A method for optimizing energy consumption of heat treatment process based on digital twin

Abstract

The present invention discloses a method for optimizing energy consumption of a heat treatment process based on digital twins, comprising the following steps: (1) formulating a heat treatment process plan according to the performance requirements of the workpiece; (2) a heat treatment furnace heat-treating the workpiece according to the heat treatment process plan, and then performing steady-state filtering on the steady-state data set to obtain an effective steady-state data set; (3) establishing a digital twin model according to the effective steady-state data set, and then performing simulation according to the digital twin model to obtain a total deformation diagram and an equivalent stress diagram of the workpiece after heat treatment; (4) judging whether the workpiece quality meets the standard, if so, calculating the energy loss during the heat treatment process, and then selecting the production process with the lowest energy consumption as the workpiece heat treatment process according to the infinite approximation principle, otherwise optimizing and correcting the input parameters and executing step (3). The technical solution of the present invention can solve the problem of time-consuming, labor-intensive and high-cost process optimization in an electric heating heat treatment furnace in the prior art.

Description

Technical Field

[0001] The present invention belongs to the field of heat treatment technology, and in particular relates to a method for optimizing energy consumption of a heat treatment process based on digital twins. Background Art

[0002] Heat treatment is an extremely important process in the production of metal materials. The heat treatment process determines the structure and performance of the material. The performance of the material can be adjusted by regulating the heat treatment process to meet the requirements of industrial production.

[0003] Judging from the current electric heating heat treatment furnace, the heat treatment furnace consumes a lot of electricity during the heat treatment process of the workpiece. The traditional heat treatment process optimization is time-consuming, labor-intensive and costly, and traditional simulation consumes a lot of time and computer computing power. Summary of the invention

[0004] The problem to be solved by the present invention is to provide a method for optimizing energy consumption of a heat treatment process based on digital twins, which is used to solve the problem of time-consuming, labor-intensive and high cost of process optimization in electric heating heat treatment furnaces in the prior art.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for optimizing energy consumption of a heat treatment process based on digital twins, comprising the following steps: (1) formulating a heat treatment process plan according to the performance requirements of the workpiece; (2) a heat treatment furnace heat-treating the workpiece according to the heat treatment process plan, collecting a steady-state data set during the heat treatment process, and then performing steady-state filtering on the steady-state data set to obtain an effective steady-state data set; (3) establishing a digital twin model according to the effective steady-state data set, and then performing simulation according to the digital twin model to obtain a total deformation diagram and an equivalent stress diagram of the workpiece after heat treatment; (4) judging whether the workpiece quality meets the standard according to the total deformation diagram and the equivalent stress diagram, and if so, calculating the energy loss during the heat treatment process, and then selecting the production process with the lowest energy consumption as the workpiece heat treatment process according to the infinite approximation principle, otherwise optimizing and correcting the input parameters according to the orthogonal experimental table and executing step (3).

[0006] Furthermore, in step (2), the steady-state data set is subjected to steady-state filtering by combining Gaussian filtering and R test method to obtain a valid steady-state data set.

[0007] Furthermore, in step (3), establishing a digital twin model based on the effective steady-state data set includes the following steps: establishing a temperature field model of the heat treatment furnace during operation through a hybrid modeling method based on the effective steady-state data set and other boundary conditions, and using a sequential coupling analysis method to apply the transient temperature field results as input conditions to the structure of the workpiece for thermal stress analysis, and then reducing the order of the established temperature field model through the modeling function of ANSYS software and the matrix calculation function of Matlab software to obtain a reduced-order model, and then following the infinite approximation principle to change the input parameters of the reduced-order model to obtain the optimal energy consumption process.

[0008] Furthermore, in step (3), establishing a temperature field model of the heat treatment furnace in operation by a hybrid modeling method includes the following steps:

[0009] Substitute the heat flux, micro-element thickness and the temperature difference between the two sides of the micro-element thickness into the thermal conductivity calculation formula φ=-λA*(dt / dx) to obtain the thermal conductivity λ and calculate the surface area of ​​the heated part to obtain the thermal conduction area A, where φ is the heat flux, λ is the thermal conductivity, A is the thermal conduction area, dt represents the temperature difference between the two sides of the micro-element thickness, and dx represents the micro-element thickness;

[0010] A 1:1 model was established in Ansys according to the size data of the heat treatment furnace and the heated workpiece. The grid was divided and the temperature field model of the heat treatment furnace was established when it was working, combining the thermal conductivity λ, thermal conductivity area A, convection coefficient K and effective steady-state data set.

[0011] Furthermore, in step (3), the established temperature field model is reduced in order by using the function command HBMAT of ANSYS software and the matrix calculation function of Matlab software, including the following steps:

[0012] Ansys outputs Cond_File.dat and Cap_File.dat files;

[0013] Matlab reads the Cond_File.dat file and obtains the heat conduction matrix Conductance and the load array LoadVector;

[0014] Matlab reads the Cap_File.dat file and obtains the heat capacity matrix Capcitance;

[0015] Perform LU decomposition on the heat conduction matrix Conductance and convert the high-order matrix into a low-order matrix.

[0016] Furthermore, the calculation of energy loss during the heat treatment process in step (4) includes the following steps: calculating the overall energy consumption of the heat treatment furnace using an electric power calculation formula.

[0017] Furthermore, in step (4), the infinite approximation principle is followed and the effective production process with the lowest energy consumption is selected as the final heat treatment process of the workpiece under the premise of controlling the error within 1%.

[0018] Furthermore, in the orthogonal experimental table in step (4), the total power of the heat treatment furnace, the heating rate, and the cooling rate in the initial heat treatment process are selected as experimental factors, and the orthogonal experimental design table is listed to determine the factor coding table, and the stress tracking data, temperature field data, and energy consumption of the workpiece are obtained.

[0019] The beneficial effects of the present invention are as follows: according to the established initial heat treatment process and the effective steady-state data set collected during the working process of the heat treatment furnace, the stress field of the heat treatment workpiece is obtained by the method of sequential coupling analysis, and then the model is reduced in order using the modeling function of the ANSYS software and the matrix calculation function of the Matlab software. The heating process of the heat treatment furnace is simulated to obtain the total deformation diagram and equivalent stress diagram of the workpiece in the heat treatment furnace and the energy consumption of the entire heat treatment furnace; at this time, it can be judged whether the workpiece quality meets the standard according to the total deformation diagram and equivalent stress diagram of the workpiece. If the standard is met, the total energy loss in the workpiece production process will be calculated. If the standard is not met, an orthogonal experimental table will be set to optimize and correct the input parameters. Finally, following the infinite approximation principle, the effective production process with the lowest energy consumption is selected as the final production process of the workpiece under the premise of controlling the error within 1%. The present invention can flexibly adjust the details of the heat treatment process and adjust the process parameters in real time. According to the processing process of the present invention, it can be highly energy-efficient, effectively save economic time and economic costs.

[0020] The present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of the method for optimizing energy consumption of a heat treatment process of the present invention;

[0022] Figure 2 Schematic diagram of the relationship between the heat transfer coefficient of the roller heat treatment and time in the embodiment of the present invention;

[0023] Figure 3 It is a schematic diagram of the temperature field of the heat treatment furnace used in the embodiment of the present invention when the temperature is raised to 925K;

[0024] Figure 4 Schematic diagram of total deformation of the roller during heat treatment in an embodiment of the present invention;

[0025] Figure 5 Schematic diagram of equivalent stress during heat treatment of a rolling mill roll in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The present invention provides a method for optimizing energy consumption of a heat treatment process based on digital twins, comprising the following steps.

[0027] (1) According to the performance requirements of the workpiece, refer to the heat treatment process plan of the same type of workpiece and formulate a heat treatment process plan.

[0028] (2) The heat treatment furnace performs heat treatment on the workpiece according to the heat treatment process plan, and collects steady-state data sets during the heat treatment process (i.e., the power Q of the heating resistor wire recorded during the operation of the heat treatment furnace). i and the ventilation fan inlet flow rate V i The steady-state data set is then processed by steady-state filtering to obtain a valid steady-state data set.

[0029] In this step, the steady-state data set is subjected to steady-state filtering by combining Gaussian filtering and R test methods to obtain a valid steady-state data set. Gaussian filtering is an image processing method used to smooth images. The noise in the image is reduced by plotting the collected data into an image and then applying a Gaussian template to the image. The Gaussian filter performs a weighted average of the pixels around each pixel in the image, making the image look smoother. The R test is a method in statistics used to test whether there is a correlation between two sets of data. The relationship between the two sets of variable data is evaluated by calculating the correlation coefficient R between them. If the value of R is close to 1, it indicates that there is a strong positive correlation between the two sets of data; if the value of R is close to -1, it indicates that there is a strong negative correlation between the two sets of data; if the value of R is close to 0, it indicates that there is no obvious correlation between the two sets of data. Use Gaussian filtering to delete and optimize the collected data, and use the R test method to delete irrelevant data, and finally obtain a valid steady-state data set.

[0030] (3) A digital twin model is established based on the effective steady-state data set. Then, simulation is performed based on the digital twin model to obtain the total deformation diagram and equivalent stress diagram of the workpiece after heat treatment.

[0031] In this step, establishing a digital twin model based on the effective steady-state data set includes the following steps: based on the effective steady-state data set and boundary conditions and through the hybrid modeling method, a temperature field model of the heat treatment furnace is established when it is working, and a sequential coupling analysis method is used to apply the transient temperature field results as input conditions to the structure of the workpiece for thermal stress analysis, and then the established temperature field model is reduced in order through the modeling function of ANSYS software and the matrix calculation function of Matlab software to obtain a reduced-order model, and then the input parameters of the reduced-order model are changed in accordance with the infinite approximation principle to obtain the optimal energy consumption process (that is, the optimal energy consumption process is found by modifying the input parameters).

[0032] Among them, the temperature field model of the heat treatment furnace when working is established by the hybrid modeling method, which includes the following steps: substituting the heat flux, micro-element thickness and the temperature difference between the two sides of the micro-element thickness into the thermal conductivity calculation formula φ=-λA*(dt / dx) to obtain the thermal conductivity λ and calculating the surface area of ​​the heated part to obtain the thermal conduction area A, where φ is the heat flux, λ is the thermal conductivity, A is the thermal conduction area, dt represents the temperature difference between the two sides of the micro-element thickness, and dx represents the micro-element thickness; according to the size data of the heat treatment furnace and the heated workpiece, the model is established in ANSYS, the grid is divided, and the temperature field model of the heat treatment furnace when working is established in combination with the thermal conductivity λ, the thermal conduction area A, the convection coefficient K and the effective steady-state data set. The effective steady-state data set, the convection coefficient K, etc. are the boundary conditions of the temperature field model.

[0033] The order reduction processing of the established temperature field model is carried out through the functional command HBMAT of ANSYS software and the matrix calculation function of Matlab software, including the following steps: Ansys outputs Cond_File.dat and Cap_File.dat files; Matlab reads the Cond_File. dat file to obtain the heat conduction matrix Conductance and the load array LoadVector; Matlab reads the Cap_File.dat file to obtain the heat capacity matrix Capcitance; LU decomposition is performed on the heat conduction matrix Conductance, thereby converting the high-order matrix into a low-order matrix.

[0034] (4) Determine whether the workpiece quality meets the standard based on the total deformation diagram and the equivalent stress diagram. If so, calculate the energy loss during the heat treatment process, and then select the production process with the lowest energy consumption as the workpiece heat treatment process based on the infinite approximation principle. Otherwise, optimize and correct the input parameters based on the orthogonal experimental table and execute step (3).

[0035] In this step, the calculation of energy loss during the heat treatment process includes the following steps: Calculate the overall energy consumption of the heat treatment furnace through the electric power calculation formula. Follow the infinite approximation principle and select the effective production process with the lowest energy consumption as the final heat treatment process of the workpiece under the premise of controlling the error within 1%.

[0036] In the orthogonal experimental table, the total power of the heat treatment furnace, the heating rate, and the cooling rate in the initial heat treatment process are selected as experimental factors, the orthogonal experimental design table is listed, the factor coding table is determined, and the stress tracking data, temperature field data, and energy consumption of the workpiece are obtained. The stress tracking data and temperature field data are exported through ANSYS software, and the energy consumption is calculated.

[0037] The technical solution of the present invention is described in detail below using a rolling mill as a specific embodiment.

[0038] (1) According to the performance requirements of the roll, refer to the heat treatment process plan of the same type of rolls, and combine the performance requirements of the roll to preliminarily formulate the heat treatment process plan as shown in the following table

[0039] .

[0040] (2) The heat treatment furnace performs heat treatment on the roll according to the heat treatment process plan, collects steady-state data sets during the heat treatment process, and then performs steady-state filtering on the steady-state data sets to obtain effective steady-state data sets.

[0041] Specifically, the power Q of the heating resistance wire during the operation of the heat treatment furnace is i , Ventilation fan inlet flow rate V i Combining the steady-state filtering process of Gaussian filtering and R test method, the power Q of the heating resistance wire of the heat treatment furnace is finally obtained. i , Power V of the ventilation fan inlet flow rate i Valid steady-state data set.

[0042] (3) A digital twin model is established based on the effective steady-state data set, and then simulation is performed on the digital twin model to obtain the total deformation diagram and equivalent stress diagram of the roller after heat treatment.

[0043] According to the hybrid modeling method, the collected steady-state effective data set can be used to obtain a schematic diagram of the relationship between the heat transfer coefficient K of the workpiece heat treatment and time, as shown in Figure 2 shown.

[0044] Specifically, the temperature field model of the heat treatment furnace is established by the hybrid modeling method, such as Figure 3 As shown. The temperature field model was reduced by using the modeling function of ANSYS software and the matrix calculation function of Matlab software. The heat treatment process of the roller was simulated by changing the input parameters. After the heat treatment process simulation, the stress tracking data of the roller was obtained. The energy loss of the heat treatment furnace during the heat treatment process and the total deformation diagram of the roller were shown in Figure 4 shown.

[0045] The establishment of stress field and calculation of energy loss during heat treatment include the following steps: using sequential coupling analysis method to apply transient temperature field results as input conditions to the structure for thermal stress analysis (such as Figure 5 The overall energy consumption of the heat treatment furnace is calculated using the electric power calculation formula.

[0046] (4) Determine whether the roll quality meets the standard based on the total deformation diagram and the equivalent stress diagram. If so, calculate the energy loss during the heat treatment process, and then select the production process with the lowest energy consumption as the roll heat treatment process based on the infinite approximation principle. Otherwise, optimize and correct the input parameters based on the orthogonal experimental table.

[0047] The following assumptions should be met when calculating the energy consumption of a heat treatment furnace: (1) During the operation of the heat treatment furnace, all heat energy comes from electrical energy conversion; (2) The initial external temperature environment of the heat treatment furnace is consistent when it is working; (3) The heat treatment furnace satisfies the law of conservation of energy during operation; (4) The heat treatment furnace satisfies the law of conservation of mass during operation; (5) The initial temperatures inside and outside the furnace are consistent.

[0048] The formula for calculating the electric work of the heat treatment furnace is as follows: W = Qt, where Q = UI, W is the work done by electrical energy, Q is the total power during the operation of the heat treatment furnace, t is the operating time of the heat treatment furnace, U is the voltage, and I is the current.

[0049] Because the voltage and current are variables during the operation of the heat treatment furnace, they need to be integrated to obtain the following formula: .

[0050] Preliminarily judge whether the performance parameters of the roll meet the requirements under the proposed heat treatment process. If not, readjust the values ​​of the input parameters. Take the input parameters as influencing factors, list the orthogonal test design table according to the orthogonal test design method, and design the heat treatment process parameter optimization plan. Among them, the orthogonal test parameters are selected as shown in the following table.

[0051] Orthogonal Experiment Design Table

[0052] .

[0053] To determine whether the heat treatment process consumes the least energy, it is first necessary to consider whether the quality of the heat-treated parts meets the standards. Whether the residual stress, dimensional deformation, hardness and other indicators meet the technical standards, the judgment standard refers to the national standard "Heat Treatment Process Quality Control GJB 509B-2008", and is combined with actual production requirements. For example, the production requirements in this embodiment are: the 55Mn2 hot rolling roller after heat treatment is used in the processing of blanking, thick plates, and steel sections. It bears strong rolling force, severe wear and thermal fatigue, and the hot rolling roller works at high temperature, so the roller allows diameter wear within the unit workload, so the surface hardness is not required to be too high, only high strength, toughness and heat resistance are required. The surface hardness requires HB190~270 hardness. Reselect the optimization parameters including: the power of the electric heater in each interval, the fan air volume, the heating time, the windshield position, the cooling temperature, the insulation time, the cooling speed, and the cooling time. The adjustment range should be formulated and adjusted according to the initial heat treatment process numerical simulation results and combined with production experience. The optimization scheme refers to the specific process parameters and variable ranges that need to be optimized.

[0054] Adjust the process plan and adjust the input parameters of the reduced-order model to derive the performance parameters of the roll, such as the total deformation diagram, equivalent stress diagram, etc., to determine whether the roll performance indicators meet the standards. If the quality meets the standards, record the energy consumption loss. If not, repeat this step.

[0055] Select the best process route, and under the premise of ensuring the quality requirements of the rolls, use the idea of ​​infinite approximation to reduce the power value of the heat treatment furnace in the input parameters. The minimum energy consumption error is controlled within 1%, and the process plan with the lowest energy consumption is obtained.

[0056] The present invention establishes a model to simulate the entire process of heating, insulation and cooling of the roller heat treatment, obtains the stress tracking data of the roller after heat treatment and the energy loss of the heat treatment furnace during the heat treatment process, and thus obtains the process scheme with the lowest energy consumption for the roller heat treatment.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or some technical features can be replaced by equivalents without departing from the spirit of the technical solution of the present invention, which should be included in the scope of the technical solution for protection of the present invention.

Claims

1. A method for optimizing energy consumption of a heat treatment process based on digital twins, characterized in that: The steps include: (1) Develop a heat treatment process plan based on the performance requirements of the workpiece; (2) The heat treatment furnace performs heat treatment on the workpiece according to the heat treatment process plan, collects a steady-state data set during the heat treatment process, and then performs steady-state filtering on the steady-state data set to obtain a valid steady-state data set; (3) A digital twin model is established based on the effective steady-state data set and other boundary conditions. Then, simulation is performed based on the digital twin model to obtain the total deformation diagram and equivalent stress diagram of the workpiece after heat treatment. (4) Determine whether the workpiece quality meets the standard based on the total deformation diagram and the equivalent stress diagram. If so, calculate the energy loss during the heat treatment process, and then select the production process with the lowest energy consumption as the workpiece heat treatment process based on the infinite approximation principle. Otherwise, optimize and correct the input parameters based on the orthogonal experimental table and execute step (3); In step (3), establishing a digital twin model according to the effective steady-state data set includes the following steps: establishing a temperature field model of the heat treatment furnace when it is working by a hybrid modeling method based on the effective steady-state data set and other boundary conditions, and using a sequential coupling analysis method to apply the transient temperature field results as input conditions to the structure of the workpiece for thermal stress analysis, and then reducing the order of the established temperature field model by using the modeling function of ANSYS software and the matrix calculation function of Matlab software to obtain a reduced-order model, and then changing the input parameters of the reduced-order model in accordance with the infinite approximation principle to obtain the optimal energy consumption process; The temperature field model of the heat treatment furnace during operation is established by the hybrid modeling method, which includes the following steps: Substitute the heat flux, micro-element thickness and the temperature difference between the two sides of the micro-element thickness into the thermal conductivity calculation formula φ=-λA*(dt / dx) to obtain the thermal conductivity λ and calculate the surface area of ​​the heated part to obtain the thermal conduction area A, where φ is the heat flux, λ is the thermal conductivity, A is the thermal conduction area, dt represents the temperature difference between the two sides of the micro-element thickness, and dx represents the micro-element thickness; A 1:1 model was established in Ansys based on the size data of the heat treatment furnace and the heated workpiece, and the grid was divided and the temperature field model of the heat treatment furnace during operation was established by combining the thermal conductivity λ, thermal conductivity area A, convection coefficient K and effective steady-state data set; The order reduction process of the established temperature field model is carried out through the function command HBMAT of ANSYS software and the matrix calculation function of Matlab software, including the following steps: Ansys outputs Cond_File.dat and Cap_File.dat files; Matlab reads the Cond_File.dat file and obtains the heat conduction matrix Conductance and the load array LoadVector; Matlab reads the Cap_File.dat file and obtains the heat capacity matrix Capcitance; Perform LU decomposition on the heat conduction matrix Conductance and convert the high-order matrix into a low-order matrix.

2. The method for optimizing energy consumption of heat treatment process based on digital twin according to claim 1 is characterized in that: In step (2), the steady-state data set is subjected to steady-state filtering by combining Gaussian filtering and R test method to obtain a valid steady-state data set.

3. The method for optimizing energy consumption of heat treatment process based on digital twin according to claim 1 is characterized in that: The calculation of energy loss during the heat treatment process in step (4) includes the following steps: calculating the overall energy consumption of the heat treatment furnace using an electric power calculation formula.

4. The method for optimizing energy consumption of heat treatment process based on digital twin according to claim 1 is characterized in that: In step (4), the infinite approximation principle is followed and the effective production process with the lowest energy consumption is selected as the final heat treatment process of the workpiece under the premise of controlling the error within 1%.

5. The method for optimizing energy consumption of heat treatment process based on digital twin according to claim 1 is characterized in that: In the orthogonal experimental table in step (4), the total power of the heat treatment furnace, the heating rate, and the cooling rate in the initial heat treatment process are selected as experimental factors, the orthogonal experimental design table is listed, the factor coding table is determined, and the stress tracking data, temperature field data, and energy consumption of the workpiece are obtained.

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