door opening are processed by data-driven processing and mechanism modeling processing to obtain a high-temperature corrosion optimization model.

[0117] The boiler efficiency optimization model, nitrogen oxide generation optimization model, carbon generation and emission optimization model and high temperature corrosion optimization model are integrated to obtain a four-dimensional combustion optimization model.

[0118] Optionally, the carbon monitoring and intelligent four-dimensional combustion optimization model includes a carbon generation and emission model, a combustion state model and a four-dimensional optimized combustion model, and the four-dimensional optimized combustion model includes a boiler efficiency optimization model, a nitrogen oxide generation optimization model, a carbon generation and emission optimization model and a high-temperature corrosion optimization model;

[0119] Policy modules are specifically used for:

[0120] Input the pending operation data into the carbon monitoring and intelligent four-dimensional combustion optimization model, and use the combustion state model to process the pending operation data to obtain the real-time boiler efficiency;

[0121] The real-time boiler efficiency and the pending operation data are processed using the boiler efficiency optimization model to obtain the optimized boiler efficiency;

[0122] The operation data to be processed is processed using the nitrogen oxide generation optimization model to obtain the optimized concentration of nitrogen oxides;

[0123] The carbon generation and emission model is used to process the pending operation data to obtain the CO2 concentration;

[0124] The CO2 concentration, real-time boiler efficiency and pending operation data are processed using the carbon generation and emission optimization model to obtain the optimized chimney CO2 concentration;

[0125] The carbon generation model is used to process the operation data to be processed to obtain the CO2 generation concentration;

[0126] The high-temperature corrosion optimization model is used to process the CO2 generation concentration and the operation data to be processed to obtain the optimized CO concentration in the furnace;

[0127] Based on the optimized furnace CO concentration, the optimized chimney CO2 concentration, the optimized boiler efficiency and the optimized nitrogen oxide concentration, the optimization strategies for the boiler's tail oxygen content, total air volume, burnout air damper opening and main combustion zone damper opening are obtained.

[0128] Optionally, the policy module is specifically used to:

[0129] Perform counter-balance calculation on the operation data to be processed to obtain the real-time boiler efficiency;

[0130] When the difference between the optimized boiler efficiency and the real-time boiler efficiency is greater than the first preset value, or the ratio of the optimized chimney CO2 concentration to the to-be-processed chimney CO2 concentration in the to-be-processed operation data is less than the second preset value, the optimization strategy for the tail oxygen content and the total air volume of the boiler is to adjust the tail oxygen content and the total air volume until the boiler efficiency of the boiler reaches the optimized boiler efficiency, or until the chimney CO2 concentration of the boiler reaches the chimney CO2 optimized concentration;

[0131] When the ratio of the optimized nitrogen oxide concentration to the to-be-processed nitrogen oxide concentration in the to-be-processed operation data is greater than a third preset value, the optimization strategy for the overburnt air damper opening of the boiler is to increase the overburnt air damper opening until the nitrogen oxide concentration of the boiler reaches the optimized nitrogen oxide concentration;

[0132] When the ratio of the furnace CO optimization concentration to the furnace CO concentration to be processed in the operating data to be processed is greater than the fourth preset value, the optimization strategy for the air damper opening in the main combustion zone of the boiler is to increase the air damper opening in the main combustion zone until the furnace CO concentration of the boiler reaches the furnace CO optimization concentration.

[0133] A computing device according to an embodiment of the present application includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, some or all steps of a boiler carbon monitoring and four-dimensional combustion optimization method provided in the present application are implemented.

[0134] Among them, the computing device can be a computer, and correspondingly, its program is computer software. The above-mentioned parameters and steps in a computing device of the present application can refer to the parameters and steps in an embodiment of a boiler carbon monitoring and four-dimensional combustion optimization method provided in the present application, and will not be repeated here.

[0135] In an embodiment of the present application, a computer-readable storage medium is provided, in which instructions are stored. When the instructions are executed, the steps of a boiler carbon monitoring and four-dimensional combustion optimization method provided in the present application are executed.

[0136] The computer-readable storage medium may be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0137] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method of the embodiment of the present disclosure. The aforementioned computer-readable storage medium may be a non-transitory computer-readable storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes, or a transient computer-readable storage medium.

[0138] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0139] Those skilled in the art know that the present application can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, generally referred to as "module" or "system" herein. In addition, in some embodiments, the present application can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code. Computer-readable storage media can be, for example, but not limited to - electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above.

[0140] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0141] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A boiler carbon monitoring and four-dimensional combustion optimization method, characterized in that: A CO2 / CO online detection device is provided at the furnace and flue of the boiler, and a CO2 online detection device is provided at the tail chimney of the boiler; the method comprises: Acquire historical operation data of the boiler; wherein the historical operation data includes data detected by the CO2 / CO online detection device and the CO2 online detection device; Performing mechanism modeling and data-driven processing on the historical operation data to obtain a carbon monitoring and intelligent four-dimensional combustion optimization model; Acquiring the operation data to be processed of the boiler in a combustion state; Analyzing the operation data to be processed by using the carbon monitoring and intelligent four-dimensional combustion optimization model to obtain an optimization strategy; The boiler is controlled to perform optimized combustion based on the optimization strategy.

2. The method according to claim 1, characterized in that: The historical operation data includes coal feeding parameters, flue gas parameters, air distribution parameters, fly ash and slag carbon content, CO2 detection parameters, and CO detection parameters; the historical operation data is subjected to mechanism modeling processing and data-driven processing to obtain a carbon monitoring and intelligent four-dimensional combustion optimization model, including: Performing mechanism modeling processing on the CO2 detection parameters and the CO detection parameters to obtain a carbon generation and emission model; Performing mechanism modeling processing on the coal feeding parameters, the flue gas parameters, the carbon content of the fly ash and slag, the CO2 detection parameters and the CO detection parameters to obtain a combustion state model; The coal feeding parameters, the flue gas parameters, the air distribution parameters, the carbon content of the fly ash and slag, the CO detection parameters and the carbon generation and emission model are subjected to mechanism modeling processing and data-driven processing to obtain a four-dimensional optimized combustion model; Based on the carbon generation and emission model, the combustion state model and the four-dimensional optimized combustion model, a carbon monitoring and intelligent four-dimensional combustion optimization model is obtained.

3. The method according to claim 2, characterized in that The CO2 detection parameters include furnace CO2 concentration, flue CO2 concentration, and chimney CO2 concentration, and the CO detection parameters include furnace CO concentration and flue CO concentration; The mechanism modeling process of the CO2 detection parameter and the CO detection parameter to obtain a carbon generation and emission model includes: Performing mechanism modeling processing on the furnace CO2 concentration, the flue CO2 concentration, the furnace CO concentration and the flue CO concentration to obtain a carbon generation model; Performing mechanism modeling on the chimney CO2 concentration to obtain a carbon emission model; The carbon generation model and the carbon emission model are integrated to obtain a carbon generation and emission model.

4. The method according to claim 3, characterized in that The coal feeding parameters include coal feeding amount, coal feeding temperature, coal quality industrial analysis / sulfur content / calorific value, and the flue gas parameters include tail oxygen content, exhaust gas temperature, and nitrogen oxide concentration; The mechanism modeling process is performed on the coal feeding parameter, the flue gas parameter, the carbon content of the fly ash and slag, the CO2 detection parameter and the CO detection parameter to obtain a combustion state model, including: Performing a counter-balance calculation on the coal feed rate, the coal feed temperature, the coal quality industrial analysis / sulfur content / calorific value, the tail oxygen content, the flue gas temperature, the fly ash and slag carbon content and the flue CO concentration to obtain the boiler efficiency; The boiler efficiency, the nitrogen oxide concentration, the carbon generation and emission model and the furnace CO concentration are subjected to mechanism modeling processing to obtain a combustion state model.

5. The method according to claim 4, characterized in that The air distribution parameters include total air volume, burnout air damper opening and main combustion zone damper opening; The coal feeding parameters, the flue gas parameters, the air distribution parameters, the carbon content of the fly ash and slag, the CO detection parameters and the carbon generation and emission model are subjected to mechanism modeling processing and data-driven processing to obtain a four-dimensional optimized combustion model, including: Performing mechanism modeling processing on the boiler efficiency and the tail oxygen content to obtain a boiler efficiency optimization model; Performing mechanism modeling processing on the nitrogen oxide concentration, the total air volume and the burnout air damper opening to obtain a nitrogen oxide generation optimization model; Performing mechanism modeling processing on the carbon generation and emission model, the boiler efficiency, the carbon content of the fly ash and slag, and the tail oxygen content to obtain a carbon generation and emission optimization model; The carbon generation model, the furnace CO concentration and the main combustion zone air door opening are subjected to a nested processing of data-driven processing and mechanism modeling processing to obtain a high-temperature corrosion optimization model; The boiler efficiency optimization model, the nitrogen oxide generation optimization model, the carbon generation and emission optimization model and the high-temperature corrosion optimization model are integrated to obtain a four-dimensional combustion optimization model.

6. The method according to any one of claims 2 to 5, characterized in that: The carbon monitoring and intelligent four-dimensional combustion optimization model includes a carbon generation and emission model, a combustion state model and a four-dimensional optimized combustion model. The four-dimensional optimized combustion model includes a boiler efficiency optimization model, a nitrogen oxide generation optimization model, a carbon generation and emission optimization model and a high-temperature corrosion optimization model. The method of analyzing the operation data to be processed by using the carbon monitoring and intelligent four-dimensional combustion optimization model to obtain an optimization strategy includes: Inputting the operation data to be processed into the carbon monitoring and intelligent four-dimensional combustion optimization model, and processing the operation data to be processed using the combustion state model to obtain real-time boiler efficiency; Processing the real-time boiler efficiency and the to-be-processed operating data using the boiler efficiency optimization model to obtain optimized boiler efficiency; Processing the operation data to be processed using the nitrogen oxide generation optimization model to obtain an optimized concentration of nitrogen oxides; Processing the operation data to be processed using the carbon generation and emission model to obtain CO2 concentration; The CO2 concentration, the real-time boiler efficiency and the to-be-processed operation data are processed using the carbon generation and emission optimization model to obtain an optimized chimney CO2 concentration; Processing the operation data to be processed using the carbon generation model to obtain CO2 generation concentration; The CO2 generation concentration and the operation data to be processed are processed using the high temperature corrosion optimization model to obtain the furnace CO optimization concentration; Based on the optimized furnace CO concentration, the optimized chimney CO2 concentration, the optimized boiler efficiency and the optimized nitrogen oxide concentration, an optimization strategy for the tail oxygen content, total air volume, burnout air damper opening and main combustion zone damper opening of the boiler is obtained.

7. The method according to claim 6, characterized in that The optimization strategy for the tail oxygen amount, total air volume, burnout air damper opening and main combustion zone damper opening of the boiler is obtained based on the furnace CO optimization concentration, the chimney CO2 optimization concentration, the optimized boiler efficiency and the nitrogen oxide optimization concentration, including: Performing a counter-balance calculation on the operation data to be processed to obtain real-time boiler efficiency; When the difference between the optimized boiler efficiency and the real-time boiler efficiency is greater than a first preset value, or the ratio of the optimized chimney CO2 concentration to the to-be-processed chimney CO2 concentration in the to-be-processed operation data is less than a second preset value, the optimization strategy for the tail oxygen content and the total air volume of the boiler is to adjust the tail oxygen content and the total air volume until the boiler efficiency of the boiler reaches the optimized boiler efficiency, or until the chimney CO2 concentration of the boiler reaches the chimney CO2 optimized concentration; When the ratio of the optimized nitrogen oxide concentration to the to-be-processed nitrogen oxide concentration in the to-be-processed operation data is greater than a third preset value, the optimization strategy for the overburnt air damper opening of the boiler is to increase the overburnt air damper opening until the nitrogen oxide concentration of the boiler reaches the optimized nitrogen oxide concentration; When the ratio of the furnace CO optimization concentration to the furnace CO concentration to be processed in the operating data to be processed is greater than a fourth preset value, the optimization strategy for the damper opening of the main combustion zone of the boiler is to increase the damper opening of the main combustion zone until the furnace CO concentration of the boiler reaches the furnace CO optimization concentration.

8. A boiler carbon monitoring and four-dimensional combustion optimization system, characterized in that: include: A first acquisition module is used to acquire historical operation data of the boiler; wherein the historical operation data includes data detected by the CO2 / CO online detection device and the CO2 online detection device; A model building module is used to perform mechanism modeling and data-driven processing on the historical operation data to obtain a carbon monitoring and intelligent four-dimensional combustion optimization model; A second acquisition module, used for acquiring the to-be-processed operation data of the boiler in a combustion state; A strategy module, used to analyze the operation data to be processed by using the carbon monitoring and intelligent four-dimensional combustion optimization model to obtain an optimization strategy; The optimized combustion module is used to control the boiler to perform optimized combustion based on the optimization strategy.

9. A computing device comprising a memory, a processor, and a program stored in the memory and running on the processor, characterized in that: When the processor executes the program, the steps of a boiler carbon monitoring and four-dimensional combustion optimization method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed on a terminal device, the terminal device executes the steps of a boiler carbon monitoring and four-dimensional combustion optimization method as described in any one of claims 1 to 7.

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