Oxygen-enriched combustion system and its control method based on CFD simulation optimization

Abstract

The present invention discloses an oxygen-enriched combustion system based on CFD simulation optimization and its control method, which relates to the field of electrical digital processing technology. The method includes: configuring sensors to collect real-time combustion data under current working conditions; setting a structured grid of the combustion device, obtaining the real-time operating status of the combustion control unit, and constructing a dynamic simulation model based on the real-time operating status, real-time combustion data and the structured grid; obtaining the current simulation results through the dynamic simulation model; the current simulation results include temperature field, flow velocity field and chemical concentration field; inputting the ideal simulation results, the current simulation results and the real-time operating status into the neural network model to obtain the control parameter instructions; and controlling the combustion control unit according to the control parameter instructions. The present invention can significantly improve the simulation accuracy and computational efficiency of the combustion device, and ensure the accurate capture of complex flow characteristics and real-time control optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital processing, and in particular to an oxygen-enriched combustion system based on CFD simulation optimization and a control method thereof. Background Art

[0002] Combustion technology is a fundamental and widely used component of traditional energy utilization. Conventional combustion methods suffer from low energy efficiency and high pollutant emissions, wasting precious energy resources and causing significant environmental pollution. This is particularly true in thermal power generation, industrial boilers, and automotive engines, where efficiency and environmental requirements for combustion processes are becoming increasingly stringent. However, traditional combustion control methods struggle to achieve both high efficiency and low emissions.

[0003] To address these issues, scientists and engineers have proposed various improvements in recent years to enhance combustion efficiency and environmental performance. Oxygen-enriched combustion technology improves combustion conditions by increasing oxygen concentration, thereby increasing combustion efficiency and reducing pollutant emissions. Computational fluid dynamics (CFD) simulation technology has also been introduced to simulate and optimize the combustion process, aiding the design of more efficient and environmentally friendly combustion systems.

[0004] However, existing oxygen-rich combustion control methods still have shortcomings in capturing complex flow characteristics and real-time control optimization. In other words, current technologies often find it difficult to balance simulation accuracy and computing resource limitations, resulting in the reliability and practicality of simulation results needing to be improved. Summary of the Invention

[0005] The present invention provides an oxygen-enriched combustion system and a control method thereof based on CFD simulation optimization to solve at least one of the problems mentioned in the above background technology.

[0006] The specific technical solutions provided by the present invention are as follows:

[0007] An oxygen-rich combustion control method based on CFD simulation optimization includes the following steps:

[0008] Configure sensors to collect real-time combustion data under current working conditions;

[0009] Set the structured grid of the combustion device, obtain the real-time operating status of the combustion control unit, and build a dynamic simulation model based on the real-time operating status, real-time combustion data and structured grid;

[0010] Obtaining current simulation results through a dynamic simulation model; the current simulation results include temperature field, flow velocity field and chemical concentration field;

[0011] Input the ideal simulation results, current simulation results and real-time operation status into the neural network model to obtain control parameter instructions;

[0012] Controlling the combustion control unit according to the control parameter instructions;

[0013] The setting of the structured grid of the combustion device specifically comprises the steps of:

[0014] Set the flow characteristics, computing resource limitations, and accuracy requirements that the dynamic simulation model needs to capture; configure the weight parameters of the fitness function based on the flow characteristics, computing resource limitations, and accuracy requirements;

[0015] Initialize and set the particle swarm parameters of the particle swarm optimization algorithm; the particle swarm parameters include the position and velocity of the particles, the inertia weight, the first learning factor, and the second learning factor; the position of the particles is used to represent the configuration parameters of each structured grid; the configuration parameters include the number of grid points in each coordinate direction, grid boundary conditions, grid alignment geometric features, and auxiliary configuration parameters;

[0016] Generate a corresponding structured grid according to the position of each particle and perform simulation, and calculate the fitness value of each particle based on the fitness function and simulation results;

[0017] Update the personal best position and the group best position of each particle according to the fitness value, and update the speed and position of each particle according to the personal best position and the group best position;

[0018] Repeating the iteration until the maximum number of iterations is reached or the fitness value is less than a set threshold, and setting the optimal position of the group as the configuration parameter of the structured grid of the combustion device;

[0019] Wherein, the fitness value is expressed as:

[0020] f=α·E ε +β·E c +γ·E q ,

[0021] Among them, E ε Represents the simulation accuracy index, E c represents the computational cost index, E q represents the mesh quality index, α, β and γ are the weight parameters of simulation accuracy index, computational cost index and mesh quality index respectively;

[0022] The grid quality index is expressed as:

[0023]

[0024] Where U represents the number of grids, T u,SM represents the node smoothness index of the u-th grid, T u,OR represents the grid orthogonality index of the u-th grid, T u,ARrepresents the aspect ratio index of the u-th grid; w1, w2 and w3 represent the weight parameters of the node smoothness index, grid orthogonality index and aspect ratio index respectively.

[0025] As a preferred solution, the real-time operating status includes fuel supply rate, fan speed and valve opening; the real-time combustion data includes temperature, pressure, oxygen concentration, fuel flow and flue gas component concentration of each sampling point.

[0026] As a preferred embodiment, the dynamic simulation model includes a turbulence model, a combustion model and a gas radiation model; the turbulence model is used to describe the turbulent characteristics of the fluid in the combustion device; the combustion model is used to describe the interaction between the fuel and the oxidant and the state of reaction product generation during the chemical reaction process; the gas radiation model is used to describe the radiation heat transfer of the gas.

[0027] As a preferred solution, the turbulence model adopts a Realizable k-ε model; the Realizable k-ε model is used to describe the process of turbulent energy transfer through turbulent kinetic energy and turbulent kinetic energy dissipation rate;

[0028] The turbulent kinetic energy is expressed as:

[0029]

[0030] in, It represents the sum of the variances of the velocity fluctuation components and is used to quantify the overall intensity of velocity fluctuations; u′1, u′2 and u′3 represent velocity fluctuation components in the first direction, the second direction and the third direction respectively; the first direction, the second direction and the third direction are perpendicular to each other;

[0031] The turbulent kinetic energy dissipation rate is expressed as:

[0032]

[0033] Where ν represents the dynamic viscosity, u′ i represents the i-th direction component of velocity fluctuation, x j Represents the j-th direction coordinate.

[0034] As a preferred embodiment, the combustion model is a Finite-Rate model; the Finite-Rate model describes the relationship between reaction rate and temperature change through the Arrhenius equation; the Arrhenius equation is expressed as:

[0035]

[0036] Among them, K represents the reaction rate, T represents the temperature, n represents the temperature index, E represents the activation energy, R represents the gas constant, A represents the set characteristic constant, and C represents the concentration of the substance participating in the reaction.

[0037] As a preferred solution, the gas radiation model adopts a DO model; the DO model discretizes the radiation transfer equation into multiple directions in space and solves it; the radiation transfer equation is expressed as:

[0038]

[0039] Where I(r,s) represents the radiation intensity at position r and direction s, k a represents the absorption coefficient, I b Represents the blackbody radiation intensity, I represents the current radiation intensity; σ s represents the scattering coefficient, I(r,s′) represents the radiation intensity transmitted in the direction s′, and Φ(s,s′) represents the radiation scattering probability from the direction s′ to the direction s.

[0040] As a preferred solution, the velocity of the particle is expressed as:

[0041]

[0042] Where ω is the inertia weight, k is the iteration round; c1 and c2 are the first learning factor and the second learning factor, respectively. The first learning factor is used to control the amplitude of the particle moving toward the individual optimal position, and the second learning factor is used to control the amplitude of the particle moving toward the group optimal position. is the personal best position of particle i; is the optimal position of the group; r1 and r2 are both random numbers in the interval [0,1].

[0043] The present invention also provides an oxygen-enriched combustion system based on CFD simulation optimization, comprising a data acquisition module, a dynamic simulation module, a distributed control module and a combustion control unit;

[0044] The data acquisition module is used to collect real-time combustion data under current working conditions through sensors, and obtain the real-time operating status of the combustion control unit;

[0045] The dynamic simulation module is used to construct a dynamic simulation model based on the real-time operating status, real-time combustion data and the structured grid by setting the structured grid of the combustion device, and obtain current simulation results through the dynamic simulation model; the current simulation results include temperature field, flow velocity field and chemical concentration field;

[0046] The distributed control module is used to input the ideal simulation results, current simulation results and real-time operating status into the neural network model, obtain the control parameter instructions and transmit them to the combustion control unit;

[0047] The combustion control unit is used to control the corresponding actuator according to the control parameter instruction.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention sets the flow characteristics, computing resource limitations and accuracy requirements that need to be captured by the dynamic simulation model to ensure that the grid generation scheme can not only accurately reflect the core characteristics of the combustion process, but also operate efficiently under the conditions permitted by computing resources; by configuring the weight parameters of the fitness function, comprehensively considering the limitations of accuracy and computing resources, ensuring the multi-objective balance in the optimization process, and improving the reliability and practicality of the simulation results; by continuously iteratively updating the speed and position of the particles, the final output of the optimal position of the group represents the optimal configuration parameters of the structured grid, which can significantly improve the simulation accuracy and computing efficiency of the combustion device, and ensure the accurate capture of complex flow characteristics and real-time control optimization.

[0050] The present invention outputs temperature fields, flow rate fields and chemical concentration fields through a dynamic simulation model, which can fully reflect the complex characteristics of the combustion process and provide an accurate data basis for the control system; through a neural network model, the ideal simulation results, current simulation results and real-time operating status are input into the model, and precise control parameter instructions are automatically generated, thereby optimizing combustion control and improving the system's response speed and control accuracy; by controlling the corresponding actuators through control parameter instructions, the combustion rate and combustion uniformity can be effectively improved, the emission of harmful gases can be reduced, and the environmental protection performance and economic benefits of oxygen-enriched combustion can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0053] Figure 1 A schematic flow chart of an oxygen-rich combustion control method based on CFD simulation optimization provided in one embodiment of the present invention;

[0054] Figure 2 A schematic diagram of a process for setting a structured grid for a combustion device according to an embodiment of the present invention;

[0055] Figure 3A schematic structural diagram of an oxygen-rich combustion control system based on CFD simulation optimization is provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0057] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0058] In addition, the descriptions of "first", "second", etc. in the present invention are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least that feature. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0059] Combustion technology is a fundamental and widely used component of traditional energy utilization. Conventional combustion methods suffer from low energy efficiency and high pollutant emissions, wasting precious energy resources and causing significant environmental pollution. This is particularly true in thermal power generation, industrial boilers, and automotive engines, where efficiency and environmental requirements for combustion processes are becoming increasingly stringent. However, traditional combustion control methods struggle to achieve both high efficiency and low emissions.

[0060] To address these issues, scientists and engineers have proposed various improvements in recent years to enhance combustion efficiency and environmental performance. Oxygen-enriched combustion technology improves combustion conditions by increasing oxygen concentration, thereby increasing combustion efficiency and reducing pollutant emissions. Computational fluid dynamics (CFD) simulation technology has also been introduced to simulate and optimize the combustion process, aiding the design of more efficient and environmentally friendly combustion systems.

[0061] However, existing oxygen-rich combustion control methods still have shortcomings in capturing complex flow characteristics and real-time control optimization. In other words, current technologies often find it difficult to balance simulation accuracy and computing resource limitations, resulting in the reliability and practicality of simulation results needing to be improved.

[0062] See also Figure 1 The present invention provides an oxygen-rich combustion control method based on CFD simulation optimization, comprising:

[0063] S1. Configure sensors to collect real-time combustion data under current working conditions;

[0064] S2. Setting a structured grid for the combustion device, obtaining the real-time operating status of the combustion control unit, and constructing a dynamic simulation model based on the real-time operating status, real-time combustion data, and the structured grid;

[0065] The real-time operating status includes fuel supply rate, fan speed and valve opening; the real-time combustion data includes temperature, pressure, oxygen concentration, fuel flow and flue gas component concentration of each sampling point.

[0066] Among them, the dynamic simulation model includes a turbulence model, a combustion model and a gas radiation model; the turbulence model is used to describe the turbulent characteristics of the fluid in the combustion device; the combustion model is used to describe the interaction between the fuel and the oxidant and the generation state of the reaction products during the chemical reaction process; the gas radiation model is used to describe the radiation heat transfer of the gas.

[0067] In one embodiment, the turbulence model adopts the Realizable k-ε model; the Realizable k-ε model is used to describe the process of turbulent energy transfer through turbulent kinetic energy and turbulent kinetic energy dissipation rate. The Realizable k-ε model is a turbulence model improved based on the k-ε model, which can realistically capture physical phenomena, especially in high shear zones and flow separation regions. Among them, turbulent kinetic energy represents the intensity of velocity fluctuations in turbulence, and the turbulent kinetic energy dissipation rate represents the rate at which turbulent kinetic energy per unit mass is converted into heat energy, that is, the rate at which turbulent energy is lost due to viscosity.

[0068] The turbulent kinetic energy is expressed as:

[0069]

[0070] in, It represents the sum of the variances of the velocity fluctuation components and is used to quantify the overall intensity of velocity fluctuations; u′1, u′2 and u′3 represent velocity fluctuation components in a first direction, a second direction and a third direction respectively; the first direction, the second direction and the third direction are perpendicular to each other.

[0071] The turbulent kinetic energy dissipation rate is expressed as:

[0072]

[0073] Where ν represents the dynamic viscosity, u′ i represents the i-th direction component of velocity fluctuation, x j Represents the j-th direction coordinate.

[0074] In this embodiment, through the calculation and interaction of turbulent kinetic energy and turbulent kinetic energy dissipation rate, the Realizable k-ε model can accurately describe the turbulent characteristics in the flow field, making the simulation results more reliable and realistic.

[0075] In one embodiment, the combustion model is a Finite-Rate model; the Finite-Rate model describes the relationship between reaction rate and temperature change through the Arrhenius equation; the Arrhenius equation is expressed as:

[0076]

[0077] Among them, K represents the reaction rate, T represents the temperature, n represents the temperature index, E represents the activation energy, R represents the gas constant, A represents the set characteristic constant, and C represents the concentration of the substance participating in the reaction.

[0078] In the Finite Rate model of this example, the Arrhenius equation is used to calculate reaction rate constants at different temperatures, thereby affecting the actual reaction rate. In combustion simulations, a large number of kinetic properties, such as ignition delay and combustion rate, are highly controlled by temperature changes. The Arrhenius equation accounts for the effects of molecular collisions and energy barriers, effectively capturing the impact of temperature on chemical reaction rates.

[0079] In one embodiment, the gas radiation model adopts the DO model (Discrete Ordinates Model). The DO model discretizes the radiation transfer equation into multiple directions in space and solves the radiation transfer equation in each direction accordingly. The radiation transfer equation is expressed as:

[0080]

[0081] Where I(r,s) represents the radiation intensity at position r and direction s, k a represents the absorption coefficient, I b Represents the blackbody radiation intensity, I represents the current radiation intensity; σ s represents the scattering coefficient, I(r,s′) represents the radiation intensity transmitted in the direction s′, and Φ(s,s′) represents the radiation scattering probability from the direction s′ to the direction s.

[0082] In one embodiment, please refer to Figure 2 The setting of the structured grid of the combustion device specifically includes the steps of:

[0083] S21. Setting the flow characteristics, computing resource limitations, and accuracy requirements that the dynamic simulation model needs to capture; configuring weight parameters of the fitness function based on the flow characteristics, computing resource limitations, and accuracy requirements;

[0084] Among them, the fitness function is used to evaluate the pros and cons of the configuration parameters of each structured grid. The fitness function consists of a simulation accuracy index, a computational cost index and a grid quality index, and each index reflects the performance of a certain flow feature. This embodiment sets the weight of each index in the fitness function according to the importance and accuracy requirements of the flow feature. Among them, complex flow features may require more detailed calculations, which will increase the computational cost; for these features, the computational cost may require appropriate weight adjustments to ensure balance; for certain flow features, high-quality grids are required (such as in eddy current areas), so the weights of related grid quality indicators may need to be increased to ensure that the grid can accurately capture these features; computational resource limitations can reduce simulation time by affecting the weight parameters of computational cost indicators within a tight time frame when computer resources are insufficient.

[0085] Furthermore, the fitness value is expressed as:

[0086] f=α·E ε +β·E c +γ·E q ,

[0087] Among them, E ε Represents the simulation accuracy index, E c represents the computational cost index, E q represents the mesh quality index, α, β and γ are the weight parameters of simulation accuracy index, computational cost index and mesh quality index respectively.

[0088] In one embodiment, the grid quality index is expressed as:

[0089]

[0090] Where U represents the number of grids, T u,SM represents the node smoothness index of the u-th grid, T u,OR represents the grid orthogonality index of the u-th grid, T u,AR represents the aspect ratio index of the u-th grid; w1, w2 and w3 represent the weight parameters of the node smoothness index, grid orthogonality index and aspect ratio index respectively.

[0091] The node smoothness index is used to describe the uniformity of the spatial distribution of grid points. Excessive changes in node spacing will reduce simulation accuracy. The measurement method of the present invention is the rate of change of the spacing between adjacent nodes. The node smoothness index is expressed as:

[0092]

[0093] Among them, T u,SM represents the node smoothness index of the u-th grid, d x,x+1 represents the distance between node x and node x+1 in the u-th grid, and n represents the number of grid nodes.

[0094] Grid orthogonality is used to represent the angle between the face normal vector of each grid cell and the face normal vector of the adjacent grid cell. The ideal orthogonality is 90 degrees. The grid orthogonality is expressed as:

[0095]

[0096] Among them, T u,OR represents the grid orthogonality index of the u-th grid, n1 represents the surface normal vector of the u-th grid; M represents the number of grids adjacent to the u-th grid, n m Represents the face normal vector of the mth adjacent mesh.

[0097] The aspect ratio metric measures the uniformity of a cell's shape. Ideally, the lengths of all sides of a cell should be as close as possible. To calculate the aspect ratio, we calculate all side lengths, find the longest and shortest sides, and then calculate the ratio of the longest to shortest sides as the aspect ratio. A grid with an aspect ratio closer to 1 indicates a cell that is closer to equilibrium and has better shape quality. A high aspect ratio generally indicates a poorly shaped cell, which may affect the accuracy and stability of the calculation results.

[0098] S22. Initialize and set the particle swarm parameters of the particle swarm optimization algorithm. These parameters include the number of particles, particle positions and velocities, inertia weights used for particle updates, a first learning factor, and a second learning factor. The particle position represents the coordinates of a possible solution in the search space for the problem. In the present invention, the particle position is used to represent the configuration parameters of each structured grid. These configuration parameters include the number of grid points in each coordinate direction, grid boundary conditions, grid alignment geometric features, and auxiliary configuration parameters.

[0099] Furthermore, the auxiliary configuration parameters include a grid stretching coefficient, smoothness, and aspect ratio; the grid stretching coefficient is used to determine the change of the grid from sparse to dense, the smoothness is used to determine the smoothness of the grid change, and the aspect ratio is used to determine the shape ratio of the grid unit.

[0100] S23, generating a corresponding structured grid according to the position of each particle and performing simulation, and calculating the fitness value of each particle according to the fitness function and the simulation results;

[0101] S24, updating the individual best position and the group best position of each particle according to the fitness value, and updating the speed and position of each particle according to the individual best position and the group best position;

[0102] The velocity of the particle is expressed as:

[0103]

[0104] Where ω is the inertia weight, k is the iteration round; c1 and c2 are the first learning factor and the second learning factor, respectively. The first learning factor is used to control the amplitude of the particle moving toward the individual optimal position, and the second learning factor is used to control the amplitude of the particle moving toward the group optimal position. is the personal best position of particle i; is the optimal position of the group; r1 and r2 are both random numbers in the interval [0,1].

[0105] S25. Repeat the iteration until the maximum number of iterations is reached or the fitness value is less than a set threshold, and set the optimal position of the group as the configuration parameter of the structured grid of the combustion device.

[0106] The threshold can be set based on the accuracy requirements of step S21, and the maximum number of iterations can be set based on the computational resource constraints of step S21. Iterations are repeated, i.e., steps S23 and S24, until the maximum number of iterations is reached or the fitness value reaches the threshold. The current optimal position of the swarm represents the final configuration parameters of the combustion device structured grid. These parameters are used to generate the optimal grid to meet the simulation accuracy and computational efficiency requirements.

[0107] The present invention sets the flow characteristics, computing resource limitations and accuracy requirements that need to be captured by the dynamic simulation model to ensure that the grid generation scheme can not only accurately reflect the core characteristics of the combustion process, but also operate efficiently under the conditions permitted by computing resources; by configuring the weight parameters of the fitness function, comprehensively considering the limitations of accuracy and computing resources, ensuring the multi-objective balance in the optimization process, and improving the reliability and practicality of the simulation results; by continuously iteratively updating the speed and position of the particles, the final output of the optimal position of the group represents the optimal configuration parameters of the structured grid, which can significantly improve the simulation accuracy and computing efficiency of the combustion device, and ensure the accurate capture of complex flow characteristics and real-time control optimization.

[0108] S3. Obtaining current simulation results through a dynamic simulation model; the current simulation results include temperature field, flow velocity field, and chemical concentration field;

[0109] This step runs the simulation model to obtain the current simulation results. The simulation results include:

[0110] Temperature field: The temperature distribution during the combustion process, showing the temperature changes in different areas through grid nodes;

[0111] Velocity field: The velocity and direction distribution of the airflow during the combustion process, which is also represented by mesh nodes;

[0112] Chemical concentration field: The concentration distribution of gas components (such as oxygen, fuel, combustion products, etc.), showing the distribution changes of chemical components through nodes.

[0113] S4, inputting the ideal simulation result, the current simulation result and the real-time operation status into the neural network model to obtain the control parameter instruction;

[0114] The ideal simulation result is based on the simulation results obtained under design or optimal operating conditions, representing the system's performance under optimal or expected operating conditions. The ideal simulation result can be determined through optimization using methods such as genetic algorithms and particle swarm optimization algorithms, with combustion efficiency and pollutant emissions as optimization targets. The current simulation result is based on the simulation results of the current actual operating parameters, reflecting the system's performance at the current moment. In this embodiment, the ideal simulation result serves as the target or benchmark, and the current simulation result monitors the system status in real time and is compared with the ideal simulation result to identify deviations or anomalies.

[0115] In this step, the ideal simulation results, current simulation results, and real-time operating status are input into the neural network model as feature vectors, and the input data is nonlinearly transformed through multiple hidden layers to extract deep-level features in the data; the deep-level features generate control parameter instructions through the output layer, and the control parameter instructions include continuous values ​​(such as adjusting the fuel supply rate) and discrete values ​​(such as switch control).

[0116] The present invention obtains control parameter instructions by inputting ideal simulation results, current simulation results and real-time operating status into a neural network model. Since the combustion process involves multiple nonlinear relationships, the neural network effectively captures these complex relationships through nonlinear activation functions and stacked hidden layer structures, and can generate accurate control parameters in real time and efficiently, thereby optimizing the performance and efficiency of the combustion system.

[0117] S5. Control the combustion control unit according to the control parameter instructions.

[0118] In this embodiment, the combustion control unit includes a fuel supply module, an oxygen supply module and a fan module; the fuel supply module is used to store and transport various fuels (such as natural gas, coal, liquid fuel, etc.) to the combustion device; the oxygen supply module is used to provide oxygen-enriched air of a set concentration to help the combustion device achieve oxygen-enriched combustion and enhance combustion efficiency; the fan module is used to control the air volume, air pressure and air flow temperature of the primary air, central air, swirl air and axial air to ensure the normal progress of the combustion process.

[0119] An oxygen-enriched combustion control system based on CFD simulation optimization is used to implement the above-mentioned oxygen-enriched combustion method based on CFD simulation optimization, including a data acquisition module, a dynamic simulation module, a distributed control module and a combustion control unit;

[0120] The data acquisition module is used to collect real-time combustion data under current working conditions through sensors, and obtain the real-time operating status of the combustion control unit;

[0121] The dynamic simulation module is used to construct a dynamic simulation model based on the real-time operating status, real-time combustion data and the structured grid by setting the structured grid of the combustion device, and obtain current simulation results through the dynamic simulation model; the current simulation results include temperature field, flow velocity field and chemical concentration field;

[0122] The distributed control module is used to input the ideal simulation results, current simulation results and real-time operating status into the neural network model, obtain the control parameter instructions and transmit them to the combustion control unit;

[0123] The combustion control unit is used to control the corresponding actuator according to the control parameter instruction.

[0124] The embodiment of the present invention adopts a particle swarm optimization algorithm to generate and optimize a structured grid, so that the grid can accurately capture the flow characteristics in the combustion process, while ensuring the effective use of computing resources, thereby improving the efficiency and accuracy of the simulation; through the dynamic simulation model, multiple parameters such as temperature field, velocity field and chemical concentration field are combined, which can comprehensively reflect the complex characteristics of the combustion process and provide an accurate data basis for the control system; through the neural network model, the ideal simulation results, current simulation results and real-time operating status are input into the model, and accurate control parameter instructions are automatically generated, thereby optimizing combustion control and improving the response speed and control accuracy of the system; by controlling the corresponding actuator through the control parameter instructions, the combustion rate and combustion uniformity can be effectively improved, and the emission of harmful gases can be reduced, thereby improving the environmental protection performance and economic benefits of oxygen-enriched combustion.

[0125] In the embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the modules can be electrical, mechanical or other forms.

[0126] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of the present embodiment according to actual needs.

[0127] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0128] If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), magnetic disk or optical disk, and other media that can store program code.

[0129] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily 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 is not intended to be limited to the embodiments shown herein, but is intended to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. An oxygen-rich combustion control method based on CFD simulation optimization, characterized by: Including steps: Configure sensors to collect real-time combustion data under current working conditions; Set the structured grid of the combustion device, obtain the real-time operating status of the combustion control unit, and build a dynamic simulation model based on the real-time operating status, real-time combustion data and structured grid; Obtaining current simulation results through a dynamic simulation model; the current simulation results include temperature field, flow velocity field and chemical concentration field; Input the ideal simulation results, current simulation results and real-time operation status into the neural network model to obtain control parameter instructions; Controlling the combustion control unit according to the control parameter instructions; The setting of the structured grid of the combustion device specifically comprises the steps of: Set the flow characteristics, computing resource limitations, and accuracy requirements that the dynamic simulation model needs to capture; configure the weight parameters of the fitness function based on the flow characteristics, computing resource limitations, and accuracy requirements; Initialize and set the particle swarm parameters of the particle swarm optimization algorithm; the particle swarm parameters include the position and velocity of the particles, the inertia weight, the first learning factor, and the second learning factor; the position of the particles is used to represent the configuration parameters of each structured grid; the configuration parameters include the number of grid points in each coordinate direction, grid boundary conditions, grid alignment geometric features, and auxiliary configuration parameters; Generate a corresponding structured grid according to the position of each particle and perform simulation, and calculate the fitness value of each particle based on the fitness function and simulation results; Update the personal best position and the group best position of each particle according to the fitness value, and update the speed and position of each particle according to the personal best position and the group best position; Repeating the iteration until the maximum number of iterations is reached or the fitness value is less than a set threshold, and setting the optimal position of the group as the configuration parameter of the structured grid of the combustion device; Wherein, the fitness value is expressed as: f=α·E ε +β·E c +γ·E q , Among them, E ε Represents the simulation accuracy index, E c represents the computational cost index, E q represents the mesh quality index, α, β and γ are the weight parameters of simulation accuracy index, computational cost index and mesh quality index respectively; The grid quality index is expressed as: Where U represents the number of grids, T u,SM represents the node smoothness index of the u-th grid, T u,OR represents the grid orthogonality index of the u-th grid, T u,AR represents the aspect ratio index of the u-th grid; w1, w2 and w3 represent the weight parameters of the node smoothness index, grid orthogonality index and aspect ratio index respectively.

2. The oxygen-rich combustion control method based on CFD simulation optimization according to claim 1, characterized in that: The real-time operating status includes fuel supply rate, fan speed and valve opening; the real-time combustion data includes temperature, pressure, oxygen concentration, fuel flow and flue gas component concentration of each sampling point.

3. The oxygen-rich combustion control method based on CFD simulation optimization according to claim 1, characterized in that: The dynamic simulation model includes a turbulence model, a combustion model and a gas radiation model; the turbulence model is used to describe the turbulent characteristics of the fluid in the combustion device; the combustion model is used to describe the interaction between the fuel and the oxidant and the generation state of the reaction products during the chemical reaction process; the gas radiation model is used to describe the radiation heat transfer of the gas.

4. The oxygen-rich combustion control method based on CFD simulation optimization according to claim 3 is characterized in that: The turbulence model adopts the Realizable k-ε model; the Realizable k-ε model is used to describe the process of turbulent energy transfer through turbulent kinetic energy and turbulent kinetic energy dissipation rate; The turbulent kinetic energy is expressed as: in, It represents the sum of the variances of the velocity fluctuation components and is used to quantify the overall intensity of velocity fluctuations; u′1, u′2 and u′3 represent velocity fluctuation components in the first direction, the second direction and the third direction respectively; the first direction, the second direction and the third direction are perpendicular to each other; The turbulent kinetic energy dissipation rate is expressed as: Where ν represents the dynamic viscosity, u′ i represents the i-th direction component of velocity fluctuation, x j Represents the j-th direction coordinate.

5. The oxygen-rich combustion control method based on CFD simulation optimization according to claim 3 is characterized in that: The combustion model is a Finite-Rate model; the Finite-Rate model describes the relationship between reaction rate and temperature change through the Arrhenius equation; the Arrhenius equation is expressed as: Among them, K represents the reaction rate, T represents the temperature, n represents the temperature index, E represents the activation energy, R represents the gas constant, A represents the set characteristic constant, and C represents the concentration of the substance participating in the reaction.

6. The oxygen-rich combustion control method based on CFD simulation optimization according to claim 3, characterized in that: The gas radiation model adopts the DO model; the DO model discretizes the radiation transfer equation into multiple directions in space and solves it; the radiation transfer equation is expressed as: Where I(r,s) represents the radiation intensity at position r and direction s, k a represents the absorption coefficient, I b Represents the blackbody radiation intensity, I represents the current radiation intensity; σ s represents the scattering coefficient, I(r,s′) represents the radiation intensity transmitted in the direction s′, and Φ(s,s′) represents the radiation scattering probability from the direction s′ to the direction s.

7. The oxygen-rich combustion control method based on CFD simulation optimization according to claim 1, characterized in that: The velocity of the particle is expressed as: Where ω is the inertia weight, k is the iteration round; c1 and c2 are the first learning factor and the second learning factor, respectively. The first learning factor is used to control the amplitude of the particle moving toward the individual optimal position, and the second learning factor is used to control the amplitude of the particle moving toward the group optimal position. is the personal best position of particle i; is the optimal position of the group; r1 and r2 are both random numbers in the interval [0,1].

8. An oxygen-enriched combustion system based on CFD simulation optimization, characterized by: Used to implement the oxygen-enriched combustion method based on CFD simulation optimization as described in any one of claims 1 to 7, comprising a data acquisition module, a dynamic simulation module, a distributed control module and a combustion control unit; The data acquisition module is used to collect real-time combustion data under current working conditions through sensors, and obtain the real-time operating status of the combustion control unit; The dynamic simulation module is used to construct a dynamic simulation model based on the real-time operating status, real-time combustion data and the structured grid by setting the structured grid of the combustion device, and obtain current simulation results through the dynamic simulation model; the current simulation results include temperature field, flow velocity field and chemical concentration field; The distributed control module is used to input the ideal simulation results, current simulation results and real-time operating status into the neural network model, obtain the control parameter instructions and transmit them to the combustion control unit; The combustion control unit is used to control the corresponding actuator according to the control parameter instruction.

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