Operation optimization method for fused magnesite smelting process

Abstract

The invention relates to the technical field of fused magnesite, and discloses a fused magnesite smelting process operation optimization method, which comprises the following steps: S1, smelting data acquisition and monitoring, and collection of original parameters during equipment operation, S2, process modeling and simulation operation, establishment of a mathematical calculation model, and simulation checking calculation through simulation software, and S3, optimization target and constraint limitation. S4, designing an intelligent control system, and establishing a corresponding intelligent control system; and S5, continuously performing optimization feedback, and performing continuous monitoring feedback according to an optimization scheme. According to the method, through the established weighted optimization step, optimization emphasis can be performed on different targets according to different added weighted percentages when multi-target parameter optimization is performed, so that other target parameters can be effectively prevented from being greatly influenced by optimization of a certain target parameter when the multi-target parameter optimization is performed, and the optimization efficiency of the multi-target parameter optimization is improved. And the overall optimization effect is influenced.

Description

Technical Field

[0001] The invention relates to the technical field of fused magnesia, and in particular to an operation optimization method for a fused magnesia smelting process. Background Art

[0002] Fused magnesia is a material used in industry, usually refers to fused magnesium oxide (MgO). It is prepared by arc melting method, has high purity and good chemical stability, and is commonly used in refractory materials, furnace lining materials in iron and steel making industry, and electric furnace lining applications. Fused magnesia is widely used in various high temperature industrial environments due to its high fire resistance, high temperature resistance, chemical corrosion resistance and other characteristics.

[0003] In the prior art, people often use natural magnesite ore or purified magnesite concentrate powder as raw materials and melt it in an electric arc furnace at a high temperature of more than 2800°C, or use a two-step calcination melting method to lightly burn magnesite ore or purified magnesite concentrate powder in a vertical kiln or rotary kiln at about 1000°C to obtain lightly burned magnesium oxide powder, which is then finely ground, pressed into a blank, and finally smelted at high temperature in an electric arc furnace to obtain fused magnesia.

[0004] In the actual production and processing process, since the current production of fused magnesia is limited by equipment and environmental influences, there is room for improvement in the processing efficiency of the equipment. Therefore, people often use optimization algorithms to optimize the processing process in the production and processing process, so as to achieve higher production efficiency while using the same equipment. However, in the optimization process, since multiple groups of optimization objectives are often interrelated, when optimizing one group of data, the other objectives are implicated and affected, thereby affecting the final optimization effect. In view of this, we propose an operation optimization method for the fused magnesia smelting process. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for optimizing the operation of an electric fused magnesia smelting process, which solves the problem in the prior art that when multi-objective optimization is performed, effective optimization cannot be performed if there are conflicts between objectives.

[0006] To achieve the above objectives, the present invention is implemented by the following technical scheme: a method for optimizing the operation of a fused magnesia smelting process, comprising the following steps:

[0007] S1: Melting data collection and monitoring

[0008] The various data in the fused magnesia smelting process are collected through the set sensor equipment and production control algorithm;

[0009] S2: Process Modeling and Simulation Operation

[0010] Establish mathematical models of the smelting process, including thermodynamics model, kinetic model and fluid mechanics model, and verify the accuracy of the model through simulation software;

[0011] S3: Optimization objectives and constraints

[0012] Establish specific optimization goals for the smelting process and set relevant constraints based on the set optimization goals;

[0013] S4: Intelligent control system design

[0014] Establish or optimize the existing fused magnesia smelting steps according to the set optimization goals, and establish a corresponding intelligent control system;

[0015] S5: Continuous Optimization Feedback

[0016] Establish a continuous optimization and improvement mechanism to continuously optimize the smelting process through data analysis and performance monitoring.

[0017] Preferably, during the smelting data collection and monitoring process in S1, data of each group of sensor equipment in the fused magnesia production will be read, including current, voltage, temperature, and furnace pressure, and real-time data analysis and processing will be performed. At the same time, during the real-time data transmission process, data preprocessing technology is used to filter outliers and noise.

[0018] Preferably, the process modeling in S2 will establish a mathematical model based on the collected detection data, use finite element analysis to simulate the thermal field and stress field in the electric molten magnesia smelting process, and use computational fluid dynamics to simulate the fluid in the furnace, while supporting custom editing of equipment parameters.

[0019] Preferably, the finite element analysis process needs to establish a finite element mesh according to the geometric model of the actual fused magnesia smelting furnace, and define the geometric model including the values ​​of thermal conductivity, specific heat capacity, and density, and at the same time define the thermal boundary conditions of the geometric model, including the heating or cooling boundary of the equipment, the temperature of the external environment and the convection heat transfer coefficient, and use the finite element method to discretize the partial differential equation and convert it into an algebraic equation, and finally use the numerical solution method to solve the discretized heat conduction equation to obtain temperature analysis data.

[0020] Preferably, the corresponding simulation working condition simulation scenario needs to be established in S2, and the simulation initial conditions and boundary conditions are set according to the process parameters and production experience in the actual smelting process. After the simulation scenario is established, the established model needs to be imported into ANSYS software for analysis and simulation. By simulating the smelting process under different working conditions, the influence of various parameter changes on the smelting results can be predicted, providing theoretical support for the subsequent optimization process.

[0021] Preferably, the optimization objectives and constraints in S3 are mainly used to establish corresponding optimization objectives for specific parameters, and after selecting the corresponding optimization objectives, determine the independent variables and parameters that affect the variables, and establish corresponding constraints based on the determined variable variation range and related parameter ranges. Subsequently, an optimization algorithm is established based on the set constraints and solved to obtain the optimal optimization result.

[0022] Preferably, the optimization target in S3 can be a single target or multiple targets. When facing multi-target optimization, the algorithm will make a secondary judgment based on whether there is a conflict between the two. If there is no data conflict between the multiple sets of target data, multiple single-target optimization parameter judgments will be completed independently. If there is a conflict between the parameters of the multiple sets of target data, the algorithm will sacrifice some optimization indicators based on the weighted state of the two sets of data, and use the weighted percentage as a benchmark to weigh and obtain the optimization parameters that approximately meet the multi-target optimization goals.

[0023] Preferably, the weighted parameters in the optimization objectives support manual custom settings. If no custom weighted settings are performed, the algorithm will assign equal weighted parameters to each group of optimization objectives, and make the final optimization parameters approximately close to each group of target optimization values. When the weighted parameters are set manually, the algorithm will optimize one or more groups of objectives with larger weighted ratios according to the emphasis of the weighted algorithm, while the changes in the target optimization values ​​with smaller weighted ratios will decrease as the weighted values ​​decrease.

[0024] Preferably, the intelligent control system in S4 is designed to control algorithms and strategies to optimize the smelting process. Based on the optimization objectives and constraints set in S3, the system receives the smelting data collected in S1 in real time, and uses the mathematical model established in S2 for prediction and control.

[0025] Preferably, the continuous optimization feedback in S5 is used to store historical smelting processing methods and monitor the optimized fused magnesia smelting process, and monitor the current smelting process and score the optimization process according to the optimization target established in S3, and evaluate the optimization effect according to the score value. If the optimization score is lower than a certain value, abnormal feedback is given to the optimization process, and the algorithms and data processing processes in S1-S4 are verified.

[0026] The present invention provides a method for optimizing the operation of a fused magnesia smelting process. It has the following beneficial effects:

[0027] 1. The present invention establishes a weighted optimization step, which can optimize different objectives according to different added weighted percentages when performing multi-objective parameter optimization. Therefore, when performing multi-objective parameter optimization, it can effectively avoid the situation where the optimization of a certain objective parameter causes other objective parameters to be excessively affected, thereby affecting the overall optimization effect.

[0028] 2. The present invention can simulate the smelting process before the actual smelting process by establishing a simulation scenario of the simulated working condition, predict the influence of various parameter changes on the smelting results, provide theoretical support for the subsequent optimization of the smelting process, and improve the stability and optimization effect of the smelting process.

[0029] 3. The present invention establishes an analysis model of the equipment through finite element analysis and retains an editable parameter modification window. When the equipment needs to be replaced later, the parameters of the replaced equipment can be conveniently entered through the parameter modification window, thereby avoiding the time cost of re-modeling after replacing the equipment or changing the processing parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of the operation optimization method of the electric fused magnesia smelting process. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the specification 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.

[0032] Please see attached Figure 1 The embodiment of the present invention provides a method for optimizing the operation of a fused magnesia smelting process, comprising the following steps:

[0033] S1: Melting data collection and monitoring

[0034] The various data in the fused magnesia smelting process are collected through the set sensor equipment and production control algorithm;

[0035] S2: Process Modeling and Simulation Operation

[0036] Establish mathematical models of the smelting process, including thermodynamics model, kinetic model and fluid mechanics model, and verify the accuracy of the model through simulation software;

[0037] S3: Optimization objectives and constraints

[0038] Establish specific optimization goals for the smelting process and set relevant constraints based on the set optimization goals;

[0039] S4: Intelligent control system design

[0040] Establish or optimize the existing fused magnesia smelting steps according to the set optimization goals, and establish a corresponding intelligent control system;

[0041] S5: Continuous Optimization Feedback

[0042] Establish a continuous optimization and improvement mechanism to continuously optimize the smelting process through data analysis and performance monitoring.

[0043] During the smelting data collection and monitoring process in S1, data of each group of sensor equipment in the production of fused magnesia will be read, including current, voltage, temperature, and furnace pressure, and real-time data analysis and processing will be performed. At the same time, during the real-time data transmission process, data preprocessing technology is used to filter outliers and noise.

[0044] The process modeling in S2 will establish a mathematical model based on the collected detection data. Finite element analysis will be used to simulate the thermal field and stress field in the electric molten magnesia smelting process, and computational fluid dynamics will be used to simulate the fluid in the furnace. At the same time, custom editing of equipment parameters is supported.

[0045] Its momentum equation is:

[0046] [\rho\l eft(\frac{\part ial\mathbf{u}}{\part ialt}+\mathbf{u}\cdo t\nab la\mathbf{u}\ri ght)=-\nab l ap+\mu\nab la^2\mathbf{u}+\mathbf{f}]

[0047] Where (p) is the pressure;

[0048] (\mu) is the dynamic viscosity;

[0049] (\mathbf{f}) is the body force.

[0050] The finite element analysis process needs to establish a finite element mesh according to the geometric model of the actual fused magnesia smelting furnace, and define the geometric model including the values ​​of thermal conductivity, specific heat capacity, and density. At the same time, the thermal boundary conditions of the geometric model are defined, including the heating or cooling boundary of the equipment, the temperature of the external environment and the convection heat transfer coefficient, and the partial differential equation is discretized by the finite element method and converted into an algebraic equation. Finally, the discretized heat conduction equation is solved by a numerical solution method to obtain temperature analysis data.

[0051] In the S2, it is necessary to establish a corresponding simulation scenario of the simulation working condition, and set the initial conditions and boundary conditions of the simulation according to the process parameters and production experience in the actual smelting process. After the simulation scenario is established, the established model needs to be imported into the ANSYS software for analysis and simulation. By simulating the smelting process under different working conditions, the influence of various parameter changes on the smelting results can be predicted, providing theoretical support for the subsequent optimization process.

[0052] The heat conduction equation is expressed as:

[0053] [\rhoc_p\frac{\part ial T}{\part ialt}=\frac{\part ial}{\parti alx}\l eft(k\frac{\part ial T}{\part ialx}\ri ght)+\dot{q}]

[0054] Where (\rho) is the material density;

[0055] (c_p) is the specific heat capacity;

[0056] (k) is thermal conductivity;

[0057] (T) is temperature;

[0058] (\dot{q}) is the heat source rate.

[0059] After discretization, we get the following algebraic equations:

[0060] [[K][T]=[F]];

[0061] Where [K] is the stiffness matrix;

[0062] [T] is the nodal temperature vector;

[0063] [F] is the load vector.

[0064] The optimization objectives and constraints in S3 are mainly used to establish corresponding optimization objectives for specific parameters. After selecting the corresponding optimization objectives, the independent variables and parameters that affect the variables are determined, and the corresponding constraints are established based on the determined variable variation range and related parameter ranges. Subsequently, an optimization algorithm is established based on the set constraints and solved to obtain the optimal optimization result.

[0065] The optimization target in S3 can be a single target or multiple targets. When facing multi-target optimization, the algorithm will make a secondary judgment based on whether there is a conflict between the two. If there is no data conflict between the multiple sets of target data, multiple single-target optimization parameter judgments will be completed independently. If there is a conflict between the parameters of the multiple sets of target data, the algorithm will sacrifice some optimization indicators based on the weighted status of the two sets of data, and use the weighted percentage as a benchmark to weigh the optimization parameters that approximately meet the multi-target optimization goals.

[0066] The weighted parameters in the optimization objectives support manual custom settings. If no custom weighted settings are performed, the algorithm will assign equal weighted parameters to each group of optimization objectives, and make the final optimization parameters approximately close to each group of target optimization values. When the weighted parameters are set manually, the algorithm will optimize one or more groups of objectives with larger weighted ratios according to the emphasis of the weighted algorithm, while the changes in the target optimization values ​​with smaller weighted ratios will decrease as the weighted values ​​decrease.

[0067] Suppose there are two objective functions (f_1(x)) and (f_2(x)), where (x) is the optimized independent variable vector. At this time, multi-objective optimization can be performed by weighted sum, with weights (w_1) and (w_2) respectively, satisfying (w_1+w_2=1), usually (w_1,w_2\geq0).

[0068] The comprehensive objective function can be expressed as: [F(x)=w_1\cdotf_1(x)+w_2\cdotf_2(x)]

[0069] Where (F(x)) is the comprehensive optimization objective function. The optimization problem can be expressed as: [\text{min imi ze}F(x)\quad\text{subjectto}x\in\Omega];

[0070] Here, (\Omega) represents the constraint domain of the independent variable (x);

[0071] At this point, we can determine the objective functions (f_1(x)) and (f_2(x)) that need to be optimized, as well as their respective weights (w_1) and (w_2);

[0072] And construct a comprehensive objective function based on the optimization goal: According to the weight distribution, construct a comprehensive optimization objective function (F(x)=w_1\cdotf_1(x)+w_2\cdotf_2(x)).

[0073] That is, suppose we want to optimize the cost and performance of the fused magnesia processing system, where cost (C(x)) and performance (P(x)) are two objective functions. If we want performance to take precedence, we can set the weight of the performance function (P(x)) to a higher value and the weight of the cost function (C(x)) to a lower value.

[0074] Assuming (w_1=0.7) and (w_2=0.3) are selected, the comprehensive objective function is: [F(x)=0.7\cdotP(x)+0.3\cdotC(x)]

[0075] By optimizing (F(x)), we can find a compromise solution (x^*) that achieves an ideal balance between performance and cost.

[0076] The intelligent control system in S4 is designed to control algorithms and strategies to optimize the smelting process. Based on the optimization objectives and constraints set in S3, the system receives the smelting data collected in S1 in real time, and uses the mathematical model established in S2 for prediction and control.

[0077] The continuous optimization feedback in S5 is used to store historical smelting processing methods and monitor the optimized fused magnesia smelting process, and monitor the current smelting process and score the optimization process according to the optimization target established in S3, and evaluate the optimization effect according to the score value. If the optimization score is lower than a certain value, abnormal feedback is given to the optimization process, and the algorithms and data processing processes in S1-S4 are verified.

[0078] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the operation of a fused magnesia smelting process, characterized in that: The following steps are involved: S1: Smelting data collection and monitoring The various data in the fused magnesia smelting process are collected through the set sensor equipment and production control algorithm; S2: Process Modeling and Simulation Operation Establish mathematical models of the smelting process, including thermodynamics model, kinetic model and fluid mechanics model, and verify the accuracy of the model through simulation software; S3: Optimization objectives and constraints Establish specific optimization goals for the smelting process and set relevant constraints based on the set optimization goals; S4: Intelligent control system design Establish or optimize the existing fused magnesia smelting steps according to the set optimization goals, and establish a corresponding intelligent control system; S5: Continuous Optimization Feedback Establish a continuous optimization and improvement mechanism to continuously optimize the smelting process through data analysis and performance monitoring.

2. The method for optimizing the operation of the fused magnesia smelting process according to claim 1, characterized in that: During the smelting data collection and monitoring process in S1, data of each group of sensor equipment in the production of fused magnesia will be read, including current, voltage, temperature, and furnace pressure, and real-time data analysis and processing will be performed. At the same time, during the real-time data transmission process, data preprocessing technology is used to filter outliers and noise.

3. The method for optimizing the operation of the fused magnesia smelting process according to claim 1, characterized in that: The process modeling in S2 will establish a mathematical model based on the collected detection data. Finite element analysis will be used to simulate the thermal field and stress field in the electric molten magnesia smelting process, and computational fluid dynamics will be used to simulate the fluid in the furnace. At the same time, custom editing of equipment parameters is supported.

4. The method for optimizing the operation of the fused magnesia smelting process according to claim 3, characterized in that: The finite element analysis process needs to establish a finite element mesh according to the geometric model of the actual fused magnesia smelting furnace, and define the geometric model including the values ​​of thermal conductivity, specific heat capacity, and density. At the same time, the thermal boundary conditions of the geometric model are defined, including the heating or cooling boundary of the equipment, the temperature of the external environment and the convection heat transfer coefficient, and the partial differential equation is discretized by the finite element method and converted into an algebraic equation. Finally, the discretized heat conduction equation is solved by a numerical solution method to obtain temperature analysis data.

5. The method for optimizing the operation of the fused magnesia smelting process according to claim 1, characterized in that: In S2, it is necessary to establish a corresponding simulation scenario of the simulation working condition, and set the initial conditions and boundary conditions of the simulation according to the process parameters and production experience in the actual smelting process. After the simulation scenario is established, the established model needs to be imported into the ANSYS software for analysis and simulation. By simulating the smelting process under different working conditions, the influence of various parameter changes on the smelting results can be predicted, providing theoretical support for the subsequent optimization process.

6. The method for optimizing the operation of the fused magnesia smelting process according to claim 1, characterized in that: The optimization objectives and constraints in S3 are mainly used to establish corresponding optimization objectives for specific parameters. After selecting the corresponding optimization objectives, the independent variables and parameters that affect the variables are determined, and the corresponding constraints are established based on the determined variable variation range and related parameter ranges. Subsequently, an optimization algorithm is established based on the set constraints and solved to obtain the optimal optimization result.

7. The method for optimizing the operation of the fused magnesia smelting process according to claim 1, characterized in that: The optimization target in S3 can be a single target or multiple targets. When facing multi-target optimization, the algorithm will make a secondary judgment based on whether there is a conflict between the two. If there is no data conflict between the multiple sets of target data, multiple single-target optimization parameter judgments will be completed independently. If there is a conflict between the parameters of the multiple sets of target data, the algorithm will sacrifice some optimization indicators based on the weighted status of the two sets of data, and use the weighted percentage as a benchmark to weigh the optimization parameters that approximately meet the multi-target optimization goals.

8. The method for optimizing the operation of the fused magnesia smelting process according to claim 7, characterized in that: The weighted parameters in the optimization objectives support manual custom settings. If no custom weighted settings are performed, the algorithm will assign equal weighted parameters to each group of optimization objectives, and make the final optimization parameters approximately close to each group of target optimization values. When the weighted parameters are set manually, the algorithm will optimize one or more groups of objectives with larger weighted ratios according to the emphasis of the weighted algorithm, while the changes in the target optimization values ​​with smaller weighted ratios will decrease as the weighted values ​​decrease.

9. The method for optimizing the operation of the fused magnesia smelting process according to claim 8, characterized in that: The intelligent control system in S4 is designed to control algorithms and strategies to optimize the smelting process. Based on the optimization objectives and constraints set in S3, the system receives the smelting data collected in S1 in real time, and uses the mathematical model established in S2 for prediction and control.

10. The method for optimizing the operation of the fused magnesia smelting process according to claim 1, characterized in that: The continuous optimization feedback in S5 is used to store historical smelting processing methods and monitor the optimized fused magnesia smelting process, and monitor the current smelting process and score the optimization process according to the optimization target established in S3, and evaluate the optimization effect according to the score value. If the optimization score is lower than a certain value, abnormal feedback is given to the optimization process, and the algorithms and data processing processes in S1-S4 are verified.

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