A method and apparatus for optimizing combustion control of low-calorific-value industrial waste gas

Abstract

The present invention relates to the field of combustion control technology, and in particular to a method and device for optimizing the combustion control of low calorific value industrial waste gas. The method comprises the following steps: obtaining waste gas composition data, and calculating the waste gas calorific value according to the waste gas composition data to obtain waste gas calorific value data; obtaining combustion chamber data, and generating air-fuel ratio parameters according to the combustion chamber data and the waste gas calorific value data to obtain air-fuel ratio parameter data; performing waste gas pretreatment according to the air-fuel ratio parameter data and the waste gas composition data to obtain waste gas pretreatment data; performing multi-stage combustion operations according to the waste gas pretreatment data to obtain waste gas combustion data. The present invention realizes intelligent management of the combustion process, maximizes the combustion potential of low calorific value waste gas through multi-stage combustion and segmented control technology, and realizes source control of pollutants, reduces emissions, and improves the overall performance of the combustion system through real-time monitoring and optimized control.

Description

Technical Field

[0001] The present invention relates to the technical field of combustion control, and in particular to a method and device for optimizing the combustion control of low calorific value industrial waste gas. Background Art

[0002] With the acceleration of industrialization, the types and quantities of waste gas generated in industrial production processes are increasing. Many industrial waste gases have the characteristics of low calorific value, high humidity, and complex composition. Direct emission not only wastes energy, but also causes serious pollution to the environment. Therefore, how to effectively utilize this type of low calorific value industrial waste gas has become one of the urgent problems to be solved in the current field of industrial energy conservation and emission reduction. The composition of industrial waste gas generated by different industries varies greatly. Even in the same production process, the composition and calorific value of the waste gas fluctuate due to changes in operating conditions. This variability in composition puts higher requirements on combustion control. Traditional fixed parameter combustion control methods are difficult to adapt to complex changes in waste gas composition. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a method and device for optimizing the combustion control of low calorific value industrial waste gas to solve at least one of the above technical problems.

[0004] The present application provides a method for optimizing combustion control of low calorific value industrial waste gas, comprising the following steps:

[0005] Step S1: obtaining exhaust gas composition data, and calculating the exhaust gas calorific value according to the exhaust gas composition data to obtain the exhaust gas calorific value data;

[0006] Step S2: Acquire combustion chamber data, and generate air-fuel ratio parameters according to the combustion chamber data and exhaust gas calorific value data to obtain air-fuel ratio parameter data;

[0007] Step S3: performing exhaust gas pretreatment according to the air-fuel ratio parameter data and the exhaust gas composition data to obtain exhaust gas pretreatment data;

[0008] Step S4: Perform multi-stage combustion operation according to the exhaust gas pretreatment data to obtain exhaust gas combustion data.

[0009] In the present invention, the actual calorific value data of the waste gas is calculated by accurately analyzing the waste gas component data, so as to avoid the situation that the low calorific value waste gas is not completely burned due to insufficient calorific value during the combustion process, thereby improving the combustion efficiency and reducing the emission of unburned combustibles. According to the combustion chamber data (such as the combustion chamber structure, the arrangement of the fuel nozzle, the combustion temperature distribution, etc.) and the waste gas calorific value data, the accurate air-fuel ratio parameters are calculated. The optimized air-fuel ratio parameters can ensure the optimal mixing ratio of fuel and air, and avoid the reduction of combustion efficiency caused by excessive air or excessive fuel. For the impurities (such as water vapor, sulfide, halide, etc.) contained in the low calorific value industrial waste gas components, necessary pretreatment (such as dehydration, desulfurization, premixing, etc.) is carried out to improve the combustion characteristics of the waste gas. Through pretreatment, the stability of combustion can be improved, and problems such as burner blockage and flame instability can be avoided. Through multi-stage combustion operation, the supply of fuel and air is divided into multiple stages, and the combustible components in the waste gas are gradually burned, so as to avoid problems such as incomplete combustion or excessive flame caused by too much fuel supplied at one time. Multi-stage combustion can effectively improve combustion efficiency and ensure complete combustion of low calorific value exhaust gas.

[0010] Preferably, step S1 specifically includes:

[0011] Step S11: obtaining exhaust gas composition data;

[0012] Step S12: extracting exhaust gas component characteristics according to the exhaust gas component data to obtain exhaust gas component characteristic data;

[0013] Step S13: performing exhaust gas component clustering processing according to the exhaust gas component characteristic data to obtain exhaust gas component clustering pattern data;

[0014] Step S14: performing component calorific value mapping according to the exhaust gas component clustering pattern data to obtain component calorific value mapping data;

[0015] Step S15: Calculate the calorific value according to the component calorific value mapping data to obtain the exhaust gas calorific value data.

[0016] In the present invention, by extracting features (such as molecular weight, proportion of combustion products, reaction activity, etc.) of exhaust gas component data, the key features of each exhaust gas component can be captured to avoid information loss caused by simple component analysis. By clustering the exhaust gas component feature data, the complex and diverse exhaust gas components can be classified into several feature modes (such as high calorific value mode, low calorific value mode, complex mixed mode, etc.), so as to more clearly identify the calorific value distribution law of the exhaust gas components, so that when processing complex exhaust gas, a known mode similar to the target exhaust gas component can be quickly found, thereby improving the efficiency and accuracy of calorific value calculation. Through component calorific value mapping, an accurate mapping relationship can be established between the characteristics of each component in the exhaust gas and its combustion calorific value, which can dynamically adapt to the changes in different exhaust gas component combinations, so that the calorific value calculation is no longer a simple linear addition, but combines the interaction between the components and their contribution to the total calorific value, thereby more truly reflecting the actual calorific value of the exhaust gas. Through the analysis of the component clustering pattern and the establishment of the component calorific value mapping, the calorific value calculation model can be dynamically adjusted according to the changes in the exhaust gas components under different working conditions to adapt to the changes in different exhaust gas types. In this way, the calorific value calculation model can still maintain a high degree of accuracy under different production conditions and fluctuations in exhaust gas composition.

[0017] Preferably, step S2 specifically includes:

[0018] Step S21: Obtain combustion chamber data;

[0019] Step S22: performing combustion chamber heat and mass transfer simulation according to the combustion chamber data to obtain combustion chamber heat and mass transfer data;

[0020] Step S23: acquiring historical operation data of the combustion chamber, and performing feature extraction based on the historical operation data of the combustion chamber to obtain historical operation feature data of the combustion chamber;

[0021] Step S24: performing calorific value combustion chamber response mapping according to the combustion chamber historical operation characteristic data and the combustion chamber heat and mass transfer data to obtain calorific value combustion chamber response mapping data;

[0022] Step S25: Calculate the air-fuel ratio parameters according to the calorific value combustion chamber response mapping data to obtain the air-fuel ratio parameter data.

[0023] In the present invention, the distribution of key physical quantities such as temperature, pressure, and velocity field inside the combustion chamber can be simulated through the heat and mass transfer simulation of the combustion chamber, and the complex heat and material transfer mechanism inside the combustion chamber can be deeply understood, reflecting the real dynamic behavior of the combustion process. The historical operation data contains the operating characteristic information of the combustion chamber under different working conditions (such as temperature, pressure fluctuations, fuel consumption, emission concentration, etc.). The typical operation mode and response characteristics of the combustion chamber can be identified through feature extraction, and the response of the combustion chamber to fuels with different calorific values ​​can be effectively captured, providing a basis for the calculation of air-fuel ratio parameters. By combining the heat and mass transfer data of the combustion chamber with the historical operation characteristic data, a calorific value combustion chamber response mapping model is established. The model can dynamically reflect the response behavior of the combustion chamber under different calorific values ​​and combustion conditions (such as temperature changes, combustion efficiency, pollutant generation, etc.), and can accurately predict the reaction of the combustion chamber to fuels with different calorific values. By combining the calorific value combustion chamber response mapping model with real-time combustion chamber data, the air-fuel ratio parameters can be dynamically adjusted to achieve rapid response of combustion control, which can effectively cope with the fluctuations of exhaust gas composition and calorific value in the combustion chamber, and ensure the stability and efficiency of the combustion process. The calorific value combustion chamber response mapping model can predict in advance the state changes of the combustion chamber under different calorific values ​​(such as flame propagation speed, combustion chamber pressure fluctuations, etc.). The combustion control system can adjust the air-fuel ratio in advance according to the prediction results to avoid combustion instability and equipment damage caused by fluctuations in the calorific value of the fuel.

[0024] Preferably, step S3 is specifically:

[0025] Step S31: selecting an exhaust gas pretreatment scheme according to the air-fuel ratio parameter data and the exhaust gas composition data to obtain exhaust gas pretreatment scheme data;

[0026] Step S32: performing desulfurization and dechlorination treatment through a chemical absorption tower according to the exhaust gas pretreatment scheme data to obtain desulfurization and dechlorination treatment data;

[0027] Step S33: performing exhaust gas drying and preheating treatment through a condenser and a heat exchanger according to the exhaust gas pretreatment scheme data and the desulfurization and dechlorination treatment data to obtain exhaust gas drying and preheating data;

[0028] Step S34: performing exhaust gas homogenization treatment through a static mixer according to the exhaust gas pretreatment scheme data and the exhaust gas drying and preheating data to obtain exhaust gas homogenization data;

[0029] Step S35: Introducing and mixing the premixed fuel according to the exhaust gas pretreatment scheme data and the exhaust gas homogenization data to obtain the exhaust gas pretreatment data.

[0030] In the present invention, the components in the exhaust gas (such as CO 2 、SO 2 , H2 S, HCl, water vapor, etc.) and the impact of these components on the combustion process, select the appropriate exhaust gas pretreatment scheme, including different treatment combinations such as desulfurization, dechlorination, dehydration, and preheating, and generate the optimal exhaust gas pretreatment scheme data. Through the online monitoring system, the treatment efficiency of the desulfurization and dechlorination process, the gas composition and concentration changes at the outlet of the absorption tower are recorded, and the desulfurization and dechlorination treatment data are generated. The desulfurization and dechlorination treatment can reduce the corrosion of acidic gases on combustion equipment (such as burners and heat exchangers), extend the service life of the equipment, and reduce maintenance costs. Record the inlet and outlet gas temperature, humidity and treatment efficiency of the condenser and heat exchanger, and generate exhaust gas drying and preheating data. The preheating treatment makes the exhaust gas temperature closer to the combustion temperature, reduces energy loss in the combustion process, and improves combustion efficiency and combustion stability. Monitor the composition uniformity, temperature distribution and pressure loss of the exhaust gas at the outlet of the mixer, and generate exhaust gas homogenization data. The homogenization treatment eliminates the risk of combustion instability caused by local high-concentration and high-temperature exhaust gas, and avoids problems such as flame instability and flashback. According to the exhaust gas pretreatment program data and exhaust gas homogenization data, high calorific value fuel (such as natural gas, propane) is premixed into the exhaust gas to increase the overall calorific value so that the low calorific value exhaust gas can burn stably. For low calorific value exhaust gas, adding high calorific value fuel can increase the overall calorific value and ensure that the exhaust gas can reach a stable combustion temperature in the combustion chamber to prevent the flame from being extinguished.

[0031] Preferably, the method is applied to a combustion chamber, the combustion chamber includes a primary combustion zone, a secondary combustion zone and a tertiary combustion zone, the primary combustion zone, the secondary combustion zone and the tertiary combustion zone are different combustion zones, and step S4 is specifically as follows:

[0032] Step S41: generating multi-stage combustion parameter data according to exhaust gas pretreatment data;

[0033] Step S42: performing a primary combustion ignition operation through the primary combustion zone according to the multi-stage combustion parameter data and monitoring in real time to obtain primary combustion ignition data;

[0034] Step S43: performing a secondary combustion ignition operation through the secondary combustion zone according to the multi-stage combustion parameter data and the primary combustion ignition data and monitoring in real time to obtain the secondary combustion ignition data;

[0035] Step S44: performing a three-stage combustion ignition operation through the three-stage combustion zone according to the multi-stage combustion parameter data and the two-stage combustion ignition data and monitoring in real time to obtain the three-stage combustion ignition data;

[0036] Step S45: constructing the combustion chamber temperature field according to the three-stage combustion ignition data to obtain the combustion chamber temperature field data;

[0037] Step S46: collecting gas composition through a preset gas analyzer to obtain combustion chamber gas composition data;

[0038] Step S47: Calculate the combustion efficiency of the combustion chamber according to the combustion chamber temperature field data and the combustion chamber gas composition data to obtain the exhaust gas combustion data for optimizing the combustion control of the combustion chamber.

[0039] In the present invention, combustion parameter data for different combustion zones are generated according to the specific composition and calorific value data of the exhaust gas after pretreatment, including the fuel supply, air supply, ignition temperature, etc. of each combustion zone, which can accurately adapt to the combustion characteristics of the exhaust gas after pretreatment and avoid problems such as unstable combustion, incomplete combustion or flame detachment caused by improper parameters.

[0040] When the ignition operation is carried out in the primary combustion zone, the flame status and temperature changes are monitored in real time through the combustion monitor and temperature sensor. Real-time monitoring can help quickly identify abnormal conditions in the ignition process, such as flame instability, flashback, incomplete combustion, etc., and adjust the ignition conditions (such as fuel injection amount, air flow, etc.) in time according to the monitoring data to ensure successful ignition and stable combustion. According to the ignition data and real-time monitoring results of the primary combustion zone, the combustion parameters of the secondary combustion zone are adjusted so that the secondary combustion zone can fully burn the components that are not completely burned after the primary combustion. For example, the CO remaining after the primary combustion zone is completely burned to CO in the secondary combustion zone. 2 , avoiding CO emission pollution. The third-stage combustion zone can further process the remaining combustible components (such as CH 4 and H 2 ), ensuring that all combustible components can be completely burned at high temperatures. Multi-stage combustion control can release the combustion characteristics of different components in stages, reduce incomplete combustion and local overheating, and improve combustion efficiency. Through the ignition data of the three-stage combustion zone, an accurate combustion chamber temperature field model is constructed.

[0041] The temperature field model can directly reflect the temperature distribution, temperature gradient and heat release of each combustion zone in the combustion chamber. The gas composition monitoring data can be used to evaluate the combustion efficiency of different combustion zones and timely detect unburned gases (such as CO and CH 4 ) content changes, and optimize combustion conditions based on real-time data to reduce pollutant emissions. Calculate combustion efficiency based on combustion chamber temperature field data and gas composition data. Evaluate overall combustion efficiency and thermal efficiency through comprehensive analysis of temperature field distribution, heat loss, and unburned gas content in different combustion zones. Combustion efficiency data can fully reflect the energy utilization of the combustion process and identify the main sources of energy waste and incomplete combustion.

[0042] Preferably, step S42 is specifically as follows:

[0043] During the primary combustion ignition operation, the flame monitor is controlled to collect flame data to obtain combustion chamber flame data, and the temperature sensor is used to collect temperature data to obtain combustion chamber temperature data;

[0044] A combustion chamber temperature model is constructed according to the combustion chamber temperature data to obtain a combustion chamber temperature model;

[0045] Perform local high temperature detection based on combustion chamber flame data and combustion chamber temperature model to obtain local high temperature data;

[0046] Conduct combustion risk assessment based on local high temperature data to obtain combustion risk assessment data;

[0047] The combustion chamber temperature control operation is performed according to the combustion risk assessment data until the first-level combustion ignition operation is terminated and monitored in real time to obtain the first-level combustion ignition data.

[0048] In the present invention, flame data (such as flame brightness, shape, color, etc.) are collected in real time by the flame monitor, so that the instability or abnormal behavior of the flame in the combustion chamber, such as flame detachment, backfire, etc., can be detected in time to ensure the safety of the ignition operation. The real-time temperature data inside the combustion chamber is collected by the temperature sensor, so that the temperature reaches the set ignition threshold during the combustion ignition process, and the ignition failure or incomplete combustion due to insufficient temperature can be prevented. The combustion chamber temperature model is constructed by temperature data, and the temperature distribution can be dynamically analyzed and predicted to ensure the uniformity of the temperature field during the ignition process and avoid the ignition instability caused by local excessive or low temperature. Local high temperature detection is performed by flame data and temperature model, and the high temperature hot spot area in the combustion chamber can be identified in time to prevent equipment damage or unstable combustion caused by local overheating. By analyzing the local high temperature data for combustion risk assessment, the combustion risks (such as high temperature hot spots, flame instability, etc.) that may exist in the ignition process can be identified in advance, and preventive measures can be taken. Combustion risk assessment can reduce the possibility of equipment damage, reduce downtime and maintenance costs. Through real-time monitoring and data feedback, the fuel-air mixing ratio during the ignition process can be optimized to ensure full combustion of the fuel, avoid fuel waste, and improve energy utilization.

[0049] Preferably, the combustion chamber temperature model is constructed as follows:

[0050] Perform local high temperature partition modeling according to combustion chamber temperature data to obtain a local high temperature partition model;

[0051] According to the local high temperature zoning model, local high temperature diffusion prediction is performed to obtain local high temperature diffusion prediction data;

[0052] Perform combustion instability analysis in the high-temperature zone based on local high-temperature diffusion prediction data to obtain combustion instability data in the high-temperature zone;

[0053] Perform combustion chamber structure thermal stress processing on the local high-temperature partition model to obtain a combustion chamber structure thermal stress model;

[0054] The combustion chamber structure thermal stress model is annotated according to the combustion instability data in the high-temperature zone to obtain the combustion chamber temperature model.

[0055] In the present invention, by performing local high-temperature partition modeling on the combustion chamber temperature data, the temperature distribution characteristics of different areas in the combustion chamber, especially the high-temperature hot spot area, can be accurately identified. Local high-temperature partition modeling can identify the temperature gradient and distribution differences in different combustion areas, avoid the problem of local information loss in the traditional overall temperature model, and make the temperature model more refined and accurate. Through local high-temperature diffusion prediction, the diffusion trend and speed of the high-temperature area can be understood in advance, so as to predict the temperature field changes that may occur during the combustion process, so that the combustion control system can adjust the fuel supply or cooling system in advance to prevent the high-temperature area from diffusing to the key equipment area and avoid equipment overheating and damage. Through the analysis of combustion instability in the high-temperature zone, potential unstable factors in the combustion process (such as flame oscillation, flame detachment, etc.) can be identified to avoid situations that lead to reduced combustion efficiency or equipment damage. Timely analysis and early warning can avoid sudden problems in the combustion process. By processing the thermal stress of the combustion chamber structure on the local high-temperature partition model, the thermal stress effect of the high-temperature area on the combustion chamber structure (such as furnace wall, burner bracket, etc.) can be analyzed, and the thermal deformation, crack propagation and other problems that may occur in the structure under high-temperature environment can be predicted, thereby preventing the equipment from failing under high-temperature environment. Structural thermal stress analysis can identify key parts that are prone to thermal fatigue and thermal cracks, and strengthen the structural design of these parts or take effective cooling measures.

[0056] Preferably, the local high temperature partition modeling is specifically as follows:

[0057] Calculate the temperature change rate according to the combustion chamber temperature data to obtain the temperature change rate data;

[0058] Perform three-dimensional temperature gradient vector field calculation on the temperature change rate data to obtain temperature gradient vector data;

[0059] Extract temperature gradient features according to temperature vector gradient data to obtain temperature gradient feature data;

[0060] According to the temperature gradient characteristic data, a heat conduction similarity matrix is ​​constructed to obtain temperature heat conduction similarity matrix data;

[0061] Based on the temperature heat conduction similarity matrix data, partition hierarchical clustering calculation is performed to obtain the temperature gradient clustering feature data;

[0062] The partition boundaries are divided and the model is constructed according to the temperature gradient clustering feature data to obtain a local high temperature partition model.

[0063] The temperature change rate data in the present invention can reflect the transient behavior during the combustion process, help understand the temperature response characteristics of the burner when it is started, stopped, and when the fuel fluctuates, and improve the dynamic response capability of the system. The three-dimensional temperature gradient vector field can intuitively display the spatial structure of the temperature field in the combustion chamber, including the direction of temperature rise, gradient strength, etc. Through the vector field, the dominant direction of temperature change and the area with steep gradients can be effectively identified, providing a more detailed description of the spatial distribution of the combustion process. Through temperature gradient feature extraction, key features such as gradient amplitude, direction change rate, gradient concentration, etc. can be extracted from the vector field data, which can effectively distinguish different temperature change modes (such as stable areas, transition areas, and drastic change areas) and are used to identify key change areas in the temperature field, facilitating further clustering and partitioning modeling. The heat conduction similarity matrix can reveal the thermophysical correlation between different regions in the temperature field by reflecting the heat conduction similarity between different temperature gradients, and can more scientifically characterize the connection between temperature change patterns, avoiding the shortcomings of traditional similarity matrices that only rely on geometric distance or simple similarity. In complex heat flow environments (such as heat exchangers, reactors, etc.), the heat conduction similarity matrix can effectively identify the heat conduction relationship between different temperature zones and provide a basis for heat flow allocation and optimization. Through partition hierarchical clustering, the temperature gradient feature data can be hierarchically aggregated to identify high temperature zones, transition zones, and cooling zones at different levels. By partitioning the temperature gradient clustering feature data, the boundary position of each high temperature zone can be accurately located, and the distribution range of different temperature zones can be clarified.

[0064] Preferably, the combustion efficiency of the combustion chamber is calculated as follows:

[0065] Calculate the combustion efficiency according to the exhaust gas composition data and the combustion chamber gas composition data to obtain the first exhaust gas combustion data;

[0066] Perform temperature field regional integral calculation based on combustion chamber temperature field data and combustion chamber gas composition data to obtain preliminary second exhaust gas combustion data;

[0067] Perform cumulative calculation of smoke exhaust loss by zone according to preliminary second exhaust gas combustion data to obtain second exhaust gas combustion data;

[0068] The combustion efficiency is iterated step by step according to the first exhaust gas combustion data and the second exhaust gas combustion data to obtain the exhaust gas combustion data.

[0069] In the present invention, by analyzing the exhaust gas composition data and the combustion chamber gas composition data, the actual utilization efficiency of the fuel during the combustion process can be accurately calculated, which can reflect the degree of fuel combustion (such as CO 2 , CO, CH 4The generation ratio of the unburned part), identifying the composition and quantity of the unburned part, helps to understand the completeness of fuel combustion. Through the regional integral calculation of the combustion chamber temperature field data, the heat distribution and temperature field distribution in different combustion areas can be accurately evaluated, which can reflect the uniformity of the temperature field and the temperature gradient changes in each area, and provide spatial distribution details for the comprehensive analysis of combustion efficiency. The cumulative calculation of partitioned exhaust loss can quantify the heat loss caused by exhaust during the combustion process. Through partition analysis, the heat loss of different exhaust channels can be accurately calculated, and the main source of exhaust heat loss can be identified. Through the step-by-step iterative optimization method of combustion efficiency, the combustion utilization efficiency and exhaust loss are considered, and the two are organically combined and iteratively optimized. Each iteration is based on the calculation results of the previous round, and the estimated value of the combustion efficiency is gradually corrected to obtain more accurate comprehensive combustion efficiency data.

[0070] Preferably, the present application also provides a low calorific value industrial waste gas combustion control optimization device, which is used to execute the low calorific value industrial waste gas combustion control optimization method as described above, and the low calorific value industrial waste gas combustion control optimization device comprises:

[0071] The exhaust gas calorific value calculation module is used to obtain the exhaust gas composition data, and calculate the exhaust gas calorific value according to the exhaust gas composition data to obtain the exhaust gas calorific value data;

[0072] The air-fuel ratio parameter generation module is used to obtain combustion chamber data, and generate air-fuel ratio parameters according to the combustion chamber data and exhaust gas calorific value data to obtain air-fuel ratio parameter data;

[0073] An exhaust gas pretreatment module is used to perform exhaust gas pretreatment according to air-fuel ratio parameter data and exhaust gas composition data to obtain exhaust gas pretreatment data;

[0074] The multi-stage combustion operation module is used to perform multi-stage combustion operations according to the exhaust gas pretreatment data to obtain exhaust gas combustion data.

[0075] The beneficial effect of the present invention is that by obtaining the waste gas composition data, the main components (such as CO 2 , CO, CH 4 , H 2 、N 2The exhaust gas composition is analyzed in detail (such as the chemical properties and calorific value contribution of each component). The calorific value evaluation can identify the impact of exhaust gas composition fluctuations on combustion performance under different working conditions, avoid incomplete combustion or excessive combustion caused by calorific value evaluation errors, and thus improve the starting point accuracy and reliability of combustion control. Air-fuel ratio control can reduce the phenomenon of excess air or insufficient air in the combustion process, avoid flame instability, incomplete combustion or overheating in the combustion process, and improve the completeness and safety of combustion. Exhaust gas pretreatment is carried out according to air-fuel ratio parameter data and exhaust gas composition data, and appropriate pretreatment processes such as desulfurization, dechlorination, dehydration, preheating, etc. are customized to remove impurities in the exhaust gas, improve the combustion characteristics of the exhaust gas, and avoid corrosion and damage to the burner and combustion chamber by harmful components. Through multi-stage combustion operations (such as primary combustion zone, secondary combustion zone, and tertiary combustion zone), the combustion process is divided into multiple stages according to the combustion characteristics and calorific value contribution of different exhaust gas components. In this way, the calorific value of the combustible components in the exhaust gas can be gradually released, avoiding combustion instability and heat loss caused by excessive combustion at one time. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting implementations made with reference to the following drawings:

[0077] Figure 1 A flowchart showing a method for optimizing combustion control of low calorific value industrial waste gas according to an embodiment of the present invention is provided;

[0078] Figure 2 A flowchart showing a method for optimizing the calculation of the calorific value of exhaust gas according to an embodiment of the present invention is shown;

[0079] Figure 3 A flowchart showing a method for generating air-fuel ratio parameters according to an embodiment of the present invention is shown;

[0080] Figure 4 A flowchart showing a method for pre-treating exhaust gas according to an embodiment of the present invention is provided;

[0081] Figure 5 A flow chart showing the steps of a multi-stage combustion operation method according to an embodiment is shown. DETAILED DESCRIPTION

[0082] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0083] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0084] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0085] Chemical plants generate a large amount of low calorific value industrial waste gas during the production process. The waste gas has complex composition, low calorific value, is not easy to burn, and contains a small amount of corrosive gases such as chlorides and sulfides. Traditional combustion treatment methods are difficult to achieve efficient and stable combustion, which can easily cause incomplete combustion, equipment damage and environmental pollution. Chemical plants use combustion control technology and data analysis methods to optimize the combustion process of waste gas and achieve efficient, safe and environmentally friendly utilization of waste gas.

[0086] The factory emits about 1000 Nm³ of industrial waste gas per hour. The composition of the waste gas has great fluctuations. The main components include: CO 2 : About 40%, derived from the combustion of hydrocarbons and other chemical reaction byproducts. CO: About 15%, produced by incomplete combustion, flammable but requires a higher ignition temperature. 4 :Accounting for about 10%, natural gas component, high calorific value, can be used as the main combustion component. 2 : About 5%, originating from chemical reactions, with high combustion temperature and high heat release, but easy to diffuse and poor combustion stability. 2 : It accounts for about 30%. As an inert gas, it does not participate in combustion, but it affects the combustion temperature and air demand. In order to accurately obtain these component data, the factory installed an online gas analyzer, which samples once a minute and uploads it to the data analysis platform in real time. The analyzer has a detection accuracy of one millionth and can monitor small changes in exhaust gas composition in real time.

[0087] Based on the exhaust gas components obtained in real time, the low calorific value of the exhaust gas is obtained through the following calorific value calculation formula ( ): Calculation formula: ,in, , , They are , , The volume fraction, , , They are , , Lower calorific value of each component: :12.63 , :35.8 , :10.78 . Calculation of exhaust gas calorific value: , the calculated exhaust gas calorific value , compared to pure natural gas ( for ) is low, but can still be effectively utilized by optimizing the combustion strategy.

[0088] The factory uses a three-stage combustion system for waste gas treatment. Each combustion zone has an independent burner and temperature control system. The primary combustion zone is used for the initial combustion of low calorific value components, such as CH 4 and H 2 . Target combustion temperature: 800°C Air preheating temperature: 150°C. Secondary combustion zone: for the residual after primary combustion Complete combustion and increase the temperature. Target combustion temperature: 1000°C. Air preheating temperature: 250°C. Three-stage combustion zone: handle unburned combustible gases, ensure complete combustion, and avoid pollutant emissions. Target combustion temperature: 1200°C. Air preheating temperature: 350°C. The burner layout, airflow distribution and temperature field characteristics in the combustion chamber are verified through CFD (computational fluid dynamics) simulation and field testing, and a sophisticated combustion chamber simulation model is established to provide basic data for air-fuel ratio optimization.

[0089] According to the combustion chamber model and exhaust gas calorific value, the following air-fuel ratio parameters are optimized: Air-fuel ratio in the primary combustion zone: 17 cubic meters of air is required for each cubic meter of exhaust gas (fuel). and For complete combustion, the preset air requirement is , with a slight oxygen-rich margin to prevent incomplete combustion. Secondary combustion zone air-fuel ratio: 15 cubic meters of air is required for every cubic meter of exhaust gas (fuel). The complete combustion of the exhaust gas requires an appropriate amount of oxygen, and the air requirement is slightly lower to prevent the burner from being damaged by excessive temperature. The air-fuel ratio of the three-stage combustion zone: 13 cubic meters of air is required for every cubic meter of exhaust gas (fuel), ensuring high-temperature combustion while avoiding excessive oxygen. Emissions increase with relatively less air demand.

[0090] According to the exhaust gas composition data and air-fuel ratio parameters, the following pretreatment steps are designed (control parameters are matched from the preset system parameter control library): Chemical absorption tower desulfurization and dechlorination treatment: Although the sulfide and chloride content in the exhaust gas is low, in order to avoid equipment corrosion and harmful gas emissions, a NaOH solution absorption tower is used to remove possible acidic gases. Treatment efficiency: The removal rate of sulfide and chloride is 95%. Condenser cooling and drying treatment: The exhaust gas is cooled from 80°C to 40°C to remove water vapor (water content 5%). After condenser treatment, the amount of condensed water is about 50 Nm 3 / h. Heat exchanger preheating treatment: The dried exhaust gas is heated to 120°C through a heat exchanger to increase the exhaust gas temperature and reduce the heat load of the combustion chamber. Static mixer homogenization treatment: The preheated exhaust gas is statically mixed to make the exhaust gas composition and temperature more uniform, avoiding the phenomenon of local uneven combustion after entering the combustion chamber. Pretreatment effect: Exhaust gas flow after pretreatment: , waste gas temperature after pretreatment: 120°C, desulfurization and dechlorination treatment efficiency: 95%, moisture content after drying treatment: < 1%.

[0091] The combustion control system uses different combustion chambers / areas to achieve multi-stage combustion operations. The specific conditions of the ignition operation in the first-stage combustion area are as follows: Fuel and air supply: Exhaust gas flow after pretreatment: ; Natural gas is introduced into the primary combustion zone as a combustion aid: ; Air flow: (Air-fuel ratio 17). Ignition and combustion monitoring: Ignition temperature: ; Real-time monitoring of flame brightness and shape to avoid flame separation or flashback; stable flame and complete combustion, , Convert to . Primary combustion zone data: Flame temperature: ; Content: 0.1%; unburned hydrocarbons ( ): 0.05%.

[0092] The details of the combustion operation in the secondary combustion zone are as follows: Fuel and air supply: Exhaust gas flow after primary combustion: (Contains a small amount of unburned components); Air flow: (Air-fuel ratio 15). Secondary combustion monitoring: Combustion temperature: ; Mainly burns CO to ensure complete combustion; the temperature field is uniform, the burner works normally, and there is no local overheating. Secondary combustion zone data: exhaust composition: 90%,O 2 2%,N 2 8%; CO content: <0.05%, close to complete combustion; Unburned hydrocarbons (UHC): almost zero; Secondary combustion zone temperature: , temperature fluctuation is small, and the temperature field is uniform. Combustion efficiency: The combustion efficiency of the secondary combustion zone is close to 99%, and CO is almost completely burned. The specific conditions of the combustion operation in the three-stage combustion zone are as follows: Fuel and air supply: Exhaust gas flow entering the three-stage combustion zone: ; Air flow in the third combustion zone: (Air-fuel ratio 13). Three-level combustion monitoring: Combustion temperature: ; In a high temperature environment, it mainly processes trace amounts of residual and , ensuring no harmful gas emissions. The three-stage combustion zone has extremely high combustion efficiency, and the unburned gas is further burned to ensure that pollutant emissions are controlled within the standard range. Data of the three-stage combustion zone: Exhaust composition: 95%,O 2 3% and the rest ; Unburned and Content: close to zero; combustion efficiency reaches 99.8%, at high temperature and Almost completely converted to and water vapor.

[0093] The exhaust gas is analyzed by an online gas analyzer to calculate the combustion efficiency of each combustion zone. 4 and H 2 The combustion efficiency is close to 95%. The combustion efficiency reaches 96.5%, almost completely converted into CO 2 The trace CH remaining in the third combustion zone 4 and H 2 Almost complete combustion at high temperature. The combustion efficiency obtained by iterative calculation of the combustion operation at each stage is 91.6%, indicating that most of the fuel has been effectively utilized.

[0094] Through the temperature sensor network in the combustion chamber, the temperature distribution of each combustion zone is monitored in real time, and regional integral calculations are performed to evaluate the uniformity of the temperature field. The temperature fluctuation in the primary combustion zone is between 800-820°C, and the regional temperature field integral uniformity is 93%. The temperature in the secondary combustion zone is relatively stable, with a fluctuation range of 990-1005°C, and the regional temperature field uniformity is 95%. The temperature in the high temperature zone of the tertiary combustion zone is stable at 1180-1220°C, and the regional temperature field uniformity is 92%. The overall temperature field uniformity is 93.3%, indicating that the temperature field distribution of the entire combustion process is relatively uniform, which can ensure stable combustion.

[0095] The smoke loss in the primary combustion zone is 10%, mainly from the incomplete combustion of CH 4 Residual and partial heat loss. The exhaust loss in the secondary combustion zone is 5%. After optimized combustion, the The content is extremely low, and the loss is reduced. The smoke loss in the third-level combustion area is 3%, and almost no combustible gas is discharged at high temperature, with the smallest loss. The overall smoke loss is 18%, mainly concentrated in the first-level combustion area.

[0096] According to the combustion utilization efficiency and exhaust loss, the combustion efficiency is preliminarily calculated to be 91.6%-18%=72.6%. By combining the temperature field uniformity data with the actual combustion situation, the air supply and fuel mixture ratio are further optimized to reduce the loss in the primary combustion zone. After adjusting the air-fuel ratio, the combustion efficiency is further improved. By reducing the air flow in the primary combustion zone and reducing the impact of excess air, the exhaust loss is reduced to 8% after adjustment. The exhaust loss in the secondary and tertiary combustion zones remains unchanged. The optimized combustion efficiency reaches 80%.

[0097] See also Figures 1 to 5 The present application provides a method for optimizing combustion control of low calorific value industrial waste gas, comprising the following steps:

[0098] Step S1: obtaining exhaust gas composition data, and calculating the exhaust gas calorific value according to the exhaust gas composition data to obtain the exhaust gas calorific value data;

[0099] In one embodiment, the main components in the exhaust gas are collected in real time by sensors or online analysis instruments, such as , CO 2 , CH 4 , H 2 , O 2 、N 2 The concentration data of each component is weighted according to certain rules to calculate the theoretical calorific value of each component (the calorific value of each component multiplied by its concentration ratio). The calorific value of all components is added to obtain the overall calorific value data of the exhaust gas. The calculated exhaust gas calorific value data is transmitted to the next control system as the basis for combustion control.

[0100] Step S2: Acquire combustion chamber data, and generate air-fuel ratio parameters according to the combustion chamber data and exhaust gas calorific value data to obtain air-fuel ratio parameter data;

[0101] In one embodiment, the operating parameters of the combustion chamber, such as temperature, pressure, combustion chamber volume, exhaust gas flow rate, etc., are monitored in real time by sensors. The optimal distribution of the combustion zone is determined based on the geometric structure and volume of the combustion chamber. The influence of the combustion chamber temperature and pressure on the exhaust gas combustion efficiency is analyzed to determine the working range. The theoretical optimal air-fuel ratio (i.e., the air-to-fuel ratio that ensures complete combustion without excess oxygen) is calculated by combining the exhaust gas calorific value data and the combustion chamber data. According to the actual working conditions, the air-fuel ratio is adjusted to cope with different exhaust gas component fluctuations to generate actual air-fuel ratio parameters. The generated air-fuel ratio parameter data is transmitted to the exhaust gas pretreatment and combustion stage.

[0102] Step S3: performing exhaust gas pretreatment according to the air-fuel ratio parameter data and the exhaust gas composition data to obtain exhaust gas pretreatment data;

[0103] In one embodiment, the working state of pretreatment equipment, such as an exhaust gas cooler, a heater, a mixer, etc., is adjusted according to the air-fuel ratio parameters. The exhaust gas flow rate is adjusted by controlling the valve to reach a range that matches the air-fuel ratio parameters. Avoid incomplete combustion or waste of energy due to excessive or insufficient flow rate. Cool the exhaust gas with too high a temperature to avoid exceeding the heat resistance limit of the combustion chamber when entering the combustion chamber. Dehydrate the exhaust gas with too high humidity to avoid affecting the combustion efficiency. Through stirring, mixing and other means, the exhaust gas from different sources and different components is evenly mixed to ensure that the exhaust gas entering the combustion chamber has a uniform composition. The pretreated exhaust gas data (such as flow, temperature, humidity, composition, etc.) is transmitted to the combustion operation stage.

[0104] Step S4: Perform multi-stage combustion operation according to the exhaust gas pretreatment data to obtain exhaust gas combustion data.

[0105] In one embodiment, the combustion zone is divided into multiple areas such as a primary combustion zone and a secondary combustion zone according to the combustion chamber structure. According to the exhaust gas pretreatment data, the pretreated exhaust gas is distributed to different combustion zones as needed to ensure that each combustion zone can maintain a suitable air-fuel ratio. The exhaust gas supply to each combustion zone is controlled to optimize the combustion efficiency. Ignition operation is performed in each combustion zone, and the combustion state is monitored in real time by a flame sensor. If the flame of a combustion zone is extinguished or the combustion is incomplete, the exhaust gas supply or air-fuel ratio parameters are automatically adjusted and re-ignited. By detecting the emissions (such as NO X , The system can adjust the combustion conditions and reduce the emission of harmful gases. The exhaust gas treatment system can further purify the emissions to ensure compliance with environmental emission standards. The temperature, pressure, flame state and other data of each combustion zone are collected in real time as feedback data for subsequent combustion control. The control system can analyze the combustion data and optimize the next combustion strategy.

[0106] Preferably, step S1 specifically includes:

[0107] Step S11: obtaining exhaust gas composition data;

[0108] In one embodiment, a gas component analyzer (such as an online gas chromatograph, an infrared gas analyzer, an electrochemical sensor or other analytical instrument with gas detection capability) is used for real-time monitoring to collect data on the main components in the exhaust gas. According to the rate of change of the exhaust gas, a reasonable collection frequency (such as once per minute) is set to ensure that the dynamic changes of the exhaust gas components are captured.

[0109] Step S12: extracting exhaust gas component characteristics according to the exhaust gas component data to obtain exhaust gas component characteristic data;

[0110] In one embodiment, the exhaust gas composition data is analyzed to extract the concentration, distribution characteristics and statistical characteristics of each component. For example, the mean and variance of the concentration of each component in the past period of time (such as 1 hour) are calculated: : mean 5.1%, variance 0.2%; CO 2 : Mean 14.7%, variance 0.3%; CH 4 : mean 1.8%, variance 0.1%; H 2 : mean 0.8%, variance 0.05%; N 2 : Mean 77.3%, variance 0.5%.

[0111] Step S13: performing exhaust gas component clustering processing according to the exhaust gas component characteristic data to obtain exhaust gas component clustering pattern data;

[0112] In one embodiment, the distance (such as Euclidean distance) between the feature vectors of different exhaust gas components is calculated to determine the similarity between each sample. A distance threshold (such as 0.01) is set, and samples with a distance less than the threshold are classified into the same class to characterize the similarity of the exhaust gas components. Several samples are randomly selected as initial cluster centers, and the remaining samples are assigned to the nearest cluster center according to the similarity. Each cluster center is adjusted iteratively, and the sample allocation in each class is updated until the clustering result converges or the maximum number of iterations is reached. Multiple cluster centers and their corresponding sample sets (i.e., clustering pattern data) are obtained, such as: Cluster 1: Center [0.05, 0.002, ...], number of samples: 20. Cluster 2: Center [0.04, 0.003, ...], number of samples: 15.

[0113] Step S14: performing component calorific value mapping according to the exhaust gas component clustering pattern data to obtain component calorific value mapping data;

[0114] In one embodiment, the component data of each cluster center is mapped to the known component calorific value correspondence relationship, and the average calorific value of each cluster center is calculated. For example, the average component of cluster 1 is [5.0, 14.8, 1.9, 0.9, 77.4], and its calorific value can be obtained by weighting the calorific value of each component. The central calorific value of each cluster is used as the representative calorific value of all samples in the cluster to generate the component calorific value mapping data of each cluster pattern. Cluster 1: , Cluster 2: .

[0115] Step S15: Calculate the calorific value according to the component calorific value mapping data to obtain the exhaust gas calorific value data.

[0116] In one embodiment, the average calorific value of the global exhaust gas is calculated by combining the proportion of each cluster sample in the entire exhaust gas data. For example, the number of samples in cluster 1 accounts for 0.6, and the number of samples in cluster 2 accounts for 0.4, then the average calorific value is calculated as: . Based on the newly collected data, the sample distribution and calorific value mapping relationship of each cluster is dynamically updated to ensure the accuracy of the real-time calorific value data. The calculated exhaust gas calorific value data (such as 1.3990 MJ / m³) is output and passed to the next combustion control system.

[0117] Step S21: Obtain combustion chamber data;

[0118] In one embodiment, sensors installed in the combustion chamber collect key data of the combustion chamber in real time, including temperature, pressure, exhaust gas flow rate, combustion product concentration, etc. The data sources include temperature sensors, pressure sensors, flow meters and other measuring instruments to ensure coverage of the working status of the combustion chamber.

[0119] In one embodiment, the combustion chamber data is obtained by collecting data through database query and combustion chamber parameter library.

[0120] Step S22: performing combustion chamber heat and mass transfer simulation according to the combustion chamber data to obtain combustion chamber heat and mass transfer data;

[0121] In one embodiment, a physical model of the combustion chamber is established based on the geometric structure of the combustion chamber (such as volume, shape, wall material, etc.) as the basis for simulation. For example, the volume of the combustion chamber is , the wall material is refractory brick, and the thermal conductivity is . Set the boundary conditions of heat and mass transfer simulation according to the actual working conditions, such as the exhaust gas temperature and pressure at the inlet and outlet of the combustion chamber, the external environment temperature, etc. The boundary conditions are the intake temperature , outlet exhaust gas temperature , ambient temperature The heat transfer and mass transfer process of the exhaust gas in the combustion chamber is simulated by heat transfer and mass transfer simulation tools, taking into account the heat release of the combustion reaction, convection heat transfer, radiation heat transfer and heat loss of the wall. Combustion chamber temperature field: , wall heat loss: , convection heat transfer coefficient: .

[0122] For example, the temperature field, heat flux distribution, heat loss and other information inside the combustion chamber can be calculated.

[0123] Step S23: acquiring historical operation data of the combustion chamber, and performing feature extraction based on the historical operation data of the combustion chamber to obtain historical operation feature data of the combustion chamber;

[0124] In one embodiment, the long-term operation data of the combustor is extracted from the historical database, including past temperature, pressure, combustion efficiency, fuel consumption and other information. The data may cover several days, several months or longer. For example, the average operation data of the combustor in the past month is: average temperature: 880°C, average pressure: 1.15 atm, combustion efficiency: 92%, fuel consumption: 1000 m³ / d. The key features are extracted from the historical data, and the data after feature extraction are: temperature mean: 880°C, standard deviation: 5°C, pressure mean: 1.15 atm, standard deviation: 0.05 atm, combustion efficiency: mean 92%, standard deviation 1.5%.

[0125] Step S24: performing calorific value combustion chamber response mapping according to the combustion chamber historical operation characteristic data and the combustion chamber heat and mass transfer data to obtain calorific value combustion chamber response mapping data;

[0126] In one embodiment, based on the relationship between heat and mass transfer data and historical operation data, a combustion chamber response model for different exhaust gas calorific values ​​is constructed. The model can consider the impact of exhaust gas composition on indicators such as combustion temperature, heat transfer efficiency, and combustion efficiency. For example, the relationship between calorific value and combustion chamber response is: as the calorific value increases, the combustion temperature increases, and the combustion efficiency improves. Using historical operation data, the response law of the combustion chamber for different fuel calorific values ​​is fitted to obtain the changing trends of combustion temperature, heat transfer coefficient, combustion efficiency, etc. The fitting results show that for every increase in calorific value, the combustion temperature increases, and the combustion efficiency increases. , the combustion temperature rises by 5°C and the combustion efficiency increases by 0.2%. According to the different exhaust gas calorific values, the range of changes in parameters such as the temperature, pressure, and combustion efficiency of the combustion chamber is mapped. For example, when the exhaust gas calorific value is 1.5 MJ / m³, the combustion temperature is 900°C and the combustion efficiency is 93%.

[0127] Step S25: Calculate the air-fuel ratio parameters according to the calorific value combustion chamber response mapping data to obtain the air-fuel ratio parameter data.

[0128] In one embodiment, the optimal air-fuel ratio is calculated based on the calorific value of the exhaust gas and the response mapping data of the combustion chamber. The air-fuel ratio refers to the ratio of the volume of air to the volume of fuel, and is optimized by ensuring complete combustion and maximizing the use of heat energy. Considering the working conditions of the combustion chamber (such as temperature fluctuations, heat transfer efficiency, etc.), make appropriate adjustments based on the theoretical air-fuel ratio. For example, add some oxygen-enriched air to improve combustion efficiency. The calorific value of exhaust gas is , the combustion efficiency is 93%, then the theoretical air-fuel ratio is: . According to the working conditions of the combustion chamber, the air-fuel ratio is adjusted to 0.75.

[0129] Preferably, step S3 is specifically:

[0130] Step S31: selecting an exhaust gas pretreatment scheme according to the air-fuel ratio parameter data and the exhaust gas composition data to obtain exhaust gas pretreatment scheme data;

[0131] In one embodiment, the need for exhaust gas pretreatment is determined by combining air-fuel ratio parameters and exhaust gas composition data. The air-fuel ratio parameter determines the ratio of combustible components to air in the exhaust gas, while the exhaust gas composition data reveals the types and concentrations of harmful substances in the exhaust gas (such as SO 2 , HCl, H 2 S, water vapor, etc.). The air-fuel ratio parameter is 0.75, and the exhaust gas composition data is: :5%,CO 2 :15%, CH 4 :2%, H 2 :1%, SO 2 :0.1%,H 2O: 10%. According to the types and concentrations of harmful substances in the exhaust gas, match the system parameters of the current combustion pretreatment system and determine the required pretreatment steps, such as desulfurization, dechlorination, drying, etc. For example, SO 2 If the concentration exceeds 0.05%, desulfurization treatment is required; if the water vapor content is high, drying treatment is required. Select a pretreatment process, such as chemical absorption for desulfurization and dechlorination, condensation for drying, heat exchanger for preheating, etc. Determine the process sequence and equipment selection. Output the determined pretreatment plan data, including each treatment step, equipment used, treatment sequence, etc.

[0132] Step S32: performing desulfurization and dechlorination treatment through a chemical absorption tower according to the exhaust gas pretreatment scheme data to obtain desulfurization and dechlorination treatment data;

[0133] In one embodiment, according to the SO 2 and HCl concentration, select the absorbent. The absorbent includes alkali solution (such as NaOH, Ca(OH) 2 ) or ammonia water. The absorbent selected this time is NaOH solution with a concentration of 5%. Set the operating parameters of the absorption tower, including waste gas flow rate, absorbent flow rate, operating temperature, etc. The waste gas flow rate is 15 m 3 / h, the absorbent flow rate is 2 L / min, and the operating temperature is 50°C. In the absorption tower, SO 2 React chemically with HCl and NaOH solutions to produce soluble salts (such as Na 2 SO 3 and NaCl), thereby removing SO from the exhaust gas 2 and HCl. According to the reaction efficiency of the absorption tower, the removal rates of desulfurization and dechlorination are calculated. The desulfurization efficiency is 95% and the dechlorination efficiency is 90%.

[0134] Step S33: performing exhaust gas drying and preheating treatment through a condenser and a heat exchanger according to the exhaust gas pretreatment scheme data and the desulfurization and dechlorination treatment data to obtain exhaust gas drying and preheating data;

[0135] In one embodiment, the exhaust gas after desulfurization and dechlorination treatment is passed into a condenser, and the water vapor in the exhaust gas is condensed and precipitated by lowering the temperature to achieve a drying effect. The operating temperature of the condenser is 20°C, and the water vapor concentration in the exhaust gas after treatment is reduced from 10% to 1%. The condensed water is collected and the pollutants in the condensed water (such as a small amount of SO 2 , HCl), to prevent secondary pollution. The dried exhaust gas is preheated by a heat exchanger to increase the exhaust gas temperature from 20°C to 300°C to achieve the optimal combustion temperature. During the preheating process, the flue gas waste heat or other heat sources are used to increase the exhaust gas temperature and reduce external energy consumption. Exhaust gas data after drying and preheating: water vapor content: 1%, exhaust gas temperature: 300°C.

[0136] Step S34: performing exhaust gas homogenization treatment through a static mixer according to the exhaust gas pretreatment scheme data and the exhaust gas drying and preheating data to obtain exhaust gas homogenization data;

[0137] In one embodiment, the structure of the static mixer is designed according to the exhaust gas flow rate and composition, including a spiral structure or a baffle structure, to ensure the uniformity of the flow of the exhaust gas in the mixer. The effective length of the mixer is 1.2 meters and contains 5 spiral units. The exhaust gas after drying and preheating passes through the static mixer, and a rotation and baffle effect is generated inside the mixer, so that the various components of the exhaust gas are evenly mixed to avoid excessive or low local concentrations. The uniformity index (such as the variance of the component concentration) is calculated by sampling and analyzing the components of the mixed exhaust gas. The variance of the exhaust gas component concentration before mixing is 0.04, and the variance of the concentration after mixing is reduced to 0.01, indicating that the uniformity is significantly improved.

[0138] Step S35: Introducing and mixing the premixed fuel according to the exhaust gas pretreatment scheme data and the exhaust gas homogenization data to obtain the exhaust gas pretreatment data.

[0139] In one embodiment, according to the calorific value requirement of the exhaust gas (obtained by querying the preset database), the corresponding premixed fuel (such as natural gas, hydrogen, light diesel, etc.) is selected. Natural gas is selected as the premixed fuel, and its calorific value is . The natural gas is introduced into the exhaust gas flow at a set ratio through the control valve. The mixing ratio is set according to the pretreatment scheme data. 0.1 cubic meters of natural gas is added to each cubic meter of exhaust gas to increase the overall calorific value of the exhaust gas. In the static mixer, the natural gas and exhaust gas are fully mixed through the internal swirl structure to ensure uniform mixing. The gas composition after mixing is detected to ensure that the concentration of the combustible component is within a safe range. Output the exhaust gas data after premixing the fuel, including the concentration and calorific value of each component: :4.2%, :11%, :1.4%, :0.9%, (Natural gas): 10%, Water vapor: 1%, Temperature: , Calorific value: .

[0140] Preferably, the method is applied to a combustion chamber, the combustion chamber includes a primary combustion zone, a secondary combustion zone and a tertiary combustion zone, the primary combustion zone, the secondary combustion zone and the tertiary combustion zone are different combustion zones, and step S4 is specifically as follows:

[0141] Step S41: generating multi-stage combustion parameter data according to exhaust gas pretreatment data;

[0142] In one embodiment, the pre-processed exhaust gas data is analyzed, including the composition of the exhaust gas (e.g. , , The combustion requirements are decomposed according to the functional positioning of different combustion zones (primary, secondary and tertiary combustion zones). The primary combustion zone is mainly used for initial combustion, the secondary combustion zone is used for supplementary combustion, and the tertiary combustion zone is used for complete combustion and exhaust gas treatment. Set the fuel, air ratio, combustion temperature, flow rate and other parameters for each combustion zone. The combustion parameter data includes the fuel input, air input, combustion temperature setting value, etc. for each combustion zone. Set the following multi-stage combustion parameters: Primary combustion zone: fuel flow , air flow , combustion temperature , Secondary combustion zone: fuel flow , air flow , combustion temperature , three-stage combustion zone: fuel flow , air flow , combustion temperature .

[0143] Step S42: performing a primary combustion ignition operation through the primary combustion zone according to the multi-stage combustion parameter data and monitoring in real time to obtain primary combustion ignition data;

[0144] In one embodiment, according to the multi-stage combustion parameter data, fuel and air are input into the primary combustion zone in a set ratio. The flow rates of fuel and air are controlled by adjusting valves and flow meters. Setting the fuel flow rate , air flow . Use an automatic ignition device (such as an electric spark or gas igniter) for ignition. Before ignition, ensure that there is no residual combustible gas in the combustion chamber to prevent deflagration. Ensure ignition safety and record the ignition time and ignition success sign. Use temperature sensors and flame sensors to monitor the temperature, flame stability, combustion efficiency and other data of the primary combustion zone in real time. Organize the real-time monitoring data into primary combustion ignition data (successful ignition, temperature: , flame state: stable, combustion efficiency: 90%) as a reference for the operation of the secondary combustion zone.

[0145] Step S43: performing a secondary combustion ignition operation through the secondary combustion zone according to the multi-stage combustion parameter data and the primary combustion ignition data and monitoring in real time to obtain the secondary combustion ignition data;

[0146] In one embodiment, the fuel and air flow rates of the secondary combustion zone are adjusted with reference to the ignition data of the primary combustion zone, and the fuel and air input ratio of the secondary combustion zone is set. , air flow Use the same ignition method as the primary combustion zone, and ignite through the automatic ignition device. During the ignition process, prevent backfire and ensure ignition safety. After ignition, confirm the flame state and temperature stability. Monitor the temperature, flame state and combustion efficiency of the secondary combustion zone through temperature sensors and flame sensors (combustion temperature: , flame state: stable, combustion efficiency: 92%).

[0147] Step S44: performing a three-stage combustion ignition operation through the three-stage combustion zone according to the multi-stage combustion parameter data and the two-stage combustion ignition data and monitoring in real time to obtain the three-stage combustion ignition data;

[0148] In one embodiment, the fuel and air input ratio of the tertiary combustion zone is set with reference to the ignition data of the secondary combustion zone. The fuel and air flow rates are adjusted to ensure stable combustion. Set the fuel flow rate , air flow . Ignition is performed through the automatic ignition device to ensure ignition safety. After ignition, observe the flame state and confirm the stability of the temperature of the three-stage combustion zone. Record the ignition time and flame state. Monitor the temperature, flame state and combustion efficiency of the three-stage combustion zone through the temperature sensor and flame sensor (combustion temperature: , flame state: stable, combustion efficiency: 95%).

[0149] Step S45: constructing the combustion chamber temperature field according to the three-stage combustion ignition data to obtain the combustion chamber temperature field data;

[0150] In one embodiment, the real-time temperature data of each combustion zone is collected by installing temperature sensors on different combustion zones and the wall of the combustion chamber. Primary combustion zone: 850°C, secondary combustion zone: 950°C, tertiary combustion zone: 1000°C, combustion chamber wall: 800°C. Based on the collected temperature data, a temperature field model of the combustion chamber is established, and an interpolation algorithm is used to calculate the temperature distribution between each measuring point to generate a temperature field distribution map in the combustion chamber. The temperature distribution of each position in the combustion chamber is calculated to generate two-dimensional or three-dimensional temperature field distribution data, which is calculated using finite element or numerical analysis methods.

[0151] Step S46: collecting gas composition through a preset gas analyzer to obtain combustion chamber gas composition data;

[0152] In one embodiment, gas sampling points are set in each combustion zone and exhaust port of the combustion chamber and connected to the gas analyzer through a sampling pipeline. Ensure that the sampling point is reasonably located and can represent the overall gas composition in the combustion chamber. The gas composition data in the combustion chamber is collected by a gas analyzer (such as an infrared gas analyzer, a gas chromatograph, etc.). The analyzed components include CO 2 , , O2 、NO X 、SO 2 Etc. The gas composition data is transmitted to the control system in real time for data recording and storage.

[0153] Step S47: Calculate the combustion efficiency of the combustion chamber according to the combustion chamber temperature field data and the combustion chamber gas composition data to obtain the exhaust gas combustion data for optimizing the combustion control of the combustion chamber.

[0154] In one embodiment, a combustion efficiency calculation model is established based on the combustion chamber temperature field data and gas composition data. , , The content and combustion temperature of the combustion efficiency = ( Amount of generation - Generation) / Theoretical Combined with the actual gas composition data, calculate the combustion process , The amount generated determines the combustion efficiency. The generation volume is 9%, The amount of generation is 0.1%, theoretically If the amount generated is 10%, the combustion efficiency is: The calculated combustion efficiency data is used as the basis for combustion optimization in the combustion chamber. According to the combustion efficiency data, the fuel and air input ratio of the combustion zone is adjusted, the combustion control strategy is optimized, and the combustion efficiency is improved. For example, the air flow in the secondary combustion zone is increased, and the Generate more and improve combustion efficiency.

[0155] Preferably, step S42 is specifically as follows:

[0156] During the primary combustion ignition operation, the flame monitor is controlled to collect flame data to obtain combustion chamber flame data, and the temperature sensor is used to collect temperature data to obtain combustion chamber temperature data;

[0157] In one embodiment, a flame monitor (such as an ultraviolet flame sensor, a visible light camera, etc.) is used to monitor the flame state in the combustion chamber in real time. The flame monitor can capture parameters such as the brightness, shape, position, flame intensity and stability of the flame. Temperature sensors are arranged at key positions in the combustion chamber, such as the center of the primary combustion zone, the wall, the combustion chamber outlet, etc. When the sensor collects temperature data, it is necessary to record the temperature changes at each point in real time to monitor the temperature distribution during the combustion process.

[0158] A combustion chamber temperature model is constructed according to the combustion chamber temperature data to obtain a combustion chamber temperature model;

[0159] In one embodiment, an interpolation algorithm or a numerical simulation method (such as a finite element method) is used to establish a combustion chamber temperature distribution model, calculate the temperature distribution at different locations in the combustion chamber, and identify high temperature areas and low temperature areas.

[0160] Perform local high temperature detection based on combustion chamber flame data and combustion chamber temperature model to obtain local high temperature data;

[0161] In one embodiment, the flame data and temperature model are combined to identify the area where the temperature exceeds the safety threshold. A high temperature threshold (such as 1000°C) is set as a judgment standard. The boundary of the high temperature area is determined by temperature gradient calculation and flame intensity analysis.

[0162] Conduct combustion risk assessment based on local high temperature data to obtain combustion risk assessment data;

[0163] In one embodiment, based on high temperature data (based on threshold judgment of area, temperature and degree of change), risk factors in the combustion chamber are identified, such as structural damage caused by excessive temperature, flame failure caused by unstable flame, etc. The risk level (low, medium, high) and the impact are evaluated. For example, risk level: medium; main risk: excessive temperature in the third-level combustion zone, unstable flame; possible impact: decreased combustion efficiency, equipment damage.

[0164] The combustion chamber temperature control operation is performed according to the combustion risk assessment data until the first-level combustion ignition operation is terminated and monitored in real time to obtain the first-level combustion ignition data.

[0165] In one embodiment, according to the risk assessment results, the fuel and air ratio is adjusted to reduce the temperature of the high temperature area or increase the temperature of the low temperature area. For example, cooling water or inert gas (such as nitrogen) is injected into the high temperature area to quickly reduce the local temperature. Injection amount calculation , is the injection volume, is the cooling water quality, is the specific heat capacity data, It is the temperature difference data. The flame and temperature changes of the adjusted combustion chamber are monitored in real time through the flame monitor and temperature sensor. When the temperature and flame parameters are stable within the safe range, stop the adjustment operation and continue to monitor the combustion status. All data in the first-level combustion ignition process are recorded as ignition data, including flame data, temperature data, risk assessment results and control operation records. First-level combustion ignition data: ignition time: 2024-03-30 10:00:00; flame intensity: 90%; flame position: stable; temperature: 900°C in the first-level combustion zone, 950°C in the second-level combustion zone, and 1020°C in the third-level combustion zone; risk level: medium; control operation: inject cooling water at 5 L / min for 5 minutes; ignition operation end time: 2024-03-30 10:15:00.

[0166] Preferably, the combustion chamber temperature model is constructed as follows:

[0167] Perform local high temperature partition modeling according to combustion chamber temperature data to obtain a local high temperature partition model;

[0168] In one embodiment, real-time temperature data at different positions in the combustion chamber are collected by temperature sensors distributed inside the combustion chamber. Sensors should be arranged in the combustion zone, the combustion chamber wall and the area where the heat flow is easily concentrated to capture detailed information on the temperature distribution. By analyzing the temperature data, local high-temperature areas inside the combustion chamber are identified. High-temperature areas are generally characterized by areas where the local temperature is significantly higher than the surrounding environment. Areas with temperatures greater than 1000°C are defined as high-temperature areas. A local high-temperature zoning model in the combustion chamber is established to associate the high-temperature area with the temperature gradient, heat flux density and other information of the surrounding environment. The zoning modeling method is used to mark the boundaries and center positions of the high-temperature areas.

[0169] Temperature sensors placed in the combustion chamber are used to collect temperature data from various areas, especially in key locations such as the combustion zone and the combustion chamber wall. The temperature threshold method is used to identify high-temperature areas. Assume that the high-temperature threshold is set to , then the center of the third-level combustion zone and its surrounding area are local high-temperature areas. Model the identified high-temperature area and define the parameters such as the range, center position, and temperature gradient of the high-temperature area in the model. Calculate the boundary of the high-temperature area and use the following formula to calculate the radius of the high-temperature area : , is the radius of the high temperature area, is the area data of high temperature area, is the constant term of pi.

[0170] According to the local high temperature zoning model, local high temperature diffusion prediction is performed to obtain local high temperature diffusion prediction data;

[0171] In one embodiment, based on the local high temperature partition model, the heat conduction and heat convection characteristics of the high temperature area are analyzed. Factors such as the diffusion path of heat in the high temperature area and the convection speed are considered. The heat diffusion rate and diffusion range are calculated, and the expansion trend of the high temperature area over time is predicted. The temperature gradient between the high temperature area and the surrounding environment is calculated, and the influence of the temperature difference on the heat diffusion is analyzed. The direction and diffusion rate of the maximum temperature gradient are determined. The maximum temperature gradient is 50°C / m, and the diffusion rate is 0.1 m / s. Combined with heat conduction and convection calculations, the diffusion path of the high temperature area in the combustion chamber is predicted. Use mathematical models or numerical simulation methods to simulate the expansion of the high temperature area over time.

[0172] Use Fourier's law of heat conduction to calculate the heat flux density diffused from the high temperature area to the surrounding area: ,in is the thermal conductivity, is the temperature data, is the temperature distance difference data. Use the convection heat transfer formula to calculate the convection heat transfer at the high temperature area boundary: , is the convective heat transfer density data, is the convective heat transfer coefficient data, is the solid surface temperature data, is the temperature data of the fluid at a distance. Based on the comprehensive calculation of heat conduction and convection heat transfer, the diffusion path and diffusion rate in the high temperature area are predicted. The diffusion rate is The diffusion path diffuses along the axis of the combustion chamber, and the radius of the high temperature zone is 1.7 m after 1 minute.

[0173] Perform combustion instability analysis in the high-temperature zone based on local high-temperature diffusion prediction data to obtain combustion instability data in the high-temperature zone;

[0174] In one embodiment, by analyzing the high-temperature diffusion prediction data, the combustion instability phenomena that may occur in the high-temperature area are identified, such as drastic temperature fluctuations, flame flashback, flameout, etc. The combustion reaction rate, fuel-air mixing conditions, etc. in the high-temperature area are analyzed to determine whether there is a risk of combustion instability. Calculate key parameters related to combustion instability, such as pressure oscillation amplitude, flame propagation speed, combustion reaction time, etc. Combined with the high-temperature diffusion prediction data, locate the area where combustion instability may occur. This area is concentrated in locations where the temperature gradient is large or the fuel / air mixing is uneven. The instability parameters and positions obtained by the analysis are organized into combustion instability data as input for structural thermal stress processing, such as pressure oscillation amplitude: 0.5 atm, flame propagation speed: 1.5 m / s, instability area: upper part of the third-level combustion zone.

[0175] When the temperature fluctuation in the combustion zone exceeds 50°C, it is considered unstable. The parameters for calculating combustion instability include the pressure oscillation amplitude: , is the pressure oscillation amplitude data, is the fluid density data, is the sound speed data, is the velocity fluctuation data, and the flame propagation velocity fluctuation range is m / s.

[0176] Perform combustion chamber structure thermal stress processing on the local high-temperature partition model to obtain a combustion chamber structure thermal stress model;

[0177] In one embodiment, a thermal stress model is established according to the structure and material properties of the combustion chamber. The model considers the thermal expansion coefficient, elastic modulus, thermal conductivity and other properties of the combustion chamber wall material. A geometric model of the combustion chamber is established, and the local high-temperature partition model is used as a heat source input to calculate the temperature distribution and stress distribution of the combustion chamber structure. The finite element analysis method is used to calculate the thermal stress distribution of the high-temperature area on the combustion chamber structure. Considering the thermal stress concentration point and stress gradient, the stress concentration area and potential structural weaknesses are identified. According to the thermal stress distribution results, the combustion chamber structure is optimized. This includes adjusting the cooling strategy of the high-temperature area, adding reinforcement measures for the stress concentration point, etc. For example, adding cooling water pipes at the stress concentration point to slow down the accumulation of thermal stress. Output the optimized structural thermal stress model, mark the stress concentration area and optimization measures, such as the stress concentration point is the wall of the third-level combustion zone, the position (x=0.5, y=1.2), the optimization measure is to add cooling water pipes, and the cooling strength is increased by 20%.

[0178] The combustion chamber structure thermal stress model is annotated according to the combustion instability data in the high-temperature zone to obtain the combustion chamber temperature model.

[0179] In one embodiment, the annotation rules of the combustion chamber temperature model are formulated based on the combustion instability data in the high temperature zone and the structural thermal stress model. The annotation rules include the annotation methods of the high temperature area, the instability area and the stress concentration point. The combustion instability data in the high temperature zone is annotated to the structural thermal stress model, including information such as the instability area, the pressure oscillation amplitude, and the flame propagation speed. High temperature zone 1: temperature 1050°C, instability area, pressure oscillation 0.5 atm; high temperature zone 2: temperature 1020°C, flame propagation speed 1.5 m / s, stress concentration. The local high temperature partition model, thermal stress model and instability annotation information are integrated to form a complete combustion chamber temperature model.

[0180] Preferably, the local high temperature partition modeling is specifically as follows:

[0181] Calculate the temperature change rate according to the combustion chamber temperature data to obtain the temperature change rate data;

[0182] In one embodiment, the temperature change rate describes the rate of change of temperature over time, indicating the rate of increase or decrease of temperature per unit time.

[0183] Perform three-dimensional temperature gradient vector field calculation on the temperature change rate data to obtain temperature gradient vector data;

[0184] In one embodiment, the temperature gradient vector represents the rate and direction of temperature change in space, and the formula is as follows: , For temperature The rate of change of direction, in K / m (Kelvin per meter), For temperature The rate of change of direction, in K / m, For temperature The rate of change of direction, in K / m.

[0185] Extract temperature gradient features according to temperature vector gradient data to obtain temperature gradient feature data;

[0186] In one embodiment, the temperature gradient vector field is subjected to feature extraction, including but not limited to the following features: gradient modulus and gradient direction. Calculate the modulus of the temperature gradient , extract the gradient direction (unit vector). For example, position (x, y, z): (0.5, 0.5, 0.5), gradient modulus: 18.7 K / m, gradient direction: (-0.53, 0.80, 0.27).

[0187] According to the temperature gradient characteristic data, a heat conduction similarity matrix is ​​constructed to obtain temperature heat conduction similarity matrix data;

[0188] In one embodiment, when constructing a heat conduction similarity matrix based on temperature gradient feature data, it is necessary to determine the similarity by comparing the heat conduction characteristics of different positions (such as temperature gradient, heat flow direction, spatial distance, etc.). The similarity matrix is ​​constructed by measuring the similarity of the temperature gradients between different positions (the similarity of the temperature gradient modulus indicates the similarity of the heat conduction intensity at different points; the similarity of the temperature gradient direction indicates the similarity of the heat flow direction; the spatial distance indicates the similarity between the positions during the heat conduction process, and the closer the positions, the more similar the heat conduction characteristics are). The similarity of the temperature gradients of each two positions is calculated (the similarity of the heat conduction characteristics is calculated using the Gaussian kernel function, or the Euclidean distance is used for calculation). Construct a similarity matrix S. For each pair of positions (i, j) in space, calculate the similarity S(i, j) between them and fill in the heat conduction similarity matrix. The similarity matrix is ​​a symmetric matrix, and each element represents the heat conduction similarity between two positions.

[0189] Based on the temperature heat conduction similarity matrix data, partition hierarchical clustering calculation is performed to obtain the temperature gradient clustering feature data;

[0190] In one embodiment, a hierarchical clustering algorithm is used to cluster the similarity matrix, and regions with high similarity are clustered into the same partition. Each location is initialized as a separate cluster. The two closest clusters are merged according to the minimum distance or maximum similarity until all clusters are merged into a whole.

[0191] In one embodiment, all positions are treated as a cluster. The clusters are split according to the maximum distance or the minimum similarity until each position is a separate cluster. The temperature gradient features are clustered using a heat conduction similarity matrix to obtain a partition structure.

[0192] The partition boundaries are divided and the model is constructed according to the temperature gradient clustering feature data to obtain a local high temperature partition model.

[0193] In one embodiment, different regions are demarcated according to the temperature gradient clustering feature data, and the boundaries of each high-temperature partition are demarcated. In the clustering results, the connection paths within each partition are found to form a minimum spanning tree and define the partition boundaries. The boundary information, temperature gradient characteristics and other data of each partition are summarized to form a local high-temperature partition model. The model data includes information such as the temperature gradient distribution within the partition, the temperature gradient extreme value, and the partition area.

[0194] In one embodiment, for the set of all points in the partition, the convex hull is calculated as the partition boundary. The boundary information, temperature gradient characteristics and other data of each partition are summarized to form a local high temperature partition model. The model data includes information such as the temperature gradient distribution in the partition, the temperature gradient extreme value, and the partition area.

[0195] Preferably, the combustion efficiency of the combustion chamber is calculated as follows:

[0196] Calculate the combustion efficiency according to the exhaust gas composition data and the combustion chamber gas composition data to obtain the first exhaust gas combustion data;

[0197] In one embodiment, the exhaust gas composition data is the exhaust gas composition before entering the combustion chamber, including combustible gas (such as , The combustion chamber gas composition data is the composition of the exhaust gas after combustion measured by the gas analyzer at the combustion chamber outlet, such as , , the remaining wait. , is the first exhaust gas combustion data, is the exhaust gas composition data, Combustion chamber gas composition data.

[0198] Perform temperature field regional integral calculation based on combustion chamber temperature field data and combustion chamber gas composition data to obtain preliminary second exhaust gas combustion data;

[0199] In one embodiment, the temperature distribution data of the temperature sensor in the combustion chamber is obtained, covering the temperature of different combustion zones and the wall surface. The gas composition data of the combustion chamber is obtained, such as the gas composition data of each combustion zone, including CO 2 , O 2 , Remaining Etc. Temperature field regional integral calculation , The combustion chamber temperature field data Temperature field volume data, is the gas density data, is the specific heat capacity data, The combustion chamber temperature field data Temperature data, The volume data.

[0200] Perform cumulative calculation of smoke exhaust loss by zone according to preliminary second exhaust gas combustion data to obtain second exhaust gas combustion data;

[0201] In one embodiment, the exhaust heat loss is calculated as: , is the second exhaust gas combustion data, is the exhaust volume flow data, is the gas density data, is the specific heat capacity data, The difference between the exhaust temperature and the ambient temperature. The cumulative calculation of the exhaust heat loss in each zone (such as the primary, secondary, and tertiary combustion zones) is performed by calculating the exhaust heat loss in each zone separately, and then accumulating the exhaust heat loss in each zone to obtain the overall exhaust heat loss.

[0202] The combustion efficiency is iterated step by step according to the first exhaust gas combustion data and the second exhaust gas combustion data to obtain the exhaust gas combustion data.

[0203] In one embodiment, in a combustion system, a step-by-step iterative calculation of the combustion efficiency is performed based on the first exhaust gas combustion data (combustion utilization efficiency) and the second exhaust gas combustion data (exhaust heat loss) to gradually optimize the combustion efficiency and obtain the exhaust gas combustion efficiency data of the entire combustion system. The iteration goal of the step-by-step iterative optimization calculation is to improve the combustion efficiency and reduce the unburned loss and exhaust loss. The amount of unburned components is reduced by adjusting the fuel-to-air ratio (air-fuel ratio). After adjustment, the unburned loss is reduced by 10%. The exhaust loss is reduced by lowering the exhaust temperature or increasing heat recovery, and the exhaust loss is reduced by 15%. The combustion efficiency is recalculated to obtain a new combustion efficiency of 46.74%. If the new combustion efficiency meets the target (for example, greater than 50%, or greater than 80%), the iteration is stopped. If not, continue to adjust the air-fuel ratio and exhaust loss, and repeat the iterative steps until the target is reached.

[0204] Preferably, the present application also provides a low calorific value industrial waste gas combustion control optimization device, which is used to execute the low calorific value industrial waste gas combustion control optimization method as described above, and the low calorific value industrial waste gas combustion control optimization device comprises:

[0205] The exhaust gas calorific value calculation module is used to obtain the exhaust gas composition data, and calculate the exhaust gas calorific value according to the exhaust gas composition data to obtain the exhaust gas calorific value data;

[0206] The air-fuel ratio parameter generation module is used to obtain combustion chamber data, and generate air-fuel ratio parameters according to the combustion chamber data and exhaust gas calorific value data to obtain air-fuel ratio parameter data;

[0207] An exhaust gas pretreatment module is used to perform exhaust gas pretreatment according to air-fuel ratio parameter data and exhaust gas composition data to obtain exhaust gas pretreatment data;

[0208] The multi-stage combustion operation module is used to perform multi-stage combustion operations according to the exhaust gas pretreatment data to obtain exhaust gas combustion data.

[0209] Therefore, from any point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the attached application documents rather than the above description, and it is intended that all changes falling within the meaning and scope of equivalent elements of the application documents are included in the present invention.

[0210] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for optimizing combustion control of low calorific value industrial waste gas, characterized in that: The following steps are involved: Step S1: Acquire exhaust gas composition data, and calculate the exhaust gas calorific value according to the exhaust gas composition data to obtain the exhaust gas calorific value data; Step S2: Acquire combustion chamber data, and generate air-fuel ratio parameters according to the combustion chamber data and exhaust gas calorific value data to obtain air-fuel ratio parameter data; Step S3: performing exhaust gas pretreatment according to the air-fuel ratio parameter data and the exhaust gas composition data to obtain exhaust gas pretreatment data; Step S4: performing a multi-stage combustion operation according to the exhaust gas pretreatment data to obtain exhaust gas combustion data; Wherein, step S1 is specifically as follows: Step S11: obtaining exhaust gas composition data; Step S12: extracting exhaust gas component characteristics according to the exhaust gas component data to obtain exhaust gas component characteristic data; Step S13: performing exhaust gas component clustering processing according to the exhaust gas component characteristic data to obtain exhaust gas component clustering pattern data; Step S14: performing component calorific value mapping according to the exhaust gas component clustering pattern data to obtain component calorific value mapping data; Step S15: Calculate the calorific value according to the component calorific value mapping data to obtain the exhaust gas calorific value data.

2. The method according to claim 1, characterized in that Step S2 is specifically as follows: Step S21: Obtain combustion chamber data; Step S22: performing combustion chamber heat and mass transfer simulation according to the combustion chamber data to obtain combustion chamber heat and mass transfer data; Step S23: acquiring historical operation data of the combustion chamber, and performing feature extraction based on the historical operation data of the combustion chamber to obtain historical operation feature data of the combustion chamber; Step S24: performing calorific value combustion chamber response mapping according to the combustion chamber historical operation characteristic data and the combustion chamber heat and mass transfer data to obtain calorific value combustion chamber response mapping data; Step S25: Calculate the air-fuel ratio parameters according to the calorific value combustion chamber response mapping data to obtain the air-fuel ratio parameter data.

3. The method according to claim 1, characterized in that Step S3 is specifically as follows: Step S31: selecting an exhaust gas pretreatment scheme according to the air-fuel ratio parameter data and the exhaust gas composition data to obtain exhaust gas pretreatment scheme data; Step S32: performing desulfurization and dechlorination treatment through a chemical absorption tower according to the exhaust gas pretreatment scheme data to obtain desulfurization and dechlorination treatment data; Step S33: performing exhaust gas drying and preheating treatment through a condenser and a heat exchanger according to the exhaust gas pretreatment scheme data and the desulfurization and dechlorination treatment data to obtain exhaust gas drying and preheating data; Step S34: performing exhaust gas homogenization treatment through a static mixer according to the exhaust gas pretreatment scheme data and the exhaust gas drying and preheating data to obtain exhaust gas homogenization data; Step S35: Introducing and mixing the premixed fuel according to the exhaust gas pretreatment scheme data and the exhaust gas homogenization data to obtain the exhaust gas pretreatment data.

4. The method according to claim 1, characterized in that Applied to a combustion chamber, the combustion chamber includes a primary combustion zone, a secondary combustion zone and a tertiary combustion zone, the primary combustion zone, the secondary combustion zone and the tertiary combustion zone are different combustion zones, and step S4 is specifically as follows: Step S41: generating multi-stage combustion parameter data according to exhaust gas pretreatment data; Step S42: performing a primary combustion ignition operation through the primary combustion zone according to the multi-stage combustion parameter data and monitoring in real time to obtain primary combustion ignition data; Step S43: performing a secondary combustion ignition operation through the secondary combustion zone according to the multi-stage combustion parameter data and the primary combustion ignition data and monitoring in real time to obtain the secondary combustion ignition data; Step S44: performing a three-stage combustion ignition operation through the three-stage combustion zone according to the multi-stage combustion parameter data and the two-stage combustion ignition data and monitoring in real time to obtain the three-stage combustion ignition data; Step S45: constructing the combustion chamber temperature field according to the three-stage combustion ignition data to obtain the combustion chamber temperature field data; Step S46: collecting gas composition through a preset gas analyzer to obtain combustion chamber gas composition data; Step S47: Calculate the combustion efficiency of the combustion chamber according to the combustion chamber temperature field data and the combustion chamber gas composition data to obtain the exhaust gas combustion data for optimizing the combustion control of the combustion chamber.

5. The method according to claim 4, characterized in that Wherein step S42 is specifically as follows: During the primary combustion ignition operation, the flame monitor is controlled to collect flame data to obtain combustion chamber flame data, and the temperature sensor is used to collect temperature data to obtain combustion chamber temperature data; A combustion chamber temperature model is constructed according to the combustion chamber temperature data to obtain a combustion chamber temperature model; Perform local high temperature detection based on combustion chamber flame data and combustion chamber temperature model to obtain local high temperature data; Conduct combustion risk assessment based on local high temperature data to obtain combustion risk assessment data; The combustion chamber temperature control operation is performed according to the combustion risk assessment data until the first-level combustion ignition operation is terminated and monitored in real time to obtain the first-level combustion ignition data.

6. The method according to claim 5, characterized in that The combustion chamber temperature model is constructed as follows: Perform local high temperature partition modeling according to combustion chamber temperature data to obtain a local high temperature partition model; According to the local high temperature zoning model, local high temperature diffusion prediction is performed to obtain local high temperature diffusion prediction data; Perform combustion instability analysis in the high-temperature zone based on local high-temperature diffusion prediction data to obtain combustion instability data in the high-temperature zone; Perform combustion chamber structure thermal stress processing on the local high-temperature partition model to obtain a combustion chamber structure thermal stress model; The combustion chamber structure thermal stress model is annotated according to the combustion instability data in the high-temperature zone to obtain the combustion chamber temperature model.

7. The method according to claim 6, characterized in that The local high temperature partition modeling is specifically as follows: Calculate the temperature change rate according to the combustion chamber temperature data to obtain the temperature change rate data; Perform three-dimensional temperature gradient vector field calculation on the temperature change rate data to obtain temperature gradient vector data; Extract temperature gradient features according to temperature vector gradient data to obtain temperature gradient feature data; According to the temperature gradient characteristic data, a heat conduction similarity matrix is ​​constructed to obtain temperature heat conduction similarity matrix data; Based on the temperature heat conduction similarity matrix data, partition hierarchical clustering calculation is performed to obtain the temperature gradient clustering feature data; The partition boundaries are divided and the model is constructed according to the temperature gradient clustering feature data to obtain a local high temperature partition model.

8. The method according to claim 4, characterized in that The combustion efficiency of the combustion chamber is calculated as follows: Calculate the combustion efficiency according to the exhaust gas composition data and the combustion chamber gas composition data to obtain the first exhaust gas combustion data; Perform temperature field regional integral calculation based on combustion chamber temperature field data and combustion chamber gas composition data to obtain preliminary second exhaust gas combustion data; Perform cumulative calculation of smoke exhaust loss by zone according to preliminary second exhaust gas combustion data to obtain second exhaust gas combustion data; The combustion efficiency is iterated step by step according to the first exhaust gas combustion data and the second exhaust gas combustion data to obtain the exhaust gas combustion data.

9. A low calorific value industrial waste gas combustion control optimization device, characterized in that: Used to execute the low calorific value industrial waste gas combustion control optimization method as claimed in claim 1, the low calorific value industrial waste gas combustion control optimization device comprises: The exhaust gas calorific value calculation module is used to obtain the exhaust gas composition data, and calculate the exhaust gas calorific value according to the exhaust gas composition data to obtain the exhaust gas calorific value data; The air-fuel ratio parameter generation module is used to obtain combustion chamber data, and generate air-fuel ratio parameters according to the combustion chamber data and exhaust gas calorific value data to obtain air-fuel ratio parameter data; An exhaust gas pretreatment module is used to perform exhaust gas pretreatment according to air-fuel ratio parameter data and exhaust gas composition data to obtain exhaust gas pretreatment data; A multi-stage combustion operation module is used to perform multi-stage combustion operations according to exhaust gas pretreatment data to obtain exhaust gas combustion data; The calculation of exhaust gas calorific value is as follows: Obtain exhaust gas composition data; Extract exhaust gas component characteristics according to exhaust gas component data to obtain exhaust gas component characteristic data; Performing exhaust gas component clustering processing according to exhaust gas component characteristic data to obtain exhaust gas component clustering pattern data; Perform component calorific value mapping according to exhaust gas component clustering pattern data to obtain component calorific value mapping data; The calorific value is calculated according to the component calorific value mapping data to obtain the exhaust gas calorific value data.

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