A flexible controlled cooling method and system for copper tube rolling

Abstract

The present application relates to the technical field of metal rolling cooling, and particularly relates to a flexible cooling control method and system for copper pipe rolling, which comprises the following steps: selecting a rolling cooling mode according to the identification result of the copper pipe composition, and obtaining a copper pipe processing target; setting a processing technological process according to the rolling cooling mode and the copper pipe processing target, and dividing the copper pipe processing parts; collecting the copper pipe surface temperature in real time according to the divided copper pipe processing parts, and inversing the copper pipe internal parameters according to the copper pipe surface temperature, and then performing cooling difference control; and controlling and compensating the cooling difference control through the rolling speed collected in real time. Through the present application, the problem that it is difficult to control the cooling while taking into account the temperature distribution of different processing parts is effectively solved, so as to effectively improve the cooling precision, reduce the quality fluctuation caused by uneven cooling, and improve the production efficiency and product quality consistency of the copper pipe rolling.

Description

Technical Field

[0001] The present invention relates to the technical field of metal rolling cooling, and in particular to a flexible cooling control method and system for copper tube rolling. Background Art

[0002] With the rapid development of modern manufacturing and the increasing demand for precision processing, copper tube rolling technology has been widely used in electrical, electronic and other fields. As an important conductive and heat transfer element, the physical properties, surface quality and processing accuracy of copper tubes have a vital impact on the performance and reliability of the final product. Therefore, in the copper tube rolling process, achieving precise control and dynamic adjustment of the processing temperature has become a key technology to improve product quality and reduce processing defects.

[0003] However, existing cooling methods mainly use fixed cooling methods or rely on a single parameter (such as rolling speed, surface temperature, etc.) to adjust the coolant flow and injection method. These traditional cooling control methods have improved the quality of copper tubes to a certain extent. However, due to differences in factors such as the composition, rolling speed, and surface temperature of copper tubes during the rolling process, it is difficult to take into account the temperature distribution of different processing parts and perform cooling control, which can easily lead to local temperatures being too high or too low, causing a series of problems such as uneven thermal stress and structural deformation, which in turn affects the quality and production efficiency of the copper tubes.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present disclosure and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a flexible cooling control method and system for copper tube rolling, which can effectively solve the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A flexible controlled cooling method for copper tube rolling, comprising:

[0008] Identify the composition of the copper tube and select the rolling cooling method based on the composition identification results;

[0009] Collecting copper tube processing targets, setting a processing process according to the rolling cooling method and the copper tube processing targets, and dividing the copper tube processing parts according to the processing process;

[0010] The surface temperature of the copper tube is collected in real time according to the copper tube processing part, the internal parameters of the copper tube are inverted according to the copper tube surface temperature, and the cooling difference control is performed on the copper tube processing part according to the internal parameters of the copper tube;

[0011] The rolling speed is collected in real time, and the cooling difference control is compensated according to the rolling speed.

[0012] Furthermore, differential cooling control is performed on the processed parts of the copper tube according to the internal parameters of the copper tube, including:

[0013] Acquiring internal parameters of the copper tube according to the processing position of the copper tube, and determining a plurality of difference detection items according to the internal parameters of the copper tube;

[0014] Allocating cooling weights to the copper tube processing parts based on historical rolling process information, and respectively allocating cooling factor weights to the difference detection items of the copper tube processing parts;

[0015] Cooling difference control is performed on the copper tube processing parts according to the cooling weight and the cooling dominant factor.

[0016] Furthermore, cooling factor weights are assigned to the difference detection items of the copper tube processing parts respectively, including:

[0017] Acquire a plurality of coordinated parameter groups according to historical rolling process information, wherein the coordinated parameter groups represent two difference detection items having positive gains;

[0018] extracting a historical cooling efficiency index corresponding to the difference detection item according to the historical rolling process information, and allocating an original cooling weight according to the historical cooling efficiency index;

[0019] Acquire a plurality of independent parameter groups according to the collaborative parameter group, and calculate collaborative gains and interference suppression of the collaborative parameter group and the independent parameter group respectively, wherein the independent parameter groups represent two difference detection items that suppress each other;

[0020] The original cooling weight is dynamically adjusted according to the collaborative gain and interference suppression to obtain a cooling factor weight, and the cooling factor weight is dynamically balanced according to an energy conservation constraint.

[0021] Furthermore, calculating the collaborative gain and interference suppression of the collaborative parameter group and the independent parameter group includes:

[0022] extracting the rolling process parameters of the coordinated parameter group according to the historical rolling process parameters to obtain a rolling process data set;

[0023] Calculating the independent cooling efficiency and the collaborative cooling efficiency of each of the difference detection items in the collaborative parameter group according to the rolling process data set to obtain a collaborative cooling efficiency improvement rate;

[0024] Constructing a heat conduction equation, and calculating a synergistic gain based on the synergistic cooling efficiency improvement rate and the heat conduction equation;

[0025] The time series of the difference detection items are extracted respectively based on the independent parameter group, the process coordination index is calculated according to the time series, and interference suppression is obtained according to the process coordination index, wherein the process coordination index represents the coordinated quantitative value of several rolling process parameters.

[0026] Furthermore, the cooling difference control is performed according to the rolling speed to compensate for the cooling difference, including:

[0027] Extracting cooling response delay and reference rolling speed based on historical rolling process parameters, and analyzing a nonlinear relationship between the cooling response delay and the rolling speed;

[0028] constructing a delay prediction model according to the nonlinear relationship, and outputting a delay time window according to the delay prediction model;

[0029] Calculating a speed deviation between a real-time rolling speed and a reference rolling speed, and pre-adjusting a cooling system output according to the speed deviation;

[0030] The cooling injection timing is phase-shifted according to the delay time window to complete control compensation.

[0031] Furthermore, the copper tube is subjected to composition identification, and a rolling cooling method is selected according to the composition identification result, including:

[0032] Scanning the copper tube to detect and obtain the metal element content;

[0033] Calculating a composition uniformity index according to the metal element content, judging the copper tube alloy type according to the composition uniformity index, and obtaining the copper tube alloy type;

[0034] A cooling method selection database is established, a mapping relationship between the copper tube composition and the rolling cooling method is extracted according to the rolling cooling method selection database, and the rolling cooling method is determined according to the mapping relationship.

[0035] Furthermore, a cooling method selection database is established, including:

[0036] Extract some copper tube composition information and rolling cooling methods based on historical rolling process information;

[0037] Clustering and managing the copper tube composition information according to the copper tube alloy type, and corresponding each copper tube alloy type to the rolling cooling method to construct a copper tube cooling mapping relationship;

[0038] An index is constructed by taking the copper tube alloy type as a data index item, and a cooling method selection database is constructed according to the copper tube cooling mapping relationship.

[0039] Furthermore, inverting the internal parameters of the copper tube according to the surface temperature of the copper tube includes:

[0040] Establishing a copper pipe heat conduction model, and inputting the copper pipe surface temperature as a boundary condition into the copper pipe heat conduction model;

[0041] Obtaining a predicted temperature according to the copper pipe heat conduction model, obtaining an absolute error between the predicted temperature and the copper pipe surface temperature, and optimizing the copper pipe heat conduction model according to the absolute error;

[0042] The internal parameters of the copper pipe are calculated and obtained according to the optimized copper pipe heat conduction model.

[0043] A flexible controlled cooling system for copper tube rolling, comprising:

[0044] The component identification module identifies the components of the copper tube and selects the rolling cooling method based on the component identification results;

[0045] The copper tube part division module collects the copper tube processing target, sets the processing process according to the rolling cooling method and the copper tube processing target, and divides the copper tube processing parts according to the processing process;

[0046] a cooling differential control module, which collects the surface temperature of the copper tube in real time according to the copper tube processing position, inverts the internal parameters of the copper tube according to the copper tube surface temperature, and performs cooling differential control on the copper tube processing position according to the copper tube internal parameters;

[0047] The cooling control compensation module collects the rolling speed in real time and performs control compensation on the cooling difference according to the rolling speed.

[0048] Furthermore, the cooling control compensation module includes:

[0049] a delay analysis unit, which extracts cooling response delay and reference rolling speed according to historical rolling process parameters, and analyzes a nonlinear relationship between the cooling response delay and the rolling speed;

[0050] a delay prediction unit, configured to construct a delay prediction model according to the nonlinear relationship and output a delay time window according to the delay prediction model;

[0051] a deviation adjustment unit, which calculates the speed deviation between the real-time rolling speed and the reference rolling speed, and pre-adjusts the output of the cooling system according to the speed deviation;

[0052] The timing compensation unit performs phase shift on the cooling injection timing according to the delay time window to complete control compensation.

[0053] The technical solution of the present invention can achieve the following technical effects:

[0054] It effectively solves the problem of cooling control that is difficult to take into account the temperature distribution of different processing parts. Through real-time monitoring and dynamic feedback adjustment of multiple variables such as copper tube composition, surface temperature, and rolling speed, this solution can intelligently select the cooling method according to the actual processing conditions of the copper tube, and accurately control the cooling differences of different processing parts, effectively improving the cooling accuracy and reducing the quality fluctuations caused by uneven cooling, thereby improving the production efficiency of copper tube rolling and the consistency of product quality.

[0055] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 The figure is a schematic flow chart of a flexible controlled cooling method for copper tube rolling;

[0058] Figure 2 This is a flow chart of cooling differential control;

[0059] Figure 3 Schematic diagram of the process of assigning cooling factor weights;

[0060] Figure 4 A schematic diagram of the relationship between cooling factor weight distribution;

[0061] Figure 5 Schematic diagram of the relationship between cooperative gain and interference suppression. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0064] Embodiment 1;

[0065] like Figure 1 As shown, the present application provides a flexible controlled cooling method for copper tube rolling, which identifies the composition of the copper tube and selects the rolling cooling method according to the composition identification result;

[0066] S10: Identify the composition of the copper tube and select the rolling cooling method according to the composition identification result;

[0067] S20: Collect copper tube processing targets, set processing technology flow according to rolling cooling method and copper tube processing targets, and divide copper tube processing parts according to processing technology flow;

[0068] S30: collecting the surface temperature of the copper tube in real time according to the processing position of the copper tube, inverting the internal parameters of the copper tube according to the surface temperature of the copper tube, and performing differential cooling control on the processing positions of the copper tube according to the internal parameters of the copper tube;

[0069] S40: collecting the rolling speed in real time, and performing control compensation for the cooling difference according to the rolling speed.

[0070] Specifically, first, the composition of the copper tube is identified. The purpose of composition identification is to determine the alloy type of the copper tube and the distribution of its metal components, obtain the specific composition and content of the copper tube, and select an appropriate cooling method based on the specific composition and content of the copper tube. After determining the cooling method, the copper tube processing target is collected, which includes setting the final shape, size requirements, and surface quality standards of the copper tube. In some embodiments, the final shape and size requirements of the copper tube are mainly determined by an automated size measurement system. During the rolling process, parameters such as the diameter and wall thickness of the copper tube will continue to change. Therefore, it is necessary to monitor these dimensional changes in real time at different rolling stages. Laser rangefinders or optical imaging systems are usually used to measure the geometric dimensions of the copper tube, such as diameter, wall thickness, and length, in real time. Sensors are arranged at different positions of the copper tube rolling line to monitor the geometric shape of the copper tube in real time. The surface quality of the copper tube directly affects the final performance of the product. The surface quality collection mainly includes multiple indicators such as surface roughness, surface defects (such as scratches, pits, etc.), and surface finish. Online visual inspection systems or surface scanners can be used for collection. Based on these collected parameters, The process objectives are preferably achieved by collecting a large amount of production data and using machine learning algorithms (such as support vector machines, decision trees, etc.) to train a model to predict the process flow under different processing objectives. On this basis, the processing parts of the copper tube are further divided according to the rolling process flow. For example, the copper tube is divided into different processing parts such as the initial, middle and final stages of rolling. The cooling method of each part can be flexibly adjusted according to the difference in the copper tube processing objectives. Afterwards, the surface temperature of the copper tube can be collected by a temperature sensor, and the temperature distribution and other key parameters (such as stress distribution, metal structure, etc.) inside the copper tube can be inverted based on the surface temperature of the copper tube. Then, the cooling difference control of the copper tube processing parts is performed based on the inverted internal parameters. In the rolling process, the rolling speed will affect the cooling process of the copper tube. When the rolling speed is fast, the temperature of the copper tube drops slowly, which may lead to uneven cooling and affect product quality. When the rolling speed is slow, the copper tube stays for a long time, which may lead to overcooling, thereby causing surface cracks or uneven internal stress. In this way, the cooling difference control is compensated by real-time collection of the rolling speed, thereby ensuring that each part is accurately cooled during the rolling process.

[0071] The technical solution of the present invention effectively solves the problem of difficulty in controlling cooling while taking into account the temperature distribution of different processing parts. By real-time monitoring and dynamic feedback adjustment of multiple variables such as copper tube composition, surface temperature, and rolling speed, this solution can intelligently select the cooling method according to the actual processing conditions of the copper tube, and accurately control the cooling differences of different processing parts, effectively improving the cooling accuracy and reducing the quality fluctuations caused by uneven cooling, thereby improving the production efficiency of copper tube rolling and the consistency of product quality.

[0072] Further, if Figure 2 As shown, cooling differential control is performed on the copper tube processing parts according to the internal parameters of the copper tube, including:

[0073] S31: obtaining internal parameters of the copper tube according to the processing position of the copper tube, and determining a number of difference detection items according to the internal parameters of the copper tube;

[0074] S32: allocating cooling weights to the copper tube processing parts based on historical rolling process information, and respectively allocating cooling factor weights to the difference detection items of the copper tube processing parts;

[0075] S33: Cooling difference control is performed on the copper tube processing parts according to the cooling weight and the cooling dominant factor.

[0076] As a preferred embodiment of the above, it is first necessary to use a temperature sensor or infrared thermal imaging equipment to collect the surface temperature of the copper tube in real time during the processing process, and invert the temperature distribution and other key parameters inside the copper tube (such as metal structure, stress distribution, etc.) based on the collected surface temperature data; then each internal parameter of the copper tube is assigned as a difference detection item; after the difference detection item is assigned, the historical rolling process information can be obtained through the sensor network and the data acquisition system, and in some embodiments, the historical rolling process data is analyzed by data mining algorithms and machine learning models to obtain the cooling weight of the part: specifically, first collect and clean the historical data, and after standardization, use feature extraction methods (such as correlation analysis and principal component analysis) to screen important features and reduce data dimensions; then use machine learning models (such as decision trees, random forests or neural networks) to train and learn the relationship between the cooling requirements and characteristics of different parts, and finally assign cooling weights to different parts according to processing stages, temperature, stress and other factors; in actual production, the system dynamically adjusts the cooling strategy according to real-time data , thereby improving production efficiency and product quality; in addition, the cooling difference control of each copper tube processing part must not only consider the cooling weight, but also need to assign corresponding cooling factor weights according to cooling factors (such as cooling speed, cooling method, etc.), and then according to the cooling weight of the part and the cooling factor weight, a closed-loop control system can be used to perform cooling difference control on the copper tube processing parts separately: first, a cooling weight is assigned to each part to determine its cooling demand. Parts with larger cooling weights require stronger cooling, while parts with smaller cooling weights require milder cooling. According to the cooling dominant factors (such as cooling method, flow rate, cooling time, etc.), the cooling strategy of each part is further adjusted to ensure a balance of cooling effects. For example, due to the high temperature of the initial rolling part, the system will choose strong spray cooling and increase the flow rate; while the cooling demand of the final rolling part is low, air cooling or light spraying is used for mild cooling. The system will also dynamically adjust the cooling strategy according to real-time data feedback, optimize the cooling intensity, cooling method and cooling time, to ensure that the temperature of each part of the copper tube is accurately controlled, thereby improving processing quality and production efficiency.

[0077] Furthermore, if Figure 3 、 Figure 4 As shown in the figure, cooling factor weights are assigned to the difference detection items of the copper tube processing parts, including:

[0078] S321: acquiring a plurality of coordinated parameter groups according to historical rolling process information, wherein the coordinated parameter groups represent two difference detection items having positive gains;

[0079] S322: extracting historical cooling efficiency indexes corresponding to the difference detection items based on historical rolling process information, and allocating original cooling weights based on the historical cooling efficiency indexes;

[0080] S333: Obtaining a plurality of independent parameter groups according to the collaborative parameter group, and calculating collaborative gains and interference suppression of the collaborative parameter group and the independent parameter group respectively, where the independent parameter group represents two difference detection items that suppress each other;

[0081] S334: Dynamically adjust the original cooling weights according to the synergistic gain and the interference suppression to obtain the cooling factor weights, and dynamically balance the cooling factor weights according to the energy conservation constraint.

[0082] In this embodiment, during the rolling process of the copper tube, the difference detection items (such as temperature changes, stress changes, cooling method differences, etc.) of each processing part have different influencing factors. Cluster analysis can be used to analyze the historical rolling process information to obtain multiple collaborative parameter groups. These collaborative parameter groups represent two difference detection items with positive gains. For example, in a certain rolling process, the changes in cooling injection pressure and surface temperature may have a positive correlation, that is, as the injection pressure increases, the surface temperature drops faster. Therefore, these two difference detection items can form a collaborative parameter group; then, the corresponding historical cooling efficiency index is extracted based on the collaborative parameter group of each difference detection item. The cooling efficiency index is based on the historical rolling process data and evaluates the actual efficiency of each cooling method by analyzing the cooling effect during the cooling process. The calculation method is as follows:

[0083] ;

[0084] in, Indicates the temperature change during the real-time cooling process (i.e., the temperature drop). represents the expected temperature change from theoretical calculations (i.e., the temperature drop during an ideal cooling process), Represents the cooling efficiency index. According to the calculated cooling efficiency index, the original cooling weight is assigned to each collaborative parameter group. The collaborative parameter group with a higher cooling efficiency index will obtain a higher cooling weight, indicating that the cooling demand of these parts is stronger and requires higher intensity cooling. When considering the collaborative parameter group, the influence of the independent parameter group must also be considered. The independent parameter group represents two difference detection items with mutual inhibition, that is, the changes between the two parameters have a mutually offsetting effect on the cooling effect. For the independent parameter group, data mining technology (such as association rule mining technology) can be used to identify those difference detection items with inhibition relationships from historical data. As the independent parameter group and the collaborative parameter group are combined, the cooling efficiency index is increased, and the cooling efficiency is increased. The synergistic gain and interference suppression are calculated separately for the same parameter group. The synergistic gain refers to the enhanced cooling efficiency brought about by the joint action of the two synergistic parameter groups, and the interference suppression refers to the mutual suppression effect between the two independent parameter groups. Finally, according to the calculation results of the synergistic gain and interference suppression, the original cooling weight is adjusted so that the parts with larger synergistic gain obtain more cooling factor weight, while the parts subject to interference suppression have their cooling factor weight appropriately reduced. During the weight adjustment process, the system needs to ensure that the sum of the total cooling factor weights is 1, that is, the energy conservation constraint is met. Through normalization processing, the reasonable distribution of the cooling factor weights is ensured to avoid uneven cooling due to excessive weighting or weighting during the adjustment process.

[0085] Further, if Figure 5 As shown, the calculation of the cooperative gain and interference suppression of the cooperative parameter group and the independent parameter group includes:

[0086] Extract rolling process parameters of the coordinated parameter group according to historical rolling process parameters to obtain a rolling process data set;

[0087] According to the rolling process data set, the independent cooling efficiency and collaborative cooling efficiency of each difference detection item in the collaborative parameter group are calculated respectively to obtain the collaborative cooling efficiency improvement rate;

[0088] Construct a heat conduction equation and calculate the synergistic gain based on the synergistic cooling efficiency improvement rate and the heat conduction equation;

[0089] The time series of difference detection items are extracted based on independent parameter groups, and the process coordination index is calculated according to the time series. The interference suppression is obtained according to the process coordination index, which represents the coordinated quantitative value of several rolling process parameters.

[0090] Specifically, first, based on the historical process data of copper tube rolling, the collaborative parameter group and the independent parameter group are identified. Based on the collaborative parameter group, cluster analysis or correlation analysis can be used to extract relevant data from the historical rolling process parameters to form a rolling process data set. These data sets contain historical data of each difference detection item (such as temperature change, coolant flow, spray pressure, etc.); then, the independent cooling efficiency and collaborative cooling efficiency of each difference detection item are calculated respectively. The independent cooling efficiency is obtained based on the ratio of the actual temperature change to the expected temperature change of each difference detection item, and the collaborative cooling efficiency is obtained based on the ratio of the actual temperature change to the expected temperature change under the joint action of the collaborative parameter group. The collaborative cooling efficiency improvement rate is calculated based on the obtained independent cooling efficiency and collaborative cooling efficiency. The calculation method is as follows:

[0091] ;

[0092] in, represents the collaborative cooling efficiency, Indicates the independent cooling efficiency, is the improvement rate of collaborative cooling efficiency; according to the improvement rate of collaborative cooling efficiency, the collaborative gain can be calculated by the heat conduction equation. The heat conduction equation can reflect the relationship between the temperature change of different parts and the cooling effect. The collaborative gain of different processing parts can be calculated according to the collaborative cooling efficiency improvement rate and the heat conduction equation; at the same time, for the independent parameter group with interference suppression, the time series data related to these parameters are first extracted, and the process coordination index is calculated based on the time series to quantify the synergy between multiple rolling process parameters. The process coordination index is calculated based on the interaction of each difference detection item and its influence on the cooling effect. It can reflect the change in cooling efficiency when multiple parameters work together. The calculation formula of the process coordination index is as follows:

[0093] ;

[0094] in, Represents the time series data of each difference detection item, n represents the number of different detection items, and the process coordination index is obtained based on the calculation to quantify the degree of mutual influence between the difference detection items, and thus determine the size of interference suppression.

[0095] Furthermore, the cooling difference control is compensated according to the rolling speed, including:

[0096] Extract cooling response delay and benchmark rolling speed based on historical rolling process parameters, and analyze the nonlinear relationship between cooling response delay and rolling speed;

[0097] Building a delay prediction model based on the nonlinear relationship, and outputting a delay time window based on the delay prediction model;

[0098] Calculate the speed deviation between the real-time rolling speed and the reference rolling speed, and pre-adjust the output of the cooling system according to the speed deviation;

[0099] The cooling injection timing is phase-shifted according to the delay time window to complete control compensation.

[0100] As a preferred embodiment of the above, cooling response data is first extracted from historical rolling process parameters, including the time delay of temperature change in each rolling cycle and the corresponding rolling speed (reference rolling speed). The Pearson correlation coefficient can be used to identify the nonlinear relationship between the two, that is, as the rolling speed changes, the time delay of the cooling system response will also change; based on the nonlinear relationship between the cooling response delay and the reference rolling speed, in some embodiments, nonlinear regression analysis or deep learning models (such as neural networks) are used to construct a delay prediction model, and the cooling response delay is predicted by the input reference rolling speed data according to the delay prediction model, and according to the results of the prediction model, a delay time window is output, indicating when the cooling system should be started in advance. The size of the delay time window is related to the change of rolling speed and the characteristics of the cooling response. In the actual production process, the real-time rolling speed often deviates from the preset reference rolling speed. In order to ensure the precise control of the cooling system, the deviation between the real-time rolling speed and the reference rolling speed is calculated. The calculation method is as follows: first, the current rolling speed can be collected through a speed sensor, and the speed deviation is calculated according to the difference between the real-time rolling speed and the reference rolling speed. Then, based on the speed deviation and the delay time window, a PID controller or a fuzzy logic controller can be used to adjust the cooling injection timing, and the delay can be compensated by phase shift. The purpose of phase shift is to advance or delay the start time of the cooling injection to compensate for the cooling delay caused by the change of rolling speed.

[0101] Furthermore, the copper tube is identified for its composition, and the rolling cooling method is selected based on the composition identification results, including:

[0102] Scan the copper tube to detect and obtain the metal element content;

[0103] Calculate the composition uniformity index according to the metal element content, judge the copper tube alloy type according to the composition uniformity index, and obtain the copper tube alloy type;

[0104] A cooling method selection database is established, and the mapping relationship between the copper tube composition and the rolling cooling method is extracted according to the rolling cooling method selection database, and the rolling cooling method is determined according to the mapping relationship.

[0105] In this embodiment, a spectrometer (e.g., an X-ray fluorescence spectrometer) can first be used to perform a high-precision scan of the copper tube. This instrument can detect the metal element composition of the copper tube surface and its microscopic regions. X-rays are irradiated onto the copper tube surface, interacting with metal atoms, and analyzing the returned fluorescence signal to determine the metal element content of the copper tube. Using the metal element data obtained from the above scan, the composition uniformity index of the copper tube is then calculated. The composition uniformity index is primarily used to measure whether the distribution of metal elements within the copper tube is uniform. Specifically, it can quantify the concentration differences of metal components (e.g., copper, zinc, nickel, etc.) in different regions on the surface or inside the copper tube. The calculation formula is as follows:

[0106] ;

[0107] in, For copper tube i The metal element concentration at each location, is the average concentration of metal elements at all locations, N is the total number of positions scanned for the copper tube. In this embodiment, the composition uniformity index can be divided into multiple levels, such as good, medium, and uneven. This index can be used to accurately determine the alloy type of the copper tube (e.g., brass, copper, etc.). For example, a copper tube with a high uniformity index can be considered high-quality and suitable for low-temperature rolling cooling. However, a copper tube with low uniformity may require a more complex cooling method, such as high-efficiency spray cooling or immersion cooling, to ensure stability during the rolling process. A database for cooling method selection is then established, containing a mapping relationship between copper tube composition and cooling methods. The database records different copper tube alloy types and their corresponding cooling methods. For example, for copper with a high copper content and uniform composition, spray cooling can be selected as the cooling method. For brass with a low copper content or uneven composition, immersion cooling or multi-stage cooling may be selected as the cooling method. After obtaining the composition analysis results of the copper tube and determining the copper tube alloy type based on the composition uniformity index, the system selects the mapping relationship in the database based on the cooling method to determine the cooling method suitable for the current copper tube alloy type.

[0108] Furthermore, a cooling method selection database is established, including:

[0109] Extract some copper tube composition information and rolling cooling methods based on historical rolling process information;

[0110] Cluster the copper tube composition information according to the copper tube alloy type, and correspond each copper tube alloy type to the rolling cooling method to build a copper tube cooling mapping relationship;

[0111] An index is constructed by taking the copper tube alloy type as a data index item, and a cooling method selection database is constructed based on the copper tube cooling mapping relationship.

[0112] Specifically, first, historical rolling process information is extracted from existing production records. The historical rolling process information includes key data such as the rolling cooling method, cooling rate, rolling speed, alloy composition, temperature change, etc. used by each copper tube alloy type in the actual production process; then, based on the historical rolling process data, the composition information of the copper tube is clustered and analyzed: First, the metal element composition of the copper tube (such as copper, zinc, nickel, lead, etc.) is classified according to the alloy type. Each type of alloy (such as red copper, brass, bronze, etc.) contains multiple copper tube samples. The system collects statistics on the data of these samples and extracts the average composition of the alloy and its performance in the production process; through cluster analysis, copper tubes with similar compositions are classified into the same category, thereby simplifying the subsequent cooling method selection process; after completing the cluster management, the mapping relationship between the copper tube composition and the rolling cooling method is constructed. Each copper tube alloy type and its corresponding cooling method are mapped through data association. For example, Take the following mapping method as a reference: for copper, it is recommended to use "spray cooling", the spray pressure is 50 bar, and the cooling time is 30 seconds; for brass, it is recommended to use "immersion cooling", the cooling time is 60 seconds, and the temperature is controlled below 300°C; in this way, a mapping relationship is constructed to obtain the cooling method; next, the copper tube alloy type is used as a data index item to build an index database to store the mapping relationship between the copper tube alloy type and the cooling method. The database query function can quickly retrieve the corresponding cooling method according to the composition information of the copper tube (such as the alloy type) and output the relevant cooling parameters; in the actual production process, when a copper tube enters the rolling stage, the system monitors the composition information of the copper tube in real time (the copper tube composition data obtained by methods such as X-ray fluorescence spectroscopy analysis) and queries the cooling method selection database. According to the alloy type of the copper tube, it selects and recommends a suitable cooling method, and adjusts the spray pressure, cooling time and other parameters of the cooling system accordingly.

[0113] Furthermore, the internal parameters of the copper tube are inverted based on the surface temperature of the copper tube, including:

[0114] Establish a copper tube heat conduction model, and input the copper tube surface temperature into the copper tube heat conduction model as a boundary condition;

[0115] The predicted temperature is obtained based on the copper tube heat conduction model, and the absolute error is obtained based on the predicted temperature and the copper tube surface temperature. The copper tube heat conduction model is optimized based on the absolute error.

[0116] The internal parameters of the copper tube are calculated and obtained based on the optimized copper tube heat conduction model.

[0117] As a preferred embodiment of the above, a heat conduction model of the copper tube is first established. The model is based on the physical principles of heat conduction and takes into account the geometric characteristics of the copper tube and the thermophysical properties of the material (such as thermal conductivity, specific heat capacity, density, etc.). The heat conduction model of the copper tube assumes that the heat inside the copper tube diffuses from the inside to the surface through thermal conduction. It mainly considers radial heat conduction, that is, heat conduction along the radius of the copper tube. It can be described by a one-dimensional heat conduction equation. This equation takes into account the geometric shape of the copper tube and the thermophysical properties of the copper material, such as thermal conductivity, specific heat capacity, and density. Therefore, by establishing a heat conduction model for the copper tube and inputting the surface temperature of the copper tube as a boundary condition into the model, the temperature distribution inside the copper tube can be calculated. In actual operation, the temperature data of the copper tube surface can be obtained in real time by installing an infrared temperature sensor or a surface thermocouple. After obtaining the preliminary calculation results of the heat conduction model, the predicted internal temperature will be compared with the copper tube surface temperature actually measured by the temperature sensor to calculate the absolute error between the predicted temperature and the actual surface temperature. This error is used to judge the accuracy of the heat conduction model. If the model prediction result is different from the actual temperature, the temperature distribution inside the copper tube can be calculated. If the error between the values ​​of the thermal conductivity and specific heat capacity is large, it means that the heat conduction model has not fully reflected the actual thermal characteristics of the copper tube and needs further optimization. To this end, an optimization algorithm is used to adjust and improve the heat conduction model. The optimization method can use optimization techniques such as least squares method or genetic algorithm to iteratively adjust key parameters in the heat conduction model, such as thermal conductivity and specific heat capacity, to minimize the error. This process can be repeated until the optimized model can accurately predict the temperature distribution inside the copper tube and the error between the predicted result and the actual measured temperature value is minimized. Based on the optimized heat conduction model, the internal parameters of the copper tube can be further inverted and calculated, including but not limited to the temperature distribution inside the copper tube, the heat flux density distribution of the copper tube, and the thermal diffusivity of the copper tube. The inversion of the temperature distribution inside the copper tube is used as an example: first, the known surface temperature is input into the model as a boundary condition. According to the optimized heat conduction model, the surface temperature will be transmitted inward to different regions of the copper tube. Then, the initial temperature of the copper tube before rolling is obtained based on the temperature sensor. Then, the finite difference method, finite element method or spectral method can be used to discretize the heat conduction equation and calculate the temperature of each internal position of the copper tube. At each moment, the temperature will be gradually updated according to the transfer of heat flow, and finally the temperature distribution inside the copper tube will be obtained.

[0118] Embodiment 2:

[0119] Based on the same inventive concept as the flexible cooling control method for copper tube rolling in the aforementioned embodiment, the present invention also provides a flexible cooling control system for copper tube rolling, such as Figure 5 As shown, the system includes:

[0120] The component identification module identifies the components of the copper tube and selects the rolling cooling method based on the component identification results;

[0121] Copper tube part division module collects copper tube processing targets, sets the processing process according to the rolling cooling method and copper tube processing targets, and divides the copper tube processing parts according to the processing process;

[0122] The cooling difference control module collects the surface temperature of the copper tube in real time according to the processing position of the copper tube, inverts the internal parameters of the copper tube based on the surface temperature of the copper tube, and performs cooling difference control on the processing position of the copper tube according to the internal parameters of the copper tube;

[0123] The cooling control compensation module collects the rolling speed in real time and performs control compensation for the cooling difference according to the rolling speed.

[0124] The above-mentioned adjustment system in the present invention can effectively realize the flexible cooling control method for copper tube rolling, and the technical effects that can be achieved are as described in the above-mentioned embodiments and will not be repeated here.

[0125] More specifically, the cooling control compensation module includes:

[0126] Delay analysis unit, which extracts cooling response delay and benchmark rolling speed based on historical rolling process parameters, and analyzes the nonlinear relationship between cooling response delay and rolling speed;

[0127] A delay prediction unit, which constructs a delay prediction model based on the nonlinear relationship and outputs a delay time window according to the delay prediction model;

[0128] Deviation adjustment unit, which calculates the speed deviation between the real-time rolling speed and the reference rolling speed and pre-adjusts the output of the cooling system according to the speed deviation;

[0129] The timing compensation unit performs phase shift on the cooling injection timing according to the delay time window to complete the control compensation.

[0130] Similarly, the above-mentioned optimization schemes for the system can also respectively achieve the corresponding optimization effects of the method in Example 1, which will not be repeated here.

[0131] Although the present application has been described with reference to specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and drawings are merely illustrative of the present application as defined herein and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, the present application is intended to include such modifications and variations as fall within the scope of the present application and its equivalents.

Claims

1. A flexible controlled cooling method for copper tube rolling, characterized in that: The method comprises: Identify the composition of the copper tube and select the rolling cooling method based on the composition identification results; Collecting copper tube processing targets, setting a processing process according to the rolling cooling method and the copper tube processing targets, and dividing the copper tube processing parts according to the processing process; The surface temperature of the copper tube is collected in real time according to the copper tube processing part, the internal parameters of the copper tube are inverted according to the copper tube surface temperature, and the cooling difference control is performed on the copper tube processing part according to the internal parameters of the copper tube; collecting rolling speed in real time, and performing control compensation on the cooling difference control according to the rolling speed; The cooling difference control is performed on the processed parts of the copper tube according to the internal parameters of the copper tube, including: Acquiring internal parameters of the copper tube according to the processing position of the copper tube, and determining a plurality of difference detection items according to the internal parameters of the copper tube; Allocating cooling weights to the copper tube processing parts based on historical rolling process information, and respectively allocating cooling factor weights to the difference detection items of the copper tube processing parts; Performing cooling differential control on the copper tube processing parts according to the cooling weight and the cooling dominant factor; The cooling difference control is performed according to the rolling speed to compensate for the cooling difference, including: Extracting cooling response delay and reference rolling speed based on historical rolling process parameters, and analyzing a nonlinear relationship between the cooling response delay and the rolling speed; constructing a delay prediction model according to the nonlinear relationship, and outputting a delay time window according to the delay prediction model; Calculating a speed deviation between a real-time rolling speed and a reference rolling speed, and pre-adjusting a cooling system output according to the speed deviation; The cooling injection timing is phase-shifted according to the delay time window to complete control compensation.

2. The flexible controlled cooling method for copper tube rolling according to claim 1, characterized in that: The cooling factor weights are respectively assigned to the difference detection items of the copper tube processing parts, including: Acquire a plurality of coordinated parameter groups according to historical rolling process information, wherein the coordinated parameter groups represent two difference detection items having positive gains; extracting a historical cooling efficiency index corresponding to the difference detection item according to the historical rolling process information, and allocating an original cooling weight according to the historical cooling efficiency index; Acquire a plurality of independent parameter groups according to the collaborative parameter group, and calculate collaborative gains and interference suppression of the collaborative parameter group and the independent parameter group respectively, wherein the independent parameter groups represent two difference detection items that suppress each other; The original cooling weight is dynamically adjusted according to the collaborative gain and interference suppression to obtain a cooling factor weight, and the cooling factor weight is dynamically balanced according to an energy conservation constraint.

3. The flexible controlled cooling method for copper tube rolling according to claim 2, characterized in that: Calculating the collaborative gain and interference suppression of the collaborative parameter group and the independent parameter group includes: extracting the rolling process parameters of the coordinated parameter group according to the historical rolling process parameters to obtain a rolling process data set; Calculating the independent cooling efficiency and the collaborative cooling efficiency of each of the difference detection items in the collaborative parameter group according to the rolling process data set to obtain a collaborative cooling efficiency improvement rate; Constructing a heat conduction equation, and calculating a synergistic gain based on the synergistic cooling efficiency improvement rate and the heat conduction equation; The time series of the difference detection items are extracted respectively based on the independent parameter group, the process coordination index is calculated according to the time series, and interference suppression is obtained according to the process coordination index, wherein the process coordination index represents the coordinated quantitative value of several rolling process parameters.

4. The flexible controlled cooling method for copper tube rolling according to claim 1, characterized in that: Identify the composition of the copper tube and select the rolling cooling method based on the composition identification results, including: Scanning the copper tube to detect and obtain the metal element content; Calculating a composition uniformity index according to the metal element content, judging the copper tube alloy type according to the composition uniformity index, and obtaining the copper tube alloy type; A cooling method selection database is established, a mapping relationship between the copper tube composition and the rolling cooling method is extracted according to the rolling cooling method selection database, and the rolling cooling method is determined according to the mapping relationship.

5. The flexible controlled cooling method for copper tube rolling according to claim 4, characterized in that: Establish a cooling method selection database, including: Extract some copper tube composition information and rolling cooling methods based on historical rolling process information; Clustering and managing the copper tube composition information according to the copper tube alloy type, and corresponding each copper tube alloy type to the rolling cooling method to construct a copper tube cooling mapping relationship; An index is constructed by taking the copper tube alloy type as a data index item, and a cooling method selection database is constructed according to the copper tube cooling mapping relationship.

6. The flexible controlled cooling method for copper tube rolling according to claim 1, characterized in that: Inverting the internal parameters of the copper tube according to the surface temperature of the copper tube includes: Establishing a copper pipe heat conduction model, and inputting the copper pipe surface temperature as a boundary condition into the copper pipe heat conduction model; Obtaining a predicted temperature according to the copper pipe heat conduction model, obtaining an absolute error between the predicted temperature and the copper pipe surface temperature, and optimizing the copper pipe heat conduction model according to the absolute error; The internal parameters of the copper pipe are calculated and obtained according to the optimized copper pipe heat conduction model.

7. A flexible cooling control system for copper tube rolling, characterized in that: The flexible controlled cooling method for copper tube rolling according to claim 1 is used, wherein the system comprises: The component identification module identifies the components of the copper tube and selects the rolling cooling method based on the component identification results; The copper tube part division module collects the copper tube processing target, sets the processing process according to the rolling cooling method and the copper tube processing target, and divides the copper tube processing parts according to the processing process; a cooling differential control module, which collects the surface temperature of the copper tube in real time according to the copper tube processing position, inverts the internal parameters of the copper tube according to the copper tube surface temperature, and performs cooling differential control on the copper tube processing position according to the copper tube internal parameters; A cooling control compensation module collects rolling speed in real time and performs control compensation on the cooling difference according to the rolling speed; The cooling control compensation module includes: a delay analysis unit, which extracts cooling response delay and reference rolling speed according to historical rolling process parameters, and analyzes a nonlinear relationship between the cooling response delay and the rolling speed; a delay prediction unit, configured to construct a delay prediction model according to the nonlinear relationship and output a delay time window according to the delay prediction model; a deviation adjustment unit, which calculates the speed deviation between the real-time rolling speed and the reference rolling speed, and pre-adjusts the output of the cooling system according to the speed deviation; The timing compensation unit performs phase shift on the cooling injection timing according to the delay time window to complete control compensation.

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