Optimization design method for realizing low NOx emission of vertical flue of coke oven

Abstract

The invention belongs to the technical field of coke oven combustion, relates to an optimal design method for realizing low NOx emission of a vertical flue of a coke oven, and aims to optimize a coke oven combustion process, reduce NOx emission and improve combustion efficiency by combining CFD simulation and a genetic algorithm. The method comprises the following steps: firstly, collecting temperature, pressure, oxygen concentration and gas component data in a coke oven combustion chamber in real time by using a sensor; secondly, the influence of different air supply strategies on combustion efficiency and NOx emission is predicted based on CFD simulation; thirdly, the air distribution rate and the air excess coefficient are optimized through a genetic algorithm, and an optimal combustion parameter combination is found; and finally, the optimized parameters are applied to the actual combustion process, and the combustion state is adjusted in real time through feedback control. According to the invention, high-efficiency and low-pollution operation of the combustion process is realized, NOx emission is remarkably reduced, meanwhile, the high-direction heating uniformity and combustion efficiency of the coke oven are improved, and remarkable economic and environmental benefits are achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of coke oven combustion, and particularly relates to an optimized design method for realizing low NOx emissions in the vertical flues of a coke oven. Background Art

[0002] Coke oven combustion technology plays an important role in modern coking industry. Its core objectives are to improve combustion efficiency, reduce pollutant emissions (especially nitrogen oxides NOx), and optimize the vertical heating uniformity of the coke oven. In recent years, with the increasingly strict environmental protection requirements, significant progress has been made in related technologies. For example, Patent CN107057721A proposes a low-nitrogen combustion method by optimizing the ejection position and supply amount of combustion-supporting air and gas, effectively suppressing the generation of NOx. However, this method mainly relies on physical adjustments during the combustion process, does not involve intelligent control, and requires a relatively high transformation of the combustion chamber structure. Patent CN113025349A significantly reduces the NOx generation amount by setting multi-stage flue gas recirculation holes to increase the flue gas recirculation amount. However, it is only optimized by the segmented setting of the flue gas recirculation holes, and its adaptability is limited. Patent CN113536590A uses Fluent software to simulate the coke oven combustion chamber, and realizes energy conservation and emission reduction by replacing part of the gas with dry quenching gas. However, this method only focuses on the replacement of dry quenching gas, does not involve the structural optimization of the combustion process, and has a relatively high dependence on software simulation. Patent CN118085894A optimizes the combustion process in the vertical flue by segmental heating of rich gas, reducing the generation of NOx. However, it is only optimized by segmental heating, lacks intelligent means, and requires a relatively high transformation of the combustion chamber structure. Although these existing technologies have their own advantages, they still have limitations in terms of intelligence, comprehensive optimization, and adaptability.

[0003] The present invention combines computational fluid dynamics (CFD) simulation and artificial intelligence technology (genetic algorithm) to achieve efficient and low-pollution operation of the combustion process, significantly making up for the deficiencies of the existing technologies. Compared with the existing technologies, the present invention has the following significant advantages: First, it has the ability of intelligent optimization. By dynamically adjusting combustion parameters through the genetic algorithm, it can adapt to different working conditions in real time, improve combustion efficiency and reduce NOx emissions. Second, it has high-precision simulation. Using CFD technology combined with real-time sensor data, it provides high-precision predictions of temperature fields, gas concentration fields, and NOx emissions. Third, it has comprehensive optimization. It not only optimizes the combustion process, but also further reduces the generation of NOx by adjusting the combustion chamber structure. Fourth, it has strong adaptability. Combining real-time feedback control, it can quickly adapt to changes during the operation of the coke oven, ensuring the timeliness and effectiveness of the optimization strategy. In summary, the present invention shows significant innovation and practicality in terms of intelligence, high-precision simulation, comprehensive optimization, and adaptability, providing a new direction for the development of coke oven combustion technology. Summary of the Invention

[0004] The object of the present invention is to overcome the limitations of existing technologies in terms of intelligence, comprehensive optimization, and adaptability, although they each have their own advantages, and to provide an optimized design method for achieving low NOx emissions in the vertical flues of coke ovens.

[0005] To achieve the above technical objectives and reach the above technical effects, the present invention is realized through the following technical solutions:

[0006] The present invention provides an optimized design method for achieving low NOx emissions in the vertical flues of coke ovens, including the following steps:

[0007] S1. Use sensors to collect data on temperature, pressure, oxygen concentration, and gas composition in the combustion chamber of the coke oven in real time;

[0008] S2. Use computational fluid dynamics analysis software to simulate the combustion process of the coke oven and predict the effects of different air supply strategies on combustion efficiency and NOx emissions;

[0009] S3. Compare the temperature field and gas concentration field in the combustion chamber of the coke oven under different air distribution ratios and different air excess coefficients;

[0010] S4. Based on the relatively small NO x concentration at the outlet of the combustion chamber as the judgment basis, determine the air sectional distribution ratio and different air excess coefficients;

[0011] S5. Determine the three-dimensional basic dimension parameters according to the two-dimensional drawings of the vertical flues of the coke oven combustion chamber;

[0012] S6. Use three-dimensional drawing software to establish an equal-size solid model based on the basic dimension parameters of the vertical flues of the coke oven combustion chamber;

[0013] S7. Use mesh generation software to mesh the equal-size solid model obtained in step S6. After mesh independence verification, determine the appropriate number of meshes and name the boundaries; when meshing the equal-size solid model, the combustion area is mainly structured hexahedral meshes, and the adjacent areas at the inlet and outlet are mainly tetrahedral unstructured meshes. Mesh independence verification is used to ensure the simulation accuracy.

[0014] S8. Set the inlet condition as the inlet gas mass flow rate, the outlet condition as the outlet pressure, and the wall temperature as the heat flux density boundary condition; further, the inlet gas is one or a mixture of several of air and gas to adapt to different combustion requirements and improve combustion efficiency. The combustible components of the gas are a combination of CO, H2, and CH4, and the optimized ratio of these components helps to improve combustion efficiency and reduce pollutant emissions.

[0015] S9. After importing the generated grid data into the computational fluid dynamics analysis software, use the simulation model to calculate the temperature distribution and gas concentration in the vertical flues of the coke oven combustion chamber. When calculating the temperature field and gas concentration field in the flues of the coke oven combustion chamber, the models used include the energy model, turbulence model, component transport model, combustion model, or radiation model. The NOx generation model is at least one of the thermal type and the prompt type to accurately predict the generation and emission of NOx.

[0016] S10. Based on the calculation results in step S9, including the temperature distribution and gas concentration in the ascending and descending flues, use the NO x generation model to initiate the NO x generation calculation to obtain the NO x spatial distribution and NO x exit emission concentration in the flues of the coke oven combustion chamber;

[0017] S11. Extract the data simulated by the computational fluid dynamics analysis software into the optimization algorithm, and then perform optimization to determine the most suitable combination of air supply ratio and excess air coefficient;

[0018] S12. Perform CFD simulation on the optimized combination to verify whether the NO x concentration is relatively small and whether the temperature field is relatively uniform.

[0019] Furthermore, in the optimization design method described above, the optimization algorithm is the genetic algorithm. The genetic algorithm (Genetic Algorithm, GA) is a search and optimization technique based on the principles of natural selection and genetics, simulating the biological evolution process. It gradually improves the quality of candidate solutions through operations such as selection, crossover (recombination), and mutation, so as to find the optimal solution or approximate optimal solution to the problem. In the present invention, the genetic algorithm is used to optimize the air supply strategy of the coke oven vertical flues to achieve low NOx emissions and efficient combustion. The specific steps are as follows:

[0020] Step 1: Encode parameters such as the air distribution rate and excess air coefficient as individuals in the genetic algorithm, using real number encoding. Randomly generate an initial population, and the population size is determined according to the problem complexity (100 individuals).

[0021] Step 2: Define the fitness function as:

[0022]

[0023] In the formula, α and β are weight coefficients used to balance the priorities of different optimization objectives.

[0024] Step 3: Select parent individuals according to the fitness of the individuals in proportion.

[0025] Step 4: Select two parent individuals, exchange gene segments at random positions, and generate two offspring individuals.

[0026] Step 5: Randomly change one or more genes of an individual with a certain probability to introduce a new solution space and avoid local optima.

[0027] Step 6: Substitute the parameters of the newly generated offspring individuals into the CFD model for simulation, and calculate their NOx emission concentration, temperature field uniformity, and combustion efficiency. Calculate the fitness of each individual according to the simulation results, and repeat the selection, crossover, and mutation operations to gradually optimize the population.

[0028] Step 7: Stop the iteration when the fitness converges or reaches the maximum number of iterations. Output the optimal solution, that is, the best combination of air distribution rate and excess air coefficient.

[0029] Further, in the genetic algorithm as described above, the comparison range of the air distribution rate is 0 - 20%, the air distribution rate of the general value is the volume ratio of the first-stage inlet accounting for 65% - 80%, and the excess air coefficient is 1 - 1.3 to find the best balance between combustion efficiency and NOx emissions.

[0030] S13. Further, in the optimization design method as described above, the basic dimension parameters include the basic dimensions of the vertical flues in the coke oven combustion chamber to optimize the number, position, and size of the circulation holes and crossover holes in the optimization design method. These parameters have an important impact on combustion efficiency and heat distribution.

[0031] Further, the present invention also provides a supplementary combustion structure for realizing low NOx emissions in the vertical flues of a coke oven, including:

[0032] A partition wall, which is arranged in the middle of the vertical flue of the coke oven, and the inner cavity of the vertical flue of the coke oven is divided into an ascending vertical flue and a descending vertical flue by the partition wall;

[0033] A crossover hole, which is located at the top of the partition wall;

[0034] A first-stage air inlet, which is located at the bottom of the vertical flue of the coke oven;

[0035] A second-stage air inlet, which is located above the first-stage air inlet;

[0036] A re-segmented air inlet, which is located above the second-stage air inlet;

[0037] A fuel inlet, which is located below the ascending vertical flue;

[0038] An exhaust gas outlet, which is located at the middle position of the descending vertical flue part;

[0039] Internal circulation holes, there are two internal circulation holes in total, which are used to connect the ascending flue and the bottom of the descending flue.

[0040] Further, in the afterburning structure for realizing low NO x emission in the coke oven flue, the crossover holes are located at the top of the coke oven flue. They allow flue gas and unburned gas to circulate between the ascending flue and the descending flue, thereby improving the thermal efficiency and reducing heat loss.

[0041] Further, in the afterburning structure for realizing low NO x emission in the coke oven flue, the internal circulation holes are responsible for guiding part of the flue gas back to the combustion chamber to preheat the newly incoming air and fuel, further enhancing the combustion efficiency.

[0042] Further, in the afterburning structure for realizing low NO x emission in the coke oven flue, the primary air inlet and the secondary air inlet are located at different heights of the flue respectively. They are responsible for supplying the necessary oxygen to the combustion chamber to support the combustion of the fuel.

[0043] Further, in the afterburning structure for realizing low NO x emission in the coke oven flue, the re-segmented air inlet further refines the air supply, by introducing air at different stages during the combustion process to optimize the combustion efficiency and reduce the generation of NO x pollutants.

[0044] Further, in the afterburning structure for realizing low NO x emission in the coke oven flue, the

[0045] waste gas outlet is the channel for the flue gas to discharge. It is located at the top or side of the flue, ensuring that the flue gas can be discharged smoothly and enter the subsequent flue gas treatment system.

[0046] Further, in the afterburning structure for realizing low NO x emission in the coke oven flue, the ascending flue and the descending flue are the paths for the combustion gas to rise and fall during the coking process of the coke oven. The temperature and gas flow distribution inside them have a direct impact on the quality of the final coke.

[0047] The beneficial effects of the present invention are:

[0048] On the premise of ensuring that the combustion temperature distribution of the coke oven and the NOx concentration of the tail gas emission are basically unchanged, the present invention optimizes the air supply strategy, improves the combustion efficiency, provides energy for coking in the carbonization chamber, and at the same time reduces unnecessary energy consumption, providing theoretical guidance for energy conservation and emission reduction. Through intelligent control and high-precision simulation, the present invention realizes the efficient and low-pollution operation of the combustion process.

[0049] Of course, it is not necessary for any product implementing the present invention to simultaneously achieve all of the above advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for describing the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0051] Figure 1 It is a supplementary combustion structure for the vertical flue of the combustion chamber of a sectional heating coke oven;

[0052] where the wall height is 6.3 m, the width of the intermediate partition wall in the vertical flue is 0.12 m, and the area of the crossover hole is 0.075 m 2 ;

[0053] Figure 2 It is the bottom view of the vertical flue;

[0054] Figure 3 It is a 1.8 million-structured hexahedron mesh diagram of the coke oven vertical flue after grid independence verification;

[0055] Figure 4 It is the temperature field distribution at the monitoring surface I of the supplementary combustion structure of the sectional heating coke oven vertical flue under different air excess coefficients;

[0056] Figure 5 It is the distribution diagram of the rising vertical flue temperature along the height at the monitoring surface II of the supplementary combustion structure of the sectional heating coke oven vertical flue under different air excess coefficients;

[0057] Figure 6 It is the NO mass fraction distribution at the monitoring surface I of the supplementary combustion structure of the sectional heating coke oven vertical flue under different air excess coefficients;

[0058] Figure 7 It is the NO mass concentration at the flue gas outlet and the average temperature of the vertical flue of the supplementary combustion structure of the sectional heating coke oven vertical flue under different air excess coefficients;

[0059] Figure 8 It is the temperature field distribution at the monitoring surface I of the supplementary combustion structure of the sectional heating coke oven vertical flue under different air distribution ratios;

[0060] Figure 9 It is the distribution diagram of the rising vertical flue temperature along the height at the monitoring surface II of the supplementary combustion structure of the sectional heating coke oven vertical flue under different air distribution ratios;

[0061] Figure 10 It is the NO mass fraction distribution at the monitoring surface I of the supplementary combustion structure of the sectional heating coke oven vertical flue under different air distribution ratios;

[0062] Figure 11 NO mass concentration at the flue gas outlet and the uniform temperature of the vertical flue of the sectional heating coke oven's supplementary combustion structure under different air distribution rates;

[0063] In the figure, the reference numerals of the relevant components are as follows:

[0064] 1 - partition wall, 2 - crossover hole, 3 - primary air inlet, 4 - secondary air inlet, 5 - re - sectional air inlet, 6 - fuel inlet, 7 - waste gas outlet, 8 - internal circulation hole, 9 - monitoring plane I, 10 - monitoring plane II, 11 - monitoring plane III. Specific embodiments

[0065] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0066] The present invention provides a method for reducing nitrogen oxide NO emissions in the combustion chamber of a sectional heating coke oven based on software simulation. Its basic idea is: aiming at reducing the NO concentration at the flue gas outlet and optimizing the temperature distribution in the vertical flue, changing the air flow distribution ratio between the primary and secondary air inlets to optimize the air excess coefficient of the design method, and finding the most suitable distribution ratio and air excess coefficient. x emissions, and its basic idea is: aiming at reducing the NO x concentration at the flue gas outlet and optimizing the temperature distribution in the vertical flue, changing the air flow distribution ratio between the primary and secondary air inlets to optimize the air excess coefficient of the design method, and finding the most suitable distribution ratio and air excess coefficient.

[0067] Specifically, it includes the following steps:

[0068] Step 1: The air distribution ratio in sectional combustion refers to the ratio of the air flow rate distribution and adjustment according to the combustion needs at different combustion stages. The setting of this ratio is crucial for achieving an efficient and low - pollution combustion process. Researchers such as Yu Mingcheng proposed in their research that the excess air coefficient in the primary combustion should be controlled within a range not exceeding 0.8 to achieve the optimal combustion effect. At the same time, scholars such as Tian Baolong found through experiments that setting the primary air flow rate to 0.65 of the total air flow rate can achieve the best combustion efficiency. Therefore, the simulated air distribution ratios in the present invention are 0.65:0.35, 0.7:0.3, 0.75:0.25, 0.8:0.2. By applying the combustion equation, the theoretical air demand for complete combustion of the gas can be determined. However, in the actual operation of the coke oven, to ensure the comprehensiveness and stability of combustion, an air volume exceeding the theoretical value is input into the coke oven. Usually, this excess air supply is 1.1 to 1.3 times the theoretical demand. Therefore, the air excess coefficients set in the present invention are 1.1, 1.14, 1.18, 1.22, 1.6, 1.3;

[0069] Step 2: According to the design drawings of the sectional heating coke oven vertical flue, determine the basic dimension parameters, and then determine the positions and numbers of the sectional air inlets for the optimized design method of the secondary air inlets;

[0070] Step 3: Based on the basic dimension parameters provided in Step 2, use 3D drawing software to establish an equal-dimension model of the sectional heating coke oven vertical flue;

[0071] Step 4: Use grid analysis software to perform grid division on the equal-dimension model, establish the fluid domain and solid domain, and then conduct grid independence verification. In order to ensure that the grid accuracy meets the requirements of combustion numerical simulation, the number of grids gradually increases from 500,000 to about 1.8 million. The calculation results show that the change in important parameters is less than 5% (see Figure 3 ). Considering the cost of computer calculation time, the number of grids in the calculation domain is finally controlled at 959,246. Then name the boundary conditions: set the fuel inlet of the optimized design method of the air inlet as a mass flow inlet, the waste gas outlet as a pressure outlet, set the thermal physical properties (density, specific heat capacity, thermal conductivity) of the partition wall brick material in the solid domain, set the heat flux density on the heat transfer surface, and set the adiabatic wall surface for the rest of the surfaces;

[0072] Step 5: Import the grid model obtained in Step 4 into the computational fluid dynamics analysis software. First, perform grid inspection to ensure that the units and entity sizes are consistent for the optimized design method, and then add gravity as -9.8m / s 2 . Select mathematical models such as the energy model, turbulence model (standard k-ε model), component transport model (five-step chemical reaction), and radiation model (P1 model);

[0073] Step 6: The fuel inlet is one inlet, set as a mass flow boundary condition, and the fuel components (mass fraction / %) are CO2: 11.79, O2: 2, CO: 14.51, CH4: 36.31, H2: 10.22, N2: 11.25, H20: 5.2, C2H4: 5.5, C2H6: 3.22; the mass flow is 0.002098 kg / s, the hydraulic diameter is 0.04038 m, the turbulence intensity is 5%, and the temperature is 873 K;

[0074] Step 7: The air inlets are two inlets, the mass flow of a single inlet is 0.0137152 kg / s, the gauge pressure is 0 Pa, the turbulence intensity is 5%, the hydraulic diameter is 0.08 m, and the temperature is 1273 K;

[0075] Step 8: The waste gas outlets are two outlets, the gauge pressure is -40 Pa, the turbulence intensity is 5%, and the hydraulic diameter is 0.08 m.

[0076] To explore a series of parameters such as the temperature field and gas concentration field in the flue of the sectional heating coke oven, a steady-state iterative calculation is carried out for the combustion process in the vertical flue model. The Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) algorithm of an independent solver is adopted. The upwind formula of the first order of accuracy is used for the radiation and turbulence models, and the upwind formula of the second order of accuracy is used for the component transport equation and the energy equation, with the relative error less than 10 -6 .

[0077] For the processes of combustion, heat transfer, and radiation heat transfer in the vertical flue of the coke oven combustion chamber, the control equations are as follows:

[0078] Continuity equation

[0079]

[0080] In the formula, ρ represents the fluid density, with the unit of kg / m 3 ; t represents time; u i represents the fluid velocity in the i direction, with the unit of m / s.

[0081] Momentum equation

[0082]

[0083] In the formula, p represents the pressure, with the unit of Pa; μ represents the fluid viscosity coefficient, with the unit of Pa·s; u j represents the fluid velocity in the j direction, with the unit of m / s.

[0084] Energy equation

[0085]

[0086] In the formula, H represents the gas enthalpy value, with the unit of J / kg; λ represents the thermal conductivity coefficient, with the unit of W·m -1 ·K -1 ; Cp represents the specific heat capacity, with the unit of J·kg -1 ·K -1 .

[0087] The k-ε turbulence equation is adopted

[0088]

[0089] In the formula, C1, C2, and C3 are constants, and σ k and σ ε are Prandtl constants, C1 = 1.44, C2 = 1.92, C3 = 0.09, σ k = 1.0, σ ε = 1.3.

[0090] Combustion model

[0091] The gas-phase combustion model adopts a component transport model, and the chemical reaction mechanism is as follows:

[0092] 2CO + O2 = 2CO2 Equation (7)

[0093] CH4 + 2O2 = CO2 + 2H20 Equation (8)

[0094] 2C2H6 + 7O2 = 4CO2 + 6H2O Equation (9)

[0095] C2H4 + 3O2 = 2CO2 + 2H2O Equation (10)

[0096] 2H2 + O2 = 2H2O Equation (11)

[0097] Radiation model

[0098] The P1 radiation model is used for burning coke oven gas (COG).

[0099]

[0100] Where q r represents the radiation energy, with the unit of W; α represents the radiation absorption coefficient, with the unit of m -1 ; σ s represents the radiation scattering coefficient, with the unit of m -1 ; C represents the linear anisotropic phase function coefficient; σ represents the Stephen-Boltzmann constant, with the unit of W·m -2 ·K -4 ; S G represents the radiation source phase, and G represents the incident radiation.

[0101] NOx generation model

[0102] Compared with the COG combustion reaction, the generation of NOx is modeled by a post-treatment method. The generation reaction of NOx is decoupled from combustion, and its impact on the flue gas flow field simulation is also ignored. This assumption is reasonable because, compared with the COG combustion reaction, the energy change associated with the NOx reaction sequence is negligible, and the NOx reaction rate is quite low with an optimized design method for NOx concentration.

[0103] Among the NOx generated by coke oven combustion, NO accounts for 95% and NO2 accounts for 5%. In the study of the formation mechanism of NOx, the formation mechanism of NOx is mainly studied, and there are the following three types of formation mechanisms for NO: thermal, prompt, and fuel type. 95% of the NO generated during coke oven combustion is thermal type.

[0104] Thermal NO

[0105] The nitrogen oxides formed by the oxidation of nitrogen in the air under the action of high temperature are called thermal NO, and the formation of thermal NO is usually predicted by the extended Zeldovich mechanism.

[0106] The main reactions that control the formation of thermal NO are:

[0107]

[0108] N + O2 → O + NO Equation (15)

[0109] N + OH → H + NO Equation (16)

[0110] The formation of thermal NO is:

[0111]

[0112] In the formula, [NO] represents the NO concentration, k f,1 , k f,2 , k f,3 represents the forward reaction rate constant, k r,1 , k r,2 represents the reverse reaction rate constant, and its value is determined by the free radical concentration during the reaction process. [O] represents the O concentration, and [N2] represents the N2 concentration.

[0113] Step 9: Change the air distribution ratio between the first-stage inlet and the second-stage inlet to optimize the air excess coefficient of the design method, repeat Steps 4 - 8, compare the vertical flue temperature field, outlet NOx concentration, and combustion conditions under different air distribution ratios, and determine the most suitable ratio to optimize the design method coefficient.

[0114] Step 10: Optimize the parameters by combining the genetic algorithm according to the data provided in Step 9 to determine the combination of the appropriate air distribution ratio and air excess coefficient in the supplementary combustion structure of the sectional coke oven vertical flue to minimize the outlet NOx concentration and achieve higher temperature distribution uniformity;

[0115] Step 11: Substitute the combined data into the CFD model to verify whether the outlet NOx concentration is the lowest. If not, repeat Steps 4 - 10.

[0116] To make the present invention more obvious and understandable, the following is a detailed description with preferred embodiments and accompanying drawings.

[0117] Embodiment

[0118] Perform numerical simulation on the combustion process of the supplementary combustion structure of the 6.3 sectional heating coke oven vertical flue in a certain coking plant. The supplementary combustion structure of the coke oven vertical flue is as Figure 1As shown, in the middle is the partition wall 1 with a wall length of 5.945 m; above is the crossing hole 2; at the bottom is a section of air inlet 3; in the middle is the second-stage air inlet 4; above 4 is the re-segmented air inlet 5; below the ascending flue is the fuel inlet, and the height of the fuel inlet from the bottom wall is 0.45 m; in the middle of the descending flue part is the waste gas outlet 7; there are two internal circulation holes 8 connecting the bottom of the ascending flue and the descending flue. Figure 2 It is the bottom view of the flue. In the figure, 9, 10, and 11 are the monitoring surfaces I, II, and III respectively, which are used to observe the variation rules of the main parameters of the whole flue in the ascending and descending channels. Figure 3 It is the mesh diagram of 1.8 million structured hexahedrons after the grid independence test of the coke oven flue.

[0119] Figure 4 It is the temperature field distribution at the monitoring surface I of the supplementary combustion structure of the sectional heating coke oven flue under different air excess coefficients. Figure 5 It is the distribution diagram of the temperature along the height of the ascending flue at the monitoring surface II of the supplementary combustion structure of the sectional heating coke oven flue under different air excess coefficients. Figure 6 It is the NO mass fraction distribution at the monitoring surface I of the supplementary combustion structure of the sectional heating coke oven flue under different air excess coefficients. Figure 7 It is the NO mass concentration and the average flue temperature at the flue gas outlet of the supplementary combustion structure of the sectional heating coke oven flue under different air excess coefficients. Figure 8 It is the temperature field distribution at the monitoring surface I of the supplementary combustion structure of the sectional heating coke oven flue under different air distribution ratios. Figure 9 It is the distribution diagram of the temperature along the height of the ascending flue at the monitoring surface II of the supplementary combustion structure of the sectional heating coke oven flue under different air distribution ratios. Figure 10 It is the NO mass fraction distribution at the monitoring surface I of the supplementary combustion structure of the sectional heating coke oven flue under different air distribution ratios. Figure 11 It is the NO mass concentration and the average flue temperature at the flue gas outlet of the supplementary combustion structure of the sectional heating coke oven flue under different air distribution ratios.

[0120] The simulation results show that with the increase of the excess air coefficient, as α increases, the temperature distribution in the flue shows a trend of first decreasing and then increasing, and the flame height decreases, indicating that the combustion is more concentrated in the lower region. This helps to improve the combustion efficiency, but at the same time, heat loss needs to be controlled. The NO emission analysis points out that with the increase of α, the NO mass fraction and mass concentration show a trend of first decreasing and then increasing, and there is a critical point. Beyond this point, the NO generation amount increases. At the same time, the change of the air distribution ratio also has an important impact on the combustion efficiency and pollutant emissions of the coke oven. An appropriate air distribution ratio helps to improve the combustion efficiency and reduce the NOx emissions. Too low an air distribution ratio may lead to incomplete combustion, while too high an air distribution ratio may increase the generation of NO.

[0121] Using a simulation model to jointly optimize the air distribution ratio and the air excess coefficient combination takes a long time and involves a large amount of work. Therefore, a genetic algorithm is used to optimize the structural parameters of the vertical flue in the coke oven combustion chamber, and the optimal values of the air distribution ratio and the air excess coefficient combination, which are the operating parameters of the supplementary combustion structure in the vertical flue, are selected. Specifically, a set of optimal air distribution rates and air excess coefficients need to be found to minimize the NOx generation in the combustion chamber while maintaining the combustion efficiency and the uniformity of the temperature field. The following are the specific implementation steps:

[0122] Step 1: Parameter coding.

[0123] Real number coding is adopted, and the air distribution rate and the air excess coefficient are coded as individuals in the genetic algorithm. Each individual contains two genes, which represent the air distribution rate (such as the volume ratio of the inlet of the primary air) and the air excess coefficient (such as 1.1, 1.2, etc.) respectively. A random initial population is generated, and the population size is set to 100 individuals.

[0124] Step 2: Generation of the initial population.

[0125] Random generation: Randomly generate the initial population within a reasonable range. For example, the range of the air distribution rate is 65%-80%, and the range of the air excess coefficient is 1.1-1.3. An individual in the initial population may be represented as [0.72, 1.2], indicating that the air distribution rate is 72% and the air excess coefficient is 1.2.

[0126] Step 3: Design of the fitness function.

[0127] The fitness function is used to evaluate the quality of each individual, taking into account NOx emissions, temperature uniformity, and combustion efficiency. It is defined as follows:

[0128]

[0129] Among them, NOx concentration: Calculate the NOx concentration at the outlet through CFD simulation.

[0130] Temperature non-uniformity: Measure it by calculating the standard deviation of the temperature field in the combustion chamber.

[0131] Combustion efficiency loss: Measure it by calculating the deviation of the combustion efficiency from the ideal value.

[0132] Weight coefficients: α and β are used to balance the priorities of different optimization objectives and are adjusted according to actual needs.

[0133] Step 4: Selection operation.

[0134] The selection operation is used to select individuals with high fitness from the current population to generate the parental population. The roulette wheel selection method is adopted: Roulette wheel selection: Select parental individuals according to the fitness ratio of individuals. Individuals with higher fitness have a higher probability of being selected.

[0135] Suppose the fitness values of individuals in the population are [0.8, 0.7, 0.9, 0.6] respectively, then their selection probabilities are [0.27, 0.23, 0.27, 0.23] respectively.

[0136] Step 5: Crossover operation.

[0137] The crossover operation is used to generate new offspring individuals by exchanging part of the gene information of parental individuals. The single-point crossover method is adopted: Single-point crossover: Randomly select two parental individuals and exchange gene segments at a random position to generate two offspring individuals. Suppose the two parental individuals are [0.72, 1.2] and [0.78, 1.1] respectively, and the crossover point is 1, then the generated offspring individuals are [0.72, 1.1] and [0.78, 1.2].

[0138] Step 6: Mutation operation.

[0139] The mutation operation is used to introduce new gene mutations to prevent the algorithm from falling into local optima. The uniform mutation method is adopted: Uniform mutation: Randomly change one or more genes of an individual with a certain probability. The mutation probability is set to 5%. Suppose the individual [0.72, 1.2] mutates, the mutated individual may be [0.75, 1.2] or [0.72, 1.15].

[0140] Step 7: Fitness evaluation and iterative optimization.

[0141] Substitute the parameters of the newly generated offspring individuals into the CFD model for simulation, calculate their NOx emission concentration, temperature field uniformity, and combustion efficiency. Calculate the fitness of each individual according to the simulation results, repeat the selection, crossover, and mutation operations, and gradually optimize the population. Iteration times: Set the maximum number of iterations to 100 times. Termination condition: When the change in the optimal fitness of the population for 10 consecutive generations is less than 1%, stop the iteration.

[0142] 8. Output the optimal solution

[0143] After multiple iterations, output the optimal solution, that is, the best combination of air distribution ratio and excess air coefficient. After optimization, the optimal solution is [0.65, 1.22], indicating that the air distribution ratio is 65% and the excess air coefficient is 1.22.

[0144] To verify the accuracy of the optimization results, the parameters obtained after the optimization results are taken: the optimal values of the air distribution ratio and the excess air coefficient combination are: 6.5:3.5, 1.22 for verification and substituted into the CFD model for calculation. The obtained NO mass concentration is 252 mg / m 3 , and the nitrogen oxide emissions after optimization are reduced by 39% compared to those before optimization.

[0145] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims of the optimization method and the supplementary combustion structure and their full scope and equivalents.

Claims

1. An optimization design method for achieving low NOx emissions in a coke oven vertical flue, characterized in that: The following steps are involved: S1: Use sensors to collect real-time data on temperature, pressure, oxygen concentration, and gas composition in the coke oven combustion chamber; S2: Use computational fluid dynamics (CFD) analysis software to simulate the coke oven combustion process and predict the effects of different air supply strategies on combustion efficiency and NOx emissions; S3: Compare the temperature field and gas concentration field of the coke oven combustion chamber under different air distribution rates and different excess air coefficients; S4: Based on the relatively small NOx concentration at the combustion chamber outlet, determine the air segment distribution ratio and different air excess coefficients; S5: Determine the three-dimensional basic size parameters according to the two-dimensional drawing of the vertical fire channel of the coke oven combustion chamber; S6: Using 3D drawing software, a solid model of equal size is established based on the basic size parameters of the vertical fire channel of the coke oven combustion chamber; S7: using meshing software to mesh the solid model of equal size obtained in step S6, determining the appropriate number of meshes after mesh independence test, and giving boundary names; S8: The inlet condition is set to the inlet gas mass flow rate, the outlet condition is set to the outlet pressure, and the wall temperature is set to the heat flux density boundary condition; S9: After the generated grid data is imported into the computational fluid dynamics analysis software, the temperature distribution and gas concentration of the vertical fire channel in the coke oven combustion chamber are calculated using the simulation model; S10: Based on the calculation results of step S9, including the temperature distribution and gas concentration of the ascending vertical fire channel and the descending vertical fire channel, the NOx generation calculation is started using the NOx generation model to obtain the NOx spatial distribution in the fire channel of the coke oven combustion chamber and the NOx outlet emission concentration; S11: extracting the data simulated by the computational fluid dynamics analysis software into the optimization algorithm, and then optimizing to determine the most appropriate combination of air supply ratio and air excess coefficient; S12: Perform CFD simulation on the optimized combination again to verify whether the outlet NOx concentration is relatively small and whether the temperature field is relatively uniform.

2. The method for optimizing the design of low NOx emissions from a coke oven vertical flue according to claim 1, characterized in that: The optimization algorithm is a genetic algorithm, comprising the following steps: Step 1: Encode the air distribution rate and excess air coefficient parameters as individuals in the genetic algorithm, use real number encoding, and randomly generate an initial population with a population size of 100 individuals; Step 2: Define the fitness function as: Among them, α and β are weight coefficients used to balance the priorities of different optimization objectives; Step 3: Select parent individuals in proportion to their fitness; Step 4: Select two parent individuals, exchange gene fragments at random positions, and generate two offspring individuals; Step 5: Randomly change one or more genes of the individual with a certain probability to introduce a new solution space and avoid local optimality; Step 6: Substitute the parameters of the newly generated offspring individuals into the CFD model for simulation, calculate their NOx emission concentration, temperature field uniformity and combustion efficiency, calculate the fitness of each individual based on the simulation results, repeat the selection, crossover and mutation operations, and gradually optimize the population; Step 7: When the fitness converges or reaches the maximum number of iterations, stop the iteration and output the optimal solution, that is, the optimal combination of air distribution rate and excess air coefficient.

3. The method for optimizing the design of low NOx emissions from a coke oven vertical flue according to claim 1, characterized in that: The air distribution rate ranges from 65% to 80%, and the air excess coefficient ranges from 1.1 to 1.

3.

4. The method for optimizing the design of low NOx emissions from a coke oven vertical flue as claimed in claim 1, characterized in that: The basic size parameters include the basic size of the vertical fire channel of the coke oven combustion chamber, as well as the number, position and size of the circulation holes and the crossing holes.

5. The optimization design method for realizing low NOx emission of coke oven vertical flue as claimed in claim 1, which is based on a supplementary combustion structure for realizing low NOx emission of coke oven vertical flue, and is characterized in that: The supplementary combustion structure comprises: A partition wall, the partition wall is arranged in the middle of the coke oven vertical fireway, and the inner cavity of the coke oven vertical fireway is divided into an ascending vertical fireway and a descending vertical fireway by the partition wall; A spanning hole, wherein the spanning hole is located at the top of the partition wall; A first stage air inlet, wherein the first stage air inlet is located at the bottom of the vertical fireway of the coke oven; A second-stage air inlet, wherein the second-stage air inlet is located above the first-stage air inlet; A re-segmented air inlet, the re-segmented air inlet being located above the second-stage air inlet; A fuel inlet, the fuel inlet being located below the ascending vertical fireway; An exhaust gas outlet, the exhaust gas outlet being located in the middle of the descending vertical fireway; There are two internal circulation holes, which are used to connect the bottom of the ascending vertical fire channel and the descending vertical fire channel.

6. The method for optimizing the design of low NOx emissions from a coke oven vertical flue as claimed in claim 5, characterized in that: The crossover holes allow smoke and incompletely burned gases to circulate between the ascending vertical fire channel and the descending vertical fire channel, thereby improving thermal efficiency and reducing heat loss.

7. The method for optimizing the design of low NOx emission in a coke oven vertical flue as claimed in claim 5, characterized in that: The internal circulation holes are responsible for guiding part of the flue gas back to the combustion chamber to preheat the newly incoming air and fuel, further enhancing the combustion efficiency.

8. The method for optimizing the design of low NOx emission in a coke oven vertical flue as claimed in claim 5, characterized in that: The first stage air inlet and the second stage air inlet are respectively located at different heights of the vertical fire channel, and they are responsible for providing necessary oxygen to the combustion chamber to support the combustion of fuel.

9. The method for optimizing the design of low NOx emission in a coke oven vertical flue as claimed in claim 5, characterized in that: The sub-staged air inlet further refines the air supply by introducing air at different stages of the combustion process to optimize combustion efficiency and reduce the generation of NOx pollutants.

10. The method for optimizing the design of low NOx emission in a coke oven vertical flue according to claim 5, characterized in that: The exhaust gas outlet is a channel for smoke to be discharged. It is located at the top or side of the vertical fire channel to ensure that the smoke can be discharged smoothly and enter the subsequent smoke treatment system.

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