Digital modeling method for steel continuous casting production

Abstract

The invention discloses a digital modeling method for steel continuous casting production, which comprises the following steps of: constructing a continuous casting digital model and initializing, and respectively dividing molten steel and a casting blank into a molten steel section and a casting blank section; the continuous casting process is converted into a model segment queue moving process, and molten steel segments are converted into casting blank segments after moving to a crystallizer; judging an equipment action area where each section is located, and recording equipment process parameters into the corresponding section; judging casting blank segments contained in the slabs after flame cutting, and integrating data of the casting blank segments into the corresponding slabs; and carrying out quality detection on the plate blank, and matching a quality evaluation result with the corresponding casting blank sections. According to the method, total-factor digital representation of component-process-quality data is realized, the integrity and availability of production data can be effectively improved, a complete process data chain is provided for advanced digital and intelligent continuous casting production such as quality analysis and process optimization, and the utilization rate of data resources is improved.

Description

Technical Field

[0001] This invention belongs to the field of steel continuous casting production, and particularly relates to a digital modeling method for steel continuous casting production. Background Technology

[0002] As a large-scale and complex modern process industry, steelmaking enterprises in advanced steelmaking companies are mostly equipped with complete data acquisition and storage systems for their continuous casting production processes, recording large-scale industrial data. Patent document CN118122976A discloses a continuous casting method, apparatus, storage medium, and computer equipment based on digital twins. This method constructs a preset continuous casting digital twin model based on actual continuous casting scenario data corresponding to the target continuous casting apparatus, including the three-dimensional dimension data and position data of the target continuous casting apparatus, the three-dimensional dimension data of the continuous casting model, and process data from the continuous casting site. Based on the real-time operating status data and process parameters of the continuous casting machine, the preset continuous casting digital twin model is used to analyze the solidification process of the cast iron in the next continuous casting process, obtaining the behavioral patterns of the cast iron solidification process in the next continuous casting process.

[0003] However, data resources in the data acquisition and storage system are fragmented across different production stages, resulting in poor data correlation and severe information silos. Due to the lack of effective digital modeling methods, it is impossible to achieve meter-level accuracy in matching continuous casting data. The collected data fails to reflect the production status of the continuous casting machine and the entire lifecycle of billet production, severely impacting the effective utilization of data resources. Patent document CN117473770A discloses an intelligent management system for steel equipment based on digital twin information, which synchronizes data generated by various production equipment spatially and temporally to collect real-time data on steel equipment and corresponding production processes. However, this patent application cannot completely solve the problem of tracking the billet process in continuous casting production. The low utilization rate of data resources continues to seriously hinder the development of digital technology in the continuous casting process. Summary of the Invention

[0004] To address the technical problems mentioned above, this invention provides a digital modeling method for steel continuous casting production. Based on existing continuous casting production information systems, and addressing the issues of information silos in the continuous casting process and the matching and tracking of billet process history information, this invention employs a segmented tracking method to digitally model the continuous casting production process, taking into account the characteristic of the continuously cast billet solidifying while moving. Combined with slab quality inspection results, this achieves a comprehensive digital representation of composition, process, and quality data. The digital model provided by this invention effectively improves the completeness and usability of production data, providing a complete process data chain for advanced digital and intelligent continuous casting production, such as quality analysis and process optimization, thereby enhancing data resource utilization.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] A digital modeling method for steel continuous casting production includes the following steps:

[0007] S1. Construct and initialize a digital model of continuous casting that includes the tundish and the casting stream. Based on the weight of the molten steel and the length of the casting stream, divide the molten steel and the billet into molten steel segments and billet segments, respectively.

[0008] S2. The continuous casting process is converted into a model segmentation queue movement process, and the steel water segmentation is converted into billet segmentation after moving to the crystallizer;

[0009] S3. Set the operating area of ​​each piece of equipment in the continuous casting machine, determine the operating area of ​​each section, and record the equipment process parameters to the corresponding section;

[0010] S4. Determine the slab segments contained in each slab after flame cutting, and integrate the data of the slab segments into the corresponding slab to obtain the comprehensive process history information of each slab.

[0011] S5. Conduct quality inspection on the slabs, match the quality assessment results with the corresponding slab segments, and form a digital information collection system for the entire continuous casting production process.

[0012] As a further explanation of the present invention, step S1 specifically involves: for a continuous casting machine with a casting flow quantity of k, dividing the molten steel in the tundish into several steel flow segments of equal weight from bottom to top, and setting the weight of each steel flow segment to m; dividing the billet of each casting flow into several billet segments of length n meters from the top edge of the crystallizer; ignoring the influence of solidification volume shrinkage, the billet segment length n is calculated according to the following formula:

[0013] (1)

[0014] Among them, h i w represents the thickness of the slab in the i-th casting flow, in mm. i ρ represents the width of the slab in the i-th casting stream, in mm, and ρ represents the density of the current steel grade being cast, in kg / m³. 3 Initialize the continuous casting digital model; at this point, the number of billet segments is 0.

[0015] Furthermore, the product of the maximum number of steel water segments and the weight m of a single steel water segment should be less than the maximum capacity of the ladle and tundish; the product of the number of billet segments in each casting stream and the length of a single billet segment should be less than the maximum length of the continuous casting machine. Therefore, the steel water segments and the maximum number of billet segments in each casting stream are determined according to the following formulas:

[0016] (2)

[0017] (3)

[0018] Where, N TunSeg M represents the maximum number of steel sections that can be divided into sections; total The maximum capacity of the ladle and tundish is expressed in tons (t); N StrandSeg L represents the maximum number of segments in each casting flow; total This represents the maximum length of the casting flow, measured in meters (m).

[0019] As a further explanation of the present invention, the aforementioned continuous casting digital model relies on the Industrial Internet to establish an efficient connection with the continuous casting production information system, and acquires on-site sensor data and production scheduling information provided by systems such as MES in real time.

[0020] As a further explanation of the present invention, S2 specifically refers to: after the continuous casting process starts, the continuous casting digital model converts the continuous casting billet pulling process into a segmented queue for synchronous advancement; a critical conversion relationship between the steel water segment and the billet segment is established; the billet pulling process of the continuous casting machine is monitored in real time through displacement sensors; when a certain steel water segment meets the critical conversion relationship judgment condition, the steel water segment is converted into a billet segment, and a new steel water segment is generated until a production end signal is received; wherein, the critical conversion relationship judgment condition is as follows:

[0021] (4)

[0022] in, The current billet length for the i-th casting flow, in meters; S is the length of the billet drawn when the steel flow segment before the i-th casting flow is converted into a billet segment, recorded in meters; i Let be the cross-sectional area of ​​the i-th casting flow billet, in meters. 2 ρ represents the density of the steel currently being cast, in kg / m³. 3 .

[0023] As a further explanation of the present invention, S3 specifically involves: determining the influence range of each piece of equipment based on the layout of each piece of equipment in the continuous casting machine, the process settings, and the material input, and utilizing the starting position of the equipment on the continuous casting machine. and finish line Define the device's effective area, then the length of the device's effective area. The steel water segmentation and billet segmentation are continuously advanced during the continuous casting production process. The position of each segment is obtained in real time, and the equipment action zone of each segment is determined. The relevant process parameter values ​​of the corresponding equipment are recorded in the corresponding segment.

[0024] Furthermore, for segments that cross the boundary of the equipment's operating area, the weight or length ratio of the segment within the equipment's operating area is calculated. When this ratio exceeds a set threshold, the segment is considered to be within the equipment's operating area, and corresponding process parameters related to the equipment will be recorded for that segment.

[0025] Furthermore, for equipment in the casting flow area, when the length of the equipment's active zone (AOI) is... length When the length of the billet segment is less than the set length n, the starting point of the working area of ​​the equipment is reset using the following formula. and the end point The effective area of ​​the equipment is extended to be equal to the length n of the billet segment:

[0026] (5)

[0027] (6)

[0028] (7)

[0029] in, Extend the length of the device's operating area.

[0030] Furthermore, for each equipment process parameter recorded in the segment, based on the full-cycle data of the segment located within the operating area of ​​a certain equipment, the data processing method is selected according to the characteristics of the equipment process parameter: for non-threshold sensitive parameters, the mean of the full-cycle data is recorded; for threshold sensitive parameters, the extreme values ​​of the full-cycle data are recorded; for difference sensitive parameters, the maximum difference of the full-cycle data is recorded; for start-up and shutdown parameters, the data value of the last update is used.

[0031] As a further explanation of the present invention, S4 specifically refers to: after the slab segment enters the flame cutting position, according to the actual flame cutting position information, the digital model matches the cut slab with the corresponding multiple slab segments, and summarizes and processes the process data contained in these slab segments to obtain the comprehensive process history information of the slab.

[0032] Furthermore, for slab segments that cross the flame-cutting position, i.e., cross-segment segments, a region division rule is set to determine the matching relationship between the cross-segment segments and the slabs. Specifically, along the casting direction, the slab located before the flame-cutting position is defined as the first slab, and the slab located after it is defined as the second slab. The following formula is used to determine whether the cross-segment segment is included in the first slab:

[0033] (9)

[0034] Where X is the proportion of the segment j across the region that falls into the first slab; R j Indicates the starting position of segment j across regions; Lj Indicates the end position of segment j across regions; B and E represent the start and end positions of the first slab during flame cutting, respectively; P represents the set effective percentage threshold; if If the casting billet is in the first slab section, it is determined that the billet falls into the second slab section; otherwise, it is determined that the billet falls into the second slab section.

[0035] As a further explanation of the present invention, in S5, computer vision processing technology is used to evaluate the quality of the slab, and the low-magnification defect image of the slab is compared with industry standards to determine the quality grade of the slab.

[0036] Furthermore, in S5, the quality assessment results and process parameter information of the slab are combined with artificial intelligence technology to achieve the prediction of slab quality and reverse process optimization.

[0037] The beneficial effects of this invention are:

[0038] This invention uses a segmented tracking method to collect the production process history of the billet, achieving continuous casting data matching accurate to the meter. This can effectively improve the integrity and availability of production data, solve the problem of information silos in continuous casting production, and provide a complete process data chain for advanced digital and intelligent continuous casting production such as quality analysis and process optimization. This effectively improves the utilization rate of data resources in the continuous casting process and promotes the digitalization of continuous casting production. Attached Figure Description

[0039] Figure 1 This is a flowchart of the digital modeling method for continuous casting production in this invention;

[0040] Figure 2 This is a schematic diagram of the continuous casting segmentation in this invention;

[0041] Figure 3 This is a schematic diagram of digital modeling for continuous casting in this invention;

[0042] Figure 4 This is a schematic diagram of the influence zone of casting process parameters in this invention;

[0043] Figure 5 This is a schematic diagram of the slab segmentation rules in this invention. Detailed Implementation

[0044] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0045] This invention proposes a digital modeling method for steel continuous casting production. Based on existing continuous casting production information systems, and taking into account the characteristic of continuous casting billets solidifying while moving, a segmented tracking method is used to digitally model the continuous casting production process. This method can monitor the operating status and key process parameters of continuous casting equipment in real time, accurately track the process history of each billet, and combine the results of continuous casting billet quality inspection to achieve full-element digital representation of composition, process, and quality data. This enables precise analysis of billet quality and provides a complete process data chain for advanced digital and intelligent continuous casting production, such as quality analysis and process optimization. This improves data resource utilization and assists production technicians in decision-making.

[0046] This embodiment uses a straight-arc type twin-strand slab continuous casting machine as an example. The modeling part of this production line includes, in process order: tundish, crystallizer, secondary cooling zone, air cooling zone, and flame cutting zone. Molten steel is first injected into the tundish through a submerged entry nozzle, and then enters the crystallizer of each casting stream. Initial solidification is completed in the crystallizer, forming a continuous slab with a certain shell thickness. The slab then moves continuously along the casting stream direction under the action of a straightening device, successively passing through a curved section, an arc-shaped fan-shaped section, a straightening fan-shaped section, and a horizontal fan-shaped section. It is cooled in the secondary cooling zone corresponding to each fan-shaped section. After passing through the air cooling zone, it is conveyed by roller conveyors to the flame cutting zone for dimensional cutting. The tundish of this continuous casting machine has a working capacity of 60t, and the total length from the top of the crystallizer to the end of the flame cutting zone is 55m. The continuous casting billets produced by both casting streams have a cross-sectional size of 1600mm × 230mm. The continuous casting machine's sector sections are divided into curved, arc-shaped, straightening, and horizontal sectors. Cooling methods in the cooling sections include full water cooling, air-water atomization cooling, and cross-spray cooling. The continuous casting digital model uses the OPC UA communication protocol to interact with the production automation system and connects to the Manufacturing Execution System (MES) via ADO data access technology to achieve data integration with the continuous casting production information system. The acquired data, after data governance and multi-source data matching, serves as the foundation for digital modeling. The digital model receives real-time data from sensors in the continuous casting production information system to collect actual process parameters for continuous casting production; simultaneously, it receives real-time signals of billet length from the continuous casting production information system to collect the real-time position of each segment, achieving segment-to-process parameter matching.

[0047] A digital modeling method for steel continuous casting production, the process is as follows: Figure 1 As shown, it includes:

[0048] 101. Construct and initialize a digital model of continuous casting that includes the tundish and the casting stream. Based on the weight of the molten steel and the length of the casting stream, divide the liquid steel and the billet into steel water segments and billet segments, respectively:

[0049] Constructing a digital model for continuous casting, such as Figure 2 and Figure 3 As shown, the standard mass m of a single steel water segment is set to 5 tons. The molten steel in the tundish is divided into segments from bottom to top and numbered sequentially. Each casting stream is divided into several billet segments with a length of n meters from the top of the crystallizer. According to formula (1), n ​​= 0.93 meters is calculated. The continuous casting digital model is initialized. At this time, the number of steel water segments = the weight of molten steel in the tundish / m, and the number of billet segments is 0.

[0050] To ensure consistency between the model and the actual production process, the segmentation must meet the following constraints:

[0051] (1) The total mass of steel in each section shall not exceed the maximum capacity of the ladle and tundish, as shown in formula (2);

[0052] (2) The total length of the billet segment shall not exceed the maximum allowable billet length of the continuous casting machine, that is, the total length from the top of the crystallizer to the outlet of the flame cutting zone, as shown in formula (3).

[0053] Specifically, in this embodiment, the density of the currently cast steel grade is taken as ρ = 7300 kg / m³. 3 The calculation shows that the maximum number of steel flow segments is 12; the maximum number of segments for each casting flow is 59.

[0054] 102. The continuous casting process is converted into a model segmentation queue movement process, where the steel water segmentation is converted into billet segmentation after moving to the crystallizer:

[0055] In the continuous casting process, molten steel flows from the ladle through the tundish into the crystallizer, where it undergoes preliminary solidification and forms a continuous billet. The continuous casting digital model converts the continuous casting billet pulling process into a segmented queue for synchronous advancement. Based on the density ρ of the current steel grade and the geometric parameters of the crystallizer, a critical conversion relationship between molten steel segments and billet segments is established, as shown in formula (4). By monitoring the weight of molten steel and the actual position of the billet in real time through sensors, when a certain molten steel segment meets the critical conversion relationship, it is considered that it has all entered the crystallizer. The model converts the molten steel segment into a billet segment and numbers the billet segments (e.g., ...). Figure 2 As shown), a new steel water segment is generated simultaneously. Specifically, in this embodiment, it is calculated using formula (4) that when the two casting streams pull out a billet of approximately 0.93m equivalent length from the moment they enter the crystallizer from the steel water segment, that is, when the product of the cross-sectional area and the incremental length of the two casting streams satisfies the volume conservation condition shown in formula (4), the model automatically triggers the conversion event from steel water segment to billet segment.

[0056] According to different stages of the continuous casting process, the model operates in three states: "Ingot Preparation Mode," "Casting Mode," and "Tail Billet Mode." The established digital model for continuous casting triggers tasks and controls the process based on the real-time status signals of the continuous casting machine. In "Ingot Preparation Mode," the system performs model initialization, resetting the segment data and clearing residual segment information generated during the previous heat or production process due to billet pulling. This ensures that the initial state of the new model matches the actual production conditions. After resetting, the initial number of molten steel segments equals the weight of molten steel in the tundish per meter, and the initial number of billet segments is 0. Upon entering "Casting Mode," the model begins to respond in real-time to various parameter data collected from the production site, including pulling speed, cooling intensity, electromagnetic stirring power, spray pressure, and roll gap changes. The digital model dynamically generates new molten steel segments based on the billet length data collected by the continuous casting production information system and continuously advances the segment queue, completing the continuous conversion process from molten steel segments to billet segments and ensuring synchronization between the digital model and the physical continuous casting process. When the model receives a "tailing billet mode" signal from the production information system, it automatically switches to "tailing billet mode." In this mode, the digital model no longer generates new steel slurry segments. After the molten steel in the tundish is emptied, the digital model stops the conversion operation from steel slurry segments to billet segments, and only continues to track the existing billet segments until all of them are ejected, marking the final billet with the "tailing billet" attribute. Through this mechanism, the model can completely record the entire production process of the casting cycle, realizing a full-process mapping from molten steel to billet.

[0057] 103. Set the operating zones of each piece of equipment in the continuous casting machine, determine the operating zone of each section, and record the equipment process parameters to the corresponding section:

[0058] During continuous casting production, the digital model collects and manages multiple key process parameters in real time, including but not limited to: cooling intensity and electromagnetic stirring status in the crystallizer area; spray intensity and cooling water distribution in the secondary cooling zone; roll gap changes in the reduction zone; and operational events such as oxygen diversion in the ladle, open casting, and nozzle replacement. Based on the layout of each piece of equipment on the continuous casting machine, process settings, and material input, the digital model sets the influence range of each piece of equipment, utilizing the starting position of the equipment on the continuous casting machine. and finish line Define the device's effective area, then the length of the device's effective area. During the advancement of the continuous casting digital model in the segmented queue, the real-time position of each segment in the physical space is continuously calculated, and the equipment action zone in which the segment is located is dynamically determined.

[0059] When a segment falls within the operating area of ​​a certain piece of equipment, the continuous casting digital model immediately writes the parameter values ​​at that moment into the corresponding segment, realizing the physical association between process parameters and the segment. For segments that cross the boundaries of equipment operating areas, the weight or length ratio of the segment within the operating areas of the two equipments is calculated and compared with a set ratio threshold. When the ratio of a segment within the operating area of ​​a certain equipment exceeds the ratio threshold, the segment is considered to be within that equipment's operating area. Taking the secondary cooling zone cooling system as an example, the positions and operating areas of equipment A, equipment B, and equipment C are as follows: Figure 4 As shown, parameter a for device A is the cooling water volume of a certain secondary cooling zone, and its effective area is located (x1~x4) m below the top of the crystallizer; parameters b and c for devices B and C are the light reduction of sector S1 and sector S2, respectively, and their effective areas are (x1~x2) m and (x3~x4) m, respectively. During the calculation process, the continuous casting digital model identifies the three billet segments within the (x1~x4)m interval as recordable objects for parameter a, segment 1, which is entirely within the (x1~x2)m interval, as a recordable object for parameter b, and segment 3, which is entirely within the (x3~x4)m interval, as a recordable object for parameter c. For segment 2, which spans the operating areas of equipment B and equipment C, the proportion of segment 2 within the operating areas of equipment B and equipment C is calculated. The calculated proportions of segment 2 within the operating areas of equipment B and equipment C are 75.6% and 16.7%, respectively. The threshold for the proportion set in this embodiment is 50%, therefore segment 2 is identified as a recordable object for parameter b.

[0060] For equipment in the casting flow area, if the length of the working area of ​​a certain equipment is less than the length n of the billet segment, in order to avoid data loss, the continuous casting digital model symmetrically expands the working area of ​​the equipment according to the preset rules, and resets the starting point and ending point of the working area of ​​the equipment using formulas (5) to (7) so that the equivalent length of the expanded working area of ​​the equipment is consistent with the length of the billet segment. For example, the working area of ​​a certain equipment is [16.2, 16.8] m, and the length is only 0.6 m. The digital model will automatically symmetrically expand the working area of ​​the equipment by 0.165 m at each end, so that the length of the expanded interval reaches 0.93 m, in order to ensure complete information collection. The calculation formula is as follows:

[0061]

[0062]

[0063]

[0064] The parameter recording strategy categorizes data based on parameter sensitivity and selects appropriate data processing methods to ensure the accuracy and representativeness of the recorded data.

[0065] For non-threshold sensitive parameters that are not sensitive to instantaneous fluctuations and are mainly concerned with the overall level, such as the cooling water volume in the secondary cooling zone, the average value of this parameter is recorded within the cycle from when a certain billet segment enters the working area of ​​the equipment to when it leaves the working area of ​​the equipment. The calculation formula is as follows (assuming that the signal is updated 6 times in each cycle time interval):

[0066]

[0067]

[0068] in, This is the average value of the parameters. This represents the total number of signal updates within the period. Let be the signal value acquired in the i-th sampling. This indicates the final recorded value of the segment within the period.

[0069] For threshold-sensitive parameters that are highly sensitive to changes in the threshold and whose fluctuation range needs to be monitored, such as the rate of change of the maximum pulling speed in the crystallizer, the extreme values ​​within the period are recorded to reflect their typical levels. The calculation formula is as follows:

[0070]

[0071]

[0072] in, For the extreme values ​​of the parameters.

[0073] For start-up and shutdown parameters that are based on the latest status and do not require historical accumulation, such as continuous casting leakage alarm, the last updated value is recorded, and the calculation formula is as follows:

[0074]

[0075] =1

[0076] in, This represents the value of the last signal within the period.

[0077] For sensitive parameters that need to reflect the difference in the magnitude of change between adjacent sampling points, such as the change in casting speed of a billet within a certain range, the difference between adjacent sampling values ​​within the period is recorded to reflect the magnitude of the parameter change. The calculation method is as follows:

[0078]

[0079]

[0080] in, This represents the change between two consecutive sampled values.

[0081] 104. Determine the slab segments contained in each slab after flame cutting, and integrate the data of the slab segments into the corresponding slabs to obtain the comprehensive process history information of each slab:

[0082] During the flame cutting stage of the slab, the digital model receives real-time cutting signals from the flame cutting control system. These signals contain information such as the start and end logical positions of the currently cut slab, the casting machine flow number to which the cutting machine belongs, and the slab number to be cut. Based on the real-time collected cumulative casting length and the movement distance of the flame cutting machine, the continuous casting digital model calculates the actual range of the slab in the casting flow. Subsequently, the digital model traverses all slab segments within this range and identifies the set of segments belonging to the current slab.

[0083] For cast billet segments that cross cutting boundaries, i.e., cross-section segments, the digital model determines segment assignment based on the "effective proportion" principle. For example... Figure 5 As shown, when slab 1 completes flame cutting, the flame cutting position B is located at 45.8m in the casting flow, which is exactly inside the slab segment N. This segment corresponds to the slab segment from 45.57 to 46.5m in the casting flow. According to formula (9), the proportion of slab segment N falling into slab 1 is X = 60%, which is greater than the effective proportion threshold of the selected area set by the digital model (set to 50%). Therefore, the continuous casting digital model assigns slab segment N to slab 1.

[0084] After determining the affiliation of all slab segments, the continuous casting digital model summarizes all segment information corresponding to the current slab and records it in the local slab production SQL Server database, realizing the archiving of the entire production process data at the slab level.

[0085] 105. Conduct quality inspections on the slabs, match the quality assessment results with the corresponding slab segments, and form a digital information collection system for the entire continuous casting production process:

[0086] Standardized computer vision processing technology is used to evaluate the quality of slabs. Low-magnification defect images of slabs are compared with industry standards in a standardized manner to obtain quality inspection data and determine the quality grade of the slab. The quality inspection data includes the length and depth of center segregation, center porosity, intermediate cracks, triangular cracks, and surface cracks.

[0087] After the slab information data is recorded, the continuous casting digital model matches and associates the process data with the corresponding inspection and testing information (steel grade, alloy content and other composition information) and quality grade to construct complete composition-process-quality data.

[0088] Optionally, the quality assessment results and process parameter information of the slab after flame cutting can be combined with artificial intelligence technology to achieve the prediction of slab quality and reverse process optimization.

[0089] Optionally, the method in this embodiment can flexibly adapt to various units of measurement (such as meters and tons) and various starting point reference benchmarks (such as liquid level, top of crystallizer, fixed positioning point, etc.). The continuous casting digital model can automatically select appropriate reference points and units of measurement according to parameter type and current production status, and dynamically adjust the length of the influence interval, thereby ensuring that the physical correspondence between each parameter and the segmented model is accurate and consistent.

[0090] Optionally, the process parameters managed by the continuous casting digital model include not only numerical data but also some textual information parameters, such as nozzle type and mold flux type. By standardizing textual parameters and converting them into numerical signals that can be used for modeling, unified parameter processing is achieved. In this way, the model can be compatible with multiple types of process parameters, ensuring that all key process elements can be digitally collected and analyzed.

[0091] Optionally, by extracting segments at different locations of the casting flow, the real-time cooling process at each location of the casting flow can be determined. By combining temperature field calculation and visualization technologies, real-time monitoring of the casting flow temperature field can be achieved.

[0092] In summary, the method proposed in this invention has wide applicability and can be adjusted according to the type of production line (slab or billet) and the amount of casting.

[0093] The above description represents a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A digital modeling method for continuous casting production of steel, characterized in that, Includes the following steps: S1. Construct and initialize a digital model of continuous casting that includes the tundish and the casting stream. Based on the weight of the molten steel and the length of the casting stream, divide the molten steel and the billet into molten steel segments and billet segments, respectively. S2. The continuous casting process is converted into a model segmentation queue movement process, and the steel water segmentation is converted into billet segmentation after moving to the crystallizer; S3. Set the operating area of ​​each piece of equipment in the continuous casting machine, determine the operating area of ​​each section, and record the equipment process parameters to the corresponding section; S4. Determine the slab segments contained in each slab after flame cutting, and integrate the data of the slab segments into the corresponding slab to obtain the comprehensive process history information of each slab. S5. Conduct quality inspection on the slabs, match the quality assessment results with the corresponding slab segments, and form a digital information collection system for the entire continuous casting production process.

2. The digital modeling method for continuous casting production of steel as described in claim 1, characterized in that, Step S1 specifically involves: For a continuous casting machine with a casting volume of k, dividing the molten steel in the tundish into several equal-weight segments from bottom to top, and setting the weight of each segment to m; dividing the billet of each casting stream into several segments of length n meters from the top edge of the crystallizer; ignoring the effect of solidification volume shrinkage, the length n of the billet segment is calculated according to the following formula: （1） Among them, h i w represents the thickness of the billet in the i-th casting flow. i Let ρ be the width of the ith casting stream and ρ be the density of the current casting steel grade; initialize the continuous casting digital model, at which point the number of slab segments is 0.

3. A digital modeling method for continuous casting production of steel as described in claim 1 or 2, characterized in that, The product of the maximum number of steel water sections and the weight m of a single steel water section should be less than the maximum capacity of the ladle and tundish; the product of the number of billet sections in each casting stream and the length of a single billet section should be less than the maximum length of the continuous casting machine.

4. The digital modeling method for continuous casting production of steel as described in claim 1, characterized in that, Specifically, S2 refers to the following: After the continuous casting process starts, the continuous casting digital model converts the continuous casting billet pulling process into a segmented queue for synchronous advancement; a critical conversion relationship between steel moisture segments and billet segments is established. When a steel moisture segment meets the critical conversion relationship judgment condition, the steel moisture segment is converted into a billet segment, and a new steel moisture segment is generated until a production end signal is received; wherein, the critical conversion relationship judgment condition is as follows: （4） in, The current billet length for the i-th casting stream; S is the length of the billet drawn when the steel flow segment before the i-th casting flow is converted into a billet segment; i ρ is the cross-sectional area of ​​the i-th casting billet; ρ is the density of the current casting steel grade.

5. The digital modeling method for steel continuous casting production according to claim 1, characterized in that, Specifically, S3 refers to: utilizing the starting position of the equipment on the continuous casting machine. and finish line Define the device's effective area, then the length of the device's effective area. The system acquires the position of each segment in real time, determines the equipment's operational area for each segment, and records the relevant process parameter values ​​for the corresponding equipment in the corresponding segment.

6. A digital modeling method for continuous casting production of steel as described in claim 1 or 5, characterized in that, For segments that cross the boundary of the equipment's operating area, calculate the proportion of the segment's weight or length within the equipment's operating area. When this proportion exceeds a set threshold, the segment is considered to be within the equipment's operating area.

7. A digital modeling method for continuous casting production of steel as described in claim 1 or 5, characterized in that, For equipment in the casting flow region, when the length of the equipment's effective zone AOI is... length When the length of the billet segment is less than the set length n, the starting point of the working area of ​​the equipment is reset using the following formula. and the end point The effective area of ​​the equipment is extended to be equal to the length n of the billet segment: （5） （6） （7） In the formula, and These are the starting and ending positions of the equipment on the continuous casting machine, respectively. Extend the length of the device's operating area.

8. A digital modeling method for continuous casting production of steel as described in claim 1 or 5, characterized in that, For each equipment process parameter recorded in the segment, based on the full-cycle data of the segment located within the operating area of ​​a certain equipment, the data processing method is selected according to the characteristics of the equipment process parameter: for non-threshold sensitive parameters, the mean of the full-cycle data is recorded; for threshold sensitive parameters, the extreme values ​​of the full-cycle data are recorded; for difference sensitive parameters, the maximum difference of the full-cycle data is recorded; for start-up and shutdown parameters, the data value of the last update is used.

9. The digital modeling method for continuous casting production of steel according to claim 1, characterized in that, Specifically, S4 involves the following steps: after the slab segments enter the flame cutting position, the digital model matches the cut slab with the corresponding multiple slab segments based on the actual flame cutting position information, and summarizes and processes the process data contained in these slab segments to obtain the comprehensive process history information of the slab.

10. A digital modeling method for continuous casting production of steel as described in claim 1 or 9, characterized in that, For a slab segment that spans the flame-cutting position, i.e., a cross-segment segment, the slab before the flame-cutting position along the casting direction is defined as the first slab, and the slab after it is defined as the second slab. The following formula is used to determine whether the cross-segment segment is included in the first slab: （9） Where X is the proportion of the segment j across the region that falls into the first slab; R j Indicates the starting position of segment j across regions; L j Indicates the end position of segment j across regions; B and E represent the start and end positions of the first slab during flame cutting, respectively; P represents the set effective percentage threshold; if If the casting billet is in the first slab section, it is determined that the billet falls into the second slab section; otherwise, it is determined that the billet falls into the second slab section.

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