Copper rod subgrain boundary-based continuous casting and rolling process regulation method and system

Abstract

The present application relates to non-ferrous metal processing technical field, especially to a kind of based on copper pole subgrain boundary continuous casting and rolling process regulation and control method and system, method includes: acquisition process parameter, constructs process parameter vector, in continuous casting crystallization zone and continuous rolling section set position, respectively acquisition casting blank surface temperature field, subgrain boundary orientation consistency index and rolling traction tension;Based on finite element method discrete calculation casting blank temperature-stress field, constructs coupling subgrain boundary model, to coupling subgrain boundary model parameter is carried out online correction;Based on coupling subgrain boundary model constructs multi-objective collaborative optimization model, using based on cuckoo search and teaching optimization algorithm hybrid algorithm optimization solution, obtain optimal process parameter vector;The optimal process parameter vector is applied to continuous casting and rolling system, and according to the deviation of online monitoring data and prediction triggers re-optimization or alarm. Can simultaneously optimize subgrain boundary size and orientation consistency, significantly improve the mechanical properties and electrical conductivity of copper pole.

Description

Technical Field

[0001] This invention relates to the field of non-ferrous metal processing technology, and in particular to a method and system for controlling continuous casting and rolling processes based on copper rod subgrain boundaries. Background Technology

[0002] The continuous casting and rolling SCR process is a key step in the efficient production of copper rods. Its basic principle is that in a five-wheeled continuous casting machine, molten copper passes through a ladle, tundish, and mold cavity, where it rapidly solidifies under the action of a carbon coating layer and cooling water. First, a fine equiaxed crystal shell layer forms on the surface, and then columnar crystals grow along the heat flow direction inside to form a cast billet. After demolding, the billet enters a continuous rolling mill, where it is rolled into shape through heating and controlled traction tension. By adjusting the casting speed, cooling intensity (carbon coating thickness, cooling water volume, and nozzle arrangement), rolling temperature, and tension, the subgrain boundary size, orientation, and distribution of the billet can be affected; and the parameters of the subgrain boundaries are highly correlated with the mechanical properties, electrical conductivity, and tensile properties of the copper rod.

[0003] In existing technologies, uneven cooling leads to insufficient local cooling intensity in the cast billet, resulting in coarse grains and bent or offset crystal lines. This causes an imbalance in the distribution of equiaxed and columnar crystal regions in the cast billet, affecting the torsional and elongation properties after rolling. Traditional empirical adjustment of process parameters is difficult to simultaneously take into account both continuous casting and continuous rolling processes, and lacks control and feedback on subgrain boundary evolution, resulting in large production fluctuations and insufficient yield.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] This invention provides a method and system for controlling the continuous casting and rolling process based on copper rod subgrain boundaries, thereby effectively solving the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for controlling continuous casting and rolling process based on copper rod subgrain boundaries, comprising the following steps:

[0007] Process parameters were collected and a process parameter vector was constructed. At set positions in the continuous casting crystallization zone and the continuous rolling section, the surface temperature field of the billet, the subgrain boundary orientation consistency index, and the rolling traction tension were collected respectively.

[0008] Based on the finite element method, the temperature-stress field of the cast billet is discretized and calculated. A coupled subgrain boundary model is constructed. The parameters of the coupled subgrain boundary model are corrected online based on the surface temperature field of the cast billet, the subgrain boundary orientation consistency index and the rolling traction tension.

[0009] A multi-objective collaborative optimization model is constructed based on the coupled subgrain boundary model. A hybrid algorithm based on cuckoo search and teaching optimization algorithm is used to optimize and solve the multi-objective collaborative optimization model to obtain the optimal process parameter vector.

[0010] The optimal process parameter vector is applied to the continuous casting and rolling system, and re-optimization or alarm is triggered based on the deviation between online monitoring data and predictions.

[0011] Furthermore, the step of discretizing the temperature-stress field of the cast billet based on the finite element method and constructing a coupled subgrain boundary model includes the following steps:

[0012] A three-dimensional model of the geometry of the casting billet in the continuous casting-rolling section is constructed.

[0013] In the finite element software, the three-dimensional model mesh is divided into several finite element elements and boundary conditions are set;

[0014] Based on the aforementioned process parameter vector, finite element simulations of temperature and stress fields are performed using heat transfer equations and mechanical equilibrium equations.

[0015] At the center of each finite element, a coupled subgrain boundary model is constructed by driving the subgrain boundary evolution through a local temperature-stress field.

[0016] Furthermore, the coupled subgrain boundary model includes:

[0017] f(t;x)=1-exp[-k(T(x,t),σ(x,t))t n ];

[0018]

[0019] In the formula, f(t;x) represents the subgrain boundary evolution fraction at position x of the finite element at time t; k(T,σ) is the temperature and stress-dependent diffusion coefficient, T(t;x) is the temperature at position x of the finite element at time t, σ(t;x) is the stress at position x of the finite element at time t; k0 is the pre-factor; Q is the activation energy; R is the gas constant; β is the stress sensitivity coefficient; and n is the growth exponent.

[0020] Furthermore, the online calibration of the evolutionary coupling model parameters includes:

[0021] The surface temperature predicted by the finite element method is compared with the surface temperature field of the cast billet at first set time intervals.

[0022] Every second predetermined time interval, the orientation consistency predicted by the model is compared with the subgrain boundary orientation consistency index.

[0023] If the deviation after comparison exceeds the set condition, parameter re-identification is triggered, including:

[0024] A model parameter vector is constructed, and the model parameter vector is updated online using the recursive least squares method. The updated model parameter vector is then applied to the coupled subgrain boundary model.

[0025] Furthermore, the construction of the multi-objective collaborative optimization model includes:

[0026] Let the process parameter vector be p = [v, ΔT, T]. r ,F] T ;

[0027] In the formula: v is the casting speed, ΔT is the undercooling degree, and T is the casting speed. r Where F is the rolling temperature and F is the traction tension;

[0028] Based on the coupled subgrain boundary model, the average subgrain boundary size and orientation consistency at a given p are predicted, and an objective function is constructed:

[0029] F(p) = w1F1(p) + w2F2(p);

[0030]

[0031] In the formula, F(P) is the comprehensive objective function, F1(P) and F2(P) are the first objective function and the second objective function, respectively, and w1 and w2 are the first weighting coefficient and the second weighting coefficient, respectively. For the average subgrain boundary size, C o (p) represents the subgrain boundary orientation consistency, which is statistically obtained from the coupled subgrain boundary model.

[0032] in, The following calculations were performed using the coupled subgrain boundary model:

[0033]

[0034] In the formula, d(t;x) represents the characteristic size of the subgrain boundary at position x of the finite element at time t, d0 is the initial characteristic size coefficient of the subgrain boundary, which is calibrated by the annealing test; f(t;x) represents the evolution fraction of the subgrain boundary at position x of the finite element at time t; and m is the sensitivity index of the subgrain size to the evolution fraction.

[0035] Furthermore, the optimization solution of the multi-objective collaborative optimization model using a hybrid algorithm based on cuckoo search and teaching optimization includes the following steps:

[0036] Using the process parameters as decision variables, the Cuckoo Search (CS) algorithm is first used to perform a global search to generate several individuals;

[0037] The optimal individual is selected as the teacher individual, and the fitness of each of the remaining individuals is calculated. If there is room for improvement, the teacher individual is replaced. The process is iterated and the optimal process parameter vector is output.

[0038] Furthermore, the step of using the Cuckoo Search CS algorithm to perform a global search and generate several individuals includes the following steps:

[0039] Randomly generate n feasible solutions:

[0040]

[0041] To ensure that it meets process safety constraints and physical feasibility constraints;

[0042] Calculate the fitness of each individual:

[0043]

[0044] For each solution Lévy flight is used to generate candidate solutions:

[0045]

[0046] In the formula, g is the number of iterations, α is the Lévy step size scaling factor, and λ is the step size;

[0047] Randomly select another solution like:

[0048]

[0049] Then the solution Replace with the candidate solution p i ′;

[0050] With probability p a Discard the worst p a There are n solutions, which are supplemented with new random solutions.

[0051] Several solutions are generated iteratively, and these solutions are the individual entities.

[0052] Furthermore, the process of selecting the optimal individual as the teacher individual, learning from and calculating the fitness of each of the remaining individuals, and replacing the teacher individual if improvement is possible, includes the following steps:

[0053] Calculate the individuals in the current population Mean:

[0054]

[0055] Select the optimal individual as the teacher individual.

[0056] For each solution renew:

[0057]

[0058] Where r∈(0,1), q∈{1,2};

[0059] If the fitness of the updated solution is less than that of the original solution, then replace it.

[0060] Randomly pair up individuals in the population, for each pair (i,j):

[0061]

[0062] right Calculate fitness; replace if improvement is needed.

[0063] Select the individual with the lowest fitness from the updated population.

[0064] like If there is no significant improvement for several consecutive generations, the iteration is terminated.

[0065] Output the optimal process parameter vector.

[0066] Furthermore, the step of triggering re-optimization or alarm based on the deviation between online monitoring data and prediction includes:

[0067] Based on the coupled subgrain boundary model, the surface temperature field of the cast billet, the subgrain boundary orientation consistency index, and the rolling traction tension are pre-calculated under a given P.

[0068] At each sampling time, the temperature field deviation, subgrain boundary orientation consistency deviation, and rolling traction tension deviation are calculated;

[0069] If at any given time there is a temperature field deviation, subgrain boundary orientation consistency deviation, or rolling traction tension deviation that exceeds the set temperature field deviation threshold, orientation consistency threshold, or tension deviation threshold, it is determined to be a working condition drift.

[0070] If there is operating condition drift in N consecutive sampling points, the last optimal process parameter vector is used as the population center, and the hybrid algorithm optimization process is re-executed; if communication timeout, model solution failure or abnormal response of the actuator occurs during the re-optimization process, an alarm is issued;

[0071] The new process parameter vector obtained from the solution will be sent out and executed;

[0072] If there is no operating condition drift within M consecutive minutes, it is judged to be in a stable state, and the re-optimization or alarm is lifted.

[0073] This invention also includes a continuous casting and rolling process control system based on copper rod subgrain boundaries, using the method described above, the system comprising:

[0074] The acquisition unit is used to collect process parameters and construct process parameter vectors. At set positions in the continuous casting crystallization zone and continuous rolling section, the surface temperature field of the billet, the subgrain boundary orientation consistency index, and the rolling traction tension are collected respectively.

[0075] The modeling unit is used to discretize the temperature-stress field of the cast billet based on the finite element method, construct a coupled subgrain boundary model, and perform online correction of the parameters of the coupled subgrain boundary model based on the surface temperature field of the cast billet, the subgrain boundary orientation consistency index, and the rolling traction tension.

[0076] The solution unit is used to construct a multi-objective collaborative optimization model based on the coupled subgrain boundary model, and to optimize and solve the multi-objective collaborative optimization model using a hybrid algorithm based on cuckoo search and teaching optimization algorithm to obtain the optimal process parameter vector;

[0077] The application unit is used to apply the optimal process parameter vector to the continuous casting and rolling system, and to trigger re-optimization or alarm based on the deviation between online monitoring data and prediction.

[0078] The present invention also includes a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described above.

[0079] The present invention also includes a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0080] The beneficial effects of this invention are as follows: Based on the finite element method, the temperature-stress field of the cast billet is discretized and calculated to construct a coupled subgrain boundary model. The parameters of the coupled subgrain boundary model are calibrated online based on the surface temperature field of the cast billet, the subgrain boundary orientation consistency index, and the rolling traction tension. This combines real-time monitoring with simulation prediction, ensuring a high degree of consistency between the model and actual working conditions, accurately predicting the subgrain boundary distribution under different process parameters, avoiding blind trial and error, and continuously improving the model's prediction accuracy through online calibration. A multi-objective collaborative optimization model is constructed based on the coupled subgrain boundary model. A hybrid algorithm based on Cuckoo Search and Teaching-Based Optimization (TLBO) is used to optimize and solve the multi-objective collaborative optimization model, obtaining the optimal process parameter vector. The Cuckoo Search (CS) algorithm ensures a global jump search, avoiding getting trapped in local optima, while the Teaching-Based Optimization (TLBO) algorithm improves local accuracy and accelerates convergence. The hybrid algorithm requires no gradient, is suitable for complex nonlinear coupled models, has fewer parameters, is easy to adjust, and is more suitable for industrial applications. It can simultaneously optimize multiple indices, achieving balanced control of subgrain boundary size and orientation, and simultaneously optimizes subgrain boundary size and orientation consistency, significantly improving the mechanical and electrical properties of copper rods. Attached Figure Description

[0081] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0082] Figure 1 This is a flowchart of the method of the present invention;

[0083] Figure 2 This is a schematic diagram of the system structure of the present invention;

[0084] Figure 3 To optimize the comparison chart of tensile strength before and after;

[0085] Figure 4 To optimize the comparison chart of the elongation of the copper rod before and after;

[0086] Figure 5 To optimize the conductivity comparison chart before and after;

[0087] Figure 6 This is a schematic diagram of the structure of the computer device of the present invention. Detailed Implementation

[0088] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0089] Example 1:

[0090] like Figure 1 As shown: A method for controlling the continuous casting and rolling process based on copper rod subgrain boundaries, comprising the following steps:

[0091] Process parameters were collected and a process parameter vector was constructed. At set positions in the continuous casting crystallization zone and the continuous rolling section, the surface temperature field of the billet, the subgrain boundary orientation consistency index, and the rolling traction tension were collected respectively.

[0092] Based on the finite element method, the temperature-stress field of the cast billet is discretized and calculated. A coupled subgrain boundary model is constructed. The parameters of the coupled subgrain boundary model are corrected online based on the surface temperature field of the cast billet, the subgrain boundary orientation consistency index and the rolling traction tension.

[0093] A multi-objective collaborative optimization model was constructed based on the coupled subgrain boundary model. A hybrid algorithm based on cuckoo search and teaching optimization was used to optimize and solve the multi-objective collaborative optimization model to obtain the optimal process parameter vector.

[0094] The optimal process parameter vector is applied to the continuous casting and rolling system, and further optimization or alarms are triggered based on the deviation between online monitoring data and predictions.

[0095] Based on the finite element method, the temperature-stress field of the cast billet is discretized, and a coupled subgrain boundary model is constructed. The parameters of the coupled subgrain boundary model are calibrated online based on the surface temperature field of the cast billet, the subgrain boundary orientation consistency index, and the rolling traction tension. Combining real-time monitoring with simulation prediction ensures a high degree of consistency between the model and actual working conditions, accurately predicts the subgrain boundary distribution under different process parameters, avoids blind trial and error, and continuously improves the model's prediction accuracy through online calibration. A multi-objective collaborative optimization model is constructed based on the coupled subgrain boundary model. A hybrid algorithm based on Cuckoo Search and Teaching-Based Optimization (TLBO) is used to optimize and solve the multi-objective collaborative optimization model, obtaining the optimal process parameter vector. The Cuckoo Search (CS) algorithm ensures a global jump search, avoiding getting trapped in local optima, while the Teaching-Based Optimization (TLBO) algorithm improves local accuracy and accelerates convergence. The hybrid algorithm requires no gradient, is suitable for complex nonlinear coupled models, has fewer parameters, is easy to adjust, and is more suitable for industrial applications. It can simultaneously optimize multiple indices, achieving balanced control of subgrain boundary size and orientation, and simultaneously optimizes subgrain boundary size and orientation consistency, significantly improving the mechanical and electrical properties of copper rods.

[0096] In this embodiment, the coupled subgrain boundary model is constructed based on the discrete calculation of the temperature-stress field of the cast billet using the finite element method, including the following steps:

[0097] A three-dimensional model of the geometry of the casting billet in the continuous casting-rolling section is constructed.

[0098] In the finite element software, the 3D model mesh is divided into several finite element elements and boundary conditions are set; tetrahedral or hexahedral elements are appropriately refined along the length direction (e.g., 100-200 elements per meter), and the density is increased according to the gradient change in the diameter direction; several circumferential elements are divided on the cross section.

[0099] Set boundary conditions:

[0100] Thermal boundary: The initial temperature field is set to the copper liquid temperature; Crystallizer section: The convective heat transfer coefficient between the surface and the carbon coating / cooling water is hc≈5000–8000W / (m²). 2 K); Atmospheric section: natural convection and radiation, combined heat transfer coefficient ha≈50–100W / (m²); 2 K);

[0101] Mechanical boundaries: Axial traction: Applying a time-varying tension F(t) to the tail of the billet; Rolling contact: Applying contact pressure and friction in the roll-to-bill contact area.

[0102] Finite element simulation of temperature and stress fields is performed based on process parameter vectors using heat transfer equations and mechanical equilibrium equations.

[0103] Heat transfer equation:

[0104]

[0105] In the formula, P is the density of copper, and c p Q is the specific heat capacity, k is the thermal conductivity, and Q is the thermal conductivity. latent (t) is the latent heat of solidification term, which is interpolated based on the change of solid fraction with temperature;

[0106] Equilibrium equations of mechanics:

[0107] ▽·σ+b=0,σ=D:(ε-ε th );

[0108] In the formula, ε th =α th (TT ref I represents thermal expansion strain; D represents the elastic or elastoplastic constitutive matrix.

[0109] Time step and alternating iteration:

[0110] Choose a time step Δt≈0.1–1s;

[0111] At each time step, the heat transfer equation is first solved to obtain T(x,t), and then the mechanical equilibrium is solved based on the new temperature field.

[0112] Iterate until the billet is completely solidified and passes through all rolling mill units.

[0113] At the center of each finite element, a coupled subgrain boundary model is constructed by driving the subgrain boundary evolution through a local temperature-stress field.

[0114] The coupled subgrain boundary model includes:

[0115] f(t;x)=1-exp[-k(T(x,t),σ(x,t))t n ];

[0116]

[0117] In the formula, f(t;x) represents the subgrain boundary evolution fraction at position x of the finite element at time t; k(T,σ) is the temperature and stress-dependent diffusion coefficient, T(t;x) is the temperature at position x of the finite element at time t, σ(t;x) is the stress at position x of the finite element at time t; k0 is the pre-factor; Q is the activation energy; R is the gas constant; β is the stress sensitivity coefficient; and n is the growth exponent.

[0118] As a preferred embodiment of the above, online calibration of the evolutionary coupling model parameters includes:

[0119] The surface temperature predicted by the finite element method is compared with the surface temperature field of the cast billet at first set time intervals.

[0120] Every second set time interval, the orientation consistency predicted by the model is compared with the subgrain boundary orientation consistency index.

[0121] If the deviation after comparison exceeds the set condition, parameter re-identification is triggered, including:

[0122] Construct a model parameter vector, update the model parameter vector online using the recursive least squares method, and apply the updated model parameter vector to the coupled subgrain boundary model.

[0123] The recursive least squares (RLS) method is used to update the model parameter vector θ = [lnk0, Q, β, n] online;

[0124] Constructing the linear regression form:

[0125] y(t)=φ(t) T θ+ε(t);

[0126]

[0127] Where: y(t) = ln[-ln(1-f)] meas )] Through the measured local f meas Inverse calculation; ε(t) is noise;

[0128] RLS update formula:

[0129]

[0130] In the formula, λ∈(0.95,1) is the forgetting factor; P is the initial value of the covariance matrix, which can be set to a large number;

[0131] High-precision finite element method (FEM) solution provides field drive for microscopic evolution; JMAK equations are seamlessly integrated with local temperature and stress to obtain size and orientation distribution; key model parameters are continuously updated with online monitoring data to ensure high consistency between simulation and field conditions, ultimately improving prediction and control accuracy.

[0132] The construction of a multi-objective collaborative optimization model includes:

[0133] Let the process parameter vector be p = [v, ΔT, T]. r ,F] T ;

[0134] In the formula: v is the casting speed, ΔT is the undercooling degree, and T is the casting speed. r Where F is the rolling temperature and F is the traction tension;

[0135] Based on the coupled subgrain boundary model, the average subgrain boundary size and orientation consistency at a given p are predicted, and an objective function is constructed:

[0136] F(p) = w1F1(p) + w2F2(p);

[0137]

[0138] In the formula, F(P) is the comprehensive objective function, F1(P) and F2(P) are the first objective function and the second objective function, respectively, and w1 and w2 are the first weighting coefficient and the second weighting coefficient, respectively. For the average subgrain boundary size, C o (p) represents the subgrain boundary orientation consistency, which is statistically obtained from the coupled subgrain boundary model;

[0139] in, Calculated using the coupled subgrain boundary model:

[0140]

[0141] In the formula, d(t;x) represents the characteristic size of the subgrain boundary at position x of the finite element at time t, d0 is the initial characteristic size coefficient of the subgrain boundary, which is calibrated by the annealing test; f(t;x) represents the evolution fraction of the subgrain boundary at position x of the finite element at time t; and m is the sensitivity index of the subgrain size to the evolution fraction.

[0142] In this embodiment, a hybrid algorithm based on cuckoo search and teaching optimization is used to optimize and solve the multi-objective collaborative optimization model, including the following steps:

[0143] Using process parameters as decision variables, the Cuckoo Search (CS) algorithm is first used to perform a global search to generate several individuals.

[0144] The optimal individual is selected as the teacher individual, and the fitness of each of the remaining individuals is calculated. If there is room for improvement, the teacher individual is replaced. The process is iterated and the optimal process parameter vector is output.

[0145] A global search is performed using the Cuckoo Search CS algorithm to generate several individuals, including the following steps:

[0146] Randomly generate n feasible solutions:

[0147]

[0148] To ensure that it meets process safety constraints and physical feasibility constraints;

[0149] Process safety constraints:

[0150] v min n≤v≤v max ,

[0151] △T min ≤△T≤△T max ,

[0152] T r，min ≤T r ≤T r，max ,

[0153] F min ≤F≤F max .

[0154] Physical feasibility constraints: The billet should be completely solidified in the crystallizer; the cooling time τ is determined by the finite element module. c (p)≥τ req Rolling mill tension and temperature matching: ensure that the rolling stress does not exceed the material's yield stress.

[0155] Calculate the fitness of each individual:

[0156]

[0157] For each solution Lévy flight is used to generate candidate solutions:

[0158]

[0159] In the formula, g is the number of iterations, α is the Lévy step size scaling factor, and λ is the step size;

[0160] Randomly select another solution like:

[0161]

[0162] Then the solution Replace with candidate solution p i ′;

[0163] With probability p a Discard the worst p a There are n solutions, which are supplemented with new random solutions.

[0164] Several solutions are generated iteratively, and each solution is an individual.

[0165] The process involves selecting the optimal individual as the teacher, learning from and calculating the fitness of each of the remaining individuals, and replacing the teacher if improvements are found. This includes the following steps:

[0166] Calculate the individuals in the current population Mean:

[0167]

[0168] Select the optimal individual as the teacher individual.

[0169] For each solution renew:

[0170]

[0171] Where r∈(0,1), q∈{1,2};

[0172] If the fitness of the updated solution is less than that of the original solution, then replace it.

[0173] Randomly pair up individuals in the population, for each pair (i,j):

[0174]

[0175] right Calculate fitness; replace if improvement is needed.

[0176] Select the individual with the lowest fitness from the updated population.

[0177] like If there is no significant improvement for several consecutive generations, the iteration is terminated.

[0178] Output the optimal process parameter vector.

[0179] The CS algorithm provides a multi-point jump-based global search, while the TLBO algorithm improves the accuracy of local searches. It requires only a few parameters such as the Lévy step size, abandonment rate, and teaching factor, facilitating on-site debugging. It is suitable for highly nonlinear and non-differentiable problems such as evolutionary coupling models. Through rapid exploration using the CS algorithm and fine-grained convergence using the TLBO algorithm, the overall number of iterations can be controlled to less than 150 generations. Using the above model and algorithm flow, the optimal process parameters can be efficiently solved based on online monitoring and numerical simulation, achieving precise control of copper rod subgrain boundaries.

[0180] In this embodiment, triggering re-optimization or an alarm based on the deviation between online monitoring data and prediction includes:

[0181] Based on the coupled subgrain boundary model, the surface temperature field of the cast billet, the subgrain boundary orientation consistency index and the rolling traction tension are pre-calculated under a given P.

[0182] At each sampling time, the temperature field deviation, subgrain boundary orientation consistency deviation, and rolling traction tension deviation are calculated;

[0183] If at any given time there is a temperature field deviation, subgrain boundary orientation consistency deviation, or rolling traction tension deviation that exceeds the set temperature field deviation threshold, orientation consistency threshold, or tension deviation threshold, it is determined to be a working condition drift.

[0184] If there is operating condition drift in N consecutive sampling points, the last optimal process parameter vector is used as the population center, and the hybrid algorithm optimization process is re-executed; if communication timeout, model solution failure or abnormal response of the actuator occurs during the re-optimization process, an alarm is issued;

[0185] The new process parameter vector obtained from the solution will be sent out and executed;

[0186] If there is no operating condition drift within M consecutive minutes, it is judged to be in a stable state, and the re-optimization or alarm is lifted.

[0187] When the operation deviates but does not meet the conditions for further optimization (such as isolated outliers), only logs are recorded and historical data is retained for offline analysis.

[0188] By employing real-time data acquisition, synchronous model prediction, and deviation quantification, a high degree of consistency between production conditions and the simulation model is ensured. A tiered triggering logic is used: isolated deviations are only recorded, continuous deviations trigger further optimization, and advanced anomalies trigger alarms, achieving dual protection of rapid adaptation and safety alarms. The overall closed-loop system, from parameter distribution to feedback and optimization, constitutes an end-to-end automated control system, maximizing the quality stability and production efficiency of copper rod continuous casting and rolling.

[0189] like Figure 2As shown, this embodiment also includes a continuous casting and rolling process control system based on copper rod subgrain boundaries, using the method described above. The system includes:

[0190] The acquisition unit is used to collect process parameters and construct process parameter vectors. At set positions in the continuous casting crystallization zone and continuous rolling section, the surface temperature field of the billet, the subgrain boundary orientation consistency index, and the rolling traction tension are collected respectively.

[0191] The modeling unit is used to discretize the temperature-stress field of the cast billet based on the finite element method, construct a coupled subgrain boundary model, and perform online correction of the parameters of the coupled subgrain boundary model based on the surface temperature field of the cast billet, the subgrain boundary orientation consistency index, and the rolling traction tension.

[0192] The solution unit is used to construct a multi-objective collaborative optimization model based on the coupled subgrain boundary model. A hybrid algorithm based on cuckoo search and teaching optimization algorithm is used to optimize and solve the multi-objective collaborative optimization model to obtain the optimal process parameter vector.

[0193] The application unit is used to apply the optimal process parameter vector to the continuous casting and rolling system, and to trigger re-optimization or alarm based on the deviation between online monitoring data and predictions.

[0194] Example 2:

[0195] On a copper rod production line (equipment models: CC-800 continuous casting machine and CRP-500 continuous rolling mill), the method of this embodiment is used to perform online process control on Φ20mm copper rods.

[0196] 1. Online monitoring;

[0197] 1.1 such as Figure 3 As shown, a temperature detection sensor is installed on the casting wheel, with a measurement frequency of 100Hz, to monitor the temperature field on the surface of the casting billet;

[0198] 1.2 An LDS-OB3000 laser backscattered grain orientation detector is installed between the second and fourth mills in the continuous rolling section. Subgrain boundary orientation distribution data is collected every 10 seconds, and the orientation consistency index C is calculated in real time using software. o ;

[0199] 1.3 Install a GT-Tension1000 tension sensor at the front end of the mill exit traction mechanism to obtain the traction tension F at a frequency of 1kHz.

[0200] 2. Initial process parameters and model verification;

[0201] Initial casting speed v0 = 1.2 m / min; initial crystallizer cooling intensity corresponding to undercooling ΔT0 = 25℃; initial rolling temperature T ro=450℃; initial traction tension F0 = 12kN. Using finite element software (ANSYS Mechanical), the continuous casting-rolling section was discretized under the above parameters, with a total of 100 elements. The temperature-stress field distribution was simulated and substituted into the JMAK equation:

[0202] f(t;x)=1-exp[-k(T(x,t),σ(x,t))t n ];

[0203]

[0204] In the formula, f(t;x) represents the subgrain boundary evolution fraction at position x of the finite element at time t; k(T,σ) is the temperature and stress-dependent diffusion coefficient, T(t;x) is the temperature at position x of the finite element at time t, σ(t;x) is the stress at position x of the finite element at time t; k0 is the pre-factor; Q is the activation energy; R is the gas constant; β is the stress sensitivity coefficient; and n is the growth exponent.

[0205] The initial average subgrain boundary size d0 was calculated to be 15 μm, and the orientation consistency index C was... o =0.72.

[0206] The surface temperature predicted by the finite element method is compared with the surface temperature field of the cast billet at first set time intervals.

[0207] Every second set time interval, the orientation consistency predicted by the model is compared with the subgrain boundary orientation consistency index.

[0208] If the deviation after comparison exceeds the set condition, parameter re-identification is triggered, including:

[0209] Construct a model parameter vector, update the model parameter vector online using the recursive least squares method, and apply the updated model parameter vector to the coupled subgrain boundary model.

[0210] Construct a multi-objective collaborative optimization model, including:

[0211] Let the process parameter vector be p = [v, ΔT, T]. r ,F] T ;

[0212] In the formula: v is the casting speed, ΔT is the undercooling degree, and T is the casting speed. r Where F is the rolling temperature and F is the traction tension;

[0213] Based on the coupled subgrain boundary model, the average subgrain boundary size and orientation consistency at a given p are predicted, and an objective function is constructed:

[0214] F(p) = w1F1(p) + w2F2(p);

[0215]

[0216] In the formula, F(P) is the comprehensive objective function, F1(P) and F2(P) are the first objective function and the second objective function, respectively, and w1 and w2 are the first weighting coefficient and the second weighting coefficient, respectively. For the average subgrain boundary size, C o (p) represents the subgrain boundary orientation consistency, which is statistically obtained from the coupled subgrain boundary model;

[0217] in, Calculated using the coupled subgrain boundary model:

[0218]

[0219] In the formula, d(t;x) represents the characteristic size of the subgrain boundary at position x of the finite element at time t, d0 is the initial characteristic size coefficient of the subgrain boundary, which is calibrated by the annealing test; f(t;x) represents the evolution fraction of the subgrain boundary at position x of the finite element at time t; and m is the sensitivity index of the subgrain size to the evolution fraction.

[0220] 3. Hybrid CS–TLBO optimization;

[0221] 3.1 Algorithm Parameters:

[0222] Population size n = 30;

[0223] Maximum Iteration G max =150;

[0224] Lévy step size factor α = 1.0;

[0225] The probability of abandoning the nest is pa = 0.2;

[0226] The target weights are w1 = w2 = 0.5.

[0227] 3.2 Execution process;

[0228] Generation 1: Randomly generate 30 sets of process vectors p = [v, ΔT, T] r ,F] T Calculate the objective function value for each vector; CS exploration: apply Levy flight to each vector, abandon the worst 6 vectors and then replace them with new solutions; TLBO refinement: use the current best vector as the "teacher", perform teacher phase and learner phase to update all individuals; repeat the iteration until the 150th generation or the optimal value changes by <0.001 within 20 consecutive generations.

[0229] 3.3 Optimization Results;

[0230] The optimal process parameters were obtained at the end of the iteration: {1.05 m / min, 30℃, 470℃, 14 kN};

[0231] The model predicts the following under this operating condition:

[0232] The average subgrain boundary size is 9 μm; the orientation consistency index is 0.85.

[0233] 4. Parameter distribution and closed-loop feedback;

[0234] 4.1 The values ​​{1.05, 30, 470, 14} are sent to the continuous casting and rolling control unit via PLC / SCADA;

[0235] 4.2 The continuous casting machine automatically adjusts the casting speed to 1.05 m / min, increases the cooling water flow rate by 10%, controls the roll temperature of the continuous rolling mill at 470℃, and adjusts the tension of the traction device to 14 kN;

[0236] 4.3 After running for 5 minutes, the online monitoring data is updated as follows:

[0237] Surface average temperature error ΔT err <2℃; measured d meas = 9.3um, Δd < 0.3um; measured C omeas =0.83, △C o <0.02.

[0238] 4.4 All deviations are within the set threshold (∈d=0.5um, ∈C) o =0.05), no further optimization will be triggered.

[0239] 5. Evaluation of results;

[0240] like Figures 3 to 6 As shown, the copper rod sample after process control in this embodiment underwent a tensile test. Figure 3 The graph shows a comparison of tensile strength before and after optimization. The yellow line represents the tensile strength of the copper rod sample before optimization, and the red line represents the tensile strength after optimization. As can be seen from the graph, the tensile strength of the copper rod increased by 5% after optimization.

[0241] Figure 4 The graph shows the comparison of the elongation of the copper rod before and after optimization. The yellow line in the graph represents the elongation of the copper rod sample before optimization, and the red line represents the elongation after optimization. As can be seen from the graph, the elongation increased by 8%.

[0242] Figure 5 The graph shows a comparison of conductivity before and after optimization. The yellow line represents the conductivity of the copper rod sample before optimization, and the red line represents the conductivity after optimization. As can be seen from the graph, the conductivity test shows an increase of 2%.

[0243] Please see Figure 6The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0244] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0245] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0246] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0247] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0248] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0249] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0250] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0251] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0252] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0253] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for controlling continuous casting and rolling processes based on copper rod subgrain boundaries, characterized in that, Includes the following steps: Process parameters were collected and a process parameter vector was constructed. At set positions in the continuous casting crystallization zone and the continuous rolling section, the surface temperature field of the billet, the subgrain boundary orientation consistency index, and the rolling traction tension were collected respectively. Based on the finite element method, the temperature-stress field of the cast billet is discretized and calculated. A coupled subgrain boundary model is constructed. The parameters of the coupled subgrain boundary model are corrected online based on the surface temperature field of the cast billet, the subgrain boundary orientation consistency index and the rolling traction tension. A multi-objective collaborative optimization model is constructed based on the coupled subgrain boundary model. A hybrid algorithm based on cuckoo search and teaching optimization algorithm is used to optimize and solve the multi-objective collaborative optimization model to obtain the optimal process parameter vector. The optimal process parameter vector is applied to the continuous casting and rolling system, and re-optimization or alarm is triggered based on the deviation between online monitoring data and predictions. The construction of the multi-objective collaborative optimization model includes: Let the process parameter vector be... ; In the formula: v is the casting speed, ΔT is the undercooling degree, and T is the casting speed. r Where F is the rolling temperature and F is the traction tension; Based on the coupled subgrain boundary model, the average subgrain boundary size and orientation consistency at a given p are predicted, and an objective function is constructed: ； ； In the formula, F(P) is the comprehensive objective function, F1(P) and F2(P) are the first objective function and the second objective function, respectively, and w1 and w2 are the first weighting coefficient and the second weighting coefficient, respectively. For the average subgrain boundary size, C o (p) represents the subgrain boundary orientation consistency, which is statistically obtained from the coupled subgrain boundary model. in, The following calculations were performed using the coupled subgrain boundary model: ； In the formula, d(t;x) represents the characteristic size of the subgrain boundary at position x of the finite element at time t, d0 is the initial characteristic size coefficient of the subgrain boundary, which is calibrated by annealing test; f(t;x) represents the evolution fraction of the subgrain boundary at position x of the finite element at time t; m is the sensitivity index of subgrain size to evolution fraction. The optimization solution of the multi-objective collaborative optimization model using a hybrid algorithm based on cuckoo search and teaching optimization includes the following steps: Using the process parameters as decision variables, the Cuckoo Search (CS) algorithm is first used to perform a global search to generate several individuals; The optimal individual is selected as the teacher individual, and the fitness of each of the remaining individuals is calculated. If there is room for improvement, the teacher individual is replaced. The process is iterated and the optimal process parameter vector is output.

2. The continuous casting and rolling process control method based on copper rod subgrain boundaries according to claim 1, characterized in that, The method of discretizing the temperature-stress field of the cast billet using the finite element method and constructing a coupled subgrain boundary model includes the following steps: A three-dimensional model of the geometry of the casting billet in the continuous casting-rolling section is constructed. In the finite element software, the three-dimensional model mesh is divided into several finite element elements and boundary conditions are set; Based on the aforementioned process parameter vector, finite element simulations of temperature and stress fields are performed using heat transfer equations and mechanical equilibrium equations. At the center of each finite element, a coupled subgrain boundary model is constructed by driving the subgrain boundary evolution through a local temperature-stress field.

3. The continuous casting and rolling process control method based on copper rod subgrain boundaries according to claim 2, characterized in that, The coupled subgrain boundary model includes: ； ； In the formula, f(t;x) represents the subgrain boundary evolution fraction at position x of the finite element at time t; k(T,σ) is the temperature and stress-dependent diffusion coefficient, T(t;x) is the temperature at position x of the finite element at time t, σ(t;x) is the stress at position x of the finite element at time t; k0 is the pre-factor; Q is the activation energy; R is the gas constant; β is the stress sensitivity coefficient; and n is the growth exponent.

4. The continuous casting and rolling process control method based on copper rod subgrain boundaries according to claim 3, characterized in that, The online calibration of the evolutionary coupling model parameters includes: The surface temperature predicted by the finite element method is compared with the surface temperature field of the cast billet at first set time intervals. Every second predetermined time interval, the orientation consistency predicted by the model is compared with the subgrain boundary orientation consistency index. If the deviation after comparison exceeds the set condition, parameter re-identification is triggered, including: A model parameter vector is constructed, and the model parameter vector is updated online using the recursive least squares method. The updated model parameter vector is then applied to the coupled subgrain boundary model.

5. The method for controlling the continuous casting and rolling process based on copper rod subgrain boundaries according to claim 1, characterized in that, The process of using the Cuckoo Search CS algorithm to perform a global search and generate several individuals includes the following steps: Randomly generate n feasible solutions: ； To ensure that it meets process safety constraints and physical feasibility constraints; Calculate the fitness of each individual: ； For each solution Lévy flight is performed to generate candidate solutions: ； In the formula, g is the number of iterations, α is the Lévy step size scaling factor, and λ is the step size; Randomly select another solution ,like: ； Then the solution Replace with the candidate solution ; With probability p a Discard the worst p a There are n solutions, which are supplemented with new random solutions. Several solutions are generated iteratively, and these solutions are the individual entities.

6. The method for controlling the continuous casting and rolling process based on copper rod subgrain boundaries according to claim 1, characterized in that, The process of selecting the optimal individual as the teacher individual, learning from and calculating the fitness of each of the remaining individuals, and replacing the teacher individual if improvement is possible includes the following steps: Calculate the individuals in the current population Mean: ； Select the optimal individual as the teacher individual. ; For each solution renew: ； Where r∈(0,1), q∈{1,2}; If the fitness of the updated solution is less than that of the original solution, then replace it. Randomly pair up individuals in the population, for each pair (i,j): ； right Calculate fitness; replace if improvement is needed. Select the individual with the lowest fitness from the updated population. ; like If there is no significant improvement for several consecutive generations, the iteration is terminated. Output the optimal process parameter vector.

7. The method for controlling the continuous casting and rolling process based on copper rod subgrain boundaries according to claim 1, characterized in that, The method of triggering re-optimization or alarm based on the deviation between online monitoring data and prediction includes: Based on the coupled subgrain boundary model, the surface temperature field of the cast billet, the subgrain boundary orientation consistency index, and the rolling traction tension are pre-calculated under a given P. At each sampling time, the temperature field deviation, subgrain boundary orientation consistency deviation, and rolling traction tension deviation are calculated; If at any given time there is a temperature field deviation, subgrain boundary orientation consistency deviation, or rolling traction tension deviation that exceeds the set temperature field deviation threshold, orientation consistency threshold, or tension deviation threshold, it is determined to be a working condition drift. If there is operating condition drift in N consecutive sampling points, the last optimal process parameter vector is used as the population center, and the hybrid algorithm optimization process is re-executed; if communication timeout, model solution failure or abnormal response of the actuator occurs during the re-optimization process, an alarm is issued; The new process parameter vector obtained from the solution will be sent out and executed; If there is no operating condition drift within M consecutive minutes, it is judged to be in a stable state, and the re-optimization or alarm is lifted.

8. A continuous casting and rolling process control system based on copper rod subgrain boundaries, characterized in that, The system for implementing the method as described in any one of claims 1 to 7 comprises: The acquisition unit is used to collect process parameters and construct process parameter vectors. At set positions in the continuous casting crystallization zone and continuous rolling section, the surface temperature field of the billet, the subgrain boundary orientation consistency index, and the rolling traction tension are collected respectively. The modeling unit is used to discretize the temperature-stress field of the cast billet based on the finite element method, construct a coupled subgrain boundary model, and perform online correction of the parameters of the coupled subgrain boundary model based on the surface temperature field of the cast billet, the subgrain boundary orientation consistency index, and the rolling traction tension. The solution unit is used to construct a multi-objective collaborative optimization model based on the coupled subgrain boundary model, and to optimize and solve the multi-objective collaborative optimization model using a hybrid algorithm based on cuckoo search and teaching optimization algorithm to obtain the optimal process parameter vector; The application unit is used to apply the optimal process parameter vector to the continuous casting and rolling system, and to trigger re-optimization or alarm based on the deviation between online monitoring data and predictions.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1-7.

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