Roller kiln parameter collaborative optimization temperature field control method and system based on orthogonal experiment
Through the collaborative optimization method of roller kiln parameters based on orthogonal experiments, the single-factor limitations of traditional roller kiln temperature control are solved, and the high-precision, low-energy consumption control and rapid response of the temperature field are achieved, and the sintering needs of multiple materials and multiple processes are adapted.
Patent Information
- Application Number
- CN202510595567.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-09
- Publication Date
- 2025-08-19
AI Technical Summary
The temperature control method of traditional roller kilns has the limitation of single-factor optimization, which leads to large uneven temperature field, high energy consumption, and lacks scientific quantitative basis, making it difficult to adapt to the sintering needs of multiple materials and multiple processes, the optimization direction is blind, and the response delay is serious.
Using an orthogonal experiment method, the system coupling optimization of parameters is achieved by defining the structure and operating parameters of the roller kiln, the coupled thermodynamic equation is established, CFD simulation and variance analysis are performed, and dynamic control is combined with the fuzzy PID algorithm.
It realizes high-precision and low-energy control of the roller kiln temperature field, improves temperature uniformity, accelerates response speed, and has significant industrial application value.
Smart Images
Figure CN120508164A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of kiln thermal control, and in particular to a roller kiln parameter collaborative optimization temperature field control method and system based on orthogonal experiments. Background Art
[0002] As a key sintering equipment in the fields of ceramics, lithium battery materials, refractory products, etc., the roller kiln's temperature field uniformity directly determines product quality and energy efficiency. However, the temperature control method of the traditional roller kiln has the following significant problems, which restricts the development of high-precision sintering technology. The traditional roller kiln has a single heating zone layout (such as fixed 3 to 5 zones), guide plate angle and roller material design, which cannot adapt to the sintering needs of multiple materials and multiple processes. For example, the uniform distribution of heating zones can easily lead to overheating in the middle of the kiln and insufficient heat at the edges, with a temperature difference of more than ±25°C. Operating parameters such as roller speed and heating power rely on manual experience to adjust, lacking a scientific and quantitative basis, resulting in poor process stability and large fluctuations in the pass rate.
[0003] Existing technologies often independently optimize single mechanisms within thermal radiation, convective heat transfer, or heat conduction, ignoring the dynamic coupling effects of the three. For example, when adjusting heating power, the guide plate angle is not simultaneously optimized, resulting in radiant heat being carried away by turbulent airflow, resulting in low energy utilization (typical energy consumption is 300-350 kWh / ton). Structural parameters (such as silicon carbon rod spacing) and operating parameters (such as roller speed) exhibit nonlinear interactions, making it difficult to effectively analyze their synergistic influence using traditional trial-and-error methods. Existing technologies gradually optimize parameters using a "single-factor rotation method," requiring dozens or even hundreds of trials. This is time-consuming (typically 3-6 months) and wastes significant resources. Furthermore, the system lacks systematic analysis of experimental data and is unable to quantify the influence of each factor on temperature uniformity, leading to uninformed optimization. Traditional PID algorithms struggle to adapt to transient heat load changes during sintering, with response delays exceeding 10 seconds and overshoot exceeding 15%, exacerbating temperature fluctuations. Fixed parameters such as guide plate angle and roller speed prevent real-time response to changes in the kiln temperature field, resulting in frequent localized over- or under-firing. Therefore, a roller kiln parameter collaborative optimization temperature field control method based on orthogonal experiments has become one of the current research directions. Summary of the Invention
[0004] The present invention provides a roller kiln parameter collaborative optimization temperature field control method and system based on orthogonal experiments, which can effectively solve the problems in the background technology.
[0005] In order to achieve the above object, the technical solution adopted by the present invention is:
[0006] A roller kiln parameter collaborative optimization temperature field control method based on orthogonal experiments, the method comprising:
[0007] Define the structural parameters and operating parameters of the roller kiln, establish coupled thermodynamic equations, and construct a parameter system;
[0008] Based on the parameter system, roller kiln parameters are selected for orthogonal experimental design, and the temperature field distribution in the roller kiln is simulated by CFD simulation method to obtain CFD simulation results;
[0009] Perform variance analysis and multi-objective optimization on the CFD simulation results to obtain the optimal parameter combination;
[0010] Combining real-time monitoring with fuzzy PID algorithm, the roller kiln operating parameters are dynamically controlled through the optimal parameter combination.
[0011] Furthermore, the structural parameters of the roller kiln include: axial spacing of silicon carbon rods, roller arrangement density, kiln cavity cross-section aspect ratio, and insulation layer thickness; the operating parameters include: heating power of the heating wire, roller running speed, inlet and outlet gas temperature, and inlet and outlet gas flow rate.
[0012] Furthermore, the coupled thermodynamic equation is established as:
[0013]
[0014] Where P is the heating power of the heating wire, η is the efficiency factor, k1 and k2 are the heat transfer coefficients, and ΔT is the temperature difference between the inlet and outlet gas (ΔT = T in -T out ), u is the inlet and outlet gas flow rate, D is the roller arrangement density (roller spacing / roller diameter ratio), v is the roller running speed, H / W is the kiln cavity cross-section aspect ratio, Q loss (d) is the heat loss caused by the thickness d of the insulation layer.
[0015] Furthermore, the orthogonal experimental design includes:
[0016] Key parameters were selected from the parameter system, and 9 experimental matrices were designed using the L9(34) orthogonal table;
[0017] Set CFD simulation boundary conditions, simulate the experimental matrix using CFD software, record temperature standard deviation and energy consumption, and obtain CFD simulation results.
[0018] Furthermore, obtaining the optimal parameter combination includes:
[0019] Perform variance analysis on the CFD simulation results to quantify the main effects and interaction effects of parameters and screen significant factors;
[0020] Constructing an optimization problem based on the significant factors, and dynamically adjusting the target weights through an adaptive weighted particle swarm algorithm to generate an initial solution set;
[0021] An optimization goal is set, and the NSGA-II genetic algorithm is used to perform a Pareto front search on the initial solution set to obtain the optimal parameter combination.
[0022] Furthermore, the dynamic control of roller kiln parameters includes:
[0023] Embedding the optimal parameter combination into a PLC control system;
[0024] Real-time monitoring of the temperature inside the roller kiln is carried out through distributed thermocouples on the kiln roof;
[0025] According to the temperature monitoring data, the fuzzy PID algorithm is used to dynamically adjust the operating parameters of the roller kiln.
[0026] Furthermore, the control law for dynamically adjusting the roller kiln operating parameters using the fuzzy PID algorithm is:
[0027]
[0028] Where, e(t) is the temperature deviation, K P , K i , K d are PID parameters.
[0029] A roller kiln parameter collaborative optimization temperature field control system based on orthogonal experiments, the system includes:
[0030] Parameter system construction module, defines the structural parameters and operating parameters of the roller kiln, establishes coupled thermodynamic equations, and constructs the parameter system;
[0031] An orthogonal experimental design module, based on the parameter system, selects roller kiln parameters for orthogonal experimental design, and simulates the temperature field distribution in the roller kiln using a CFD simulation method to obtain CFD simulation results;
[0032] A data analysis and optimization module performs variance analysis and multi-objective optimization on the CFD simulation results to obtain the optimal parameter combination;
[0033] The parameter dynamic control module combines real-time monitoring with fuzzy PID algorithm to dynamically control the roller kiln parameters through the optimal parameter combination.
[0034] Furthermore, the data analysis and optimization module includes:
[0035] A variance analysis unit is used to perform variance analysis on the CFD simulation results, quantify the main effects and interaction effects of parameters, and screen significant factors;
[0036] A multi-objective optimization unit constructs an optimization problem based on the significant factors, and dynamically adjusts the objective weights through an adaptive weighted particle swarm algorithm to generate an initial solution set;
[0037] The optimal combination acquisition unit sets an optimization target and uses the NSGA-II genetic algorithm to perform a Pareto front search on the initial solution set to obtain the optimal parameter combination.
[0038] Furthermore, the parameter dynamic control module includes:
[0039] A parameter embedding unit, which embeds the optimal parameter combination into a PLC control system;
[0040] The temperature monitoring unit monitors the temperature inside the roller kiln in real time through distributed thermocouples on the kiln roof;
[0041] The parameter adjustment unit uses fuzzy PID algorithm to dynamically adjust the roller kiln operating parameters according to temperature monitoring data.
[0042] The technical solution of the present invention can achieve the following technical effects:
[0043] It effectively solves the single-factor limitation problem of roller kiln temperature field control. Through the systematic coupling and intelligent optimization of structure-operation parameters, the influence weight of parameter combination on temperature field is determined through a limited number of experiments. Combined with numerical simulation and real-time feedback, high-precision and low-energy consumption control of temperature field is achieved. An intelligent optimization chain is established, breaking through the limitation of local optimal solution. At the same time, parameter fine-tuning based on real-time feedback can achieve long-term stability of temperature field, which has significant industrial application value.
[0044] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS
[0045] 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, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0046] Figure 1 This is a flow chart of the temperature field control method for roller kiln parameter collaborative optimization based on orthogonal experiments;
[0047] Figure 2 Design table for orthogonal experimental matrix;
[0048] Figure 3 Schematic diagram of the process for obtaining the optimal parameter combination. DETAILED DESCRIPTION
[0049] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.
[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.
[0051] Example 1:
[0052] like Figure 1 As shown in FIG, a roller kiln parameter collaborative optimization temperature field control method based on orthogonal experiment includes:
[0053] S1: Define the structural parameters and operating parameters of the roller kiln, establish coupled thermodynamic equations, and construct a parameter system;
[0054] Specifically, when defining the structural parameters and operating parameters of the roller kiln, the dynamic relationship between the parameters can be established through thermodynamic equations, and the basic framework of the roller kiln simulation model can be formed. Laser scanning, three-dimensional imaging technology or other high-precision equipment can be used to measure the actual geometric structure of the kiln body. Focus on measuring the size, shape, inlet / outlet position, kiln wall thickness, material properties, etc. of the kiln body. Based on the measurement results, computer-aided design software (such as SolidWorks, AutoCAD, etc.) is used to convert the actual structure of the kiln body into a three-dimensional model. The model should include physical properties such as heat conduction, fluid flow, and radiation inside and outside the kiln body. In the simulation software (such as ANSYS, Fluent, COMSOL, etc.), three-dimensional meshing is performed, and the meshes of key areas (such as high-temperature areas, airflow distribution areas, etc.) are refined to ensure the accuracy of the simulation results.
[0055] S2: Based on the parameter system, the roller kiln parameters are selected for orthogonal experimental design, and the temperature field distribution in the roller kiln is simulated by CFD simulation method to obtain CFD simulation results;
[0056] Specifically, this step involves using orthogonal experimental design to set different parameter combinations based on the kiln's operating parameters (such as air velocity, temperature, and pressure). The orthogonal array design reduces the number of experiments while ensuring that the main effects of each factor are fully evaluated. In CFD software, simulations are performed based on each parameter set in the orthogonal array. The flow field, thermal field, and boundary conditions (such as inlet temperature and flow rate, outlet pressure, etc.) are set. CFD simulations are then run for each parameter combination to obtain the temperature, flow, and pressure distributions for each case.
[0057] S3: Perform variance analysis and multi-objective optimization on the CFD simulation results to obtain the optimal parameter combination;
[0058] This example performs variance analysis on the results extracted from CFD simulations to determine the extent to which different process parameters affect the kiln's thermal and flow fields. This analysis identifies the primary factors (such as temperature and air velocity). Based on the variance analysis results, an optimization problem is constructed, and a genetic algorithm is used to find the optimal solution. Through continuous iteration, various operating parameters (such as inlet temperature and flow velocity) are optimized to achieve the optimal thermal effect and flow field distribution.
[0059] S4: Combine real-time monitoring with fuzzy PID algorithm to dynamically control the roller kiln operating parameters through the optimal parameter combination.
[0060] Specifically, the optimal operating parameters are extracted from the genetic algorithm's optimization results. These optimized operating parameters can be downloaded to the PLC system, and a control program can be written to achieve dynamic feedback. The PLC program should be able to monitor parameters such as temperature, pressure, and airflow within the kiln in real time and adjust each control parameter based on the set optimal solution. During system operation, the PLC should make real-time adjustments based on sensor data (such as temperature and pressure) to ensure that the kiln operates under optimized conditions. This dynamic feedback control ensures that the kiln's thermal effects and flow field remain in an optimal state.
[0061] The present invention effectively solves the problem of single-factor limitation in the temperature field control of roller kilns. Through the systematic coupling and intelligent optimization of structure-operation parameters, the weight of the influence of parameter combinations on the temperature field is determined through a limited number of experiments. Combined with numerical simulation and real-time feedback, high-precision and low-energy consumption control of the temperature field is achieved, an intelligent optimization chain is established, and the limitation of local optimal solutions is broken. At the same time, parameter fine-tuning based on real-time feedback can achieve long-term stability of the temperature field, which has significant industrial application value.
[0062] Furthermore, the structural parameters of the roller kiln include: axial spacing of silicon carbon rods, roller arrangement density, aspect ratio of the kiln cavity section, and thickness of the insulation layer; the operating parameters include: heating power of the heating wire, roller running speed, inlet and outlet gas temperature, and inlet and outlet gas flow rate.
[0063] In order to describe the dynamic relationship between the parameters, the coupled thermodynamic equation is established as:
[0064]
[0065] Where P is the heating power of the heating wire, η is the efficiency factor, k1 and k2 are the heat transfer coefficients, and ΔT is the inlet and outlet gas temperature difference (ΔT = T in -T out ), u is the inlet and outlet gas flow rate, D is the roller arrangement density (roller spacing / roller diameter ratio), v is the roller running speed, H / W is the kiln cavity cross-section aspect ratio, Q loss (d) is the heat loss caused by the thickness d of the insulation layer.
[0066] Specifically, the thermodynamic equation relates structural parameters to operating parameters through physical mechanisms such as heat conduction and fluid flow. This thermodynamic formula reveals that when the roller speed v increases, P or T needs to be increased simultaneously. in To compensate for the shortened residence time of the material; when the gas flow rate u increases, T can be reduced in However, it is necessary to ensure that P is not lower than the lower limit; when the kiln cavity aspect ratio H / W is high, the roller arrangement density needs to be reduced (the roller spacing / roller diameter ratio takes an upper limit of 4.0) to improve the airflow distribution; structural parameters (such as insulation layer thickness, roller arrangement density) can be adjusted by coefficients k1, k2 and Q loss (d) Indirectly constrain the operating parameter range to ensure the thermodynamic stability of the system. Based on thermodynamic formulas and historical data, the feasible range of each parameter can be clarified and the parameter constraints can be set. The constraints include: 80kW≤P≤120kW, 0.5m / min≤v≤2.5m / min, T in ≥100℃,T out ≤300℃, 0.5m / s≤u≤2.5m / s, 2.5≤roller spacing / roller diameter ratio≤4.0, 0.6≤H / W≤1.2, d=314mm (lightweight brick + fiber blanket).
[0067] Based on the above embodiment, performing orthogonal experimental design includes:
[0068] S21: Select key parameters in the parameter system and use L9(34) orthogonal table to design 9 experimental matrices;
[0069] S22: Set CFD simulation boundary conditions, simulate the experimental matrix using CFD software, record the temperature standard deviation and energy consumption, and obtain CFD simulation results.
[0070] like Figure 2As shown, this step can use the L9 (34) orthogonal table to generate 9 groups of experiments, select the four key parameters P, v, u, H / W, and set 3 levels for each parameter. The level setting is required to cover extreme working conditions (such as v min =0.5m / min,v max =2.5m / min) to ensure the comprehensiveness of the experimental matrix; the obtained experimental matrix can be used to simulate the temperature field distribution in the kiln under each combination through ANSYS Fluent, and record the temperature standard deviation σ (target σ≤±10℃) and energy consumption Q (kWh / ton).
[0071] As a preferred embodiment of this invention, Figure 3 As shown, obtaining the optimal parameter combination includes:
[0072] S31: Perform variance analysis on CFD simulation results to quantify the main and interactive effects of parameters and screen significant factors;
[0073] Specifically, this step quantifies the main and interaction effects of parameters through variance analysis. For example, it is found that the P˙v interaction term contributes 30% to temperature uniformity. The F value of each factor is calculated, and significant parameters are screened. Heating power, silicon carbon rod spacing, and roller speed are significant factors.
[0074] S32: Construct an optimization problem based on significant factors, and dynamically adjust the target weights through the adaptive weighted particle swarm algorithm to generate an initial solution set;
[0075] Based on the results of variance analysis, temperature uniformity σ and energy consumption Q can be set as conflicting objectives in multi-objective optimization. The adaptive weighted particle swarm optimization algorithm (AW-PSO) can be used to dynamically adjust the objective weights to solve the premature convergence problem of the traditional algorithm. The dynamic weight adjustment formula is:
[0076] w i (t) = w min +(w max -w min )·e -λt ;
[0077] Among them, w i is the weight of the i-th target, and λ is the attenuation coefficient.
[0078] Since the dynamic weight mechanism adapts to the changes in the intensity of conflicts between targets, it can quickly generate high-quality initial solutions, which can provide high-quality populations for the subsequent NSGA-II and accelerate convergence.
[0079] S33: Set the optimization goal and use the NSGA-II genetic algorithm to perform Pareto front search on the initial solution set to obtain the optimal parameter combination.
[0080] This step uses the solution set of AW-PSO as the initial population. Through the selection, crossover, and mutation operations of the NSGA-II genetic algorithm, a better non-dominated solution can be searched globally to ensure the diversity and convergence of the solution set.
[0081] Based on the above embodiment, the dynamic control of roller kiln parameters includes:
[0082] S41: embedding the optimal parameter combination into the PLC control system;
[0083] S42: Real-time monitoring of the temperature inside the roller kiln through distributed thermocouples on the kiln roof;
[0084] S43: Based on the temperature monitoring data, the fuzzy PID algorithm is used to dynamically adjust the roller kiln operating parameters.
[0085] Specifically, the optimal parameter combination is embedded in a PLC control system (such as a Siemens S7-1200). Real-time temperature monitoring is performed using distributed thermocouples (K type, with an accuracy of ±1.5°C) on the kiln roof. A fuzzy PID algorithm is used to address nonlinear time lags, adapting to the dynamic characteristics of different operating conditions and reducing control errors caused by hysteresis. This step continuously optimizes temperature field uniformity and suppresses external disturbances through closed-loop control. For example, under a ±10% disturbance, the temperature fluctuation range is reduced from ±15°C to ±3°C, shortening response time by 40%.
[0086] Furthermore, the control law for dynamically adjusting the roller kiln operating parameters using the fuzzy PID algorithm is:
[0087]
[0088] Where, e(t) is the temperature deviation, K P , K i , K d are PID parameters.
[0089] Example 2:
[0090] The roller kiln parameter collaborative optimization temperature field control system based on orthogonal experiments includes:
[0091] Parameter system construction module, defines the structural parameters and operating parameters of the roller kiln, establishes coupled thermodynamic equations, and constructs the parameter system;
[0092] The orthogonal experimental design module selects roller kiln parameters for orthogonal experimental design based on the parameter system, and simulates the temperature field distribution in the roller kiln through CFD simulation method to obtain CFD simulation results;
[0093] Data analysis and optimization module, which performs variance analysis and multi-objective optimization on CFD simulation results to obtain the optimal parameter combination;
[0094] The parameter dynamic control module combines real-time monitoring with fuzzy PID algorithm to dynamically control the roller kiln parameters through the optimal parameter combination.
[0095] The above-mentioned adjustment system in the present invention can effectively realize the roller kiln parameter collaborative optimization temperature field control method based on orthogonal experiment. The technical effects that can be achieved are as described in the above embodiments and will not be repeated here.
[0096] Furthermore, the data analysis and optimization module includes:
[0097] Variance analysis unit, which performs variance analysis on CFD simulation results, quantifies the main effects and interaction effects of parameters, and screens significant factors;
[0098] The multi-objective optimization unit constructs the optimization problem based on significant factors and dynamically adjusts the objective weights through the adaptive weighted particle swarm algorithm to generate the initial solution set;
[0099] The optimal combination acquisition unit sets the optimization target and uses the NSGA-II genetic algorithm to perform Pareto front search on the initial solution set to obtain the optimal parameter combination.
[0100] Furthermore, the parameter dynamic control module includes:
[0101] Parameter embedding unit, embeds the optimal parameter combination into the PLC control system;
[0102] The temperature monitoring unit monitors the temperature inside the roller kiln in real time through distributed thermocouples on the kiln roof;
[0103] The parameter adjustment unit uses fuzzy PID algorithm to dynamically adjust the roller kiln operating parameters according to temperature monitoring data.
[0104] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the corresponding optimization effects of the method in Example 1, which will not be repeated here.
[0105] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and drawings are merely illustrative of the present application as defined herein and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. It is apparent that various modifications and variations of the present application may be made by those skilled in the art without departing from the scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the present application and its equivalents.
Claims
1. A roller kiln parameter collaborative optimization temperature field control method based on orthogonal experiments, characterized in that: The method comprises: Define the structural parameters and operating parameters of the roller kiln, establish coupled thermodynamic equations, and construct a parameter system; Based on the parameter system, roller kiln parameters are selected for orthogonal experimental design, and the temperature field distribution in the roller kiln is simulated by CFD simulation method to obtain CFD simulation results; Perform variance analysis and multi-objective optimization on the CFD simulation results to obtain the optimal parameter combination; Combining real-time monitoring with fuzzy PID algorithm, the roller kiln operating parameters are dynamically controlled through the optimal parameter combination.
2. The roller kiln parameter collaborative optimization temperature field control method based on orthogonal experiment according to claim 1 is characterized in that: The structural parameters of the roller kiln include: axial spacing of silicon carbon rods, roller arrangement density, kiln cavity cross-section aspect ratio, and insulation layer thickness; the operating parameters include: heating wire heating power, roller running speed, inlet and outlet gas temperature, and inlet and outlet gas flow rate.
3. The roller kiln parameter collaborative optimization temperature field control method based on orthogonal experiment according to claim 2 is characterized in that: The coupled thermodynamic equation is established as: Where P is the heating power of the heating wire, η is the efficiency factor, k1 and k2 are the heat transfer coefficients, and ΔT is the temperature difference between the inlet and outlet gas (ΔT = T in -T out ), u is the inlet and outlet gas flow rate, D is the roller arrangement density (roller spacing / roller diameter ratio), v is the roller running speed, H / W is the kiln cavity cross-section aspect ratio, Q loss (d) is the heat loss caused by the thickness d of the insulation layer.
4. The roller kiln parameter collaborative optimization temperature field control method based on orthogonal experiment according to claim 1 is characterized in that: The orthogonal experimental design comprises: Key parameters were selected from the parameter system, and 9 experimental matrices were designed using the L9(34) orthogonal table; Set CFD simulation boundary conditions, simulate the experimental matrix using CFD software, record temperature standard deviation and energy consumption, and obtain CFD simulation results.
5. The roller kiln parameter collaborative optimization temperature field control method based on orthogonal experiment according to claim 1 is characterized in that: Obtaining the optimal parameter combination includes: Perform variance analysis on the CFD simulation results to quantify the main effects and interaction effects of parameters and screen significant factors; Constructing an optimization problem based on the significant factors, and dynamically adjusting the target weights through an adaptive weighted particle swarm algorithm to generate an initial solution set; An optimization goal is set, and the NSGA-II genetic algorithm is used to perform a Pareto front search on the initial solution set to obtain the optimal parameter combination.
6. The roller kiln parameter collaborative optimization temperature field control method based on orthogonal experiment according to claim 1 is characterized in that: The dynamic control of roller kiln parameters includes: Embedding the optimal parameter combination into a PLC control system; Real-time monitoring of the temperature inside the roller kiln is carried out through distributed thermocouples on the kiln roof; According to the temperature monitoring data, the fuzzy PID algorithm is used to dynamically adjust the operating parameters of the roller kiln.
7. The roller kiln parameter collaborative optimization temperature field control method based on orthogonal experiment according to claim 6 is characterized in that: The control law for dynamically adjusting the roller kiln operating parameters using the fuzzy PID algorithm is: Where, e(t) is the temperature deviation, K P , K i , K d are PID parameters.
8. The roller kiln parameter collaborative optimization temperature field control system based on orthogonal experiment is characterized by: The system comprises: Parameter system construction module, defines the structural parameters and operating parameters of the roller kiln, establishes coupled thermodynamic equations, and constructs the parameter system; An orthogonal experimental design module, based on the parameter system, selects roller kiln parameters for orthogonal experimental design, and simulates the temperature field distribution in the roller kiln using a CFD simulation method to obtain CFD simulation results; A data analysis and optimization module performs variance analysis and multi-objective optimization on the CFD simulation results to obtain the optimal parameter combination; The parameter dynamic control module combines real-time monitoring with fuzzy PID algorithm to dynamically control the roller kiln parameters through the optimal parameter combination.
9. The roller kiln parameter collaborative optimization temperature field control system based on orthogonal experiment according to claim 8 is characterized in that: The data analysis and optimization module includes: A variance analysis unit is used to perform variance analysis on the CFD simulation results, quantify the main effects and interaction effects of parameters, and screen significant factors; A multi-objective optimization unit constructs an optimization problem based on the significant factors, and dynamically adjusts the objective weights through an adaptive weighted particle swarm algorithm to generate an initial solution set; The optimal combination acquisition unit sets an optimization target and uses the NSGA-II genetic algorithm to perform a Pareto front search on the initial solution set to obtain the optimal parameter combination.
10. The roller kiln parameter collaborative optimization temperature field control system based on orthogonal experiment according to claim 8, characterized in that: The parameter dynamic control module includes: A parameter embedding unit, which embeds the optimal parameter combination into a PLC control system; The temperature monitoring unit monitors the temperature inside the roller kiln in real time through distributed thermocouples on the kiln roof; The parameter adjustment unit uses fuzzy PID algorithm to dynamically adjust the roller kiln operating parameters according to temperature monitoring data.