Steelmaking steel ladle management system and steel ladle temperature prediction method

Abstract

The invention discloses a steel-making steel ladle management system and a temperature prediction method. The system and the method have an automatic data acquisition function, a data transmission function, a data statistics function, a record query function, a replacement period alarm prompt function, a steel ladle turnover prediction model and a steel ladle optimization matching model. The method comprises a steel ladle heat storage prediction model and a molten steel temperature prediction model. Automatic signal data are automatically collected from field equipment, real-time logic analysis and judgment are supported by using a hardware network and database resources, and the field signal data are stored. A field operator operates on a client side, steel ladle information is input in time, the state of the steel ladle is tracked in real time, and the entry temperature and exit temperature of the steel ladle are predicted. Through real-time display of the human-computer interface, the steel ladle turnover efficiency of the steel-making workshop of the iron and

Description

Technical fields:

[0001] This invention belongs to the fields of steelmaking ladle management, ladle temperature prediction, and computer application technology, specifically a steelmaking ladle management system and temperature prediction method. Background technology:

[0002] Currently, steelmaking processes are highly complex and require strong coordination between them. The ladle, as a container for holding and transporting molten steel and undergoing secondary metallurgical processes, has its heat storage state as a major factor affecting molten steel temperature. The ladle's condition also influences the tapping temperature, heating rate, and cooling rate of the molten steel. Currently, ladle temperature status relies on manual communication via telephone or walkie-talkie. Ladle turnover management primarily depends on experience, resulting in low efficiency and poor accuracy. Furthermore, the lack of quantitative differentiation in ladle condition levels leads to problems such as unstable molten steel temperature and large temperature drops during processes, which negatively impacts production stability, energy conservation, and product quality. Summary of the Invention:

[0003] The primary objective of this invention is to provide a steel ladle management system that, based on automation and intelligent technologies, improves ladle turnover efficiency in steelmaking workshops.

[0004] The second objective is to provide a method for predicting ladle temperature and to solve the problem of ladle temperature compensation.

[0005] A steelmaking ladle management system includes automatic data acquisition, data transmission, data statistics, ladle turnover prediction, ladle temperature prediction, and optimized ladle matching functions, wherein:

[0006] Automatic data acquisition function: used to collect relevant data from steel ladles and transmit the data to the database;

[0007] Data transmission function: used to transmit data from various processes including converter, refining, continuous casting, hot repair, and cold repair;

[0008] Data statistics function: used to collect, record, and query the entire life cycle and process tracking records of the steel ladle;

[0009] Steel ladle turnover prediction function: used to predict steel ladle turnover routes and transportation time;

[0010] Ladle temperature prediction function: used to predict the heat storage status of the ladle;

[0011] Steel ladle optimization matching function: used to select the best steel ladle.

[0012] The automatic data acquisition function of this invention includes ladle data such as ladle entering and exiting the converter, ladle entering and exiting the refining process, ladle entering and exiting the continuous casting process, ladle entering and exiting the hot repair process, and ladle weight, temperature, and location information for the hot repair process.

[0013] In the automatic data acquisition function of this invention, the automatic acquisition of data such as ladle position, weight, time, and baking temperature is collected by the PLC.

[0014] The data transmission function of this invention enables real-time interactive transmission of ladle data information with the secondary system and the MES system.

[0015] The data statistics function of this invention refers to classifying and analyzing the data of each ladle, automatically identifying equipment and recording usage data, saving furnace records, and providing data support for the safe use of ladles.

[0016] The ladle turnover prediction function of this invention is based on the production process route, and statistically analyzes historical data on converter charging time, blowing time, tapping time, BOF-LF transportation time, LF smelting time, LF-CC transportation time, RH smelting time, RH-CC transportation time, waiting time for casting, casting time, and hot repair time to calculate the transportation time and smelting standard time for each process.

[0017] The ladle temperature prediction function of this invention can calculate the target value of ladle heat storage saturation based on the process path, actual temperature measurement and ladle heat transfer state, and calculate the ladle heat enthalpy loss based on hot repair time and transportation time to obtain the ladle heat enthalpy value.

[0018] The ladle temperature prediction function also includes:

[0019] Ladle enthalpy loss model: Wherein, ΔG S : Ladle thermal enthalpy loss, kJ; Heat transfer coefficient of empty wall, kJ / min; Heat transfer coefficient at the bottom of the empty container, kJ / min; t y Empty package delivery time, in minutes; t x : Hot repair time for empty package, in minutes.

[0020] Steel ladle heat storage model: Where: ΔG x : Ladle heat storage capacity, kJ; Heat transfer coefficient of the ladle wall during tapping, kJ / min; Heat transfer coefficient at the bottom of the ladle during the tapping process, kJ / min; Heat transfer coefficient of the ladle wall when the ladle is full of molten steel, kJ / min; Heat transfer coefficient at the bottom of the ladle when it is full of molten steel, kJ / min

[0021] t c : tapping time, min; t m Time to fill ladle with molten steel, in minutes.

[0022] Furthermore, the ladle temperature prediction function establishes a temperature model based on steel grade parameters, where the temperature parameters are adjustable.

[0023] The ladle optimization matching function of the present invention prioritizes the selection of turnover ladles, provided that the rules of ladle age, special steel type, residual element, and ladle off-line time are met.

[0024] The ladle matching rules for the optimized ladle matching function are as follows:

[0025] Establish scoring rules for ladles: 90 points for continuous use and last used in LF smelting; 95 points for continuous use and last used in RH smelting; 95 points for continuous use and last used in RH+LF smelting; -8 points for the first use of a new ladle and in furnace B (empty time > 300 min); -5 points for the second and third use of a new ladle and in furnace A (150 min < empty time < 300 min); -5 points for a ladle without a cover during the first use.

[0026] The optimal ladle is recommended by comparing the scores of all ladles in the ladle pool. Attached image description:

[0027] Figure 1 This is a functional configuration diagram of the present invention.

[0028] Figure 2 This is a flowchart of the steel ladle turnover process of the present invention.

[0029] Figure 3 This is the network topology diagram of the present invention.

[0030] Figure 4 This represents the hit rate after the application of this invention.

[0031] Figure 5 This demonstrates the improved performance indicators after the application of this invention. Detailed implementation method:

[0032] The technical solutions in the examples of this invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0033] The steelmaking ladle management system described in this invention, such as Figure 1 As shown, it includes automatic data acquisition, data transmission, data statistics, ladle turnover prediction, ladle temperature prediction, and optimized ladle matching functions, among which:

[0034] Automatic data acquisition function: used to collect relevant data from steel ladles and transmit the data to the database;

[0035] Data transmission function: used to transmit data from various processes including converter, refining, continuous casting, hot repair, and cold repair;

[0036] Data statistics function: used to collect, record, and query the entire life cycle and process tracking records of the steel ladle;

[0037] Steel ladle turnover prediction function: used to predict steel ladle turnover routes and transportation time;

[0038] Ladle temperature prediction function: used to predict the heat storage status of the ladle;

[0039] Steel ladle optimization matching function: used to select the best steel ladle.

[0040] The automatic data acquisition function includes ladle data for ladle entry and exit from converters, ladle entry and exit from refining processes, ladle entry and exit from continuous casting processes, ladle entry and exit from hot repair processes, and ladle entry and exit from hot repair processes, including ladle weight, temperature, and location information.

[0041] The automatic data acquisition function automatically collects data such as ladle position, weight, time, and baking temperature from the PLC.

[0042] The data transmission function enables real-time interactive transmission of ladle data information with the secondary system and MES system.

[0043] The data statistics function classifies and analyzes the data of each ladle, automatically identifies equipment and records usage data, and saves furnace records, providing data support for the safe use of ladles.

[0044] See Figure 2 The ladle turnover prediction function calculates the transportation time and standard smelting time for each process based on historical data of converter charging time, blowing time, tapping time, BOF-LF transportation time, LF smelting time, LF-CC transportation time, RH smelting time, RH-CC transportation time, waiting time for casting, casting time, and hot repair time, according to the production process route.

[0045] The ladle temperature prediction function can calculate the target value of ladle heat storage saturation based on the process path, actual temperature measurement and ladle heat transfer status, and calculate the ladle heat enthalpy loss based on hot repair time and transportation time to obtain the ladle heat enthalpy value.

[0046] The calculation model for the ladle temperature prediction function is as follows:

[0047] Ladle enthalpy loss model:

[0048]

[0049] Wherein, ΔG S : Ladle thermal enthalpy loss, kJ; Heat transfer coefficient of empty wall, kJ / min; Heat transfer coefficient at the bottom of the empty container, kJ / min; t y Empty package delivery time, in minutes; t x : Hot repair time for empty package, in minutes.

[0050] Steel ladle heat storage model:

[0051]

[0052] Where: ΔG x : Ladle heat storage capacity, kJ; Heat transfer coefficient of the ladle wall during tapping, kJ / min; Heat transfer coefficient at the bottom of the ladle during the tapping process, kJ / min; Heat transfer coefficient of the ladle wall when the ladle is full of molten steel, kJ / min; Heat transfer coefficient at the bottom of the ladle when it is full of molten steel, kJ / min

[0053] t c : Steel tapping time, min; y m Time to fill ladle with molten steel, in minutes.

[0054] The ladle temperature prediction function establishes a temperature model based on steel grade parameters, and these temperature parameters are adjustable.

[0055] The ladle optimization matching function prioritizes the selection of turnover ladles, provided that the rules on ladle age, special steel type, residual element, and ladle off-line time are met.

[0056] The ladle matching rules for the optimized ladle matching function are as follows: A. Establish ladle scoring rules: Continuous use and last used in LF smelting: 90 points; continuous use and last used in RH smelting: 95 points; continuous use and last used in RH+LF smelting: 95 points; first use of a new ladle and use in furnace B (empty time > 300 min): -8 points; second and third use of a new ladle and use in furnace A (150 min < empty time < 300 min): -5 points; ladle without a cover during the first furnace run: -5 points. B. Compare the scores of all ladles in the ladle pool and recommend the optimal ladle.

[0057] Example 1

[0058] In a certain steelmaking plant workshop, the temperature status of the ladle is communicated manually via telephone / walkie-talkie, resulting in delayed and inconvenient information transmission. The ladle thermal status is categorized into four types (A, B, C, and D) without being quantified, making it impossible to guide the setting of the converter tapping temperature. This leads to problems such as unstable molten steel temperature and large temperature drops during the process, resulting in unstable production quality and high costs.

[0059] According to the content of this invention, refer to Figure 3A system network was established, utilizing IoT technologies such as overhead crane positioning, trolley positioning, and wireless communication to collect and process information from overhead cranes, trolleys, the secondary production system, and PLCs, enabling ladle number identification and ladle tracking. Data statistics, record query, and reporting functions were developed to achieve statistical storage of ladle-related data. Ladle temperature prediction models were established for two smelting paths: converter-LF-continuous casting and converter-RH-continuous casting, respectively, enabling ladle temperature prediction. Using historical data, ladle management time parameters were optimized: converter transport time 10-->16; LF cycle 55-->50; converter to ladle placement 20-->26; LF transport 10-->20. Temperature parameters were modified: tapping heat transfer 400000-->600000; full ladle heat coefficient 60000-->100000; molten steel heat coefficient 60000-->100000. Before and after the application of this invention, the accuracy rate of LF outlet temperature prediction and converter ladle temperature prediction significantly improved, as shown in the following results. Figure 4 As shown. The optimization of the continuous casting tundish temperature compliance rate and the converter tapping-continuous casting start time under the RH path is as follows: Figure 5 As shown.

[0060] The method of this invention is used for ladle management and ladle temperature prediction in steelmaking plants of iron and steel enterprises. It has functions such as automatic data acquisition, data transmission, data statistics, ladle turnover prediction, ladle temperature prediction, and optimized ladle allocation. Based on the actual ladle usage in the field, it conducts ladle turnover studies for different process routes, establishes a temperature model based on the ladle reaction enthalpy, and completes the functions of ladle recommendation and temperature prediction.

[0061] The beneficial effects of this invention are that it automatically tracks, collects, and stores field equipment data, uses database data and process parameter data to support real-time logical analysis and judgment, realizes the function of recommending the optimal ladle and predicting ladle temperature, and improves the turnover efficiency of steelmaking ladles and the accuracy of temperature control throughout the entire process.

Claims

1. A steel ladle management system, characterized in that... It features automatic data acquisition, data transmission, data statistics, ladle turnover prediction, ladle temperature prediction, and optimized ladle matching functions, including: Automatic data acquisition function: used to collect relevant data from the ladle and transmit the data to the database; Data transmission function: used to transmit data from various processes including converter, refining, continuous casting, hot repair, and cold repair. Data statistics function: used to collect, record, and query the entire life cycle and process tracking records of the ladle; Ladle turnover prediction function: used to predict the turnover route and transportation time of the ladle; Ladle temperature prediction function: used to predict the heat storage status of the ladle; Steel ladle optimization matching function: used to select the best steel ladle.

2. The steelmaking ladle management system according to claim 1, characterized in that... The ladle data mentioned in the automatic data acquisition function includes the ladle's weight, temperature, and location information for ladle entering and exiting the converter, ladle entering and exiting the refining process, ladle entering and exiting the continuous casting process, ladle entering and exiting the hot repair process, and ladle entering and exiting the hot repair process.

3. The steelmaking ladle management system according to claim 1, characterized in that... The automatic data acquisition function for steel ladles refers to the collection of data on ladle position, weight, time, and baking temperature by the PLC.

4. The steelmaking ladle management system according to claim 1, characterized in that... The data transmission function enables real-time interactive transmission of ladle data with the secondary system and MES system.

5. The steelmaking ladle management system according to claim 1, characterized in that... The data statistics function classifies and analyzes the data of each ladle, automatically identifies equipment and records usage data, saves furnace records, and has record query and report functions.

6. The steelmaking ladle management system according to claim 1, characterized in that... The ladle turnover prediction function calculates historical data on converter charging time, blowing time, tapping time, BOF-LF transportation time, LF smelting time, LF-CC transportation time, RH smelting time, RH-CC transportation time, waiting time for casting, casting time, and hot repair time based on the production process route, and determines the transportation time and standard smelting time for each process.

7. The steelmaking ladle management system according to claim 1, characterized in that... The ladle temperature prediction function calculates the target value of ladle heat storage saturation based on the process path, actual temperature measurement, and ladle heat transfer status. It also calculates the ladle enthalpy loss based on hot repair time and transportation time, thus obtaining the ladle enthalpy value.

8. The steelmaking ladle management system according to claim 1, characterized in that... Ladle temperature prediction function: Ladle enthalpy loss model: Wherein, ΔG S : Ladle thermal enthalpy loss, kJ Heat transfer coefficient of empty wall, kJ / min Heat transfer coefficient at the bottom of the empty container, kJ / min t y Empty package delivery time, in minutes t x Empty package hot repair time, min Steel ladle heat storage model: Where: ΔG x : Ladle thermal storage heat storage, kJ Heat transfer coefficient of ladle wall during tapping process, kJ / min Heat transfer coefficient at the bottom of the ladle during tapping process, kJ / min Heat transfer coefficient of the ladle wall when the ladle is full of molten steel, kJ / min Heat transfer coefficient at the bottom of the ladle when it is full of molten steel, kJ / min t c : Steel tapping time, min t m Time to fill ladle with molten steel, in minutes.

9. The steelmaking ladle management system according to claim 1, characterized in that... The ladle temperature prediction function establishes a temperature model based on steel grade parameters, and these temperature parameters are adjustable.

10. The steelmaking ladle management system according to claim 1, characterized in that... The optimized ladle matching function prioritizes the selection of reusable ladles, provided that the rules regarding ladle age, special steel grades, residual elements, and ladle off-line time are met.

11. The steelmaking ladle management system according to claim 1, characterized in that... The ladle matching rules for the optimized ladle matching function are as follows: A. Establish ladle scoring rules: 90 points for continuous use and last used in LF smelting; 95 points for continuous use and last used in RH smelting; 95 points for continuous use and last used in RH+LF smelting; -8 points for the first use of a new ladle and in furnace B (empty time > 300 min); -5 points for the second and third use of a new ladle and in furnace A (150 min < empty time < 300 min); -5 points for the ladle without a cover during the first use. B. Based on the comparison of the scores of all ladles in the ladle pool, the optimal ladle is recommended.

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