A kiln temperature monitoring method based on coupled simulation and Kalman filtering

Abstract

The present invention discloses a kiln temperature monitoring method based on coupled simulation and Kalman filtering, which belongs to the technical field of kiln temperature monitoring. The method simulates the temperature field distribution inside the kiln through simulation software, and uses Kalman filtering technology to fuse the simulation results and actual measurement data, thereby obtaining more accurate temperature monitoring results. The method can effectively improve the accuracy and real-time performance of monitoring, optimize the thermal energy utilization efficiency of the kiln, reduce energy consumption and carbon emissions, and provide technical support for industrial production to achieve carbon neutrality goals.

Description

Technical Field

[0001] The present invention belongs to the technical field of kiln temperature monitoring, and specifically relates to a method for realizing kiln temperature monitoring by utilizing coupling simulation data and actual observation data and fusing them through Kalman filtering technology. Background Art

[0002] As global climate change and environmental problems become increasingly severe, achieving carbon neutrality has become a global consensus. As a high-energy consumption and high-emission production equipment, the energy conservation, emission reduction and optimization control of kilns are key links in achieving carbon neutrality in industrial production. Traditional kiln temperature monitoring methods rely on limited sensors for temperature measurement, which has problems such as large measurement errors and poor real-time performance. This not only affects the thermal efficiency of the kiln, but also increases energy consumption and greenhouse gas emissions.

[0003] In order to respond to the carbon neutrality goal, improve the energy efficiency of the kiln and reduce carbon emissions, simulation technology can be used to iteratively develop and optimize the kiln structure, and the kiln temperature needs to be monitored. In existing technologies, simulation technology can simulate the temperature field distribution inside the kiln, but there is a deviation between simulation accuracy and actual application; and traditional sensor monitoring methods are limited by measurement accuracy and coverage. Therefore, how to combine simulation technology with actual measurement data to improve the accuracy and real-time performance of kiln temperature monitoring has become a key technical issue in achieving kiln energy conservation, emission reduction and carbon neutrality goals. Summary of the invention

[0004] The purpose of the present invention is to provide a kiln temperature monitoring method based on coupled simulation and Kalman filtering, which uses Kalman filtering technology to fuse the data of local observation points with the simulation data of the whole field, so as to realize accurate monitoring and control of the temperature field inside the kiln. Through this method, we only need to deploy limited sensors at key positions of the kiln to obtain local temperature observation data, and then we can update the simulation data of the whole field, so that the original preliminary simulated data can be obtained through Kalman filtering to obtain data closer to the real physical field after the whole field is updated. The present invention simulates the temperature field inside the kiln through coupled simulation technology, and then uses Kalman filtering technology to fuse the simulation results and the actual measurement data, so as to obtain more accurate temperature monitoring results, which not only solves the problem of data accuracy, but also solves the problem of data acquisition at many locations that cannot be directly measured in actual industry.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A kiln temperature monitoring method based on coupled simulation and Kalman filtering includes the following steps:

[0007] SS1. Establish kiln model: Based on the geometric model and physical properties of the kiln, establish the kiln heat transfer mathematical model;

[0008] SS2, simulation: simulate the temperature field distribution inside the kiln through the kiln heat transfer mathematical model to generate initial simulation data, i.e., estimated value;

[0009] SS3. Select observation points: deploy limited sensors at key locations of the kiln to obtain local temperature observation data;

[0010] SS4, Kalman filter: Use the Kalman filter algorithm to fuse the simulation results and actual measurement data to update the temperature field simulation data of the entire field;

[0011] SS5, time progression: Use the updated temperature field data of the full-field simulation as the initial simulation data for the next time step, and perform simulation again through the kiln mathematical model;

[0012] SS6, control system feedback: The updated physical field monitoring results at each time step are fed back to the control system to achieve real-time control of the kiln temperature.

[0013] Furthermore, the simulation method uses a finite volume method to simulate the temperature field.

[0014] Furthermore, the Kalman filter algorithm includes state prediction and observation update steps, and uses data from local observation points to update the simulation data of the entire field.

[0015] Furthermore, the control system optimizes the thermal energy utilization efficiency of the kiln according to the updated monitoring results.

[0016] Beneficial effects of the present invention:

[0017] 1. The present invention deploys limited sensors at key positions of the kiln to obtain local temperature observation data, and uses Kalman filtering technology to update the simulation data of the entire field, thereby solving the problem of data acquisition at many locations that cannot be directly measured in actual industry.

[0018] 2. The Kalman filter is used to fuse simulation data and observation data, which improves the accuracy and real-time performance of kiln temperature monitoring.

[0019] 3. The updated monitoring results will help achieve intelligent control and optimized operation of kilns, reduce energy consumption and carbon emissions, and provide technical support for industrial production to achieve carbon neutrality goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below in conjunction with the accompanying drawings.

[0021] Figure 1 It is the block diagram of the kiln temperature monitoring system;

[0022] Figure 2 It is the flow chart of coupled simulation and Kalman filtering;

[0023] Figure 3 It is a schematic diagram of the kiln mathematical model grid. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] A kiln temperature monitoring method based on coupled simulation and Kalman filtering, such as Figure 1 As shown, the following steps are included:

[0026] SS1. Establishing a kiln model: First, based on the geometric model and physical properties of the kiln, a mathematical model of kiln heat transfer is established. The present invention adopts a Fourier heat conduction model based on the finite volume method and numerically discretized into a linear system;

[0027] SS2, simulation: simulate the temperature field distribution inside the kiln through the kiln heat transfer mathematical model to generate initial simulation data, i.e., estimated value;

[0028] SS3. Select observation points: deploy limited sensors at key locations of the kiln to obtain local temperature observation data;

[0029] SS4, Kalman filter: The Kalman filter algorithm is used to fuse the simulation results and the actual measurement data. The core of the Kalman filter lies in its recursive estimation mechanism. Through the state prediction and observation update steps, the data of the local observation point is combined with the simulation data of the whole field, and the temperature field simulation data of the whole field is updated to make it closer to the actual situation of the actual physical field;

[0030] SS5, time progression: Use the updated temperature field data of the full-field simulation as the initial simulation data for the next time step, and perform simulation again through the kiln mathematical model;

[0031] SS6, control system feedback: Feedback the updated physical field monitoring results at each time step to the control system to achieve real-time control of the kiln temperature, optimize the thermal energy utilization efficiency of the kiln, and reduce energy consumption and carbon emissions.

[0032] From the above, it can be seen that in order to realize kiln temperature monitoring, the present invention, through coupling simulation and Kalman filtering technology, not only realizes the global update of local observation, solves the data acquisition problem at many locations that cannot be directly measured in actual industry, but also achieves the purpose of improving monitoring accuracy. By fusing simulation data and observation data through Kalman filtering, the accuracy and real-time performance of kiln temperature monitoring are improved, and real-time control of kiln temperature is realized.

[0033] The mathematical model of kiln heat transfer is the Fourier heat conduction model. Figure 2 , describing the temperature ( The basic equation for the transfer of ) inside the kiln is:

[0034] (1)

[0035] in ( ) is the density, ( ) is the specific heat capacity, ( ) is the thermal conductivity coefficient, ( ) is the speed, ( ) is the heat source per unit volume;

[0036] Figure 3 This is a simplified mesh diagram of the kiln numerical model. The black part is the interior of the kiln, and the other colored part is the insulation layer on the surface of the kiln, which is a solid phase with no flow velocity. Ignoring the input of external heat sources, the equation can be simplified to:

[0037] (2)

[0038] By using the finite volume method to discretize the Gaussian integral of equation (2), we can obtain:

[0039] (3)

[0040] The superscript n , n -1 represents the current time step and the previous time step, respectively. P , nb Represent the control volume grid center and the neighbor grid center respectively, are the grid center coordinates, In order to control the area of ​​the interface between the volume grid and the neighboring grid, equation (3) is reorganized and merged to obtain:

[0041] (4)

[0042] By traversing equation (4) for each control volume grid on the solid phase, the assembled linear equation system can be obtained:

[0043] (5)

[0044] in and The dimension is , M is the number of grids, The dimension is is the system matrix for numerical simulation. The above is the mathematical model of heat transfer of the solid layer on the surface of the kiln. N Sensors, get the observation data of the current time , the dimension is ,according to Figure 2 The Kalman filter model in can be obtained:

[0045] (6)

[0046] (7)

[0047] in For the simulation system matrix mentioned above, The dimension of the measurement system matrix is , and The dimensions of the system and measured noise matrices are and , assuming that they are all Gaussian white noise, their covariance matrices are respectively expressed as and Indicates that the dimensions are and , the steps of Kalman filtering are as follows:

[0048] Step 1: Time Update

[0049] (8)

[0050] (9)

[0051] The time update equation of the Kalman filter can be derived from equations (6) and (7), which includes the calculation of the time recursive state variables and the calculation of the forward error covariance, where is the prior estimate of the current time, is the optimal estimate of the previous time step, is the optimal estimate of the previous time step. The variance matrix dimension is , is the prior error covariance matrix with dimensions , and further find the Kalman gain matrix The dimension is :

[0052] (10)

[0053] Step 2: Prediction Update

[0054] (11)

[0055] (12)

[0056] Based on the observed value at the current moment and the estimated value at the previous moment, the state update equation of the Kalman filter can be obtained, including the predicted value Updated Estimates and Optimal Estimate Variance Update, the above is the description of the kiln temperature monitoring method based on coupled simulation and Kalman filtering.

[0057] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. A kiln temperature monitoring method based on coupled simulation and Kalman filtering, characterized in that: The steps include: SS1. Establish kiln model: Based on the geometric model and physical properties of the kiln, establish the kiln heat transfer mathematical model; SS2, simulation: simulate the temperature field distribution inside the kiln through the kiln heat transfer mathematical model to generate initial simulation data, i.e., estimated value; The mathematical model of kiln heat transfer is the Fourier heat conduction model, which describes the temperature The basic equation for the transfer inside the kiln is: ； in is the density, is the specific heat capacity, is the thermal conductivity coefficient, For speed, is the heat source per unit volume; The equation can be simplified to: ； By using the finite volume method to discretize the Gaussian integral of equation (2), we can obtain: ； The superscript n , n -1 represents the current time step and the previous time step, respectively. P , nb Represent the control volume grid center and the neighbor grid center respectively, x is the grid center coordinate, In order to control the area of ​​the interface between the volume grid and the neighboring grid, equation (3) is reorganized and merged to obtain: ; By traversing equation (4) for each control volume grid on the solid phase, the assembled linear equation system can be obtained: ; in The dimension is , M is the number of grids, and the dimension of A is is the system matrix of numerical simulation; SS3. Select observation points: deploy limited sensors at key locations of the kiln to obtain local temperature observation data; SS4, Kalman filter: Use the Kalman filter algorithm to fuse the simulation results and actual measurement data to update the temperature field simulation data of the entire field; SS5, time progression: Use the updated temperature field data of the full-field simulation as the initial simulation data for the next time step, and perform simulation again through the kiln mathematical model; SS6, control system feedback: The updated physical field monitoring results at each time step are fed back to the control system to achieve real-time control of the kiln temperature.

2. A kiln temperature monitoring method based on coupled simulation and Kalman filtering according to claim 1, characterized in that: The simulation method uses the finite volume method to simulate the temperature field.

3. The kiln temperature monitoring method based on coupled simulation and Kalman filtering according to claim 1 is characterized in that: The Kalman filter algorithm includes state prediction and observation update steps, and uses data from local observation points to update the simulation data of the entire field.

4. The kiln temperature monitoring method based on coupled simulation and Kalman filtering according to claim 3 is characterized in that: According to the Kalman filter model, we can get: ; in To simulate the system matrix, The dimension of the measurement system matrix is , and The dimensions of the system and measured noise matrices are , assuming that they are all Gaussian white noise, their covariance matrices are respectively expressed as and Indicates that the dimensions are and , the steps of Kalman filtering are: Step 1: Time Update ； in is the prior estimate of the current time, is the optimal estimate of the previous time step, is the optimal estimate of the previous time step. The variance matrix dimension is , is the prior error covariance matrix with dimensions , and further find the Kalman gain matrix The dimension is : ； Step 2: Prediction Update ； Based on the observed value at the current moment and the estimated value at the previous moment, the state update equation of the Kalman filter can be obtained, including the predicted value Updated Estimates and Optimal Estimate Variance renew.

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