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

Abstract

The invention provides a borax adding method and device in the continuous casting process of TP2 copper alloy, the borax adding method and device are applied to the technical field of continuous casting of copper alloy, and a system can accurately judge the current melt state by obtaining real-time data of the melt surface temperature, the oxidation degree and the protective film thickness. Based on the data, the system calculates the most suitable borax adding rate and accurately adds the borax through a quantitative conveying device. After adding operation, the system obtains feedback information of the melt state again, and the borax adding rate is dynamically adjusted according to the information. The device has the advantages that the adding amount and adding time of borax are accurately controlled, the adding strategy of the borax is adjusted in real time, the production efficiency and the safety of workers are improved, and the continuity and uniformity of a protective film are guaranteed.

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 a TP2 copper alloy. Background Art

[0002] In factories that produce high-performance copper alloy parts, TP2 copper alloy is easily corroded by oxygen and impurities in the air during high-temperature smelting, causing oxidation of the melt, which affects the quality of castings. In order to protect the melt and improve product quality, engineers usually add borax during high-temperature smelting to form a protective film covering the surface of the melt to reduce oxidation and impurity contamination.

[0003] In order to ensure product quality, the amount and timing of borax addition must be precisely controlled during the production process to ensure the formation of a protective film. In addition, most of the existing TP2 copper alloy production lines are continuous casting production lines, which requires continuous and precise addition of borax during the continuous smelting process to ensure the continuity and stability of the protective film. At the same time, the factory must also ensure the safety of workers and reduce the risk of direct contact between workers and high-temperature melts.

[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: 1. It is difficult to accurately control the amount and timing of addition: manual operation is difficult to ensure the continuity and uniformity of the protective film, which affects the protective effect of the melt; 2. Lack of real-time adjustment capability: It is impossible to flexibly adjust the borax addition strategy according to the melt state (such as melt temperature and oxidation status); 3. There are safety hazards: workers need to frequently approach high-temperature melt, increasing the risk of burns and accidents; 4. Low production efficiency: Manual operation efficiency is low and it is difficult to meet the needs of large-scale production; 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.

[0005] Therefore, the prior art lacks a method for achieving accurate 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

[0006] In view of the above-mentioned shortcomings of the prior art, the present application provides a method for adding borax in the continuous casting process of TP2 copper alloy, 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.

[0007] In a first aspect, a method for adding borax in a TP2 copper alloy continuous casting process comprises the steps of: S1: Obtain real-time parameter information of melt surface temperature, melt oxidation degree and protective film thickness; S2: Calculating the borax addition rate according to the real-time parameter information; S3: controlling the borax quantitative conveying device to perform a borax adding operation according to the borax adding rate; S4: obtaining feedback parameter information of melt surface temperature, melt oxidation degree and protective film thickness after borax addition operation; S5: Dynamically update the borax addition rate according to the feedback parameter information.

[0008] The present application proposes a method for adding borax in the continuous casting process of TP2 copper alloy. By obtaining real-time data of 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 of the melt state again, and dynamically adjusts the borax addition rate according to 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 contacting high-temperature melts, and improves work safety. Therefore, this scheme 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.

[0009] Further, in step S2, 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), H(t) are the melt surface temperature, oxidation degree and protective film thickness in the real-time parameter information, respectively; T_target, O_target, 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), 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.

[0010] The present application proposes a method for adding borax in the continuous casting process of TP2 copper alloy, which achieves the goal of accurately calculating the borax addition rate according to real-time parameter information in the continuous casting process of TP2 copper alloy by introducing an adaptive control calculation formula. The formula takes into account multiple key parameters such as melt surface temperature, oxidation degree and protective film thickness, and achieves accurate calculation of the borax addition rate by comparing with the preset target value, combining the dynamically adjusted control coefficient and the compensation function that comprehensively considers multiple factors.

[0011] 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, and 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: Calculate and update 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.

[0012] The present application proposes a method for adding borax in a TP2 copper alloy continuous casting process. 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. Then, the system uses these deviation values ​​to update the control coefficients, which determine the degree of response of the system to different parameter changes. Finally, the system adjusts the borax addition rate according to the updated control coefficients, thereby realizing dynamic optimization of the borax addition process.

[0013] Further, 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 value 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: 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.

[0014] The present application proposes a method for adding borax in the continuous casting process of TP2 copper alloy, which realizes accurate update of the temperature control coefficient, oxidation degree control coefficient and protective film thickness control coefficient by introducing the square of the deviation value and the first learning rate, the second learning rate and the third learning rate. This method can dynamically adjust the control coefficient according to the difference between the actual process parameters and the target parameters, thereby realizing accurate control of the borax addition process.

[0015] Further, step S512 includes: S5121: Obtain historical temperature deviation values ​​within a preset time period; S5122: Calculating a temperature fluctuation coefficient according to 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.

[0016] Further, 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.

[0017] Further, in step S51223, the preset fluctuation coefficient calculation formula is: 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.

[0018] Furthermore, the method further comprises: S6: Acquire 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 update the borax addition rate according to the protective film coverage control coefficient and the feedback parameter information.

[0019] Further, step S7 includes: S71: performing image segmentation on the protective film image information to obtain a protective film area; S72: Calculate the gray value distribution of the protective film area; S73: Calculating the protection film coverage rate according to the gray value distribution of the protection film area.

[0020] In a second aspect, a borax adding device in a TP2 copper alloy continuous casting process is applied to any one of the above-mentioned borax adding methods in a TP2 copper alloy continuous casting process, and the device comprises: Parameter acquisition module: used to obtain real-time parameter information of melt surface temperature, melt oxidation degree and protective film thickness; Calculation module: used to calculate the borax addition rate according to the real-time parameter information; A control module: used for controlling the borax quantitative conveying device to perform a borax adding operation according to the borax adding 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.

[0021] Beneficial effects: The present application provides a method and device for adding borax in the continuous casting process of TP2 copper alloy. By obtaining real-time data on the surface temperature, degree of oxidation and thickness of the melt, 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 contacting 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

[0022] Figure 1 A flow chart of a method for adding borax in a TP2 copper alloy continuous casting process provided in the present application.

[0023] Figure 2 A schematic structural diagram of a borax adding device in a TP2 copper alloy continuous casting process provided in the present application.

[0024] Description of reference numerals: 201, parameter acquisition module; 202, calculation module; 203, control module; 204, feedback module; 205, update module. DETAILED DESCRIPTION

[0025] 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 the embodiments. The components of the embodiments of the present application usually 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 claimed 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 belong to the scope of protection of the present application.

[0026] 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, further definition and explanation thereof is not required in subsequent drawings.

[0027] 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 that it is difficult to accurately and automatically add borax in the TP2 continuous casting process to form a stable and uniform protective film while improving production efficiency and worker safety.

[0028] 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: S1: Obtain real-time parameter information of melt surface temperature, melt oxidation degree and protective film thickness; S2: Calculate the borax addition rate based on real-time parameter information; S3: controlling the borax quantitative conveying device to perform the borax adding operation according to the borax adding rate; S4: obtaining feedback parameter information of melt surface temperature, melt oxidation degree and protective film thickness after borax addition operation; S5: Dynamically update the borax addition rate according to the feedback parameter information.

[0029] In the continuous casting process of TP2 copper alloy, borax addition is a key step to protect the melt from oxidation and impurity contamination. However, there are multiple technical problems with the existing manual operation method. 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 the borax addition strategy cannot be flexibly adjusted according to the temperature and oxidation conditions of the melt. In addition, manual operation is inefficient and difficult to meet the needs of large-scale production, while also increasing the safety risks of workers exposed to high-temperature melts. These problems directly affect the product quality, production efficiency and safety of the continuous casting process of TP2 copper alloy.

[0030] Specifically, in a typical TP2 copper alloy continuous casting production line, the temperature of the copper alloy melt in the smelting furnace is usually maintained at around 1100°C. A uniform borax protective film needs to be formed on the surface of the melt, with an ideal thickness of 20-30nm. However, due to the inaccuracy of manual operation, the thickness of the protective film may fluctuate between 10-50nm. The degree of oxidation of the melt is measured by the oxygen content, and the target value should be less than 10ppm. However, in actual production, due to untimely or insufficient addition of borax, the oxygen content may exceed 20ppm, resulting in a decrease in the quality of the melt. In addition, during the continuous casting process, the melt flows at a speed of 0.5-1m / min, requiring the borax addition system to respond to changes in the melt state in real time. However, the response time of manual operation is usually more than 30 seconds, which cannot meet the needs of rapid adjustment. The deviation of these technical parameters directly affects the performance and quality stability of the final casting.

[0031] 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 surface of the TP2 copper alloy melt, which can be measured by an infrared thermometer or a thermocouple.

[0032] The degree of melt oxidation refers to the content of dissolved oxygen in the melt, which can be measured by an oxygen probe or a spectrometer.

[0033] The thickness of the protective film refers to the thickness of the borax protective layer covering the surface of the melt, which can be measured by a laser rangefinder or an ultrasonic thickness gauge.

[0034] In step S2, the borax addition rate refers to the mass of borax added to the melt surface per unit time, which can be specifically controlled by a mass flow meter or a screw feeder.

[0035] 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.

[0036] The core innovation of this application is to achieve 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 accurately control the addition amount through a quantitative conveying device. After the addition operation, the system obtains feedback parameter information again, and dynamically updates the addition rate based on this to form 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 according to the effect after addition, so as to always maintain the best protective film state during the continuous casting process.

[0037] The working principle of this application can be described in detail as follows: First, during the continuous casting process of TP2 copper alloy, the real-time parameter information of melt surface temperature, melt oxidation degree and protective film thickness is obtained through the sensor system installed above the melting furnace. The temperature sensor adopts a high-precision infrared thermometer, which is installed above the melting furnace and keeps an appropriate distance from the melt surface to ensure measurement accuracy. The oxidation degree sensor adopts an electrochemical oxygen probe, which is inserted into the melt for real-time monitoring. The protective film thickness sensor adopts a laser rangefinder, which is installed above the melting furnace and calculates the protective film thickness by measuring the distance from the melt surface to a fixed reference point.

[0038] Next, the control system calculates the current required borax addition rate based on the real-time parameter information obtained through a preset algorithm. This algorithm takes into account the complex relationship between melt temperature, oxidation degree and protective film thickness, as well as their impact on borax demand. For example, when the melt temperature is high or the oxidation degree is high, the system will increase the borax addition rate; when the protective film thickness has reached the ideal value, the system will reduce or suspend borax addition.

[0039] After calculating the borax addition rate, the control system sends a control signal to the borax quantitative delivery device. The quantitative delivery device uses a high-precision screw feeder, which accurately controls the amount of borax added by adjusting the screw speed. The discharge port of the screw feeder is located above the smelting furnace to ensure that the borax is evenly spread on the surface of the melt.

[0040] After the borax addition operation is completed, the system again obtains feedback parameter information of the melt surface temperature, melt oxidation degree and protective film thickness through sensors. This feedback information is used to evaluate the effect of borax addition and provide a basis for subsequent adjustments.

[0041] Finally, the control system dynamically updates the borax addition rate based on the feedback parameter information and the preset target parameter values ​​(such as target protective film thickness, target oxidation degree, 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.

[0042] This method can achieve precise control and real-time adjustment of borax addition, and effectively cope with the dynamic changes of melt state during continuous casting. Through real-time monitoring and rapid response, the system can always maintain the best protective film state, significantly improving the stability of product quality. At the same time, automated operation greatly reduces manual intervention, improves production efficiency, reduces the risk of workers being exposed to high-temperature melt, and improves work safety.

[0043] In some of the above embodiments, during the implementation of the present application, there is still a problem of how to accurately calculate the borax addition rate according to the real-time parameter information. Therefore, further, in step S2, 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), H(t) are the melt surface temperature, oxidation degree and protective film thickness in the real-time parameter information, respectively; T_target, O_target, 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), 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.

[0044] The adaptive control calculation formula takes into account multiple key factors, including real-time parameter information, preset target process parameters, control coefficients and compensation functions. The comprehensive consideration of these factors makes the calculation of borax addition rate more accurate and flexible.

[0045] The real-time parameter information reflects the current state of the melt, including surface temperature, oxidation degree and protective film thickness. These parameters are compared with the preset target process parameters to calculate the deviation value. The deviation value is multiplied by the corresponding control coefficient to obtain the amount to be adjusted. The control coefficient can be adjusted dynamically to make the control more flexible and precise. Finally, the compensation function F(t) is used to comprehensively consider factors such as temperature change rate, production rate and process cycle to further optimize the calculation results of borax addition rate.

[0046] 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 will increase the borax addition rate to cool down; otherwise, the addition rate will be reduced. The oxidation degree control coefficient k2(t) and the protective film thickness control coefficient k3(t) are similar, and are used to adjust the effects of the oxidation degree and protective film thickness on the borax addition rate, respectively.

[0047] The introduction of the compensation function F(t) enables 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 to temperature changes more quickly; when the production rate is high, F(t) will increase accordingly to ensure sufficient borax supply; in different process cycles, F(t) will have different values ​​to meet the needs of different stages.

[0048] As a preferred embodiment, the control coefficients k1(t), k2(t), and k3(t) can be dynamically optimized by a machine learning algorithm. The system can continuously adjust the values ​​of these coefficients based on historical data and current production conditions to achieve the best control effect. For example, a gradient descent method or a genetic algorithm can be used to optimize these coefficients.

[0049] Furthermore, the F(t) function can be designed as a composite function, which contains multiple sub-functions to consider the effects of temperature change rate, production rate and process cycle respectively. For example: F(t) = f1(dT / dt) * f2(R) * f3(P) 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.

[0050] In practical applications, the specific forms of these functions can be determined according to specific production requirements and process characteristics. For example, f1 (dT / dt) can be designed as a linear function or exponential function to reflect the effect of temperature change rate on borax addition rate; f2 (R) can be designed as a piecewise function with different values ​​in different production rate ranges; f3 (P) can be designed as a periodic function to meet the needs of different process stages.

[0051] Therefore, through this adaptive control calculation formula, the present application can realize accurate calculation and dynamic adjustment of the borax addition rate. This method can not only be adjusted 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

[0052] The borax addition method of the present application is used in a TP2 copper alloy continuous casting production line. The target process parameters of the production line are set as follows: target surface temperature T_target = 1150°C, target oxidation degree O_target = 0.05 (assuming that it is expressed in some standardized index), and target protective film thickness H_target = 20nm.

[0053] The real-time monitoring system collects data every 5 seconds. At a certain time t, the real-time parameters collected by the system are: T(t) = 1160°C, O(t) = 0.07, H(t) = 18nm.

[0054] The initial control coefficients are set as: k1(t) = 0.1, k2(t) = 50, k3(t) = 0.2.

[0055] The compensation function F(t) is designed as: F(t) = (1 + 0.01 * |dT / dt|) * (1 + 0.005 * R) * (1 + 0.1 * sin(2π * P / 24)) Where dT / dt is the average temperature change rate in the last 10 minutes, in °C / min; R is the current production rate, in kg / h; P represents the current process cycle stage, which in the calculation is specifically the current time of the process cycle in the 24-hour system.

[0056] Assume that currently dT / dt = 0.5°C / min, R = 1000 kg / h, and P = 14 (i.e., 2 p.m.).

[0057] Substituting these values ​​into the formula: 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)) = [1 + 1 - 4] * 1.005 * 6 * 1.0866 = -2 * 1.005 * 6 * 1.0866 = -13.09 g / min 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.

[0058] The system will adjust the delivery rate of the borax quantitative delivery device based on this calculation result. At the same time, the system will continuously monitor the melt state and dynamically adjust the control coefficient and compensation function based on the feedback parameter information to achieve more precise control.

[0059] Compared with the prior art, the present 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.

[0060] In some of the above embodiments, during the implementation of the present application, there is still a technical problem of how to solve the technical problem of dynamically adjusting the borax addition rate during the continuous casting process of TP2 copper alloy. Further, in step S5, the feedback parameter information at least includes the real-time surface temperature of the melt, the real-time oxidation degree and the real-time protective film thickness, and 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, the target oxidation degree, and the target protective film thickness in the preset target process parameters; S52: Calculate and update the temperature control coefficient, oxidation degree control coefficient and protective film thickness control coefficient according to the deviation value; S53: updating the borax addition rate according to the updated temperature control coefficient, oxidation degree control coefficient and protective film thickness control coefficient.

[0061] Specifically, the technical solution of this application includes the following key features: First, calculate the deviation between the feedback parameter information and the target process parameter. This step can be achieved in a variety of ways. For example, a simple difference calculation can be used, that is, directly subtracting the target parameter from the real-time parameter. Another method is to use a percentage deviation, that is, (real-time parameter - target parameter) / target parameter * 100%. You can also consider using weighted deviation to assign different weights to different parameters according to their importance.

[0062] Secondly, update the control coefficient according to the deviation value. This step can adopt a variety of algorithms. A simple method is linear update, that is, new control coefficient = old control coefficient + learning rate * deviation value. Another method is to use the gradient descent algorithm to adjust the control coefficient according to the gradient of the deviation value. You can also consider using an adaptive learning rate to dynamically adjust the learning rate according to the changing trend of the deviation value.

[0063] Finally, the borax addition rate is updated according to the updated control coefficient. This step can be done by weighted sum, that is, 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.

[0064] In some of the above embodiments, during the implementation of the present application, there is still a 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: S521: respectively obtaining a temperature deviation value, an oxidation degree deviation value, and a protective film thickness deviation value from the deviation values; S522: Calculate an updated temperature control coefficient according to the product of the square value 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: 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.

[0065] Specifically, the method first obtains the deviation values ​​of temperature, oxidation degree and protective film thickness from the deviation values. Then, the updated control coefficient is calculated by multiplying the square of these deviation values ​​with the corresponding learning rate. The advantages of this method are: 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.

[0066] The introduction of learning rate can adjust the update amplitude and prevent the control coefficient from changing too much and causing system instability.

[0067] The three control coefficients are updated separately to achieve independent and precise control of the three key parameters, namely, temperature control coefficient, oxidation degree control coefficient and protective film thickness control coefficient.

[0068] This method enables the borax addition system to respond quickly 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.

[0069] In the technical solution proposed in the present application, the process of obtaining the deviation value and updating the control coefficient can be implemented in a variety of ways. For example, the deviation value can be directly calculated by the difference between the real-time parameter information measured in real time and the preset target process parameter, or it can be calculated by the difference between the average value of the real-time parameter information within a certain period of time and the preset target process parameter.

[0070] 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.

[0071] The updated control coefficient will be used to calculate the next borax addition rate, thereby achieving precise control of the borax addition process.

[0072] This application not only ensures the sensitivity of control but also avoids system instability caused by excessive changes in control coefficients by introducing a learning rate and using the square of the deviation value, thereby achieving more stable and reliable control.

[0073] In some of the above embodiments, during the implementation of the present application, there is still the problem of the influence of temperature fluctuation on the accuracy during the temperature control coefficient calculation process. Further, step S512 includes: S5121: Obtain historical temperature deviation values ​​within a preset time period; S5122: Calculate the temperature fluctuation coefficient according to the 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 the temperature control coefficient according to the product of the corrected temperature deviation value and the first learning rate.

[0074] 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: First, obtain the historical temperature deviation value within the preset time period. This step can be implemented in a variety of ways. For example, a temperature sensor can be used to collect the melt surface temperature in real time, and the collected temperature data can be compared with the target temperature to obtain the temperature deviation value. The preset time period can be adjusted according to actual production needs, and the most recent 5 to 10 minutes can usually be selected as a reference time period.

[0075] Secondly, the temperature fluctuation coefficient is calculated based on the change trend of the historical temperature deviation value within the preset time period. This step can adopt a variety of statistical methods, such as calculating the standard deviation or variance of the temperature deviation value, or using time series analysis methods such as moving average or exponential smoothing. The calculation formula of the temperature fluctuation coefficient can be adjusted according to the actual situation to better reflect the dynamic characteristics of temperature changes.

[0076] Next, multiply the temperature fluctuation coefficient by the square of the temperature deviation value to obtain the corrected temperature deviation value. The purpose of this step is to take the impact of historical temperature fluctuations into account in the current temperature control. By multiplying the temperature fluctuation coefficient by the square of the temperature deviation value, the correction amplitude can be increased when the temperature fluctuation is large and reduced when the temperature fluctuation is small, thereby achieving more accurate temperature control.

[0077] Finally, the temperature control coefficient is calculated according to the product of the corrected temperature deviation value 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 response speed and stability of the system can be balanced.

[0078] By introducing historical temperature data and temperature fluctuation coefficient, the scheme of the present application can more comprehensively consider the dynamic characteristics of temperature change, thereby improving the accuracy and stability of temperature control. At the same time, this method also produces a synergistic effect with the aforementioned borax addition rate calculation method. Through a more accurate temperature control coefficient, the calculation of the borax addition rate also becomes more accurate, thereby improving the effect of borax addition in the entire TP2 copper alloy continuous casting process.

[0079] 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.

[0080] Further, 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 the preset fluctuation coefficient calculation formula.

[0081] Among them, preset time window: different time ranges can be set according to actual production needs, such as 5 minutes, 10 minutes or 30 minutes. The selection of time window needs to balance the timeliness and representativeness of data. Shorter time window can reflect temperature changes more quickly, while longer time window can provide more stable trend information.

[0082] 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. These data can be collected in real time by the temperature sensor and compared with the target temperature.

[0083] Standard deviation calculation: Standard deviation is an important statistic to measure the degree of data dispersion. In this solution, by calculating the standard deviation of the historical temperature deviation value sequence, the degree of temperature fluctuation can be quantified. The larger the standard deviation, the more drastic the temperature fluctuation; the smaller the standard deviation, the relatively stable the temperature.

[0084] Preset fluctuation coefficient calculation formula: This is the key step to convert the standard deviation into the temperature fluctuation coefficient. A possible calculation formula is: Temperature fluctuation coefficient = (standard deviation / (average value + minimum constant)) × weight coefficient. Among them, the average value is the arithmetic mean of the historical temperature deviation value sequence, the minimum constant is a small positive number added to avoid the denominator being zero, and the weight coefficient is used to adjust the sensitivity of the fluctuation coefficient.

[0085] The preset time window determines the range of the historical temperature deviation value sequence, which in turn affects the calculation result of the standard deviation. As a quantitative indicator of the degree of fluctuation, the standard deviation is converted into the temperature fluctuation coefficient through a preset calculation formula. This method not only considers the absolute size of the temperature deviation, but also reflects the dynamic characteristics of temperature changes through the standard deviation, thereby providing a more comprehensive basis for adjusting the borax addition rate.

[0086] 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.

[0087] 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.

[0088] The technical solution of the present application solves the accuracy and adaptability problems when calculating the temperature fluctuation coefficient by introducing a preset fluctuation coefficient calculation formula. The formula takes into account the discrete degree (standard deviation) and overall level (average value) of the temperature deviation, while avoiding possible calculation errors through the minimum constant and introducing a weight coefficient to adapt to different production needs. This method can more accurately reflect the temperature fluctuation and provide a reliable basis for the subsequent adjustment of the borax addition rate, thereby helping to maintain the stability of the melt temperature and the uniformity of the protective film.

[0089] Among them, standard deviation reflects the degree of dispersion of the temperature deviation value sequence. The larger the standard deviation, the more severe the temperature fluctuation. In practical applications, different time windows can be selected to calculate the standard deviation, such as the temperature data of the last 5 minutes, 10 minutes or 30 minutes. In this way, the sensitivity to temperature fluctuations can be adjusted according to production needs.

[0090] Mean value: represents the central trend of the temperature deviation value series. The introduction of the mean value enables the coefficient of fluctuation to reflect the overall level of temperature deviation, rather than just the amplitude of fluctuation. In the calculation, the same time window as the standard deviation can be selected to calculate the mean value to maintain data consistency.

[0091] Minimum constant: used to prevent the denominator from being zero and increase the stability of the calculation. In practical applications, the minimum constant can be set according to 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 the stability of the calculation without significantly affecting the accuracy of the result.

[0092] Weight coefficient: used to adjust the influence of temperature fluctuation coefficient and improve the flexibility and adaptability of calculation. The weight coefficient can be adjusted dynamically according to different production stages or product requirements. For example, more sensitive temperature control may be required in the early stage of casting, and the weight coefficient can be set larger; while in the stable production stage, the weight coefficient can be appropriately reduced to reduce unnecessary adjustments.

[0093] The combination of these parameters enables the fluctuation coefficient calculation formula to flexibly adapt to different production situations. For example, when the temperature fluctuates greatly, the standard deviation increases, and the fluctuation coefficient increases accordingly, prompting the system to need more adjustments; when the absolute value of the temperature deviation is large, the average value increases, and the fluctuation coefficient decreases relatively, avoiding over-adjustment; and the introduction of the weight coefficient allows the sensitivity of the fluctuation coefficient to be adjusted according to actual needs.

[0094] In this way, the technical solution of the present application can more accurately reflect the temperature fluctuation and provide a reliable basis for the subsequent adjustment of 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.

[0095] In some of the above embodiments, during the implementation of the present application, there is still a problem that it is difficult to accurately monitor and control the coverage of the protective film. Further, the method also includes: S6: Acquire 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: Calculate the deviation between the protective film coverage and the target protective film coverage; S9: Calculating a protective film coverage control coefficient according to a deviation value of the protective film coverage; S10: Dynamically update the borax addition rate according to the protective film coverage control coefficient and the feedback parameter information.

[0096] This application introduces a visual recognition system to monitor the coverage of the protective film. By obtaining the protective film image information and calculating the protective film coverage, the effect of borax addition can be intuitively evaluated. This method can more accurately reflect the actual protection status of the melt surface and provide more comprehensive information than relying solely on parameters such as temperature, oxidation degree and protective film thickness.

[0097] The deviation between the protective film coverage and the target coverage is calculated, and the control coefficient is calculated based on this, which reflects the adaptive ability of the system. This method can dynamically adjust the borax addition rate according to the actual coverage situation, which helps to maintain a stable protective effect.

[0098] The protective film coverage control coefficient is combined with other feedback parameters to further optimize the adjustment process of the borax addition rate. This multi-parameter comprehensive control method can take various factors into account more comprehensively and achieve more accurate borax addition control.

[0099] Specifically, the visual recognition system can use a high-resolution camera with an appropriate light source to ensure that the image of the melt surface can be clearly captured in a high-temperature environment. The image acquisition frequency can be adjusted according to actual production needs, such as collecting 5-10 frames of images per second.

[0100] The present application may use an image segmentation algorithm, such as a threshold-based segmentation method or a deep learning segmentation model, to accurately identify the protective film area. Furthermore, the coverage rate may be calculated by analyzing the grayscale value distribution of the protective film area. For example, a grayscale threshold may be set, and pixels above the threshold may be considered as effective coverage areas, and then the ratio of the effective coverage area to the total melt surface area may be calculated, which is the protective film coverage rate.

[0101] Compared with the prior art, 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. This application introduces a visual recognition system that can monitor the coverage of the protective film in real time and accurately, 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 can adjust parameters according to the characteristics of different production lines to meet diverse production needs.

[0102] Further, step S7 includes: S71: performing image segmentation on the protective film image information to obtain a protective film area; S72: Calculate the gray value distribution of the protective film area; S73: Calculate the coverage of the protective film according to the gray value distribution of the protective film area.

[0103] First, the protective film image information is segmented to obtain the protective film area. This step can be achieved through a variety of image segmentation algorithms, such as threshold segmentation, edge detection, region growing, etc. Among them, threshold segmentation is a common and effective method. It can set a suitable threshold according to the gray value characteristics of the image to separate the protective film area from the background. The edge detection algorithm can identify the boundary of the protective film area, thereby accurately locating the range of the protective film. The region growing algorithm can start from a preset seed point and gradually expand to the entire protective film area. These algorithms can be selected and combined according to specific image features and computing resources to obtain the best segmentation effect.

[0104] Secondly, calculate the grayscale value distribution of the protective film area. After obtaining the segmented protective film area, the grayscale values ​​of all pixels in the area can be counted to generate a grayscale value histogram or other statistical representation. The grayscale value distribution can reflect the density and uniformity of the protective film and is an important basis for calculating the coverage. For example, the statistical characteristics of the grayscale value, such as the mean, standard deviation, skewness and kurtosis, can be calculated, which can fully describe the grayscale distribution of the protective film.

[0105] Finally, the coverage of the protective film is calculated based on the grayscale distribution of the protective film area. This step can be calculated in a variety of ways. For example, a grayscale threshold can be set, and pixels above the threshold are considered as effective coverage areas. Then, the ratio of the effective coverage area to the entire protective film area is calculated as the coverage. Another method is to use the weighted average of the grayscale values, assign weights according to the coverage degree corresponding to different grayscale values, and calculate the overall coverage. In addition, it is also possible to combine machine learning algorithms to predict coverage by training models. This method can better adapt to different image features and production conditions.

[0106] In this embodiment, a weighted average method can be used. First, the grayscale value range (usually 0-255) is divided into multiple intervals, and each interval corresponds to a coverage weight. For example, the grayscale value can be divided into 5 intervals: [0,50], [51,100], [101,150], [151,200], [201,255], and the corresponding coverage weights are 0.1, 0.3, 0.6, 0.8, and 1.0, respectively. Then calculate the proportion of pixels in each interval, multiply it by the corresponding weight and sum it to get the final coverage rate.

[0107] By using this method, the technical solution of the present application can quickly and accurately calculate the coverage of the protective film. Compared with the traditional manual observation or simple threshold method, this method takes into account the continuous change of the gray value and can more accurately reflect the actual coverage of the protective film.

[0108] Please refer to Figure 2, a borax adding device in a TP2 copper alloy continuous casting process, applied to any of the above-mentioned borax adding methods in a TP2 copper alloy continuous casting process, the device comprising: Parameter acquisition module 201: used to obtain real-time parameter information of melt surface temperature, melt oxidation degree and protective film thickness; Calculation module 202: used to calculate the borax addition rate according to the real-time parameter information; Control module 203: used for controlling the borax quantitative conveying device to perform the borax adding operation according to the borax adding rate; Feedback module 204: used to obtain feedback parameter information of melt surface temperature, melt oxidation degree and protective film thickness after borax addition operation; Update module 205: used to dynamically update the borax addition rate according to the feedback parameter information.

[0109] The parameter acquisition module 201 is used to obtain real-time parameter information of 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.

[0110] The calculation module 202 calculates the borax addition rate according to 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.

[0111] The control module 203 controls the borax quantitative conveying device to perform the borax adding operation according to the calculated borax adding rate, thereby automating the borax adding process and reducing the error of manual operation.

[0112] The feedback module 204 obtains feedback parameter information of the melt surface temperature, melt oxidation degree and protective film thickness after the borax addition operation. These feedback parameter information are used to evaluate the effect of borax addition and provide a basis for subsequent adjustments.

[0113] The updating module 205 dynamically updates the borax addition rate according to the feedback parameter information, which realizes the continuous optimization of the borax addition process and ensures the stability and uniformity of the protective film.

[0114] The parameter acquisition module 201 may include devices such as a temperature sensor, an oxidation degree detector, and a thickness measuring instrument, etc. These devices may be installed at different positions of the furnace to obtain comprehensive real-time parameter information.

[0115] The calculation module 202 may use an embedded processor or an industrial computer to run a specially designed algorithm to calculate the borax addition rate. The algorithm may be based on a machine learning model and consider the complex relationship of multiple parameters to obtain the optimal addition rate.

[0116] 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 according to the output of the calculation module to achieve accurate borax addition.

[0117] 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.

[0118] 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.

[0119] The cooperation between these modules can form a closed-loop control system. The parameter acquisition module 201 provides initial data, the calculation module 202 determines the addition strategy based on these data, the control module 203 performs the addition operation, the feedback module 204 evaluates the operation effect, 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.

[0120] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in 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: Calculating the borax addition rate according to the real-time parameter information; S3: controlling the borax quantitative conveying device to perform a borax adding operation according to the borax adding rate; S4: obtaining feedback parameter information of melt surface temperature, melt oxidation degree and protective film thickness after borax addition operation; S5: Dynamically update the borax addition rate according to the feedback parameter information.

2. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 1, characterized in that: In step S2, 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), H(t) are the melt surface temperature, oxidation degree and protective film thickness in the real-time parameter information, respectively; T_target, O_target, 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), 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.

3. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 2, characterized in that: 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: 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: Calculate and update 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.

4. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 3, characterized in that: 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 value 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: 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.

5. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 4, characterized in that: Step S512 includes: S5121: Obtain historical temperature deviation values ​​within a preset time period; S5122: Calculating a temperature fluctuation coefficient according to 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.

6. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 5, 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.

7. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 6, characterized in that: In step S51223, the preset fluctuation coefficient calculation formula is: 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.

8. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 1, characterized in that: The method further comprises: S6: Acquire 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 update the borax addition rate according to the protective film coverage control coefficient and the feedback parameter information.

9. The method for adding borax in a TP2 copper alloy continuous casting process according to claim 8, characterized in that: Step S7 includes: S71: performing image segmentation on the protective film image information to obtain a protective film area; S72: Calculate the gray value distribution of the protective film area; S73: Calculating the protection film coverage rate according to the gray value distribution of the protection film area.

10. A borax adding device in a 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 9, 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 to calculate the borax addition rate according to the real-time parameter information; A control module: used for controlling the borax quantitative conveying device to perform a borax adding operation according to the borax adding 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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