Method, device, equipment, storage medium and product for optimizing parameters of smelting furnace group

Abstract

The present application discloses a method, device, equipment, storage medium and product for optimizing the parameters of a melting furnace group, relating to the technical field of optimizing the melting furnace group. The method for optimizing the parameters of the melting furnace group includes: obtaining multi-dimensional data of the melting furnace group, confirming the working conditions corresponding to each melting furnace according to a preset working condition prediction model, and further determining the adaptive optimization parameters of each melting furnace; inputting the adaptive optimization parameters of each melting furnace into a preset multi-objective particle swarm optimization model for group optimization of the melting furnace group to obtain the optimal parameter combination of the melting furnace group. By obtaining the multi-dimensional data of the melting furnace group, using the preset working condition prediction model to confirm the working conditions corresponding to each melting furnace, determining the adaptive optimization parameters, and inputting these parameters into the multi-objective particle swarm optimization model for group optimization, the present application outputs the optimal parameter combination of the melting furnace group, realizing the efficient collaborative control and overall performance improvement of the melting furnace group.

Description

Technical Field

[0001] This application relates to the technical field of smelting furnace group optimization, and particularly to a method, device, equipment, storage medium and product for optimizing the parameters of a smelting furnace group. Background Art

[0002] In the operation management of a smelting furnace group, how to optimize the working parameters of each smelting furnace in real time to improve production efficiency and product quality has always been a key issue of concern in the industrial field. Traditional optimization methods often rely on fixed empirical parameters, and this single mode is difficult to adapt to complex working conditions changes and production requirements, resulting in low production efficiency and energy waste. To address this challenge, people often adopt data-driven methods for optimization, by collecting the operation data and running parameters of the smelting furnace, analyzing and predicting the changes in working conditions, and then adjusting the working parameters of the smelting furnace.

[0003] However, due to the different working condition characteristics and spatio-temporal dependencies of each furnace in the smelting furnace group, the optimization of a single furnace cannot fully utilize the synergy effect of the entire smelting furnace group. Therefore, how to optimize the parameters of the smelting furnace group is an urgent problem to be solved. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, equipment, storage medium and product for optimizing the parameters of a smelting furnace group, aiming to solve the technical problem of being unable to optimize the parameters of the smelting furnace group.

[0005] To achieve the above object, this application proposes a method for optimizing the parameters of a smelting furnace group, and the method includes:

[0006] Obtain multi-dimensional data of the smelting furnace group, confirm the working conditions corresponding to each smelting furnace according to a preset working condition prediction model, and then determine the adaptive optimization parameters of each smelting furnace;

[0007] Input the adaptive optimization parameters of each smelting furnace into a preset multi-objective particle swarm optimization model for group optimization of the smelting furnace group to obtain the optimal parameter combination of the smelting furnace group.

[0008] In one embodiment, the step of inputting the adaptive optimization parameters of each smelting furnace into a preset multi-objective particle swarm optimization model for group optimization of the smelting furnace group to obtain the optimal parameter combination of the smelting furnace group includes:

[0009] Initialize the multi-objective particle swarm optimization model, where a preset penalty function is used as a process constraint condition;

[0010] Based on the adaptive tuning parameters of each smelting furnace in the input, iterate and update the particle velocity and position through the multi-objective particle swarm optimization model, record the historical optimal position and the global optimal position, and output the optimal parameter combination of the smelting furnace group.

[0011] In one embodiment, the steps of obtaining multi-dimensional data of the smelting furnace group, confirming the working conditions corresponding to each smelting furnace according to a preset working condition prediction model, and further determining the adaptive tuning parameters of each smelting furnace include:

[0012] Obtain multi-dimensional operation data of the smelting furnace group, and preprocess it to obtain multi-dimensional data of the smelting furnace;

[0013] Based on the random forest algorithm, extract key features from the multi-dimensional data of the smelting furnace to determine the corresponding key features of the smelting furnace;

[0014] Input the key features of the smelting furnace into a preset working condition prediction model to predict the working condition law result of the molten aluminum;

[0015] According to the working condition law result of the molten aluminum, calculate the time interval between the corresponding smelting furnace and the preset working condition, and dynamically output the adaptive tuning parameters as a constraint condition.

[0016] In one embodiment, the steps of inputting the key features of the smelting furnace into a preset working condition prediction model to predict the working condition law result of the molten aluminum include:

[0017] Adopt time series conversion technology to convert the key features of the smelting furnace into the input format of the time series neural network;

[0018] Adjust the structure parameters and training parameters of the time series neural network through parameter optimization methods;

[0019] Based on multi-dimensional evaluation indicators, verify the model performance, predict the on-off state of the molten aluminum in the future period, and output the working condition law result of the molten aluminum.

[0020] In one embodiment, the steps of calculating the time interval between the corresponding smelting furnace and the preset working condition according to the working condition law result of the molten aluminum, and dynamically outputting the adaptive tuning parameters as a constraint condition include:

[0021] If the time interval is less than the first preset threshold, increase the opening of the gas valve and the control value of the burner air motor;

[0022] If the time interval is between the first preset threshold and the second preset threshold, keep the current parameters;

[0023] If the time interval is greater than the second preset threshold, decrease the opening of the gas valve and the control value of the burner air motor.

[0024] In one embodiment, the method further includes a step of collaborative control of the melting furnace group:

[0025] According to the optimal parameter combination of the melting furnace group, dynamically allocate the flow path of the molten aluminum between the melting furnace and the holding furnace;

[0026] Real-time monitor the molten aluminum capacity status of each holding furnace. When it is detected that the capacity is insufficient, automatically adjust the flow distribution of the aluminum scrap furnace;

[0027] Based on the closed-loop feedback mechanism, optimize the production control parameters between the melting furnace and the holding furnace groups.

[0028] In addition, to achieve the above object, the present application also proposes a device for optimizing the parameters of a melting furnace group. The device for optimizing the parameters of a melting furnace group includes:

[0029] An acquisition module for acquiring multi-dimensional data of the melting furnace group, confirming the working conditions corresponding to each melting furnace according to a preset working condition prediction model, and further determining the adaptive optimization parameters of each melting furnace;

[0030] An optimization module for inputting the adaptive optimization parameters of each melting furnace into a preset multi-objective particle swarm optimization model for group optimization of the melting furnace group to obtain an optimal parameter combination of the melting furnace group.

[0031] In addition, to achieve the above object, the present application also proposes a device for optimizing the parameters of a melting furnace group. The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The computer program is configured to implement the steps of the method for optimizing the parameters of the melting furnace group as described above.

[0032] In addition, to achieve the above object, the present application also proposes a storage medium. The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the method for optimizing the parameters of the melting furnace group as described above.

[0033] In addition, to achieve the above object, the present application also provides a computer program product. The computer program product includes a computer program. When the computer program is executed by a processor, it implements the steps of the method for optimizing the parameters of the melting furnace group as described above.

[0034] One or more technical solutions proposed by the present application have at least the following technical effects:

[0035] Compared with traditional optimization methods in related technologies that often rely on fixed empirical parameters, this application obtains multi-dimensional data of a group of smelting furnaces, determines the working conditions corresponding to each smelting furnace according to a preset working condition prediction model, and then determines the adaptive tuning parameters for each smelting furnace; inputs the adaptive tuning parameters of each smelting furnace into a preset multi-objective particle swarm optimization model for group optimization of the group of smelting furnaces to obtain the optimal parameter combination of the group of smelting furnaces. It can be understood that this method for optimizing the parameters of a group of smelting furnaces obtains multi-dimensional data of the group of smelting furnaces, uses a preset working condition prediction model to confirm the working conditions corresponding to each smelting furnace, determines the adaptive tuning parameters, and then inputs these parameters into a preset multi-objective particle swarm optimization model for group optimization, and finally outputs the optimal parameter combination of the group of smelting furnaces to achieve efficient collaborative control and overall performance improvement of the group of smelting furnaces. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0037] In order to more clearly illustrate the technical solutions in the embodiments of this application or in the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0038] Figure 1 It is a schematic flowchart provided for the first embodiment of the method for optimizing the parameters of a group of smelting furnaces according to this application;

[0039] Figure 2 It is a schematic diagram of the convergence curve of the optimization algorithm provided in the embodiments of the method for optimizing the parameters of a group of smelting furnaces according to this application;

[0040] Figure 3 It is a schematic flowchart of the single-furnace optimization provided in the embodiments of the method for optimizing the parameters of a group of smelting furnaces according to this application;

[0041] Figure 4 It is a schematic flowchart of the collaborative optimization provided in the embodiments of the method for optimizing the parameters of a group of smelting furnaces according to this application;

[0042] Figure 5 It is a schematic diagram of the module structure of the device for optimizing the parameters of a group of smelting furnaces in the embodiments of this application;

[0043] Figure 6 It is a schematic diagram of the device structure of the hardware operating environment involved in the method for optimizing the parameters of a group of smelting furnaces in the embodiments of this application.

[0044] The realization of the purpose, functional features, and advantages of this application will be further described in combination with the embodiments with reference to the accompanying drawings. Specific Embodiments

[0045] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not used to limit the present application.

[0046] To better understand the technical solutions of the present application, the following will be described in detail in combination with the specification drawings and specific embodiments.

[0047] The main solution of the embodiment of the present application is:

[0048] Obtain multi-dimensional data of the smelting furnace group, confirm the working conditions corresponding to each smelting furnace according to a preset working condition prediction model, and then determine the adaptive tuning parameters of each smelting furnace;

[0049] Input the adaptive tuning parameters of each smelting furnace into a preset multi-objective particle swarm optimization model for group optimization of the smelting furnace group to obtain the optimal parameter combination of the smelting furnace group.

[0050] In this embodiment, the present application takes the smelting furnace group parameter tuning device as the execution subject. For the convenience of description, it is hereinafter simply referred to as the "device" for specific description.

[0051] Since the prior art often relies on fixed empirical parameters, this single mode is difficult to adapt to complex working condition changes and production requirements, resulting in low production efficiency and energy waste. To address this challenge, a data-driven method is adopted for optimization. By collecting the operation data and running parameters of the smelting furnace, analyzing and predicting the working condition changes, and then adjusting the working parameters of the smelting furnace.

[0052] The present application provides a solution. By obtaining multi-dimensional data of the smelting furnace group, using a preset working condition prediction model to confirm the working conditions corresponding to each smelting furnace, and determining the adaptive tuning parameters, and then inputting these parameters into a preset multi-objective particle swarm optimization model for group optimization, and finally outputting the optimal parameter combination of the smelting furnace group. The specific working principle is that first, the key features of the smelting furnace are extracted through the random forest algorithm, and then these features are input into the working condition prediction model after time series conversion and parameter optimization to further predict the working condition law of the molten aluminum. According to the prediction results, the time interval required for the smelting furnace to reach the preset working condition is calculated, and the opening degree of the gas valve and the control value of the burner air motor are dynamically adjusted accordingly, so as to achieve the adaptive tuning of the smelting furnace. Further, the multi-objective particle swarm optimization model is used to perform group optimization on these adaptive tuning parameters to optimize the optimal parameter combination. The optimization module not only optimizes the parameters of the smelting furnace itself, but also dynamically distributes the flow path of the molten aluminum between the smelting furnace and the holding furnace through a closed-loop feedback mechanism, and real-time monitors and adjusts the capacity state, ultimately realizing the efficient collaborative control and overall performance improvement of the smelting furnace group.

[0053] Based on this, the embodiment of the present application provides a method for tuning parameters of a smelting furnace group, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for optimizing parameters of a smelting furnace group according to the present application.

[0054] In this embodiment, the method for optimizing parameters of a smelting furnace group includes steps S10 to S20:

[0055] Step S10: Acquire multi-dimensional data of the smelting furnace group, confirm the corresponding working condition of each smelting furnace according to the preset working condition prediction model, and then determine the adaptive tuning parameters of each smelting furnace;

[0056] It should be noted that a melting furnace group refers to an equipment group consisting of multiple melting furnaces and static furnaces, which is used for operations such as metal melting, slag removal and casting.

[0057] Multi-dimensional data refers to multi-source heterogeneous data including natural gas meter data, material feeding information, molten aluminum flow and furnace group operation data.

[0058] The operating condition prediction model is a prediction model based on the LSTM neural network, which is used to predict the switching state and operating condition change rules of the aluminum liquid in the future period.

[0059] Adaptive tuning parameters are control parameters that are dynamically adjusted based on the operating condition prediction results, such as gas valve opening, burner air motor control value, etc.

[0060] Understandably, a metal processing company uses a melting furnace group for aluminum liquid production. The group includes, but is not limited to, a pre-treatment furnace, melting furnace A, melting furnace B, aluminum chip furnace, static furnace A, and static furnace B. To optimize production efficiency and energy consumption, the company adopted a melting furnace group parameter tuning method. The specific implementation steps are as follows:

[0061] Data collection and preprocessing: Sensors are used to collect multi-dimensional operating data from the smelting furnace group, including instantaneous natural gas flow, molten aluminum temperature, and charging time. The collected data is cleaned to remove outliers (such as abnormally high instantaneous natural gas flow), and the data is standardized using the Min-Max normalization method.

[0062] Feature extraction: Use the random forest algorithm to screen the features of the preprocessed data, evaluate the importance of each feature, and retain the top N key features (such as aluminum liquid temperature and natural gas instantaneous flow rate).

[0063] Operating condition prediction: Input the key features into the operating condition prediction model based on the LSTM neural network, and convert the time series data into the model input format through the sliding window technique. Optimize the model hyperparameters (such as the number of hidden layers is 2 and the number of hidden units is 64) using grid search to predict the switching state of the molten aluminum within the next T minutes.

[0064] Adaptive tuning parameter calculation: According to the prediction results, calculate the time interval for each melting furnace from the current state to the "next molten aluminum discharging open state". For example, if the predicted interval is 10 minutes (less than the first preset threshold of 15 minutes), increase the opening degree of the gas valve and the control value of the burner air motor; if the interval is 20 minutes (between 15 - 30 minutes), maintain the current parameters; if the interval is 40 minutes (greater than the second preset threshold of 30 minutes), reduce the opening degree of the gas valve to reduce energy consumption.

[0065] Group optimization: Input the adaptive tuning parameters of each melting furnace into the multi - objective particle swarm optimization model, initialize the model and set the penalty function to handle process constraints (such as temperature fluctuations and standing time). By iteratively updating the particle velocity and position, finally output the optimal parameter combination of the melting furnace group to achieve the global optimization of the production rhythm among furnace groups.

[0066] Through the above - mentioned method, the enterprise has successfully achieved the intelligent energy - saving control of the melting furnace group, significantly reducing energy consumption and improving production efficiency.

[0067] In a feasible implementation manner, step S10 may include:

[0068] Obtain the multi - dimensional operation data of the melting furnace group, and pre - process it to obtain the multi - dimensional data of the melting furnace;

[0069] Extract key features from the multi - dimensional data of the melting furnace based on the random forest algorithm to determine the corresponding key features of the melting furnace;

[0070] Input the key features of the melting furnace into a preset operating condition prediction model to predict the operating condition law results of the molten aluminum;

[0071] According to the operating condition law results of the molten aluminum, calculate the time interval of the corresponding melting furnace from the preset operating condition, and dynamically output the adaptive tuning parameters as constraint conditions.

[0072] It should be noted that the random forest algorithm is an ensemble learning method that improves the prediction accuracy and stability by constructing multiple decision trees and integrating their results.

[0073] The operating condition prediction model is a prediction model based on the LSTM neural network, which is used to predict the switching state and operating condition change law of the molten aluminum in the future time period.

[0074] The aluminum liquid condition law result is the law of the state change of the aluminum liquid in the future period obtained through the condition prediction model, including the switch state, etc.

[0075] Exemplarily, a metal processing enterprise uses a group of smelting furnaces for aluminum liquid production. To optimize production efficiency and energy consumption, the enterprise implements the following parameter tuning method for the group of smelting furnaces, with particular emphasis on the intelligence of data processing, feature extraction, and condition identification:

[0076] Data collection and preprocessing: Collect multi-dimensional operation data of the group of smelting furnaces through sensors, covering instantaneous natural gas flow, aluminum liquid temperature, feeding time, etc. The data is cleaned to remove outliers (such as extremely high values of instantaneous natural gas flow), and is standardized using the Min-Max normalization method to ensure data consistency and accuracy.

[0077] Key feature extraction: Use the random forest algorithm to screen features from the preprocessed data and evaluate the importance of each feature. For example, features such as aluminum liquid temperature and instantaneous natural gas flow are identified as key features, providing accurate inputs for subsequent predictions.

[0078] Condition prediction: Input the key features into the condition prediction model based on the LSTM neural network, and convert the time series data format through the sliding window technique. Use grid search to optimize the model hyperparameters (such as the number of hidden layers is 2 and the number of hidden units is 64), predict the switch state of the aluminum liquid within the next T minutes, and output the aluminum liquid condition law result.

[0079] Adaptive tuning parameter calculation: According to the prediction results, calculate the time interval for each smelting furnace from the current state to the "next aluminum liquid discharge open state". For example:

[0080] If the time interval is less than 10 minutes, increase the gas valve opening and the control value of the burner air motor to quickly preheat the aluminum liquid;

[0081] If the time interval is between 10 - 30 minutes, maintain the current parameters for stable operation;

[0082] If the time interval is greater than 30 minutes, reduce the gas valve opening to reduce energy consumption.

[0083] Intelligent optimization: Through the multi-objective particle swarm optimization model, combined with the adaptive tuning parameters, achieve global optimization of the production rhythm among the furnace groups. The optimized parameter combination significantly improves production efficiency and energy utilization efficiency.

[0084] This method realizes rapid response and precise control of complex working conditions through intelligent data processing and prediction, effectively reducing energy consumption and enhancing the stability and reliability of the production process.

[0085] In a feasible implementation manner, the step of inputting the key features of the smelting furnace into a preset working condition prediction model to predict the working condition law result of the molten aluminum includes:

[0086] Converting the key features of the smelting furnace into the input format of a time series neural network by using a time series conversion technique;

[0087] Adjusting the structural parameters and training parameters of the time series neural network through a parameter optimization method;

[0088] Verifying the model performance based on multi-dimensional evaluation metrics, predicting the on-off state of the molten aluminum in the future period, and outputting the working condition law result of the molten aluminum.

[0089] It should be noted that the time series conversion technique is a technique for converting time series data into a format suitable for neural network input, usually generating input sequences and corresponding target values through a sliding window method.

[0090] The multi-dimensional evaluation metrics are multiple metrics used to comprehensively evaluate the model performance, including accuracy, recall rate, F1 value, etc.

[0091] Exemplarily, a metal processing enterprise uses a group of smelting furnaces to produce molten aluminum. To optimize production efficiency and energy consumption, the enterprise implements the following parameter tuning method for the group of smelting furnaces, with particular emphasis on the intelligence of time series conversion and model optimization:

[0092] Time series conversion: Using the sliding window technique, convert the key features of the smelting furnace (such as molten aluminum temperature, instantaneous natural gas flow) into the input format of a time series neural network. Set the time window to 120 time steps, generate input sequences and corresponding target values, and make it into a supervised learning format.

[0093] Parameter optimization: Adjust the structural parameters and training parameters of the LSTM neural network through the grid search method. The optimized hyperparameters include the number of hidden layers (2 layers), the number of hidden units (64), the learning rate (0.0001), and the number of iterations (100 times). By traversing the predefined hyperparameter combinations, find the optimal configuration.

[0094] Model verification and prediction: By evaluating the performance of each hyperparameter combination on the test set, select the optimal hyperparameter combination to complete the final prediction. The evaluation metric uses the Macro Average Accuracy, and also uses recall rate, F1 value, etc. to evaluate the prediction performance of the LSTM model, as shown in the following formula:

[0095] Where T is the time length, and Accuracy is the accuracy at the T-th step;

[0096] The predicted results of each furnace model are shown in Table 1.

[0097] Table 1: Predicted Results of Each Furnace Model

[0098]

[0099] Use the optimized LSTM model to predict the on - off state of the molten aluminum within the next T minutes and output the working condition law results of the molten aluminum.

[0100] Intelligent optimization: Dynamically adjust the operating parameters of the melting furnace according to the prediction results. For example, if it is predicted that the on - off state of the molten aluminum will change in 10 minutes, increase the opening degree of the gas valve and the control value of the burner air motor to ensure that the temperature of the molten aluminum meets the requirements; if the predicted interval is long, reduce the parameter value to reduce energy consumption.

[0101] It can be understood that through intelligent time - series conversion and parameter optimization, this method significantly improves the prediction accuracy and response speed of the model, realizes rapid adaptation and precise control of complex working conditions, effectively reduces energy consumption, and improves the stability and reliability of the production process.

[0102] In a feasible implementation manner, the step of calculating the time interval between the corresponding melting furnace and the preset working condition according to the working condition law results of the molten aluminum and dynamically outputting the adaptive optimization parameters as a constraint condition includes:

[0103] If the time interval is less than the first preset threshold, increase the opening degree of the gas valve and the control value of the burner air motor;

[0104] If the time interval is between the first preset threshold and the second preset threshold, keep the current parameters;

[0105] If the time interval is greater than the second preset threshold, reduce the opening degree of the gas valve and the control value of the burner air motor.

[0106] It should be noted that the first preset threshold is a set time threshold used to determine whether the time interval is small, so as to decide whether to increase the opening degree of the gas valve and the control value of the burner air motor.

[0107] The second preset threshold is a set time threshold used to determine whether the time interval is large, so as to decide whether to reduce the opening degree of the gas valve and the control value of the burner air motor.

[0108] It can be understood that a metal processing enterprise uses a group of melting furnaces for molten aluminum production. To optimize production efficiency and energy consumption, the enterprise implements the following parameter tuning method for the melting furnace group, with special emphasis on the dynamic parameter adjustment mechanism based on the time interval:

[0109] Data collection and preprocessing: Multidimensional operating data of the smelting furnace group is collected through sensors, including instantaneous natural gas flow rate, molten aluminum temperature, feeding time, etc. The data is cleaned and normalized to ensure data consistency and accuracy.

[0110] Key feature extraction: The random forest algorithm is used to screen features from the preprocessed data, evaluate the importance of each feature, and retain the top N key features (such as molten aluminum temperature, instantaneous natural gas flow rate).

[0111] Operating condition prediction: The key features are input into the operating condition prediction model based on the LSTM neural network. The time series data is converted into the model input format through the sliding window technique. The hyperparameters of the model (such as the number of hidden layers is 2 and the number of hidden units is 64) are optimized using grid search, and the on / off state of the molten aluminum in the next T minutes is predicted.

[0112] Dynamic parameter adjustment: According to the prediction results, calculate the time interval for each smelting furnace from the current state to the "next molten aluminum discharge open state", and dynamically adjust the gas valve opening and burner air motor control values based on the following logic:

[0113] If the time interval is less than the first preset threshold (such as 10 minutes): Increase the gas valve opening and burner air motor control values to quickly preheat the molten aluminum and ensure that the requirements are met during the next molten aluminum discharge.

[0114] If the time interval is between the first preset threshold (10 minutes) and the second preset threshold (30 minutes): Keep the current parameters and operate stably.

[0115] If the time interval is greater than the second preset threshold (30 minutes): Decrease the gas valve opening and burner air motor control values to reduce energy consumption.

[0116] Intelligent optimization: Through the multi-objective particle swarm optimization model, combined with the dynamically adjusted parameters, global optimization of the production rhythm among furnace groups is achieved. The optimized parameter combination significantly improves production efficiency and energy utilization efficiency.

[0117] Exemplarily, referring to Figure 3 A certain metal processing enterprise uses a smelting furnace group for molten aluminum production. To optimize the operating efficiency and energy consumption of a single furnace, the enterprise implements the following LSTM-based single-furnace optimization method:

[0118] Collect historical operating data of Furnace A, including instantaneous natural gas flow rate, molten aluminum temperature, feeding time, etc. The data sampling frequency is once per minute. The collected data is cleaned to remove outliers (such as extremely high instantaneous natural gas flow rate values), and the data is standardized using the Min-Max normalization method.

[0119] Use the random forest algorithm to perform feature screening on the preprocessed data, evaluate the importance of each feature, and retain the top N key features (such as the molten aluminum temperature, instantaneous natural gas flow).

[0120] Input the key features into the prediction model based on the LSTM neural network. Convert the time series data into the model input format through the sliding window technique. Set the time window to 120 time steps to generate the input sequence and the corresponding target values. Optimize the model hyperparameters (such as the number of hidden layers is 2, the number of hidden units is 64, and the learning rate is 0.0001) using grid search to predict the on / off state of the molten aluminum within the next T minutes.

[0121] Among them, Figure 3 medium represents the input information; represents the memory cell of the previous time step; represents the hidden state of the previous time step; σ and tanh represent activation functions; represents the updated memory cell; represents the generated hidden state; represents the forget gate; represents the input gate; represents the output gate; represents the candidate memory cell.

[0122] Verify the model performance based on multi-dimensional evaluation metrics (such as macro-average accuracy, recall rate, F1 value). For example, the macro-average accuracy of Furnace A reaches 92.87%. Use the optimized LSTM model to predict the on / off state of the molten aluminum within the next T minutes and output the result of the molten aluminum working condition law.

[0123] According to the prediction results, calculate the time interval of Furnace A from the "next molten aluminum opening state". For example, if the predicted interval is 10 minutes, increase the opening of the gas valve and the control value of the burner air motor; if the predicted interval is 20 minutes, keep the current parameters; if the predicted interval is 40 minutes, reduce the opening of the gas valve to reduce energy consumption.

[0124] This method realizes the rapid response and precise control of complex working conditions through dynamic parameter adjustment based on the time interval, effectively reduces energy consumption, and improves the stability and reliability of the production process.

[0125] Step S20: Input the adaptive tuning parameters of each melting furnace into a preset multi-objective particle swarm optimization model for group optimization of the melting furnace group to obtain the optimal parameter combination of the melting furnace group.

[0126] It should be noted that the multi-objective particle swarm optimization model is a model based on the particle swarm optimization algorithm, which is used to simultaneously optimize multiple objective functions (such as maximizing the aluminum liquid discharge efficiency, minimizing temperature fluctuations and waiting time), and handle process constraints through penalty functions.

[0127] Swarm optimization is to globally optimize the parameters of the melting furnace group through a multi-objective particle swarm optimization model to achieve coordination of production rhythm among furnace groups and improve overall efficiency.

[0128] Understandably, a metal processing company uses a melting furnace group for aluminum liquid production. The group includes melting furnace A, melting furnace B, aluminum chip furnace, and static furnace A and static furnace B. To optimize production efficiency and energy consumption, the company implemented the following melting furnace group parameter tuning method:

[0129] Data collection and preprocessing: Sensors are used to collect multi-dimensional operating data from the smelting furnace group, including instantaneous natural gas flow, molten aluminum temperature, and charging time. The collected data is cleaned to remove outliers (such as abnormally high instantaneous natural gas flow), and the data is standardized using the Min-Max normalization method.

[0130] Feature extraction: Use the random forest algorithm to screen the features of the preprocessed data, evaluate the importance of each feature, and retain the top N key features (such as aluminum liquid temperature and natural gas instantaneous flow rate).

[0131] Operating Condition Prediction: Key features are input into an operating condition prediction model based on an LSTM neural network. A sliding window technique is used to convert the time series data into a model input format. Grid search is used to optimize model hyperparameters (e.g., 2 hidden layers and 64 hidden units) to predict the on / off state of the molten aluminum within the next T minutes.

[0132] Adaptive tuning parameter calculation: Based on the prediction results, the time interval until the next aluminum discharge is reached for each smelting furnace is calculated. For example, if the prediction interval is 10 minutes (less than the first preset threshold of 15 minutes), the gas valve opening and burner air motor control value are increased; if the interval is 20 minutes (between 15 and 30 minutes), the current parameters are maintained; if the interval is 40 minutes (greater than the second preset threshold of 30 minutes), the gas valve opening is reduced to reduce energy consumption.

[0133] Swarm Optimization: The adaptive tuning parameters of each melting furnace are input into a multi-objective particle swarm optimization model. The model is initialized and a penalty function is set to address process constraints (such as temperature fluctuations and rest time). By iteratively updating particle speed and position, the optimal parameter combination for the melting furnace group is ultimately output, achieving global optimization of the production rhythm across the furnace groups.

[0134] Dynamic Path Allocation and Feedback Optimization: According to the optimized parameter combination, dynamically allocate the flow path of the molten aluminum between the melting furnace and the holding furnace. Real-time monitor the molten aluminum capacity status of each holding furnace. When the capacity is detected to be insufficient, automatically adjust the flow path allocation of the aluminum scrap furnace. Through a closed-loop feedback mechanism, further optimize the production control parameters between the melting furnace and the holding furnace groups.

[0135] Through the above method, the enterprise has successfully achieved intelligent energy-saving control of the melting furnace group, significantly reducing energy consumption and improving production efficiency.

[0136] In a feasible implementation, step S20 may include:

[0137] Initialize the multi-objective particle swarm optimization model, where the preset penalty function is used as the process constraint condition;

[0138] According to the input adaptive tuning parameters of each melting furnace, iterate and update the particle velocity and position through the multi-objective particle swarm optimization model, record the historical optimal position and the global optimal position, and output the optimal parameter combination of the melting furnace group.

[0139] It should be noted that the penalty function is a function used to handle process constraint conditions. By imposing penalties on particles that violate the constraint conditions during the optimization process, its fitness value is reduced to ensure that the optimization result meets the process requirements.

[0140] Exemplarily, referring to Figure 2 , Figure 2 shows the convergence process of the particle swarm optimization algorithm in the coordinated optimization of the furnace group. Set the particle swarm size to 40 particles, the initial value of the inertia weight to 0.9, linearly decreasing to 0.4, the learning factor c1 = 2.0, c2 = 2.0, and the maximum number of iterations to 200 times. Use the preset penalty function as the process constraint condition to handle hard constraints such as temperature fluctuations and holding time.

[0141] Input the adaptive tuning parameters of each melting furnace into the multi-objective particle swarm optimization model, and iterate and update the particle velocity and position. In each iteration, simulate the operation of the furnace group, calculate the molten aluminum discharge efficiency, temperature change, and holding waiting time corresponding to the current particle. If the constraint conditions are violated (such as insufficient holding time, temperature exceeding the limit), reduce its fitness value through the penalty function. Record the historical optimal position and the global optimal position of each particle.

[0142] Draw the convergence curve of the particle swarm optimization algorithm to show the change of fitness value during the iteration process. The curve shows that the algorithm converges rapidly in the first 50 iterations and the fitness value gradually stabilizes, indicating that the model has found a relatively optimal control parameter combination.

[0143] After 200 iterations, the algorithm converges and outputs the optimal parameter combination of the melting furnace group, including the opening degree of the gas valve, the flow direction selection of the aluminum scrap furnace, and the aluminum liquid discharge rate, etc.

[0144] Exemplarily, a metal processing enterprise uses a melting furnace group to produce aluminum liquid. To optimize production efficiency and energy consumption, the enterprise implements the following parameter tuning method for the melting furnace group:

[0145] Initialize the multi-objective particle swarm optimization model: Set the particle swarm size to 40 particles, the initial value of the inertia weight to 0.9, linearly decreasing to 0.4, the learning factor c1 = 2.0, c2 = 2.0, and the maximum number of iterations to 200 times. Use the preset penalty function as the process constraint condition to handle hard constraints such as temperature fluctuations and standing time.

[0146] Iterative optimization: Input the adaptive tuning parameters of each melting furnace into the multi-objective particle swarm optimization model, and update the particle velocity and position through iteration. In each iteration, simulate the operation of the furnace group, and calculate the aluminum liquid discharge efficiency, temperature change, and standing waiting time corresponding to the current particle. If the constraint conditions are violated (such as insufficient standing time, temperature exceeding the limit), reduce its fitness value through the penalty function. Record the historical optimal position and global optimal position of each particle.

[0147] Output the optimal parameter combination: After 200 iterations, the algorithm converges and outputs the optimal parameter combination of the melting furnace group, including the opening degree of the gas valve, the flow direction selection of the aluminum scrap furnace, and the aluminum liquid discharge rate, etc.

[0148] Through the above method, the enterprise has successfully achieved intelligent energy-saving control of the melting furnace group, significantly reducing energy consumption and improving production efficiency.

[0149] In a feasible implementation manner, the method further includes the step of coordinated control of the melting furnace group:

[0150] According to the optimal parameter combination of the melting furnace group, dynamically allocate the flow path of aluminum liquid between the melting furnace and the standing furnace;

[0151] Real-time monitor the aluminum liquid capacity status of each standing furnace, and when it is detected that the capacity is insufficient, automatically adjust the flow direction allocation of the aluminum scrap furnace;

[0152] Based on the closed-loop feedback mechanism, optimize the production control parameters between the melting furnace and the standing furnace of the furnace group.

[0153] It should be noted that the closed-loop feedback mechanism is a mechanism that optimizes the system performance by real-time monitoring the system output and comparing it with the target output, thereby adjusting the system input.

[0154] In this embodiment, a metal processing enterprise uses a smelting furnace group to produce aluminum liquid. To optimize production efficiency and energy consumption, the enterprise implements the following parameter optimization method for the smelting furnace group, with particular emphasis on the real-time and intelligent nature of the closed-loop feedback mechanism:

[0155] Dynamic path allocation: According to the optimal parameter combination of the smelting furnace group, dynamically allocate the flow path of aluminum liquid between the smelting furnace and the holding furnace. For example, the aluminum liquid from Smelting Furnace A preferentially flows to Holding Furnace A, and the aluminum liquid from Smelting Furnace B preferentially flows to Holding Furnace B. The flow direction of the aluminum scrap furnace is dynamically adjusted according to the real-time capacity and production demand.

[0156] Real-time monitoring and automatic adjustment: Real-time monitor the aluminum liquid capacity status of each holding furnace. When it is detected that the capacity of Holding Furnace A is insufficient (such as the remaining capacity is less than 20%), the system automatically adjusts the flow direction of the aluminum scrap furnace to preferentially flow to Holding Furnace A to avoid the risk of overflow and ensure production continuity.

[0157] Closed-loop feedback optimization: Based on the closed-loop feedback mechanism, the system continuously collects production data (such as aluminum liquid temperature, discharge rate, holding time, etc.) and compares it with the target parameters. Through the multi-objective particle swarm optimization model, dynamically adjust the production control parameters of the smelting furnace and the holding furnace, such as the opening degree of the gas valve, the control value of the burner air motor, etc., to ensure that the system operates in the optimal state.

[0158] It can be understood that this method realizes the real-time monitoring and intelligent adjustment of the production process through the closed-loop feedback mechanism, effectively avoids production bottlenecks, improves the stability and reliability of the production process, and meets the industry's requirements for efficient, energy-saving, and intelligent production.

[0159] This embodiment provides a parameter optimization method for a smelting furnace group. By obtaining multi-dimensional data of the smelting furnace group, using a preset working condition prediction model to confirm the working conditions corresponding to each smelting furnace, and determining the adaptive optimization parameters, and then inputting these parameters into a preset multi-objective particle swarm optimization model for population optimization, finally output the optimal parameter combination of the smelting furnace group to achieve the efficient collaborative control and overall performance improvement of the smelting furnace group.

[0160] In a feasible embodiment, referring to Figure 3 , Figure 3 shows the specific implementation steps of the parameter optimization of the smelting furnace group, aiming to achieve the optimal parameter combination of the smelting furnace group through multi-dimensional data acquisition, feature extraction, working condition prediction model, and multi-objective particle swarm optimization model, so as to improve production efficiency and product quality.

[0161] Data acquisition and preprocessing: Obtain multi-dimensional operation data of the melting furnace group, including natural gas usage information, feeding plans, aluminum liquid quality inspection records, furnace group operation data, etc. Preprocess the acquired data to ensure data quality and consistency.

[0162] Feature extraction: Based on the random forest algorithm, extract key features from the preprocessed multi-dimensional data of the melting furnace to determine the corresponding key features of the melting furnace.

[0163] Working condition prediction model: Input the extracted key features into a pre-constructed working condition prediction model (such as the LSTM neural network model exemplified in Figure 3 ), and predict the working condition law results of the aluminum liquid. Use time series conversion technology to convert the key features into the input format of the time series neural network, and adjust the network structure parameters and training parameters through parameter optimization methods. Verify the model performance based on multi-dimensional evaluation indicators, predict the on / off state of the aluminum liquid in the future period, and output the working condition law results of the aluminum liquid.

[0164] Adaptive tuning parameter calculation: According to the working condition law results of the aluminum liquid, calculate the time interval between the corresponding melting furnace and the preset working condition, and dynamically output the adaptive tuning parameters as a constraint condition.

[0165] If the time interval is less than the first preset threshold, increase the gas valve opening and the control value of the burner air motor; if the time interval is between the first preset threshold and the second preset threshold, keep the current parameters; if the time interval is greater than the second preset threshold, decrease the gas valve opening and the control value of the burner air motor.

[0166] Multi-objective particle swarm optimization model: Initialize the multi-objective particle swarm optimization model, where the preset penalty function is used as the process constraint condition.

[0167] According to the adaptive tuning parameters of each melting furnace input, iteratively update the particle velocity and position through the multi-objective particle swarm optimization model, record the historical optimal position and the global optimal position, and output the optimal parameter combination of the melting furnace group.

[0168] Coordinated control of the melting furnace group: According to the optimal parameter combination of the melting furnace group, dynamically allocate the flow path of the aluminum liquid between the melting furnace and the holding furnace. Real-time monitor the aluminum liquid capacity status of each holding furnace, and automatically adjust the flow distribution of the aluminum scrap furnace when the capacity is detected to be insufficient. Based on the closed-loop feedback mechanism, optimize the production control parameters between the melting furnace and the holding furnace groups.

[0169] Production Execution: Executes production operations based on the optimal parameter combination, including opening the burner and adjusting the gas valve air motor door opening and valve opening. During the production process, real-time monitoring of smoke detection signals, aluminum liquid quality inspections, furnace group operation data, natural gas usage data, etc. is carried out to ensure production process stability and product quality.

[0170] It can be understood that the present application can effectively optimize the parameters of the smelting furnace group, improve production efficiency and product quality, and reduce energy consumption and production costs.

[0171] For example, in order to help understand the implementation process of the method for optimizing parameters of the smelting furnace group obtained by combining this embodiment with the above-mentioned embodiment 1, please refer to Figure 4 , Figure 4 A brief flow chart of a method for tuning parameters of a melting furnace group is provided, specifically:

[0172] Sensors collect multi-dimensional operating data from the smelting furnace group, including instantaneous natural gas flow, molten aluminum temperature, and charging time. The collected data is cleaned to remove outliers (such as abnormally high instantaneous natural gas flow), and the data is standardized using the Min-Max normalization method.

[0173] The random forest algorithm is used to screen the features of the preprocessed data, evaluate the importance of each feature, and retain the top N key features (such as aluminum liquid temperature and natural gas instantaneous flow rate).

[0174] Key features are input into an operating condition prediction model based on an LSTM neural network. The time series data is converted into a model input format using a sliding window technique. Grid search is used to optimize model hyperparameters (e.g., 2 hidden layers and 64 hidden units) to predict the on / off state of the molten aluminum within the next T minutes.

[0175] Based on the prediction results, the time interval from each smelting furnace to the "next aluminum liquid release state" is calculated, and the gas valve opening and burner air motor control value are dynamically adjusted;

[0176] The adaptive tuning parameters for each melting furnace are fed into a multi-objective particle swarm optimization model. The model is initialized and a penalty function is set to account for process constraints (such as temperature fluctuations and rest time). By iteratively updating particle velocity and position, the optimal parameter combination for the melting furnace group is ultimately output, achieving global optimization of the production rhythm across the furnace groups.

[0177] Based on the optimized parameter combination, the flow path of molten aluminum between the melting furnace and the static furnace is dynamically allocated. The aluminum liquid capacity of each static furnace is monitored in real time. When insufficient capacity is detected, the flow distribution of the aluminum chip furnace is automatically adjusted. Through a closed-loop feedback mechanism, the production control parameters between the melting furnace and the static furnace group are further optimized.

[0178] It should be noted that the above examples are only for understanding the present application and do not constitute a limitation on the method for optimizing the parameters of the melting furnace group of the present application. Based on this technical concept, more forms of simple transformation are within the protection scope of the present application.

[0179] The present application also provides a device for optimizing the parameters of a melting furnace group. Please refer to Figure 5 The device for optimizing the parameters of the melting furnace group includes:

[0180] An acquisition module 10, configured to acquire multi-dimensional data of the melting furnace group, confirm the working conditions corresponding to each melting furnace according to a preset working condition prediction model, and further determine the adaptive optimization parameters of each melting furnace;

[0181] An optimization module 20, configured to input the adaptive optimization parameters of each melting furnace into a preset multi-objective particle swarm optimization model for population optimization of the melting furnace group to obtain an optimal parameter combination of the melting furnace group.

[0182] And / or, the optimization module 20 includes:

[0183] A first initialization module, configured to initialize the multi-objective particle swarm optimization model, where a preset penalty function is used as a process constraint condition;

[0184] A first iteration module, configured to iteratively update the particle velocity and position through the multi-objective particle swarm optimization model according to the input adaptive optimization parameters of each melting furnace, record the historical optimal position and the global optimal position, and output an optimal parameter combination of the melting furnace group.

[0185] And / or, the acquisition module 10 includes:

[0186] A first acquisition module, configured to acquire multi-dimensional operation data of the melting furnace group and preprocess it to obtain multi-dimensional data of the melting furnace;

[0187] A first extraction module, configured to perform key feature extraction on the multi-dimensional data of the melting furnace based on the random forest algorithm to determine the corresponding key features of the melting furnace;

[0188] A first prediction module, configured to input the key features of the melting furnace into a preset working condition prediction model to predict and obtain the result of the aluminum liquid working condition law;

[0189] A first calculation module, configured to calculate the time interval between the corresponding melting furnace and the preset working condition according to the result of the aluminum liquid working condition law, and dynamically output the adaptive optimization parameters as a constraint condition.

[0190] And / or, the first prediction module includes:

[0191] The first conversion module is used to convert the key features of the smelting furnace into the input format of a time series neural network by using time series conversion technology;

[0192] The first adjustment module is used to adjust the structural parameters and training parameters of the time series neural network by using a parameter optimization method;

[0193] The second prediction module is used to verify the model performance based on multi-dimensional evaluation indicators, predict the switching state of the molten aluminum in the future period, and output the operating condition law results of the molten aluminum.

[0194] And / or, the first calculation module includes:

[0195] The first increase module is used to increase the opening degree of the gas valve and the control value of the burner air motor if the time interval is less than the first preset threshold;

[0196] The first holding module is used to keep the current parameters if the time interval is between the first preset threshold and the second preset threshold;

[0197] The first decrease module is used to decrease the opening degree of the gas valve and the control value of the burner air motor if the time interval is greater than the second preset threshold.

[0198] And / or, the smelting furnace group parameter tuning device includes:

[0199] The first allocation module is used to dynamically allocate the flow path of the molten aluminum between the smelting furnace and the holding furnace according to the optimal parameter combination of the smelting furnace group;

[0200] The first monitoring module is used to monitor the molten aluminum capacity status of each holding furnace in real time, and automatically adjust the flow allocation of the aluminum scrap furnace when it detects insufficient capacity;

[0201] The first optimization module is used to optimize the production control parameters between the smelting furnace and the holding furnace group based on a closed-loop feedback mechanism.

[0202] The smelting furnace group parameter tuning device provided in this application adopts the smelting furnace group parameter tuning method in the above embodiment, and can solve the technical problem that the parameters of the smelting furnace group cannot be tuned. Compared with the prior art, the beneficial effects of the smelting furnace group parameter tuning device provided in this application are the same as those of the smelting furnace group parameter tuning method provided in the above embodiment, and other technical features in the smelting furnace group parameter tuning device are the same as those disclosed in the method of the above embodiment, and will not be elaborated here.

[0203] The present application provides an equipment for optimizing parameters of a melting furnace group. The equipment for optimizing parameters of a melting furnace group includes: at least one processor; and a memory communicatively connected to the at least one processor. Wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for optimizing parameters of a melting furnace group in the first embodiment above.

[0204] Reference is made below Figure 6 , which shows a schematic structural diagram of an equipment for optimizing parameters of a melting furnace group suitable for implementing the embodiments of the present application. The equipment for optimizing parameters of a melting furnace group in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, tablet computers, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The equipment for optimizing parameters of a melting furnace group shown is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present application.

[0205] As Figure 6As shown, the melting furnace group parameter tuning device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the melting furnace group parameter tuning device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the melting furnace group parameter tuning device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a melting furnace group parameter tuning device having various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems can be alternatively implemented or had.

[0206] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0207] The melting furnace group parameter tuning device provided by the present application adopts the melting furnace group parameter tuning method in the above embodiment, and can solve the technical problem of being unable to tune the parameters of the melting furnace group. Compared with the prior art, the beneficial effects of the melting furnace group parameter tuning device provided by the present application are the same as those of the melting furnace group parameter tuning method provided by the above embodiment, and other technical features in the melting furnace group parameter tuning device are the same as the features disclosed in the method of the previous embodiment, and will not be elaborated here.

[0208] It should be understood that each part disclosed in this application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0209] As described above, the above are only specific embodiments of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0210] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the smelting furnace group parameter tuning method in the above embodiments.

[0211] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or combined with an instruction execution system, device, or device. The program code contained on the computer-readable storage medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0212] The above computer-readable storage medium can be included in the smelting furnace group parameter tuning device; or it can exist alone without being assembled into the smelting furnace group parameter tuning device.

[0213] The above computer-readable storage medium carries one or more programs, which, when executed by the smelting furnace group parameter tuning device, cause the smelting furnace group parameter tuning device to: obtain multi-dimensional data of the smelting furnace group, confirm the working conditions corresponding to each smelting furnace according to a preset working condition prediction model, and then determine the adaptive tuning parameters of each smelting furnace; input the adaptive tuning parameters of each smelting furnace into a preset multi-objective particle swarm optimization model for group optimization of the smelting furnace group to obtain the optimal parameter combination of the smelting furnace group.

[0214] Computer program code for performing the operations of the present application may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0215] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0216] The modules described in the embodiments of the present application may be implemented in software or in hardware. Wherein, the name of the module does not constitute a limitation to the unit itself in some cases.

[0217] The readable storage medium provided by this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned parameter tuning method for a melting furnace group, and can solve the technical problem that the parameters of the melting furnace group cannot be tuned. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by this application are the same as those of the parameter tuning method for a melting furnace group provided in the above embodiment, and will not be elaborated here.

[0218] This application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the parameter tuning method for a melting furnace group as described above.

[0219] The computer program product provided by this application can solve the technical problem that the parameters of the melting furnace group cannot be tuned. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as those of the parameter tuning method for a melting furnace group provided in the above embodiment, and will not be elaborated here.

[0220] The above are only partial embodiments of this application, and thus do not limit the patent scope of this application. Any equivalent structural transformation made by using the content of the specification and drawings of this application under the technical concept of this application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of this application.

Claims

1. A method for optimizing the parameters of a smelting furnace group, characterized in that, The method described above includes: Obtaining multi-dimensional data of the smelting furnace group, confirming the working conditions corresponding to each smelting furnace according to a preset working condition prediction model, and then determining the adaptive tuning parameters of each smelting furnace; The steps of obtaining multi-dimensional data of the smelting furnace group, confirming the working conditions corresponding to each smelting furnace according to a preset working condition prediction model, and then determining the adaptive tuning parameters of each smelting furnace include: Obtaining multi-dimensional operation data of the smelting furnace group and preprocessing to obtain multi-dimensional data of the smelting furnace; Performing key feature extraction on the multi-dimensional data of the smelting furnace based on the random forest algorithm to determine the corresponding key features of the smelting furnace; Inputting the key features of the smelting furnace into a preset working condition prediction model to predict the result of the aluminum liquid working condition law; The steps of inputting the key features of the smelting furnace into a preset working condition prediction model to predict the result of the aluminum liquid working condition law include: Using time series conversion technology to convert the key features of the smelting furnace into the input format of a time series neural network; Adjusting the structural parameters and training parameters of the time series neural network through a parameter optimization method; Verifying the model performance based on multi-dimensional evaluation indicators, predicting the on-off state of the aluminum liquid in the future period, and outputting the result of the aluminum liquid working condition law; According to the result of the aluminum liquid working condition law, calculating the time interval between the corresponding smelting furnace and the preset working condition, and dynamically outputting the adaptive tuning parameters as a constraint condition; Inputting the adaptive tuning parameters of each smelting furnace into a preset multi-objective particle swarm optimization model for group optimization of the smelting furnace group to obtain the optimal parameter combination of the smelting furnace group; The steps of inputting the adaptive tuning parameters of each smelting furnace into a preset multi-objective particle swarm optimization model for group optimization of the smelting furnace group to obtain the optimal parameter combination of the smelting furnace group include: Initializing the multi-objective particle swarm optimization model, where a preset penalty function is used as a process constraint condition; According to the input adaptive tuning parameters of each smelting furnace, iteratively updating the particle velocity and position through the multi-objective particle swarm optimization model, recording the historical optimal position and the global optimal position, and outputting the optimal parameter combination of the smelting furnace group.

2. The method according to claim 1, characterized in that, The steps of calculating the time interval between the corresponding smelting furnace and the preset working condition according to the result of the aluminum liquid working condition law, and dynamically outputting the adaptive tuning parameters as a constraint condition include: If the time interval is less than the first preset threshold, then increase the opening degree of the gas valve and the control value of the burner air motor; If the time interval is between the first preset threshold and the second preset threshold, then keep the current parameters; If the time interval is greater than the second preset threshold, then decrease the opening degree of the gas valve and the control value of the burner air motor.

3. The method according to claim 1, wherein The method further includes the collaborative control steps of the smelting furnace group: Dynamically allocating the flow path of the aluminum liquid between the smelting furnace and the holding furnace according to the optimal parameter combination of the smelting furnace group; Real-time monitoring the aluminum liquid capacity status of each holding furnace, and automatically adjusting the flow distribution of the aluminum scrap furnace when it is detected that the capacity is insufficient; Optimizing the production control parameters between the smelting furnace and the holding furnace group based on a closed-loop feedback mechanism.

4. A device for optimizing the parameters of a melting furnace group, characterized in that, The device includes: An acquisition module, configured to acquire multi-dimensional data of a smelting furnace group, confirm the working conditions corresponding to each smelting furnace according to a preset working condition prediction model, and further determine the adaptive tuning parameters of each smelting furnace; The steps of acquiring multi-dimensional data of the smelting furnace group, confirming the working conditions corresponding to each smelting furnace according to a preset working condition prediction model, and further determining the adaptive tuning parameters of each smelting furnace include: Acquire multi-dimensional operation data of the smelting furnace group, and preprocess to obtain multi-dimensional data of the smelting furnace; Based on the random forest algorithm, extract key features from the multi-dimensional data of the smelting furnace to determine the corresponding key features of the smelting furnace; Input the key features of the smelting furnace into a preset working condition prediction model to predict the working condition law result of the molten aluminum; The steps of inputting the key features of the smelting furnace into a preset working condition prediction model to predict the working condition law result of the molten aluminum include: Use time series conversion technology to convert the key features of the smelting furnace into the input format of a time series neural network; Adjust the structural parameters and training parameters of the time series neural network through a parameter optimization method; Based on multi-dimensional evaluation indicators, verify the model performance, predict the on-off state of the molten aluminum in the future period, and output the working condition law result of the molten aluminum; According to the working condition law result of the molten aluminum, calculate the time interval between the corresponding smelting furnace and the preset working condition, and dynamically output the adaptive tuning parameters as a constraint condition; An optimization module, configured to input the adaptive tuning parameters of each smelting furnace into a preset multi-objective particle swarm optimization model to perform group optimization on the smelting furnace group, and obtain the optimal parameter combination of the smelting furnace group; The steps of inputting the adaptive tuning parameters of each smelting furnace into a preset multi-objective particle swarm optimization model to perform group optimization on the smelting furnace group and obtaining the optimal parameter combination of the smelting furnace group include: Initialize the multi-objective particle swarm optimization model, where a preset penalty function is used as a process constraint condition; According to the input adaptive tuning parameters of each smelting furnace, use the multi-objective particle swarm optimization model to iteratively update the particle velocity and position, record the historical optimal position and the global optimal position, and output the optimal parameter combination of the smelting furnace group.

5. An equipment for optimizing the parameters of a smelting furnace group, characterized in that The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the smelting furnace group parameter tuning method according to any one of claims 1 to 3.

6. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, it implements the steps of the smelting furnace group parameter tuning method according to any one of claims 1 to 3.

7. A computer program product, characterized in that, The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps of the smelting furnace group parameter tuning method according to any one of claims 1 to 3.

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