Borax adding method and device in TP2 copper alloy continuous casting process

Abstract

The present application provides a borax addition method and device in the TP2 copper alloy continuous casting process, which is applied to the field of copper alloy continuous casting technology. By obtaining real-time data on the melt surface temperature, oxidation degree and protective film thickness, the system can accurately judge the current melt state. Based on these data, the system calculates the most suitable borax addition rate and accurately adds it through a quantitative conveying device. After the addition operation, the system obtains feedback information on the melt state again and dynamically adjusts the borax addition rate based on this information. It has the advantages of accurately controlling the amount and timing of borax addition, adjusting the borax addition strategy in real time, improving production efficiency and worker safety, and ensuring the continuity and uniformity of the protective film.

Description

Technical Field

[0001] The present application relates to the technical field of continuous casting of copper alloys, and in particular to a method and device for adding borax in a continuous casting process of TP2 copper alloys. Background Art

[0002] In factories producing high-performance copper alloy parts, TP2 copper alloy is susceptible to corrosion from oxygen and impurities in the air during the high-temperature smelting process, leading to oxidation of the melt and thus affecting the quality of the castings. To protect the melt and improve product quality, engineers typically add borax during the high-temperature smelting process to form a protective film covering the melt surface, reducing oxidation and impurity contamination.

[0003] To ensure product quality, the amount and timing of borax addition must be precisely controlled during the production process to ensure the formation of an effective protective film. Furthermore, most existing TP2 copper alloy production lines are continuous casting lines, which requires the continuous and precise addition of borax during the continuous melting process to ensure the durability and stability of the protective film. At the same time, factories must also ensure worker safety and reduce the risk of direct contact with the high-temperature melt.

[0004] However, at present, the addition of borax in the TP2 continuous casting process mainly relies on manual operation, but this method has the following problems:

[0005] 1. It is difficult to precisely control the amount and timing of addition: manual operation cannot ensure the continuity and uniformity of the protective film, which affects the protective effect of the melt;

[0006] 2. Lack of real-time adjustment capability: The borax addition strategy cannot be flexibly adjusted according to the melt state (such as melt temperature and oxidation status);

[0007] 3. Safety hazards: Workers need to frequently approach high-temperature melts, increasing the risk of burns and accidents;

[0008] 4. Low production efficiency: Manual operation efficiency is low and it is difficult to meet the needs of large-scale production;

[0009] 5. Unstable protective film effect: Due to the unstable addition of borax, the antioxidant effect of the melt fluctuates greatly, which may affect the consistency of casting quality.

[0010] Therefore, the prior art lacks a method for achieving precise and automated addition of borax during the TP2 continuous casting process to form a stable and uniform protective film while improving production efficiency and worker safety. Summary of the Invention

[0011] In view of the above-mentioned shortcomings of the prior art, the present application provides a borax addition method in the TP2 copper alloy continuous casting process, which is applied to the field of copper alloy continuous casting technology. It has the advantages of accurately controlling the amount and timing of borax addition, adjusting the borax addition strategy in real time, improving production efficiency and worker safety, and ensuring the continuity and uniformity of the protective film.

[0012] In a first aspect, a method for adding borax during a TP2 copper alloy continuous casting process is provided, the method comprising the steps of:

[0013] S1: Obtain real-time parameter information of melt surface temperature, melt oxidation degree and protective film thickness;

[0014] S2: Calculating the borax addition rate according to the real-time parameter information;

[0015] S3: controlling the borax quantitative conveying device to perform a borax addition operation according to the borax addition rate;

[0016] S4: obtaining feedback parameter information of the melt surface temperature, melt oxidation degree, and protective film thickness after the borax addition operation;

[0017] S5: Dynamically update the borax addition rate according to the feedback parameter information.

[0018] The present application proposes a method for adding borax in the continuous casting process of TP2 copper alloy. By obtaining real-time data on the melt surface temperature, degree of oxidation and thickness of the protective film, the system can accurately judge the current state of the melt. Based on these data, the system calculates the most suitable borax addition rate and accurately adds it through a quantitative conveying device. After the addition operation, the system obtains feedback information on the melt state again and dynamically adjusts the borax addition rate based on this information. This closed-loop control method ensures the continuity and uniformity of the protective film and effectively improves the protective effect of the melt. At the same time, automated operation greatly reduces manual intervention, improves production efficiency, reduces the risk of workers coming into contact with high-temperature melts, and improves work safety. Therefore, this solution has the advantages of accurately controlling the amount and timing of borax addition, adjusting the borax addition strategy in real time, improving production efficiency and worker safety, and ensuring the continuity and uniformity of the protective film.

[0019] Furthermore, in step S2, the adaptive control calculation formula for calculating the borax addition rate according to the real-time parameter information is:

[0020] B(t) = [k1(t) * (T(t) - T_target) + k2(t) * (O(t) - O_target) + k3(t)* (H(t) - H_target)] * F(t);

[0021] Among them, B(t) is the borax addition rate, T(t), O(t), and H(t) are the melt surface temperature, oxidation degree, and protective film thickness in the real-time parameter information, respectively; T_target, O_target, and H_target are the target surface temperature, target oxidation degree, and target protective film thickness in the preset target process parameters; k1(t), k2(t), and k3(t) are the temperature control coefficient, oxidation degree control coefficient, and protective film thickness control coefficient; F(t) is a compensation function that comprehensively considers the temperature change rate, production rate, and process cycle.

[0022] This application proposes a borax addition method for the continuous casting of TP2 copper alloy. By introducing an adaptive control calculation formula, this method achieves the goal of accurately calculating the borax addition rate during the continuous casting of TP2 copper alloy based on real-time parameter information. The formula considers multiple key parameters, such as melt surface temperature, oxidation level, and protective film thickness. By comparing the calculated borax addition rate with preset target values, combined with a dynamically adjusted control coefficient and a compensation function that comprehensively considers multiple factors, the formula achieves accurate calculation of the borax addition rate.

[0023] Furthermore, in step S5, the feedback parameter information includes at least the real-time surface temperature of the melt, the real-time oxidation degree, and the real-time protective film thickness. Step S5 includes:

[0024] S51: Calculating deviations between the real-time surface temperature, the real-time oxidation degree, and the real-time protective film thickness and the target surface temperature, target oxidation degree, and target protective film thickness in the preset target process parameters;

[0025] S52: Calculating and updating the temperature control coefficient, the oxidation degree control coefficient, and the protective film thickness control coefficient according to the deviation value;

[0026] S53: updating the borax addition rate according to the updated temperature control coefficient, the oxidation degree control coefficient, and the protective film thickness control coefficient.

[0027] This application proposes a method for adding borax during the continuous casting process of TP2 copper alloy. First, the system obtains real-time parameter information and compares it with preset target parameters to calculate deviation values. These deviation values ​​reflect the gap between the current borax addition effect and the ideal state. The system then uses these deviation values ​​to update control coefficients, which determine the system's responsiveness to different parameter changes. Finally, the system adjusts the borax addition rate based on the updated control coefficients, thereby achieving dynamic optimization of the borax addition process.

[0028] Furthermore, step S52 includes:

[0029] S521: respectively obtaining a temperature deviation value, an oxidation degree deviation value, and a protective film thickness deviation value from the deviation values;

[0030] S522: Calculating an updated temperature control coefficient according to the product of the square of the temperature deviation value and the first learning rate;

[0031] S523: Calculating an updated oxidation degree control coefficient according to the product of the square value of the oxidation degree deviation value and the second learning rate;

[0032] S524: Calculating an updated protective film thickness control coefficient according to the product of the square value of the protective film thickness deviation value and the third learning rate.

[0033] This application proposes a borax addition method for the continuous casting of TP2 copper alloy. By introducing the squared sum of deviation values ​​(first, second, and third learning rates), this method accurately updates the temperature control coefficient, the oxidation degree control coefficient, and the protective film thickness control coefficient. This method dynamically adjusts the control coefficients based on the difference between actual process parameters and target parameters, thereby achieving precise control of the borax addition process.

[0034] Furthermore, step S512 includes:

[0035] S5121: Obtain historical temperature deviation values ​​within a preset time period;

[0036] S5122: Calculating a temperature fluctuation coefficient based on a change trend of the historical temperature deviation value within a preset time period;

[0037] S5123: Multiplying the temperature fluctuation coefficient by the square of the temperature deviation value to obtain a corrected temperature deviation value;

[0038] S5124: Calculate a temperature control coefficient according to the product of the corrected temperature deviation value and the first learning rate.

[0039] Furthermore, step S5122 includes:

[0040] S51221: Obtain a historical temperature deviation value sequence within a preset time window;

[0041] S51222: Calculate the standard deviation of the historical temperature deviation value sequence;

[0042] S51223: Calculate the temperature fluctuation coefficient using the standard deviation according to a preset fluctuation coefficient calculation formula.

[0043] Furthermore, in step S51223, the preset fluctuation coefficient calculation formula is: temperature fluctuation coefficient = (standard deviation / (average value + minimum value constant)) × weight coefficient, and the average value is the average value of the historical temperature deviation value sequence.

[0044] Furthermore, the method further comprises:

[0045] S6: Obtaining the protective film image information collected by the visual recognition system;

[0046] S7: calculating the protective film coverage of the melt surface according to the protective film image information;

[0047] S8: Calculating a deviation between the protective film coverage and a target protective film coverage;

[0048] S9: calculating a protective film coverage control coefficient according to the deviation value of the protective film coverage;

[0049] S10: Dynamically updating the borax addition rate according to the protective film coverage control coefficient and feedback parameter information.

[0050] Furthermore, step S7 includes:

[0051] S71: performing image segmentation on the protective film image information to obtain a protective film area;

[0052] S72: Calculating the grayscale value distribution of the protective film area;

[0053] S73: Calculating the protection film coverage rate according to the grayscale value distribution of the protection film area.

[0054] In a second aspect, a borax adding device for a TP2 copper alloy continuous casting process is provided, which is applied to any of the above-mentioned borax adding methods for a TP2 copper alloy continuous casting process, and the device comprises:

[0055] Parameter acquisition module: used to obtain real-time parameter information of melt surface temperature, melt oxidation degree and protective film thickness;

[0056] Calculation module: used for calculating the borax addition rate according to the real-time parameter information;

[0057] A control module is used to control the borax quantitative delivery device to perform the borax addition operation according to the borax addition rate;

[0058] Feedback module: used to obtain feedback parameter information of melt surface temperature, melt oxidation degree and protective film thickness after borax addition operation;

[0059] Update module: used to dynamically update the borax addition rate according to the feedback parameter information.

[0060] Beneficial effects: The present application provides a method and device for adding borax in the continuous casting process of TP2 copper alloy. By acquiring real-time data on the surface temperature of the melt, the degree of oxidation and the thickness of the protective film, the system can accurately judge the current state of the melt. Based on these data, the system calculates the most suitable borax addition rate and accurately adds it through a quantitative conveying device. After the addition operation, the system obtains feedback information on the melt state again, and dynamically adjusts the borax addition rate based on this information. This closed-loop control method ensures the continuity and uniformity of the protective film and effectively improves the protective effect of the melt. At the same time, automated operation greatly reduces manual intervention, improves production efficiency, reduces the risk of workers coming into contact with high-temperature melts, and improves work safety. Therefore, this solution has the advantages of accurately controlling the amount and timing of borax addition, adjusting the borax addition strategy in real time, improving production efficiency and worker safety, and ensuring the continuity and uniformity of the protective film. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of a method for adding borax during the continuous casting process of TP2 copper alloy provided in this application.

[0062] Figure 2 This is a schematic structural diagram of a borax adding device in the continuous casting process of TP2 copper alloy provided in this application.

[0063] Description of reference numerals: 201, parameter acquisition module; 202, calculation module; 203, control module; 204, feedback module; 205, update module. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and marked in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0065] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0066] The disclosure below provides many different embodiments or examples for achieving the purpose of the present invention, which solves the problem in the prior art of difficulty in accurately and automatically adding borax during the TP2 continuous casting process to form a stable and uniform protective film while improving production efficiency and worker safety.

[0067] For details, please refer to Figure 1 A method for adding borax in a TP2 copper alloy continuous casting process, the method comprising the steps of:

[0068] S1: Obtain real-time parameter information of melt surface temperature, melt oxidation degree and protective film thickness;

[0069] S2: Calculate the borax addition rate based on real-time parameter information;

[0070] S3: controlling the borax quantitative conveying device to perform the borax addition operation according to the borax addition rate;

[0071] S4: obtaining feedback parameter information of the melt surface temperature, melt oxidation degree, and protective film thickness after the borax addition operation;

[0072] S5: Dynamically update the borax addition rate according to the feedback parameter information.

[0073] In the continuous casting process of TP2 copper alloy, borax addition is a key step in protecting the melt from oxidation and impurity contamination. However, existing manual operation methods have multiple technical problems. First, the amount and timing of borax addition are difficult to accurately control, resulting in the inability to ensure the continuity and uniformity of the protective film, affecting the protective effect of the melt. Secondly, there is a lack of real-time adjustment capabilities, and it is impossible to flexibly adjust the borax addition strategy according to the temperature and oxidation status of the melt. In addition, manual operation is inefficient and difficult to meet the needs of large-scale production. It also increases the safety risks of workers exposed to high-temperature melts. These problems directly affect the product quality, production efficiency and safety of the TP2 copper alloy continuous casting process.

[0074] Specifically, on a typical TP2 copper alloy continuous casting line, the temperature of the copper alloy melt in the melting furnace is typically maintained at around 1100°C. A uniform borax protective film, ideally 20-30nm thick, is required to form on the melt surface. However, due to manual inaccuracies, this film thickness can fluctuate between 10-50nm. The degree of oxidation in the melt is measured by its oxygen content, with a target value of less than 10ppm. However, in actual production, due to untimely or insufficient borax addition, the oxygen content can exceed 20ppm, resulting in degraded melt quality. Furthermore, during the continuous casting process, the melt flows at a speed of 0.5-1m / min, requiring the borax addition system to respond in real time to changes in the melt state. However, manual operation typically requires a response time of over 30 seconds, which is insufficient for rapid adjustments. Deviations in these technical parameters directly impact the performance and quality stability of the final casting.

[0075] To solve these problems, the present application proposes a closed-loop control solution. Specifically, in step S1, the melt surface temperature refers to the real-time temperature of the TP2 copper alloy melt surface, which can be measured using an infrared thermometer or a thermocouple.

[0076] The degree of melt oxidation refers to the content of dissolved oxygen in the melt, which can be measured using an oxygen probe or a spectrometer.

[0077] The protective film thickness refers to the thickness of the borax protective layer covering the surface of the melt, which can be measured using a laser rangefinder or an ultrasonic thickness gauge.

[0078] In step S2, the borax addition rate refers to the mass of borax added to the melt surface per unit time, and can be specifically controlled by a mass flow meter or a screw feeder.

[0079] Among them, the borax quantitative delivery device refers to a device that can accurately control the amount of borax added, which can be specifically achieved by a screw feeder or a vibrating feeder.

[0080] The core innovation of this application lies in the real-time monitoring, precise control, and dynamic adjustment of the borax addition process. By obtaining real-time parameter information of the melt state, the system can accurately calculate the required borax addition rate and precisely control the addition amount through a quantitative delivery device. After the addition operation, the system obtains feedback parameter information again and dynamically updates the addition rate accordingly, forming a complete closed-loop control system. This method can not only accurately control the addition of borax according to the real-time state of the melt, but also make adaptive adjustments based on the effect after addition, thereby always maintaining the optimal protective film state during the continuous casting process.

[0081] The working principle of this application can be described in detail as follows:

[0082] First, during the continuous casting process of TP2 copper alloy, a sensor system installed above the melting furnace acquires real-time information on the melt surface temperature, melt oxidation level, and protective film thickness. The temperature sensor is a high-precision infrared thermometer, installed above the melting furnace and maintained at an appropriate distance from the melt surface to ensure measurement accuracy. The oxidation level sensor is an electrochemical oxygen probe, inserted into the melt for real-time monitoring. The protective film thickness sensor is a laser rangefinder, installed above the melting furnace, which measures the distance from the melt surface to a fixed reference point to calculate the protective film thickness.

[0083] Next, the control system calculates the required borax addition rate based on the real-time parameter information using a pre-set algorithm. This algorithm considers the complex relationships among melt temperature, oxidation level, and protective film thickness, and their impact on borax demand. For example, when the melt temperature or oxidation level is high, the system increases the borax addition rate; when the protective film thickness reaches the desired value, the system reduces or even stops borax addition.

[0084] After calculating the borax addition rate, the control system sends a control signal to the borax dosing device. This dosing device uses a high-precision screw feeder, which precisely controls the amount of borax added by adjusting the screw speed. The screw feeder's outlet is located above the melting furnace, ensuring that the borax is evenly distributed across the melt surface.

[0085] After the borax addition is complete, the system uses sensors to obtain feedback parameters such as melt surface temperature, melt oxidation level, and protective film thickness. This feedback is used to evaluate the effectiveness of the borax addition and provide a basis for subsequent adjustments.

[0086] Finally, the control system dynamically updates the borax addition rate based on the feedback parameter information and preset target parameter values ​​(such as target protective film thickness, target oxidation level, and target surface temperature). This step uses an adaptive control algorithm to continuously optimize the borax addition strategy based on actual results to adapt to the dynamic changes in the melt state.

[0087] This method enables precise control and real-time adjustment of borax addition, effectively addressing the dynamic changes in the melt state during continuous casting. Through real-time monitoring and rapid response, the system consistently maintains an optimal protective film, significantly improving product quality and stability. Furthermore, automated operation significantly reduces manual intervention, improves production efficiency, and reduces the risk of worker exposure to high-temperature melt, enhancing workplace safety.

[0088] In some of the above embodiments, during the implementation of the present application, there is still the problem of how to accurately calculate the borax addition rate based on the real-time parameter information. Therefore, further, in step S2, the adaptive control calculation formula for calculating the borax addition rate based on the real-time parameter information is:

[0089] B(t) = [k1(t) * (T(t) - T_target) + k2(t) * (O(t) - O_target) + k3(t)* (H(t) - H_target)] * F(t);

[0090] Among them, B(t) is the borax addition rate, T(t), O(t), and H(t) are the melt surface temperature, oxidation degree, and protective film thickness in the real-time parameter information, respectively; T_target, O_target, and H_target are the target surface temperature, target oxidation degree, and target protective film thickness in the preset target process parameters; k1(t), k2(t), and k3(t) are the temperature control coefficient, oxidation degree control coefficient, and protective film thickness control coefficient; F(t) is a compensation function that comprehensively considers the temperature change rate, production rate, and process cycle.

[0091] The adaptive control formula takes into account several key factors, including real-time parameter information, preset target process parameters, control coefficients, and compensation functions. This comprehensive consideration makes the calculation of borax addition rate more accurate and flexible.

[0092] Real-time parameter information reflects the current state of the melt, including surface temperature, oxidation level, and protective film thickness. These parameters are compared with the preset target process parameters to calculate the deviation. This deviation is multiplied by the corresponding control coefficient to determine the required adjustment. The control coefficient can be adjusted dynamically, making control more flexible and precise. Finally, the compensation function F(t) comprehensively considers factors such as temperature change rate, production rate, and process cycle time to further optimize the calculated borax addition rate.

[0093] Specifically, the temperature control coefficient k1(t) is used to adjust the effect of temperature deviation on the borax addition rate. When the actual temperature is higher than the target temperature, the system increases the borax addition rate to reduce the temperature; otherwise, the addition rate is reduced. Similarly, the oxidation degree control coefficient k2(t) and the protective film thickness control coefficient k3(t) are used to adjust the effects of oxidation degree and protective film thickness on the borax addition rate, respectively.

[0094] The introduction of the compensation function F(t) allows the system to more comprehensively consider various factors in the production process. For example, when the temperature change rate is large, F(t) will increase to respond more quickly to temperature changes; when the production rate is high, F(t) will increase accordingly to ensure sufficient borax supply; and F(t) will have different values ​​in different process cycles to meet the needs of different stages.

[0095] As a preferred embodiment, the control coefficients k1(t), k2(t), and k3(t) can be dynamically optimized using a machine learning algorithm. The system can continuously adjust the values ​​of these coefficients based on historical data and current production conditions to achieve optimal control results. For example, gradient descent or genetic algorithms can be used to optimize these coefficients.

[0096] Furthermore, the F(t) function can be designed as a composite function that contains multiple sub-functions to consider the effects of temperature change rate, production rate, and process cycle respectively. For example:

[0097] F(t) = f1(dT / dt) * f2(R) * f3(P)

[0098] Among them, f1(dT / dt) is a function of the temperature change rate, dT / dt represents the rate of change of temperature over time; f2(R) is a function of the production rate, R represents the current production rate; f3(P) is a function of the process cycle, P represents the current stage of the process cycle.

[0099] In practical applications, the specific forms of these functions can be determined based on specific production requirements and process characteristics. For example, f1(dT / dt) can be designed as a linear or exponential function to reflect the impact of the temperature change rate on the borax addition rate; f2(R) can be designed as a piecewise function with different values ​​in different production rate ranges; and f3(P) can be designed as a periodic function to meet the needs of different process stages.

[0100] Thus, through this adaptive control calculation formula, the present application can achieve accurate calculation and dynamic adjustment of the borax addition rate. This method can not only adjust according to the real-time state of the melt, but also adapt to various changes in the production process, thereby ensuring the accuracy of borax addition and the stability of the continuous casting process. Specific embodiments

[0101] The borax addition method described in this application was used on a TP2 copper alloy continuous casting production line. The target process parameters for this production line were set as follows: target surface temperature T_target = 1150°C, target oxidation degree O_target = 0.05 (assuming it is expressed in some standardized index), and target protective film thickness H_target = 20nm.

[0102] The real-time monitoring system collects data every 5 seconds. At a certain time t, the system collects the following real-time parameters: T(t) = 1160°C, O(t) = 0.07, and H(t) = 18nm.

[0103] The initial control coefficients are set as: k1(t) = 0.1, k2(t) = 50, k3(t) = 0.2.

[0104] The compensation function F(t) is designed as:

[0105] F(t) = (1 + 0.01 * |dT / dt|) * (1 + 0.005 * R) * (1 + 0.1 * sin(2π * P / 24))

[0106] Where dT / dt is the average temperature change rate over the last 10 minutes, in °C / min; R is the current production rate, in kg / h; and P represents the current process cycle stage, which in the calculation is the current time of the process cycle in 24-hour format.

[0107] Assume that the current dT / dt = 0.5°C / min, R = 1000 kg / h, and P = 14 (i.e., 2 p.m.).

[0108] Substituting these values ​​into the formula:

[0109] B(t) = [0.1 * (1160 - 1150) + 50 * (0.07 - 0.05) + 0.2 * (18 - 20)] *(1 + 0.01 * |0.5|) * (1 + 0.005 * 1000) * (1 + 0.1 * sin(2π * 14 / 24))

[0110] = [1 + 1 - 4] * 1.005 * 6 * 1.0866

[0111] = -2 * 1.005 * 6 * 1.0866

[0112] = -13.09 g / min

[0113] This result indicates that the borax addition rate needs to be increased by 13.09 g / min. A negative value means that the current protective film state is worse than the target state and the borax addition rate needs to be increased to avoid insufficient protection.

[0114] The system adjusts the delivery rate of the borax quantitative delivery device based on this calculation result. At the same time, the system continuously monitors the melt state and dynamically adjusts the control coefficient and compensation function based on feedback parameter information to achieve more precise control.

[0115] Compared with the existing technology, this application considers multiple key parameters and introduces an adaptive control calculation formula. This method can more accurately calculate the required borax addition rate, and can better meet actual production needs compared with traditional fixed-rate addition or simple linear adjustment methods.

[0116] In some of the above embodiments, during the implementation of this application, there is still a technical problem of how to solve the technical problem of how to dynamically adjust the borax addition rate during the continuous casting process of TP2 copper alloy. Further, in step S5, the feedback parameter information includes at least the real-time surface temperature of the melt, the real-time oxidation degree, and the real-time protective film thickness. Step S5 includes:

[0117] S51: Calculating deviations between the real-time surface temperature, the real-time oxidation degree, and the real-time protective film thickness and the target surface temperature, target oxidation degree, and target protective film thickness in preset target process parameters;

[0118] S52: Calculate and update the temperature control coefficient, oxidation degree control coefficient, and protective film thickness control coefficient according to the deviation value;

[0119] S53: updating the borax addition rate according to the updated temperature control coefficient, oxidation degree control coefficient, and protective film thickness control coefficient.

[0120] Specifically, the technical solution of this application includes the following key features:

[0121] First, calculate the deviation between the feedback parameter information and the target process parameter. This step can be implemented in a variety of ways. For example, a simple difference calculation can be used: directly subtracting the target parameter from the real-time parameter. Another method is to use a percentage deviation: (real-time parameter - target parameter) / target parameter * 100%. Another approach is to consider using a weighted deviation, assigning different weights to different parameters based on their importance.

[0122] Next, the control coefficient is updated based on the deviation. This step can be performed using a variety of algorithms. A simple method is linear update, where the new control coefficient = old control coefficient + learning rate * deviation. Another method uses gradient descent to adjust the control coefficient based on the gradient of the deviation. Another approach is to consider using an adaptive learning rate, dynamically adjusting the learning rate based on the changing trend of the deviation.

[0123] Finally, the borax addition rate is updated based on the updated control coefficients. This step can be done using a weighted sum method: updated borax addition rate = updated temperature control coefficient * surface temperature deviation + updated oxidation degree control coefficient * oxidation degree deviation + updated protective film thickness control coefficient * protective film thickness deviation.

[0124] In some of the above embodiments, during the implementation of the present application, there is still the problem of how to accurately update the temperature control coefficient, the oxidation degree control coefficient, and the protective film thickness control coefficient. Further, step S52 includes:

[0125] S521: Obtaining a temperature deviation value, an oxidation degree deviation value, and a protective film thickness deviation value from the deviation values ​​respectively;

[0126] S522: Calculating an updated temperature control coefficient according to the product of the square of the temperature deviation value and the first learning rate;

[0127] S523: Calculating an updated oxidation degree control coefficient according to the product of the square of the oxidation degree deviation value and the second learning rate;

[0128] S524: Calculate an updated protective film thickness control coefficient according to the product of the square value of the protective film thickness deviation value and the third learning rate.

[0129] Specifically, this method first obtains the deviation values ​​of temperature, oxidation degree, and protective film thickness from the deviation values. Then, by multiplying the squares of these deviation values ​​by the corresponding learning rates, the updated control coefficients are calculated. The advantages of this method are:

[0130] By using the square of the deviation value, the deviation can be amplified, making the control more sensitive, while avoiding the problem of positive and negative deviations canceling each other out.

[0131] The introduction of learning rate can adjust the update amplitude and prevent the control coefficient from changing too much and causing system instability.

[0132] The three control coefficients are updated separately to achieve independent and precise control of the three key parameters: temperature control coefficient, oxidation degree control coefficient and protective film thickness control coefficient.

[0133] This method enables the borax addition system to quickly respond to changes in process parameters and make corresponding adjustments, thereby ensuring the stability and uniformity of the protective film during the continuous casting of TP2 copper alloy and improving product quality and production efficiency.

[0134] In the technical solution proposed in this application, the process of obtaining the deviation value and updating the control coefficient can be implemented in various ways. For example, the deviation value can be directly calculated from the difference between the real-time parameter information measured in real time and the preset target process parameters, or it can be calculated from the difference between the average value of the real-time parameter information over a certain period of time and the preset target process parameters.

[0135] The first learning rate, the second learning rate, and the third learning rate are preset values, which may be 0.001, 0.1, and 0.05.

[0136] The updated control coefficient will be used to calculate the next borax addition rate, thereby achieving precise control of the borax addition process.

[0137] This application introduces a learning rate and uses the square of the deviation value to ensure the sensitivity of the control and avoid system instability caused by excessive changes in the control coefficient, thereby achieving more stable and reliable control.

[0138] In some of the above embodiments, during the implementation of the present application, there is still the problem of the impact of temperature fluctuations on the accuracy of the temperature control coefficient calculation process. Further, step S512 includes:

[0139] S5121: Obtain historical temperature deviation values ​​within a preset time period;

[0140] S5122: Calculating the temperature fluctuation coefficient based on the change trend of the historical temperature deviation value within a preset time period;

[0141] S5123: Multiplying the temperature fluctuation coefficient by the square of the temperature deviation value to obtain a corrected temperature deviation value;

[0142] S5124: Calculate the temperature control coefficient according to the product of the corrected temperature deviation value and the first learning rate.

[0143] The technical solution of this application optimizes the calculation process of the temperature control coefficient by introducing historical temperature data and temperature fluctuation coefficient. Specifically, the solution includes the following key steps:

[0144] First, obtain historical temperature deviation values ​​within a preset time period. This step can be accomplished in a variety of ways. For example, a temperature sensor can be used to collect real-time melt surface temperature and compare the collected temperature data with the target temperature to obtain the temperature deviation value. The preset time period can be adjusted based on actual production needs; typically, the last 5 to 10 minutes is used as a reference period.

[0145] Next, the temperature fluctuation coefficient is calculated based on the trend of historical temperature deviation values ​​over a preset time period. This step can employ various statistical methods, such as calculating the standard deviation or variance of the temperature deviation values, or using time series analysis methods such as moving average or exponential smoothing. The formula for calculating the temperature fluctuation coefficient can be adjusted based on actual conditions to better reflect the dynamic characteristics of temperature changes.

[0146] Next, the temperature fluctuation coefficient is multiplied by the square of the temperature deviation to obtain the corrected temperature deviation. This step aims to factor the impact of historical temperature fluctuations into the current temperature control. By multiplying the temperature fluctuation coefficient by the square of the temperature deviation, the correction amplitude can be increased for large temperature fluctuations and reduced for smaller temperature fluctuations, thereby achieving more precise temperature control.

[0147] Finally, the temperature control coefficient is calculated based on the product of the corrected temperature deviation and the first learning rate. The first learning rate is a preset parameter used to adjust the update speed of the temperature control coefficient. By adjusting the learning rate, the system's response speed and stability can be balanced.

[0148] By incorporating historical temperature data and the temperature fluctuation coefficient, the solution of the present application can more comprehensively consider the dynamic characteristics of temperature changes, thereby improving the accuracy and stability of temperature control. Simultaneously, this method also synergizes with the aforementioned borax addition rate calculation method. With a more precise temperature control coefficient, the calculation of the borax addition rate also becomes more accurate, thereby improving the effectiveness of borax addition throughout the TP2 copper alloy continuous casting process.

[0149] In some of the above embodiments, during the implementation of the present application, there is still the problem of how to accurately calculate the temperature fluctuation coefficient to optimize the borax addition rate.

[0150] Furthermore, step S5122 includes:

[0151] S51221: Obtain a historical temperature deviation value sequence within a preset time window;

[0152] S51222: Calculate the standard deviation of the historical temperature deviation value sequence;

[0153] S51223: Calculate the temperature fluctuation coefficient using the standard deviation according to the preset fluctuation coefficient calculation formula.

[0154] Preset time windows can be set to different time ranges based on actual production needs, such as 5 minutes, 10 minutes, or 30 minutes. The selection of a time window requires a balance between data timeliness and representativeness. Shorter time windows can more quickly reflect temperature changes, while longer time windows can provide more stable trend information.

[0155] Historical temperature deviation value series: This is a set of temperature deviation data collected within a preset time window. For example, if the time window is set to 3 minutes and the sampling interval is 6 seconds, 30 temperature deviation values ​​will be obtained. This data can be collected in real time by a temperature sensor and compared with the target temperature.

[0156] Standard Deviation Calculation: Standard deviation is an important statistic that measures the degree of data dispersion. In this solution, by calculating the standard deviation of a series of historical temperature deviation values, we can quantify the degree of temperature fluctuation. A larger standard deviation indicates more severe temperature fluctuations; a smaller standard deviation indicates relatively stable temperatures.

[0157] Preset Fluctuation Coefficient Calculation Formula: This is the key step in converting the standard deviation into the temperature fluctuation coefficient. One possible calculation formula is: Temperature Fluctuation Coefficient = (Standard Deviation / (Average Value + Minimum Constant)) × Weighting Factor. The average value is the arithmetic mean of the historical temperature deviation series, the minimum constant is a small positive number added to avoid zero in the denominator, and the weighting factor is used to adjust the sensitivity of the fluctuation coefficient.

[0158] The preset time window determines the range of the historical temperature deviation sequence, which in turn influences the calculated standard deviation. As a quantitative indicator of the degree of fluctuation, the standard deviation is converted into a temperature fluctuation coefficient using a preset calculation formula. This method not only considers the absolute magnitude of temperature deviation but also reflects the dynamic characteristics of temperature changes through the standard deviation, providing a more comprehensive basis for adjusting the borax addition rate.

[0159] In some of the above embodiments, during the implementation of the present application, there are still problems with accuracy and adaptability when calculating the temperature fluctuation coefficient.

[0160] In this regard, the present application further proposes that in the preset fluctuation coefficient calculation formula, the temperature fluctuation coefficient = (standard deviation / (average value + minimum constant)) × weight coefficient, and the average value is the average value of the historical temperature deviation value sequence.

[0161] The technical solution of this application addresses the accuracy and adaptability issues associated with calculating the temperature fluctuation coefficient by introducing a preset fluctuation coefficient calculation formula. This formula considers both the discrete degree (standard deviation) and the overall level (average) of temperature deviations, while simultaneously avoiding potential calculation errors through the use of a minimum constant and introducing a weighting factor to accommodate varying production requirements. This approach more accurately reflects temperature fluctuations, providing a reliable basis for subsequent adjustments to the borax addition rate, thereby helping to maintain melt temperature stability and protective film uniformity.

[0162] The standard deviation reflects the degree of dispersion in the temperature deviation series. A larger standard deviation indicates more severe temperature fluctuations. In practical applications, you can choose different time windows to calculate the standard deviation, such as the last 5, 10, or 30 minutes of temperature data. This allows you to adjust sensitivity to temperature fluctuations based on production needs.

[0163] Mean: Represents the central tendency of the temperature deviation series. The introduction of the mean allows the coefficient of fluctuation to reflect the overall level of temperature deviation, rather than just the amplitude of fluctuation. In the calculation, the mean can be calculated over the same time window as the standard deviation to maintain data consistency.

[0164] Minimum constant: This prevents the denominator from reaching zero, increasing calculation stability. In practical applications, the minimum constant can be set based on the specific temperature range and accuracy requirements. For example, if the temperature accuracy requirement is 0.1°C, the minimum constant can be set to 0.01 or 0.001 to ensure calculation stability without significantly affecting the accuracy of the result.

[0165] Weighting factor: This is used to adjust the impact of temperature fluctuations, improving calculation flexibility and adaptability. The weighting factor can be dynamically adjusted based on different production stages or product requirements. For example, in the early stages of casting, where more sensitive temperature control may be required, a higher weighting factor can be set. During the stable production phase, the weighting factor can be appropriately lowered to reduce unnecessary adjustments.

[0166] The combination of these parameters allows the coefficient of fluctuation calculation formula to flexibly adapt to different production situations. For example, when temperature fluctuations are large, the standard deviation increases, and the coefficient of fluctuation also increases, indicating that the system needs more adjustment. When the absolute value of the temperature deviation is large, the average value increases, and the coefficient of fluctuation is relatively reduced, avoiding over-adjustment. The introduction of the weighting factor allows the sensitivity of the coefficient of fluctuation to be adjusted according to actual needs.

[0167] In this way, the technical solution of the present application can more accurately reflect temperature fluctuations, providing a reliable basis for subsequent adjustments to the borax addition rate. For example, when the calculated temperature fluctuation coefficient is large, the system may increase the borax addition rate to stabilize the melt temperature; conversely, when the fluctuation coefficient is small, the system may reduce the borax addition rate to avoid over-adjustment.

[0168] In some of the above embodiments, during the implementation of the present application, there is still the problem of difficulty in accurately monitoring and controlling the coverage of the protective film. Further, the method also includes:

[0169] S6: Obtaining the protective film image information collected by the visual recognition system;

[0170] S7: calculating the protective film coverage of the melt surface according to the protective film image information;

[0171] S8: Calculating the deviation between the protective film coverage and the target protective film coverage;

[0172] S9: Calculating a protective film coverage control coefficient according to a deviation value of the protective film coverage;

[0173] S10: Dynamically update the borax addition rate according to the protective film coverage control coefficient and the feedback parameter information.

[0174] This application introduces a visual recognition system to monitor the coverage of the protective film. By acquiring protective film image information and calculating the protective film coverage, the effectiveness of borax addition can be intuitively evaluated. This method can more accurately reflect the actual protection status of the melt surface, providing more comprehensive information than relying solely on parameters such as temperature, oxidation level, and protective film thickness.

[0175] Calculating the deviation between the protective film coverage and the target coverage and using this information to calculate the control coefficient demonstrates the system's adaptive capabilities. This method allows for dynamic adjustment of the borax addition rate based on actual coverage, helping to maintain a stable protective effect.

[0176] Combining the protective film coverage control coefficient with other feedback parameters further optimizes the adjustment process of the borax addition rate. This multi-parameter integrated control method can more comprehensively consider various factors and achieve more precise control of borax addition.

[0177] Specifically, the visual recognition system can use a high-resolution camera with an appropriate light source to ensure clear capture of the melt surface in a high-temperature environment. The image acquisition frequency can be adjusted according to actual production needs, for example, 5-10 frames per second.

[0178] This application can use image segmentation algorithms, such as threshold-based segmentation methods or deep learning segmentation models, to accurately identify the protective film area. Furthermore, the coverage rate can be calculated by analyzing the grayscale value distribution of the protective film area. For example, a grayscale threshold can be set, and pixels above the threshold are considered to be the effective coverage area. The ratio of the effective coverage area to the total melt surface area is then calculated, which is the protective film coverage rate.

[0179] Compared with the existing technology, the method proposed in this application has significant advantages. Traditional methods mainly rely on manual observation and empirical judgment to control the addition of borax, which is difficult to achieve precise control and poses safety hazards. However, this application introduces a visual recognition system that can accurately monitor the coverage of the protective film in real time and achieve precise control through an adaptive algorithm. This not only improves the stability of the protective effect, but also greatly improves production efficiency and safety. In addition, the method of this application also has good adaptability and scalability, and parameters can be adjusted according to the characteristics of different production lines to meet diverse production needs.

[0180] Furthermore, step S7 includes:

[0181] S71: performing image segmentation on the protective film image information to obtain a protective film area;

[0182] S72: Calculate the grayscale value distribution of the protective film area;

[0183] S73: Calculate the protective film coverage rate according to the gray value distribution of the protective film area.

[0184] First, the protective film image information is segmented to obtain the protective film region. This step can be achieved using a variety of image segmentation algorithms, such as threshold segmentation, edge detection, and region growing. Threshold segmentation is a common and effective method. It can set an appropriate threshold based on the grayscale value characteristics of the image to separate the protective film region from the background. The edge detection algorithm can identify the boundaries of the protective film region, thereby accurately locating the scope of the protective film. The region growing algorithm can start from a preset seed point and gradually expand to the entire protective film region. These algorithms can be selected and combined based on the specific image characteristics and computing resources to achieve the best segmentation effect.

[0185] Next, calculate the grayscale distribution of the protective film area. After obtaining the segmented protective film area, the grayscale values ​​of all pixels within the area can be counted to generate a grayscale histogram or other statistical representation. The grayscale distribution reflects the density and uniformity of the protective film and is an important basis for calculating coverage. For example, statistical characteristics such as the mean, standard deviation, skewness, and kurtosis of the grayscale values ​​can be calculated to comprehensively describe the grayscale distribution of the protective film.

[0186] Finally, the protective film coverage is calculated based on the grayscale distribution of the protective film area. This step can be calculated using a variety of methods. For example, a grayscale threshold can be set, and pixels above this threshold are considered to be effectively covered. The coverage ratio is then calculated as the ratio of the effective coverage area to the entire protective film area. Another method is to use a weighted average of the grayscale values, assigning weights based on the coverage level corresponding to different grayscale values, to calculate the overall coverage ratio. Furthermore, machine learning algorithms can be combined to train models to predict coverage ratios. This method can better adapt to different image characteristics and production conditions.

[0187] In this embodiment, a weighted average method can be used. First, the grayscale value range (usually 0-255) is divided into multiple intervals, each of which corresponds to a coverage weight. For example, the grayscale value can be divided into five intervals: [0,50], [51,100], [101,150], [151,200], and [201,255], with corresponding coverage weights of 0.1, 0.3, 0.6, 0.8, and 1.0, respectively. Then, the proportion of pixels in each interval is calculated, multiplied by the corresponding weight, and summed to obtain the final coverage rate.

[0188] Through this method, the technical solution of the present application can quickly and accurately calculate the protective film coverage. Compared with traditional manual observation or simple threshold methods, this method takes into account the continuous change of grayscale values ​​and can more accurately reflect the actual coverage of the protective film.

[0189] Please refer to Figure 2 A borax adding device for a TP2 copper alloy continuous casting process, applicable to any of the above-mentioned borax adding methods for a TP2 copper alloy continuous casting process, comprising:

[0190] Parameter acquisition module 201: used to obtain real-time parameter information of melt surface temperature, melt oxidation degree and protective film thickness;

[0191] Calculation module 202: used to calculate the borax addition rate based on real-time parameter information;

[0192] Control module 203: used for controlling the borax quantitative delivery device to perform borax addition operation according to the borax addition rate;

[0193] Feedback module 204: used to obtain feedback parameter information of melt surface temperature, melt oxidation degree and protective film thickness after the borax addition operation;

[0194] Update module 205: used to dynamically update the borax addition rate according to the feedback parameter information.

[0195] The parameter acquisition module 201 is used to obtain real-time parameter information of the melt surface temperature, melt oxidation degree and protective film thickness. These parameter information are key indicators in the borax addition process and directly affect the formation effect of the protective film and the quality of the melt.

[0196] The calculation module 202 calculates the borax addition rate based on the real-time parameter information. This step ensures the accuracy of the borax addition amount and can dynamically adjust the addition strategy according to the current melt state.

[0197] The control module 203 controls the borax quantitative delivery device to perform the borax addition operation according to the calculated borax addition rate, thereby automating the borax addition process and reducing errors caused by manual operation.

[0198] Feedback module 204 obtains feedback parameter information of melt surface temperature, melt oxidation degree and protective film thickness after borax addition operation. These feedback parameter information are used to evaluate the effect of borax addition and provide a basis for subsequent adjustments.

[0199] The updating module 205 dynamically updates the borax addition rate based on the feedback parameter information, thereby achieving continuous optimization of the borax addition process and ensuring the stability and uniformity of the protective film.

[0200] The parameter acquisition module 201 may include devices such as a temperature sensor, an oxidation degree detector, and a thickness gauge, etc. These devices may be installed at different locations of the furnace to obtain comprehensive real-time parameter information.

[0201] The calculation module 202 can be an embedded processor or an industrial computer that runs a specially designed algorithm to calculate the borax addition rate. The algorithm can be based on a machine learning model and consider the complex relationship between multiple parameters to determine the optimal addition rate.

[0202] The control module 203 may include a programmable logic controller (PLC) and an actuator, such as a quantitative feeder driven by a stepper motor or a servo motor. The PLC controls the operation of the feeder based on the output of the calculation module to achieve accurate borax addition.

[0203] The feedback module 204 may utilize the same or similar sensing device as the parameter acquisition module, but its acquisition position may be different from the initial parameter acquisition position to obtain parameter changes after the addition operation.

[0204] The updating module 205 may adopt an adaptive control algorithm to dynamically adjust various coefficients in the calculation formula of the borax addition rate according to the deviation between the feedback parameter and the target parameter.

[0205] The collaborative operation of these modules forms a closed-loop control system. The parameter acquisition module 201 provides initial data, the calculation module 202 determines the addition strategy based on this data, the control module 203 executes the addition operation, the feedback module 204 evaluates the operation results, and the update module 205 optimizes the strategy based on the evaluation results. This closed-loop system can continuously optimize the borax addition process and adapt to different production conditions and melt states.

[0206] The foregoing is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. Persons skilled in the art will readily appreciate that the present application may be modified and altered in various ways. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A method for adding borax in a TP2 copper alloy continuous casting process, characterized in that: The method comprises the steps of: S1: Obtain real-time parameter information of melt surface temperature, melt oxidation degree and protective film thickness; S2: Calculate the borax addition rate according to the real-time parameter information; the adaptive control calculation formula for calculating the borax addition rate according to the real-time parameter information is: B(t) = [k1(t) * (T(t) - T_target) + k2(t) * (O(t) - O_target) + k3(t) *(H(t) - H_target)] * F(t); Wherein, B(t) is the borax addition rate, T(t), O(t), and H(t) are the melt surface temperature, oxidation degree, and protective film thickness in the real-time parameter information, respectively; T_target, O_target, and H_target are the target surface temperature, target oxidation degree, and target protective film thickness in the preset target process parameters; k1(t), k2(t), and k3(t) are the temperature control coefficient, oxidation degree control coefficient, and protective film thickness control coefficient; and F(t) is a compensation function that comprehensively considers the temperature change rate, production rate, and process cycle. S3: controlling the borax quantitative conveying device to perform a borax addition operation according to the borax addition rate; S4: obtaining feedback parameter information of the melt surface temperature, melt oxidation degree, and protective film thickness after the borax addition operation; S5: Dynamically updating the borax addition rate according to the feedback parameter information; Step S5 includes: S51: Calculating deviations between the real-time surface temperature, the real-time oxidation degree, and the real-time protective film thickness and the target surface temperature, target oxidation degree, and target protective film thickness in the preset target process parameters; S52: Calculating and updating the temperature control coefficient, the oxidation degree control coefficient, and the protective film thickness control coefficient according to the deviation value; S53: updating the borax addition rate according to the updated temperature control coefficient, the oxidation degree control coefficient, and the protective film thickness control coefficient.

2. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 1, wherein: Step S52 includes: S521: respectively obtaining a temperature deviation value, an oxidation degree deviation value, and a protective film thickness deviation value from the deviation values; S522: Calculating an updated temperature control coefficient according to the product of the square of the temperature deviation value and the first learning rate; S523: Calculating an updated oxidation degree control coefficient according to the product of the square value of the oxidation degree deviation value and the second learning rate; S524: Calculating an updated protective film thickness control coefficient according to the product of the square value of the protective film thickness deviation value and the third learning rate.

3. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 2, wherein: Step S512 includes: S5121: Obtain historical temperature deviation values ​​within a preset time period; S5122: Calculating a temperature fluctuation coefficient based on a change trend of the historical temperature deviation value within a preset time period; S5123: Multiplying the temperature fluctuation coefficient by the square of the temperature deviation value to obtain a corrected temperature deviation value; S5124: Calculate a temperature control coefficient according to the product of the corrected temperature deviation value and the first learning rate.

4. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 3, characterized in that: Step S5122 includes: S51221: Obtain a historical temperature deviation value sequence within a preset time window; S51222: Calculate the standard deviation of the historical temperature deviation value sequence; S51223: Calculate the temperature fluctuation coefficient using the standard deviation according to a preset fluctuation coefficient calculation formula.

5. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 4, wherein: In step S51223, the preset fluctuation coefficient calculation formula is: temperature fluctuation coefficient = (standard deviation / (average value+minimum value constant)) × weight coefficient, where the average value is the average value of the historical temperature deviation value sequence.

6. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 1, wherein: The method further comprises: S6: Obtaining the protective film image information collected by the visual recognition system; S7: calculating the protective film coverage of the melt surface according to the protective film image information; S8: Calculating a deviation between the protective film coverage and a target protective film coverage; S9: calculating a protective film coverage control coefficient according to the deviation value of the protective film coverage; S10: Dynamically updating the borax addition rate according to the protective film coverage control coefficient and feedback parameter information.

7. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 6, characterized in that: Step S7 includes: S71: performing image segmentation on the protective film image information to obtain a protective film area; S72: Calculating the grayscale value distribution of the protective film area; S73: Calculating the protection film coverage rate according to the grayscale value distribution of the protection film area.

8. A borax adding device in the TP2 copper alloy continuous casting process, characterized in that: A method for adding borax in a TP2 copper alloy continuous casting process according to any one of claims 1 to 7, the device comprising: Parameter acquisition module: used to obtain real-time parameter information of melt surface temperature, melt oxidation degree and protective film thickness; Calculation module: used for calculating the borax addition rate according to the real-time parameter information; A control module is used to control the borax quantitative delivery device to perform the borax addition operation according to the borax addition rate; Feedback module: used to obtain feedback parameter information of melt surface temperature, melt oxidation degree and protective film thickness after borax addition operation; Update module: used to dynamically update the borax addition rate according to the feedback parameter information.

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