Slab continuous casting dynamic secondary cooling water distribution method based on data driving

Abstract

The invention discloses a slab continuous casting dynamic secondary cooling water distribution method based on data driving, and belongs to the technical field of metallurgy, according to the method, water volume prediction of each section of a secondary cooling area is achieved through temperature prediction and water distribution prediction, according to temperature prediction, characteristic parameters I are input into a Transform model according to the sequence of a time sequence after being preprocessed, and the characteristic parameters I are input into the Transform model according to the sequence of the time sequence; the model captures the time dependency relationship in the sequence and the interaction between the features by encoding the feature parameter I and learning the self-attention mechanism, so as to realize the accurate prediction of the temperature of the position point of the secondary cooling area, and the water distribution prediction is realized through the ensemble learning of the random forest and by utilizing the internal law and relevance of the feature parameter II. The method achieves the prediction of the water quantity of the to-be-predicted zone of the secondary cooling zone, achieves the precise distribution of the water quantity of each zone of the secondary cooling zone, optimizes the continuous casting process, and improves the casting blank quality and the production efficiency.

Description

Technical Field

[0001] The invention belongs to the field of metallurgical technology, and in particular relates to a data-driven dynamic secondary cooling water distribution method for slab continuous casting. Background Art

[0002] Continuous casting is an important process for billet forming and quality assurance. It is also the core of long-process steelmaking to promote the dynamic, orderly, coordinated and continuous operation of the whole process. The essence of continuous casting is the process of molten steel releasing superheat, latent heat and physical sensible heat to solidify into billets. The molten steel needs to release about 50% of the heat to completely solidify after passing through the crystallizer-secondary cooling zone-air cooling zone. The cooling intensity of the crystallizer and the secondary cooling zone has an important influence on the continuous casting efficiency and billet quality, especially the non-steady-state conditions such as the start-up, final pouring, and changing the tundish with large process fluctuations such as the drawing speed and cooling water. In the continuous casting production process, the intensity of secondary cooling, cooling method, distribution and control of cooling water are important factors affecting the quality of billets. Most of the surface defects appear in the crystallizer stage, while other defects such as cracks, de-squareness, and bulging appear in the secondary cooling stage. The molten steel forms a billet shell of a certain thickness in the crystallizer to ensure that the billet is not pulled out of the crystallizer. After entering the secondary cooling zone, the cooling water sprays the billet to accelerate the transfer of heat inside the billet and gradually solidify it. The main function of secondary cooling is to accelerate the solidification process of the ingot, further cool the ingot that has not yet solidified from the crystallizer, ensure the quality of the ingot, and try to avoid various defects on the surface and inside of the ingot due to improper secondary cooling. At present, in the continuous casting control process, the water distribution in the secondary cooling zone has the problems of inaccurate water regulation and insufficient stability, and the stability and accuracy of the water distribution in the secondary cooling zone are closely related to the quality of the ingot. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention discloses a data-driven dynamic secondary cooling water distribution method for slab continuous casting.

[0004] The present invention adopts the following technical solution:

[0005] A data-driven dynamic secondary cooling water distribution method for slab continuous casting, the water distribution method is used to predict the water volume in each section of the secondary cooling zone, and the dynamic secondary cooling water distribution method includes a temperature prediction method and a secondary cooling water distribution prediction method;

[0006] According to the different distances between the second cooling zone and the meniscus, a plurality of position points are sequentially set on the second cooling zone, and the position points divide the second cooling zone into a plurality of sections, and a section is formed between two adjacent position points, and a position point of a current section close to one end of the meniscus is a previous position point of the current section, and a position point of the other end of the current section is a next position point of the current section;

[0007] The segment adjacent to the current segment in the meniscus direction is the previous segment, and the previous position point of the current segment is the next position point of the previous segment;

[0008] The segment adjacent to the current segment in the opposite direction of the meniscus is the next segment, and the next position point of the current segment is the previous position point of the next segment;

[0009] The temperature prediction method includes: first preprocessing the characteristic parameter I, and then inputting the characteristic parameter I into the Transformer model in the order of the time series, the Transformer model captures the time dependency and the interaction between the features in the time series by encoding the characteristic parameter I and learning the self-attention mechanism, and obtains the predicted value of the temperature of the next position point of the current section, the characteristic parameter I is a parameter affecting the temperature of the second cold zone, and the predicted value of the temperature of the next position point is obtained by predicting the temperature of the previous position point of the current section through the temperature prediction method;

[0010] The secondary cooling water distribution prediction method includes: through the integrated learning of random forests, using the inherent rules and correlations of characteristic parameters II, to predict the water volume of the section to be predicted in the secondary cooling zone, the characteristic parameters II include section specifications, pulling speed and superheat, the characteristic parameters II also include the previous position point temperature and the next position point temperature corresponding to the section to be predicted in the secondary cooling zone, the next position point temperature is predicted by the temperature prediction method, and the water volume of the section to be predicted in the secondary cooling zone is predicted according to the characteristic parameters II.

[0011] Furthermore, the characteristic parameter I includes: section specifications, pulling speed, superheat, distance, temperature and water volume, the section specifications are the width and length of the ingot, the pulling speed is the speed at which the ingot is pulled out of the crystallizer, the superheat is the difference between the pouring temperature of the molten steel and its liquidus temperature, the distance is the distance between each section of the secondary cooling zone and the meniscus, the temperature is the temperature of the previous position point, the temperature of the previous position point is the center temperature of the ingot at that position point, and the water volume is the water volume in the secondary cooling zone section.

[0012] Furthermore, the temperature prediction method comprises:

[0013] S1.1 Collect relevant characteristic parameters of continuous casting machine production, the relevant characteristic parameters include distance, temperature, and water volume, the distance is the distance between each section of the secondary cooling zone and the meniscus, the temperature is the central temperature of the position point of the secondary cooling zone corresponding to the position of the casting, and the water volume is the water volume of each section of the secondary cooling zone;

[0014] S1.2 Determine characteristic parameters of temperature prediction method I;

[0015] S1.3 inputs the feature parameter I into the Transformer model in the order of the time series. The Transformer model captures the time dependency in the sequence and the interaction between features by encoding the feature parameter I and learning the self-attention mechanism, and obtains the predicted value of the temperature at the next location point.

[0016] Furthermore, S1.3 includes:

[0017] S1.3.1 Data preprocessing: preprocess characteristic parameter I to ensure that characteristic parameter I meets the requirements of the model;

[0018] S1.3.2 constructing a Transformer model, wherein the Transformer model includes an encoder and an output layer, wherein the encoder uses multiple stacked Transformer encoding layers, each encoding layer includes a self-attention mechanism and a feedforward neural network, and the output layer converts the output of the encoder into a final predicted temperature value;

[0019] S1.3.3 Training process: using the training data set to train the Transformer model, the Transformer model first calculates the predicted value through forward propagation, compares it with the true value, calculates the loss function, and then updates the parameters of the model through the back propagation algorithm to minimize the loss function;

[0020] S1.3.4 Model evaluation and tuning: Evaluate the performance of the Transformer model on the validation set or test set, use mean square error, root mean square error, and mean absolute error indicators to evaluate the prediction accuracy of the model, and tune the model based on the evaluation results;

[0021] S1.3.5 Prediction and application, use the trained Transformer model to predict temperature. In actual production, by continuously inputting new time series data, real-time updated prediction results are obtained.

[0022] Furthermore, the preprocessing described in S1.3.1 includes feature scaling and sequence filling.

[0023] Furthermore, the training data set described in S1.3.3 is composed of the related feature parameters.

[0024] Furthermore, the tuning described in S1.3.4 includes adjusting model parameters and changing model structure.

[0025] Furthermore, the secondary cooling water distribution prediction method includes:

[0026] S2.1 Determine characteristic parameters II of the secondary cooling water distribution prediction method, which include section specifications, pulling speed, superheat, temperature of the previous position point and temperature of the next position point;

[0027] S2.2 inputs characteristic parameter II into the random forest model. Through the integrated learning of the random forest, the random forest model uses the inherent rules and correlations of the input data to obtain the water volume between the previous position point and the next position point, thereby realizing the prediction of the water volume in each section of the secondary cooling zone.

[0028] Furthermore, S2.2 includes:

[0029] S2.2.1 Data preprocessing: preprocessing feature parameter II, including feature scaling and sequence filling to ensure that the input data meets the requirements of the model;

[0030] S2.2.2 Model construction, build a random forest regression model in the Scikit-learn library and set parameters to control the behavior of the random forest;

[0031] S2.2.3 training the model, using the training data set to train the random forest regression model; the random forest regression model will construct multiple decision trees based on the samples in the training set and the values ​​of the target variable, and perform feature selection and partitioning on each tree;

[0032] S2.2.4 Prediction results: Use the trained random forest regression model to predict the samples in the test set; the trained random forest regression model averages or weighted averages the prediction results of each decision tree to obtain the final regression prediction results;

[0033] S2.2.5 Model evaluation and tuning: evaluate the performance of the trained random forest regression model on the validation set or test set, and use the mean square error, root mean square error, and mean absolute error indicators to evaluate the prediction accuracy of the trained random forest regression model; tune the model according to the evaluation results, and the tuning includes adjusting model parameters and changing the model structure;

[0034] S2.2.6 Prediction and application: Use the trained random forest regression model to predict the water volume in the secondary cooling zone.

[0035] Furthermore, the parameters described in S2.2.2 include: the number of decision trees, feature selection method and decision tree growth method.

[0036] Beneficial effects:

[0037] The present invention discloses a data-driven dynamic secondary cooling water distribution method for slab continuous casting, which makes full use of the data accumulated in the production process, mines the inherent laws and correlations in the production data, and realizes precise control of the continuous casting process. The present invention constructs a prediction method that first predicts the temperature of each position point based on the Transform regression prediction method, and then accurately predicts the water volume of each section of the secondary cooling zone based on the random forest regression prediction and the temperature of each position point predicted above. The method is trained based on a large amount of actual production data, and can accurately predict the temperature of the position point of the secondary cooling zone and distribute the water volume of the secondary cooling zone section. The quality of the casting can be significantly improved in actual production, and the accuracy and stability of the water distribution in the secondary cooling zone are significantly improved compared with the existing methods. At the same time, the prediction method is based on relevant characteristic parameters, characteristic parameters I and characteristic parameters II, is not limited by fixed parameters and expert experience, and can automatically adapt to changes in the production process. The method provides a reference for dynamic secondary cooling water distribution in the continuous casting process, can effectively optimize the continuous casting process, and improve the quality of the casting and production efficiency.

[0038] The prediction model in the present invention adopts the leave-one-out method for cross-validation. This verification method maximizes the use of all data for training and verification, reducing the overfitting problem caused by the small data size. At the same time, each sample will be used as a verification set, thereby reducing the problem of unstable evaluation results caused by uneven sample division, and can more accurately evaluate the generalization ability of the model on the entire data set. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 It is a flow chart of a data-driven slab continuous casting dynamic secondary cooling water distribution method according to the present invention;

[0041] Figure 2 It is a schematic diagram of a temperature prediction process of a data-driven slab continuous casting dynamic secondary cooling water distribution method according to the present invention;

[0042] Figure 3 It is a schematic diagram of prediction results of a temperature prediction method of a slab continuous casting dynamic secondary cooling water distribution method based on data-driven according to the present invention;

[0043] Figure 4 It is a schematic diagram of prediction results of a secondary cooling water distribution prediction method of a slab continuous casting dynamic secondary cooling water distribution method based on data-driven according to the present invention. DETAILED DESCRIPTION

[0044] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0045] It should be clear that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Example 1

[0047] A data-driven dynamic secondary cooling water distribution method for slab continuous casting, the water distribution method is used to predict the water volume in each section of the secondary cooling zone, and the dynamic secondary cooling water distribution method includes a temperature prediction method and a secondary cooling water distribution prediction method;

[0048] According to the different distances between the second cooling zone and the meniscus, a plurality of position points are sequentially set on the second cooling zone, and the position points divide the second cooling zone into a plurality of sections, and a section is formed between two adjacent position points, and a position point of a current section close to one end of the meniscus is a previous position point of the current section, and a position point of the other end of the current section is a next position point of the current section;

[0049] The segment adjacent to the current segment in the meniscus direction is the previous segment, and the previous position point of the current segment is the next position point of the previous segment;

[0050] The segment adjacent to the current segment in the opposite direction of the meniscus is the next segment, and the next position point of the current segment is the previous position point of the next segment;

[0051] The temperature prediction method includes: first preprocessing the characteristic parameter I, and then inputting the characteristic parameter I into the Transformer model in the order of the time series, the Transformer model captures the time dependency and the interaction between the features in the time series by encoding the characteristic parameter I and learning the self-attention mechanism, and obtains the predicted value of the temperature of the next position point of the current section, the characteristic parameter I is a parameter affecting the temperature of the second cold zone, and the predicted value of the temperature of the next position point is obtained by predicting the temperature of the previous position point of the current section through the temperature prediction method;

[0052] The secondary cooling water distribution prediction method includes: through the integrated learning of random forests, using the inherent rules and correlations of characteristic parameters II, to predict the water volume of the section to be predicted in the secondary cooling zone, the characteristic parameters II include section specifications, pulling speed and superheat, the characteristic parameters II also include the previous position point temperature and the next position point temperature corresponding to the section to be predicted in the secondary cooling zone, the next position point temperature is predicted by the temperature prediction method, and the water volume of the section to be predicted in the secondary cooling zone is predicted according to the characteristic parameters II.

[0053] Furthermore, the characteristic parameter I includes: section specifications, pulling speed, superheat, distance, temperature and water volume, the section specifications are the width and length of the ingot, the pulling speed is the speed at which the ingot is pulled out of the crystallizer, the superheat is the difference between the pouring temperature of the molten steel and its liquidus temperature, the distance is the distance between each section of the secondary cooling zone and the meniscus, the temperature is the temperature of the previous position point, the temperature of the previous position point is the center temperature of the ingot at that position point, and the water volume is the water volume in the secondary cooling zone section.

[0054] Furthermore, the temperature prediction method comprises:

[0055] S1.1 Collect relevant characteristic parameters of continuous casting machine production, the relevant characteristic parameters include distance, temperature, and water volume, the distance is the distance between each section of the secondary cooling zone and the meniscus, the temperature is the central temperature of the position point of the secondary cooling zone corresponding to the position of the casting, and the water volume is the water volume of each section of the secondary cooling zone;

[0056] S1.2 Determine characteristic parameters of temperature prediction method I;

[0057] S1.3 Input the feature parameter I into the Transformer model in the order of the time series. The Transformer model encodes the feature parameter I and learns the self-attention mechanism to capture the time dependency and interaction between features in the sequence, and obtain the predicted value of the temperature at the next location. Figure 1 shown.

[0058] Furthermore, S1.3 includes:

[0059] S1.3.1 Data preprocessing: preprocess characteristic parameter I to ensure that characteristic parameter I meets the requirements of the model;

[0060] S1.3.2 constructing a Transformer model, wherein the Transformer model includes an encoder and an output layer, wherein the encoder uses multiple stacked Transformer encoding layers, each encoding layer includes a self-attention mechanism and a feedforward neural network, and the output layer converts the output of the encoder into a final predicted temperature value;

[0061] S1.3.3 Training process: using the training data set to train the Transformer model, the Transformer model first calculates the predicted value through forward propagation, compares it with the true value, calculates the loss function, and then updates the parameters of the model through the back propagation algorithm to minimize the loss function;

[0062] S1.3.4 Model evaluation and tuning: Evaluate the performance of the Transformer model on the validation set or test set, use mean square error, root mean square error, and mean absolute error indicators to evaluate the prediction accuracy of the model, and tune the model based on the evaluation results;

[0063] S1.3.5 Prediction and application, use the trained Transformer model to predict temperature. In actual production, by continuously inputting new time series data, real-time updated prediction results are obtained.

[0064] Furthermore, the preprocessing described in S1.3.1 includes feature scaling and sequence filling.

[0065] Furthermore, the training data set described in S1.3.3 is composed of the related feature parameters.

[0066] Furthermore, the tuning described in S1.3.4 includes adjusting model parameters and changing model structure.

[0067] Furthermore, the secondary cooling water distribution prediction method includes:

[0068] S2.1 Determine characteristic parameters II of the secondary cooling water distribution prediction method, which include section specifications, pulling speed, superheat, temperature of the previous position point and temperature of the next position point;

[0069] S2.2 Input the characteristic parameter II into the random forest model. Through the integrated learning of the random forest, the random forest model uses the inherent rules and correlation of the input data to obtain the water volume between the previous position point and the next position point, and realizes the prediction of the water volume in each section of the secondary cooling zone. Figure 1 shown.

[0070] Furthermore, S2.2 includes:

[0071] S2.2.1 Data preprocessing: preprocessing feature parameter II, including feature scaling and sequence filling to ensure that the input data meets the requirements of the model;

[0072] S2.2.2 Model construction, build a random forest regression model in the Scikit-learn library and set parameters to control the behavior of the random forest;

[0073] S2.2.3 training the model, using the training data set to train the random forest regression model; the random forest regression model will construct multiple decision trees based on the samples in the training set and the values ​​of the target variable, and perform feature selection and partitioning on each tree;

[0074] S2.2.4 Prediction results: Use the trained random forest regression model to predict the samples in the test set; the trained random forest regression model averages or weighted averages the prediction results of each decision tree to obtain the final regression prediction results;

[0075] S2.2.5 Model evaluation and tuning: evaluate the performance of the trained random forest regression model on the validation set or test set, and use the mean square error, root mean square error, and mean absolute error indicators to evaluate the prediction accuracy of the trained random forest regression model; tune the model according to the evaluation results, and the tuning includes adjusting model parameters and changing the model structure;

[0076] S2.2.6 Prediction and application: Use the trained random forest regression model to predict the water volume in the secondary cooling zone.

[0077] Furthermore, the parameters described in S2.2.2 include: the number of decision trees, feature selection method and decision tree growth method.

[0078] Example 2

[0079] A temperature prediction method based on a Transformer model, comprising:

[0080] S101: Collect relevant characteristic parameters of the continuous casting machine, collect production data of the continuous casting machine, obtain the distance between each section of the secondary cooling zone and the meniscus, and the corresponding temperature data during the casting process, which are used as training data for the model.

[0081] The distance parameters of each section of the secondary cooling zone from the meniscus are shown in Table 1. The temperature data of the start and end positions of each zone during the billet production process are shown in Table 2. The billet section specifications in this table are 1250mm wide, 15℃ overheat, and 1m / min pulling speed.

[0082] Table 1 Second cooling zone parameters

[0083]

[0084] Table 2 Corresponding temperature data extracted during the casting process

[0085]

[0086]

[0087] S102: Determine characteristic parameter I of the temperature prediction method and determine the input characteristic sequence of the temperature prediction model. Characteristic parameter I includes characteristics such as section specifications, temperature at the previous position, pulling speed, superheat, and water volume over a period of time. The model output is the temperature at the next position.

[0088] S103: The feature parameters I are input into the Transformer model in the order of the time series. The model captures the time dependency in the sequence and the interaction between features by encoding these feature parameters and learning the self-attention mechanism, thereby achieving accurate prediction of the temperature at the next position.

[0089] When constructing the temperature prediction model, the present invention adopts the Transformer model, which is suitable for the regression prediction task in the present invention. In the field of machine learning and data science, regression analysis is a basic method for predicting numerical target variables. Traditionally, models such as linear regression, decision tree regression, and random forest are widely used to solve regression problems. However, with the rapid development of deep learning technology, especially the rise of the Transformer model, new possibilities have been brought to regression analysis. Transformer has achieved great success in the field of natural language processing (NLP) with its excellent ability to process sequence data. The Transformer model was originally proposed by Vaswani et al. in 2017 to solve NLP tasks such as machine translation. Its core lies in the self-attention mechanism, which can capture the dependency between any two positions in the input sequence, thereby effectively modeling long-distance dependencies. Transformer consists of two parts: an encoder and a decoder, but in regression tasks, the encoder part is mainly concerned because it can convert the input sequence into a high-dimensional representation, which can then be used for regression prediction.

[0090] The core of the temperature prediction function is to use the Transformer model to capture the temporal dependencies and correlations between features in the input feature sequence. The Transformer model captures the dependencies in the sequence through the self-attention mechanism, and uses a multi-layer stacked attention mechanism between the encoder and decoder to better capture the information in the input sequence. The temperature prediction model accepts a feature sequence as input and outputs the predicted temperature value at the next time point in the corresponding sequence.

[0091] Feature parameters I include section specifications, pulling speed, superheat, distance, temperature, and water volume characteristics of the secondary cooling zone over a period of time. After preprocessing, these data are input into the Transformer model in the order of time series. The model captures the long-term dependencies and interactions between features in the sequence by encoding these features and learning the self-attention mechanism. Then, the model outputs a predicted value, which is the temperature of the next position point.

[0092] The specific steps of the temperature prediction method include:

[0093] 1) Data preprocessing: First, feature parameter I is preprocessed, including feature scaling, sequence filling and other operations to ensure that the input data meets the requirements of the model.

[0094] 2) Model construction: Construct a Transformer model, including an encoder part and an output layer. The encoder part uses multiple stacked Transformer encoding layers, each of which contains a self-attention mechanism and a feedforward neural network. The output layer converts the encoder output into the final predicted temperature value.

[0095] 3) Training process: The model is trained using the training data set. During the training process, the model calculates the predicted value through forward propagation, compares it with the true value, and calculates the loss function. Then, the parameters of the model are updated through the back propagation algorithm to minimize the loss function. The training uses the leave-one-out method for cross-validation to avoid problems caused by a small training set. This cross-validation method is suitable for situations where the data set is small.

[0096] 4) Model evaluation and tuning: Evaluate the performance of the model on the validation set or test set, and use indicators such as mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) to evaluate the prediction accuracy of the model. Tune the model based on the evaluation results, including adjusting model parameters and changing model structure.

[0097] 5) Prediction and application: Use the trained model to predict temperature. In practical applications, new time series data can be continuously input and the prediction results of the model can be updated in real time. The prediction results can be used to guide temperature control, early warning and decision support in the production process.

[0098] In summary, the temperature prediction function based on the Transformer model achieves accurate prediction of future temperature by capturing the time dependency and correlation between features in the input feature sequence. The modeling method adopts the Transformer architecture in deep learning and completes the entire modeling and prediction process through data preprocessing, model construction, training process, evaluation and tuning, and prediction and application. The temperature prediction process is as follows Figure 2 shown.

[0099] The model prediction results are as follows Figure 3 As shown in the figure, the combinations of superheat and casting speed are (1) 15-1.0, (2) 15-1.1, (3) 15-1.2, and (4) 15-1.3, respectively. The cross-sectional specifications are all 1750 mm. The evaluation indicators of the training results are shown in Table 3. It can be seen from the results that the model prediction is accurate and can achieve accurate prediction of the temperature at each position in the secondary cooling zone.

[0100] Table 3 Evaluation indicators of temperature prediction results

[0101]

[0102] Secondary cooling water distribution prediction method, including:

[0103] S104: Determine the input characteristic parameters II of the dynamic secondary cooling water distribution model. Specifically, the input data includes the section specifications, the temperature of the previous position, the temperature of the next position obtained by the temperature prediction model, the pulling speed, the superheat and other characteristics. The model output is the water volume between the front and back positions.

[0104] The water volume data between the start and end positions of each zone during the casting process are shown in Table 4. The casting section specifications in this table are 1250mm wide, 15°C superheat, and 1m / min pulling speed.

[0105] Table 4 Extracted water volume data between the start and end positions of each zone during the casting process

[0106]

[0107]

[0108] S105: Input characteristic parameter II into the random forest model. Through the integrated learning of random forest, the model can make full use of the inherent laws and correlations of the data to achieve accurate prediction of the water volume in each section, so as to optimize the continuous casting process and improve the quality of the ingot and production efficiency.

[0109] When constructing a dynamic secondary cooling water distribution model, the present invention adopts a random forest algorithm. Random forest regression is an algorithm based on ensemble learning, which belongs to the bagging type. It performs regression tasks by constructing multiple decision trees and integrating their prediction results. In a random forest, each decision tree is independent and trained on a randomly selected subsample, which can effectively reduce the risk of overfitting, so that the results of the overall model have higher accuracy and generalization performance. The random forest averages or weighted averages the prediction results of multiple decision trees to obtain the final regression result. It can achieve good results, mainly due to "random" and "forest", one makes it anti-overfitting ability, and the other makes it more accurate. Its advantages are as follows: multiple decision trees can be trained at the same time, each decision tree is independently constructed, and large-scale data sets can be processed in parallel to achieve high parallelization; by randomly selecting features and samples for training, the risk of overfitting of a single decision tree is reduced; it has good robustness for missing data and outliers, and is not easily disturbed by noise; it can provide feature importance ranking to help analysts understand the prediction process of the model, and has strong explanatory power.

[0110] Specifically, characteristic parameter II includes section specifications, position information, temperature difference between adjacent positions, pulling speed and superheat, etc., among which the temperature of the next position of the temperature difference between adjacent positions is predicted by the temperature prediction model, and the output is the water volume between adjacent positions.

[0111] Secondary cooling water distribution prediction methods include:

[0112] 1) Data preprocessing: First, the input data is preprocessed, including feature scaling, sequence padding and other operations to ensure that the input data meets the requirements of the model.

[0113] 2) Model construction: Building a random forest: In the Scikit-learn library, use the RandomForestRegressor class to build a random forest regression model. Set parameters to control the behavior of the random forest, such as the number of decision trees, feature selection method, and decision tree growth method.

[0114] 3) Training model: Use the training set to train the random forest regression model. The model will build multiple decision trees based on the samples in the training set and the value of the target variable, and perform feature selection and partitioning on each tree.

[0115] 4) Prediction results: Use the trained random forest regression model to predict the samples in the test set. The model will average or weighted average the prediction results of each decision tree to obtain the final regression prediction results.

[0116] 5) Model evaluation and tuning: Evaluate the performance of the model on the validation set or test set, and use indicators such as mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE) to evaluate the prediction accuracy of the model. Tune the model based on the evaluation results, including adjusting model parameters and changing model structure.

[0117] 6) Prediction and application: Use the trained model to predict the water volume in each section of the secondary cooling zone. In practical applications, new time series data can be continuously input and the prediction results of the model can be updated in real time. The prediction results can be used to guide water volume control, early warning and decision support in the production process.

[0118] The model prediction results are as follows Figure 4 As shown in the figure, the superheat and casting speed combinations are (1) 15-1.0, (2) 15-1.1, (3) 15-1.15, and (4) 15-1.3, respectively. The cross-sectional specifications are all 1750 mm. The training also uses the leave-one-out method. The evaluation indicators of the training results are shown in Table 5. It can be seen from the results that the model prediction is accurate and can achieve accurate prediction of the water volume in each section of the secondary cooling zone.

[0119] Table 5 Evaluation indicators of temperature prediction results

[0120]

[0121] The embodiments of the present invention are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A data-driven dynamic secondary cooling water distribution method for slab continuous casting, the water distribution method is used to predict the water volume in each section of the secondary cooling zone, characterized in that: The dynamic secondary cooling water distribution method includes a temperature prediction method and a secondary cooling water distribution prediction method; According to the different distances between the second cooling zone and the meniscus, a plurality of position points are sequentially set on the second cooling zone, and the position points divide the second cooling zone into a plurality of sections, and a section is formed between two adjacent position points, and a position point of a current section close to one end of the meniscus is a previous position point of the current section, and a position point of the other end of the current section is a next position point of the current section; The segment adjacent to the current segment in the meniscus direction is the previous segment, and the previous position point of the current segment is the next position point of the previous segment; The segment adjacent to the current segment in the opposite direction of the meniscus is the next segment, and the next position point of the current segment is the previous position point of the next segment; The temperature prediction method includes: first preprocessing the characteristic parameter I, and then inputting the characteristic parameter I into the Transformer model in the order of the time series, the Transformer model captures the time dependency and the interaction between the features in the time series by encoding the characteristic parameter I and learning the self-attention mechanism, and obtains the predicted value of the temperature of the next position point of the current section, the characteristic parameter I is a parameter affecting the temperature of the second cold zone, and the predicted value of the temperature of the next position point is obtained by predicting the temperature of the previous position point of the current section through the temperature prediction method; The secondary cooling water distribution prediction method includes: through the integrated learning of random forests, using the inherent rules and correlations of characteristic parameters II, to predict the water volume of the section to be predicted in the secondary cooling zone, the characteristic parameters II include section specifications, pulling speed and superheat, the characteristic parameters II also include the previous position point temperature and the next position point temperature corresponding to the section to be predicted in the secondary cooling zone, the next position point temperature is predicted by the temperature prediction method, and the water volume of the section to be predicted in the secondary cooling zone is predicted according to the characteristic parameters II.

2. The dynamic secondary cooling water distribution method according to claim 1, characterized in that: The characteristic parameter I includes: section specifications, pulling speed, superheat, distance, temperature and water volume, the section specifications are the width and length of the ingot, the pulling speed is the speed at which the ingot is pulled out of the crystallizer, the superheat is the difference between the pouring temperature of the molten steel and its liquidus temperature, the distance is the distance between each section of the secondary cooling zone and the meniscus, the temperature is the temperature of the previous position point, the temperature of the previous position point is the center temperature of the ingot at that position point, and the water volume is the water volume in the secondary cooling zone section.

3. The dynamic secondary cooling water distribution method according to claim 2, characterized in that: The temperature prediction method comprises: S1.1 Collect relevant characteristic parameters of continuous casting machine production, the relevant characteristic parameters include distance, temperature, and water volume, the distance is the distance between each section of the secondary cooling zone and the meniscus, the temperature is the central temperature of the position point of the secondary cooling zone corresponding to the position of the casting, and the water volume is the water volume of each section of the secondary cooling zone; S1.2 Determine characteristic parameters of temperature prediction method I; S1.3 inputs the feature parameter I into the Transformer model in the order of the time series. The Transformer model captures the time dependency in the sequence and the interaction between features by encoding the feature parameter I and learning the self-attention mechanism, and obtains the predicted value of the temperature at the next location point.

4. The dynamic secondary cooling water distribution method according to claim 3, characterized in that: S1.3 includes: S1.3.1 Data preprocessing: preprocess characteristic parameter I to ensure that characteristic parameter I meets the requirements of the model; S1.3.2 constructing a Transformer model, wherein the Transformer model includes an encoder and an output layer, wherein the encoder uses multiple stacked Transformer encoding layers, each encoding layer includes a self-attention mechanism and a feedforward neural network, and the output layer converts the output of the encoder into a final predicted temperature value; S1.3.3 Training process: using the training data set to train the Transformer model, the Transformer model first calculates the predicted value through forward propagation, compares it with the true value, calculates the loss function, and then updates the parameters of the model through the back propagation algorithm to minimize the loss function; S1.3.4 Model evaluation and tuning: Evaluate the performance of the Transformer model on the validation set or test set, use mean square error, root mean square error, and mean absolute error indicators to evaluate the prediction accuracy of the model, and tune the model based on the evaluation results; S1.3.5 Prediction and application, use the trained Transformer model to predict temperature. In actual production, by continuously inputting new time series data, real-time updated prediction results are obtained.

5. The dynamic secondary cooling water distribution method according to claim 4, characterized in that: The preprocessing described in S1.3.1 includes feature scaling and sequence filling.

6. The dynamic secondary cooling water distribution method according to claim 4, characterized in that: S1.3.3 The training data set is composed of the relevant feature parameters.

7. The dynamic secondary cooling water distribution method according to claim 4, characterized in that: The tuning described in S1.3.4 includes adjusting model parameters and changing model structure.

8. The dynamic secondary cooling water distribution method according to claim 1, characterized in that: The secondary cooling water distribution prediction method comprises: S2.1 Determine characteristic parameters II of the secondary cooling water distribution prediction method, which include section specifications, pulling speed, superheat, temperature of the previous position point and temperature of the next position point; S2.2 inputs characteristic parameter II into the random forest model. Through the integrated learning of the random forest, the random forest model uses the inherent rules and correlations of the input data to obtain the water volume between the previous position point and the next position point, thereby realizing the prediction of the water volume in each section of the secondary cooling zone.

9. The dynamic secondary cooling water distribution method according to claim 8, characterized in that: S2.2 includes: S2.2.1 Data preprocessing: preprocessing feature parameter II, including feature scaling and sequence filling to ensure that the input data meets the requirements of the model; S2.2.2 Model construction, build a random forest regression model in the Scikit-learn library and set parameters to control the behavior of the random forest; S2.2.3 training the model, using the training data set to train the random forest regression model; the random forest regression model will construct multiple decision trees based on the samples in the training set and the values ​​of the target variable, and perform feature selection and partitioning on each tree; S2.2.4 Prediction results: Use the trained random forest regression model to predict the samples in the test set; the trained random forest regression model averages or weighted averages the prediction results of each decision tree to obtain the final regression prediction results; S2.2.5 Model evaluation and tuning: evaluate the performance of the trained random forest regression model on the validation set or test set, and use the mean square error, root mean square error, and mean absolute error indicators to evaluate the prediction accuracy of the trained random forest regression model; tune the model according to the evaluation results, and the tuning includes adjusting model parameters and changing the model structure; S2.2.6 Prediction and application: Use the trained random forest regression model to predict the water volume in the secondary cooling zone.

10. The dynamic secondary cooling water distribution method according to claim 9, characterized in that: The parameters described in S2.2.2 include: the number of decision trees, feature selection method and decision tree growth method.

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