A data model driven method for grouping low alloy plate continuous casting heats

Abstract

The application provides a data model driven low-alloy medium plate continuous casting furnace campaign grouping method, which comprises the following steps: constructing a furnace position sequence data set and an in-furnace grouping data set in a low-alloy medium plate continuous casting process grouping; determining the total number of furnaces required for smelting all customized steel grades, and determining the characteristic value of the grouping sequence of each furnace campaign of any customized steel grade in a pouring period; sequencing the characteristic value of the grouping sequence of each furnace campaign to determine the grouping sequence of each furnace campaign of any customized steel grade in the pouring period; determining the total number of continuous casting billets required by all customized steel grades, matching the continuous casting billets of all customized steel grades to the grouping sequence of each furnace campaign, determining the casting billet grouping position characteristic value in each smelting furnace campaign in the pouring period, and sequencing the casting billet grouping position characteristic value to determine the position sequence of each block of casting billets. The application can improve the chemical composition continuity between furnaces in the pouring period of the low-alloy medium plate under the condition of hot delivery and hot charging, and avoid the under-amount or over-amount of the composition of continuous casting billets and mixed casting billets.

Description

Technical Field

[0001] This invention belongs to the field of intelligent production of medium and heavy plates driven by furnace grouping technology, and specifically relates to a data model-driven method for grouping low alloy medium and heavy plate continuous casting furnaces. Background Technology

[0002] The hot-charging and hot-feeding process for medium and heavy plates refers to a green and efficient production method in which continuously cast billets are directly fed into the heating furnace in the rolling zone after manufacturing without being cooled offline. Once heated to the target temperature, they are rolled. As an important technology for energy conservation and emission reduction in the steel industry, it has always received widespread attention. The advantages of this production method include high billet entry temperature, high heating efficiency, low furnace gas consumption, and strong production continuity. It is of great significance for improving the production efficiency of medium and heavy plates, enhancing quality assurance capabilities, and reducing manufacturing costs.

[0003] Low-alloy medium and heavy plates with strength ranging from 235MPa to 420MPa are characterized by small batches, diverse specifications, and short delivery cycles. They are widely used in construction, urban bridges, wind turbine towers, boilers and pressure vessels, shipbuilding, and other fields, holding an important position among medium and heavy plate products. Over the years, most steel mills in my country have continuously improved their quality assurance capabilities for this type of product. To further improve production efficiency, reduce manufacturing costs, and decrease energy consumption, most medium and heavy plate production lines adopt hot-charging and hot-feeding processes, which effectively improve production efficiency, reduce energy consumption, and shorten delivery cycles.

[0004] However, under hot charging and delivery conditions, the design of furnace grouping for the continuous casting process of low-alloy medium-thick plates still relies mainly on manual experience and offline methods. This is labor-intensive, and the design principles for continuous casting process grouping are simply determined based on a single alloying element, resulting in a very crude process. For example, the furnace grouping plan for continuous casting is determined by the decreasing or increasing content of C or Mn. The strength and toughness of the rolled product, the specifications of the rolled steel plate, and other constraints during billet assembly are not considered, leading to numerous problems such as poor continuity of the rolling process, large leaps in the rolling process, high energy consumption, low equipment utilization, low production efficiency, and unsuitable mechanical properties. Therefore, under the fast production pace, the hot charging and delivery process cannot effectively support the improvement of key technical indicators and is difficult to adapt to the needs of large-scale customized production of personalized medium-thick plate products. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a data model-driven method for grouping furnaces in hot-charging and hot-delivery continuous casting of low-alloy medium-thick plates. This method can improve the continuity of chemical composition between furnaces during the casting period of low-alloy medium-thick plates under hot-charging and hot-delivery conditions, reduce chemical composition fluctuations, reduce the number of continuously cast and mixed-cast billets between furnaces, and avoid "insufficient" or "excessive" composition in continuously cast and mixed-cast billets.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A data model-driven method for grouping low-alloy medium-thick plate continuous casting furnaces includes the following steps:

[0008] Construct a dataset of furnace position sequence and in-furnace billet assembly data in the continuous casting process of low alloy medium-thick plates under hot charging and hot delivery conditions.

[0009] Determine the total number of heats required for all custom steel grades; determine the characteristic value of the heat grouping order for each custom steel grade during the casting period based on the influence of each parameter in the heat position sequence data on the grouping order of each heat; sort the characteristic values ​​of the heat grouping order to determine the grouping order of each heat for each custom steel grade during the casting period.

[0010] Determine the total number of continuously cast billets required for all customized steel grades. Match all billets corresponding to customized steel grades into the grouping sequence of each heat according to the in-furnace billet grouping dataset. Determine the billet grouping position feature values ​​in each smelting heat during the casting period according to the continuity of the rolling process. Sort the billet grouping position feature values ​​to determine the position order of each billet.

[0011] Furthermore, the process of constructing the heat position sequence data set in the grouping of the low alloy medium and heavy plate continuous casting process is as follows: using the content of each alloy element in the smelting steel grade, order weight, smelting cost index of customized steel grade and delivery date to construct the data set required for the heat position sequence;

[0012] The data set required for the furnace position sequence is:

[0013]

[0014] in, The planned order weight for the i-th order in the k-th contract;

[0015] The delivery date difference index for the i-th steel grade in the k-th contract;

[0016] Let i be the manufacturing cost index for the i-th customized steel grade in the k-th contract;

[0017] The weight percentage of alloying elements for the i-th customized steel grade in the k-th contract;

[0018] The influence of alloy composition on the characteristic value of furnace grouping sequence during the casting period in the continuous casting process; The impact of contract delivery date differences on the characteristic value of furnace grouping sequence during the continuous casting process; The influence of the customized steel grade smelting cost index on the characteristic value of the furnace grouping sequence during the continuous casting process.

[0019] Furthermore, the process of constructing the in-furnace billet data set in the low-alloy medium-thick plate continuous casting process grouping is as follows: the data set required for the in-furnace billet grouping is constructed using the customized steel plate width difference index, customized steel plate thickness difference index, customized steel plate strength, customized steel plate toughness, finishing mill start rolling temperature and finishing mill finish rolling temperature.

[0020] The data set required for in-furnace billet assembly is as follows:

[0021]

[0022] in, For the k-th contract and the i-th steel type The m-th billet in a furnace; where k, i, and m are all positive integers; The steel delivery mark for the i-th steel type ordered in the k-th contract;

[0023] For the k-th contract and the i-th steel type The width difference index of the rolled steel plate of the m-th billet in a furnace; This is the width difference penalty coefficient;

[0024] For the k-th contract and the i-th steel type The thickness difference index of the rolled steel plate of the m-th billet in a furnace; This is the thickness difference penalty coefficient;

[0025] For the k-th contract and the i-th steel type The yield strength variation index of the rolled steel plate of the m-th billet in the furnace; This is the penalty coefficient for differences in intensity levels;

[0026] For the k-th contract and the i-th steel type The toughness grade difference index of the rolled steel plate of the m-th billet in the furnace; This is the penalty coefficient for differences in toughness levels;

[0027] For the k-th contract and the i-th steel type The temperature difference index between the finishing and initial rolling of the m-th billet in a furnace; This is the penalty coefficient for the difference in rolling temperature.

[0028] For the k-th contract and the i-th steel type The temperature difference index between the finishing and final rolling of the m-th billet in a furnace; This is the penalty coefficient for the difference in final rolling temperature.

[0029] Furthermore, the process of determining the total number of heats required to smelt all customized steel grades includes:

[0030] Group all I customized steel types from K order contracts into a set C = {g_t} ki};g_t ki Let N be the smelting symbol for the i-th steel grade in the k-th order contract; the total number of furnaces required to smelt all i-th customized steel grades in the k order contracts is N.

[0031]

[0032] Among them, t ki Let represent the order weight of the i-th steel grade in the k-th order contract; The number of steelmaking furnaces required for the i-th steel grade; ω is an integer greater than 0 and rounded up; ω is the rated output of the converter.

[0033] Furthermore, the characteristic value of the grouping order of each heat of any customized steel grade during the casting period is determined based on the influence of each parameter in the heat position sequence data on the grouping order of each heat. The process includes:

[0034]

[0035] To ensure the original furnace position sequence for all customized steel grades, adjacent furnaces must have the same or similar chemical composition.

[0036]

[0037] The effect of the content of each alloying element on the grouping sequence of each furnace batch;

[0038] To mitigate the impact of delivery cycles on the grouping sequence of each heat during the casting period, it is required that the delivery dates of all order contracts during the continuous casting period be the same or similar.

[0039] To assess the impact of manufacturing costs on the grouping sequence of each heat, the required selling price of ordered steel grades must be greater than or equal to the selling price of surplus steel without contracts.

[0040] Let the silicon content in the steel be the i-th steel grade from k contracts.

[0041] Let be the chromium content of the i-th steel grade in k contracts;

[0042] Let the molybdenum content be the molybdenum content of the i-th steel grade in k contracts;

[0043] Let vanadium content be the vanadium content of the i-th steel grade in k contracts;

[0044] Let the manganese content be the value of the i-th steel grade in k contracts.

[0045] Let the nickel content be the value of the i-th steel grade in k contracts.

[0046] Let the copper content of the i-th steel grade in k contracts be denoted by ;

[0047] Let the carbon content of the i-th steel grade in k contracts be denoted as .

[0048] Let be the niobium content of the i-th steel grade in k contracts;

[0049] Let represent the titanium content of the i-th steel grade in k contracts.

[0050] Furthermore, the process of determining the grouping order of each heat for any custom steel grade within the casting period by sorting the grouping sequence characteristic values ​​of each heat includes: sorting the grouping sequence characteristic values ​​of each heat... The order of increasing or decreasing determines the grouping sequence of the heats for the i-th steel grade ordered under the k-th contract during the casting period, denoted as . The above iterative process determines the grouping order of each heat during the casting period for K orders, I steel grades, and a total of N heats of molten steel.

[0051] Furthermore, the thickness and width of all continuously cast billets are exactly the same during the casting period.

[0052] Furthermore, the process of determining the total number of continuously cast billets required for all custom steel grades includes:

[0053] Collect all continuously cast billets corresponding to all I customized steel grades in K order contracts; Let m be the number of continuously cast billets for the i-th steel grade in the k-th order contract; the total number of continuously cast billets required for all I customized steel grades in the k order contracts is M; the formula for calculating M and the conditions it meets are as follows:

[0054]

[0055] in, The total number of actual continuously cast billets required for the i-th customized steel grade in the k-th order contract; ρ represents the output quantity of the i-th customized steel grade under the k-th contract; θThe weight loss coefficient for converting the output of the i-th customized steel grade in the k-th contract into the weight of the cast billet, where 0 < ρ θ <1; 7.85 refers to the density of molten steel, in g / cm³. 3 ; The width of the billet corresponding to the i-th customized steel grade in the k-th contract; The length of the billet corresponding to the i-th customized steel grade in the k-th contract; The thickness of the billet corresponding to the i-th customized steel grade in the k-th contract; ξ represents the number of smelting furnaces for the i-th customized steel grade in the k-th contract; ξ represents the segmented cutting length coefficient of the continuously cast billet.

[0056] Furthermore, the process of determining the billet assembly position characteristic values ​​in each smelting furnace during the casting period according to the continuity of the rolling process includes:

[0057] Determine the characteristic value of the assembly sequence of m billets in the furnace for the i-th customized steel grade of the k-th contract.

[0058]

[0059]

[0060] Furthermore, the process of sorting the positional feature values ​​of the billet grouping to determine the positional order of each billet includes: calculating the furnace grouping sequence feature values ​​of all customized steel grades in any group during continuous casting; determining the furnace grouping sequence position of all billets of a specific furnace number for any steel grade in the continuous casting grouping sequence according to a preset order; and iterating the above steps to obtain the positional order of each billet.

[0061] The effects described in the invention are merely those of the embodiments, and not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0062] This invention proposes a data model-driven method for grouping heats in continuous casting of low-alloy medium-thick plates. The method includes the following steps: constructing a heat position sequence dataset and an in-furnace billet assembly dataset for the continuous casting process of low-alloy medium-thick plates under hot charging and delivery conditions; determining the total number of heats required for all custom steel grades; determining the heat grouping sequence characteristic value for each custom steel grade during the casting period based on the influence of various parameters in the heat position sequence dataset on the grouping sequence of each heat; sorting the heat grouping sequence characteristic values ​​to determine the heat grouping sequence for each custom steel grade during the casting period; determining the total number of continuously cast billets required for all custom steel grades; matching all custom steel grade corresponding billets to the heat grouping sequence based on the in-furnace billet assembly dataset; determining the billet assembly position characteristic value within each smelting heat during the casting period according to the continuity of the rolling process; and sorting the billet assembly position characteristic values ​​to determine the position sequence of each billet. This invention can improve the continuity of chemical composition between furnaces during the casting period of low alloy medium-thick plates under hot delivery and charging conditions, reduce chemical composition fluctuations, reduce the number of continuous casting billets and mixed casting billets between furnaces, and avoid "insufficient" or "excessive" composition of continuous casting billets and mixed casting billets.

[0063] This invention proposes a data model-driven method for grouping heats in continuous casting of low-alloy medium-thick plates. Considering key data such as order weight, smelting cost index of customized steel grades, and delivery time difference index, a characteristic value model for the heat grouping sequence in the continuous casting process is constructed. Based on these characteristic values, the heat sequence position of each customized steel grade during the casting period is determined. In the rolling process, a characteristic value model for the in-furnace billet grouping sequence is established, integrating key data elements such as the width and thickness of the rolled steel plate, strength and toughness grades, and rolling process. This model determines the sequential position of all billets in the order during the casting period, effectively avoiding large jumps in quality grade, size specifications, and rolling process parameters during the hot-charged continuous casting billet rolling process, thereby improving the continuity and stability of production.

[0064] When applied to actual production, this invention can take into account multiple highly relevant factors, such as the C, Si, Mn, Cr, Ni, Mo, Nb, V, and Ti elements contained in the smelted steel, order weight, smelting cost of customized steel, delivery time, product strength and toughness grades, steel plate thickness and width specifications, and rolling cycle during the rolling process. It can construct a grouping model for the hot-feeding and hot-charging continuous casting process of low-alloy medium-thick plates, replacing the relatively crude furnace grouping method for the continuous casting process of low-alloy medium-thick plates that relies on manual experience.

[0065] Under hot charging and hot delivery process conditions, this invention can improve the continuity of the rolling process for products with strength of 235MPa-420MPa, reduce rolling process jumps, improve equipment utilization, significantly improve production line efficiency and product quality control capabilities, and significantly enhance the ability to customize personalized medium and heavy plate products on a large scale. Attached Figure Description

[0066] like Figure 1 This is a flowchart of a data model-driven method for grouping low-alloy medium-thick plate continuous casting furnaces according to Embodiment 1 of the present invention.

[0067] like Figure 2 This is a schematic diagram of the grouping of each heat in the N-heat continuous casting process of the present invention as proposed in Embodiment 1;

[0068] like Figure 3 This is a schematic diagram illustrating the sequence of M billets within a specific heat batch during the casting period, as proposed in Embodiment 1 of the present invention. Detailed Implementation

[0069] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure of the invention, components and arrangements of specific examples are described below. Furthermore, reference numerals and / or letters may be repeated in different examples. This repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. Descriptions of well-known components, processing techniques, and processes are omitted in this invention to avoid unnecessarily limiting the invention.

[0070] Example 1

[0071] Embodiment 1 of this invention proposes a data model-driven method for grouping furnace batches in continuous casting of low-alloy medium-thick plates. Based on multiple highly relevant information such as the elements C, Si, Mn, Cr, Ni, Mo, Nb, V, and Ti contained in the smelted steel grade, order weight, smelting cost of customized steel grades, delivery time, as well as the product strength and toughness grades, steel plate thickness and width specifications, and rolling process during the rolling process, a data model-driven grouping model for the hot-charging and hot-feeding continuous casting process of low-alloy medium-thick plates is constructed to achieve a more scientific and reasonable grouping of furnace batches in the continuous casting process of low-alloy medium-thick plates.

[0072] like Figure 1 This is a flowchart of a data model-driven method for grouping low-alloy medium-thick plate continuous casting furnaces according to Embodiment 1 of the present invention.

[0073] In step S100, a dataset of furnace position sequence and a dataset of in-furnace billet assembly are constructed in the grouping of low alloy medium-thick plate continuous casting process under hot charging and hot delivery conditions.

[0074] The data set related to the furnace position sequence in the continuous casting process of low alloy medium and heavy plates should at least include the weight percentage (wt, %) of C, Si, Mn, Cr, Ni, Mo, Nb, V, and Ti elements contained in the steel grade being smelted, as well as information such as order weight, smelting cost index of customized steel grades, and delivery time.

[0075] The data set A required for the furnace position sequence is:

[0076]

[0077] in, This represents the planned order weight for the i-th order under the k-th contract, in tons.

[0078] This is the delivery date difference index for the i-th steel grade in the k-th contract, which is related to the difference in delivery date between the i-th steel grade in the k-th contract and any other steel grade ordered in the k-th contract; the value of this item is 1 when the difference is less than ε, and 0 when the difference is greater than ε.

[0079] This is the manufacturing cost index for the i-th customized steel grade in the k-th contract; it is related to the difference between the selling price of the i-th steel grade in the k-th contract and the spot selling price of molten steel without a contract when the steel grade is insufficient to fill a whole furnace; the value of this item is 0 when the difference between the two is less than 0, and the value of this item is 1 when the difference is greater than 0.

[0080] The weight percentage of alloying elements for the i-th customized steel grade in the k-th contract;

[0081] The influence of alloy composition on the characteristic value of furnace grouping sequence during the casting period in the continuous casting process; The impact of contract delivery date differences on the characteristic value of furnace grouping sequence during the continuous casting process; The influence of the customized steel grade smelting cost index on the characteristic value of the furnace grouping sequence during the continuous casting process.

[0082] The data set related to the in-furnace billet assembly in the continuous casting process of low alloy medium and heavy plates includes information such as the width and thickness difference index of customized steel plates, the strength ReL and toughness Akv of customized steel plates, the initial rolling temperature Tsta of finishing mill, and the final rolling temperature Tend of finishing mill.

[0083] The data set required for in-furnace billet assembly is as follows:

[0084]

[0085] in, For the k-th contract and the i-th steel type The m-th billet in a furnace; where k, i, and m are all positive integers; The steel delivery mark for the i-th steel type ordered in the k-th contract;

[0086] The k-th contract, the i-th steel type The width difference index of the rolled steel plate from the m-th billet in the furnace, compared with the... It is related to the difference in the width of any other order in the furnace batch. The width difference penalty coefficient is used when the difference between the two is less than ε. When the value is 1, and the difference is greater than ε. The value is 0. It is required that the width difference of all rolled steel plates from the same cast billet in the same furnace should not be too large.

[0087] The k-th contract, the i-th steel type The thickness difference index of the rolled steel plate from the m-th billet in the furnace, compared with the thickness difference index of the m-th billet in the furnace. It is related to the difference in thickness of any other order in the same batch. This is the penalty coefficient for the thickness difference; when the difference between the two is less than ε... When the value is 1, and the difference is greater than ε. The value is 0. The thickness difference of all steel plates rolled from the same cast billet in the same furnace must not be too large.

[0088] The k-th contract, the i-th steel type The yield strength variation index of the m-th billet rolled steel plate in the furnace is similar to that of the m-th billet. It is related to the difference in yield strength of any other ordered steel plate in the same heat. This is the penalty coefficient for the difference in strength level; when the difference between the two is less than 50 MPa. When the value is 1 and the difference is greater than 60 MPa The yield strength should be 0. This requires that the difference in yield strength between all steel plates rolled from the same cast billet in the same furnace should not be too large.

[0089] The k-th contract, the i-th steel type The toughness grade difference index of the rolled steel plate from the m-th billet in the furnace, compared with the... It is related to the difference in toughness grade between any other ordered steel plate in the same heat. The penalty coefficient for differences in toughness grade is calculated when the experimental temperature is less than 20℃. The value is 1, and the experimental temperature is greater than 20℃. The value is 0, which requires that the difference in toughness grade between all the steel plates rolled from the same billet in the same furnace should not be too large;

[0090] The k-th contract, the i-th steel type The temperature difference index of the finishing rolling of the m-th billet rolled steel plate in the furnace is compared with that of the first... It is related to the difference in the initial rolling temperature of any other ordered steel plate in the same heat. This is the penalty coefficient for the temperature difference during rolling, when the temperature is less than 20℃. The value is 1, and the experimental temperature is greater than 20℃. The value is 0. It is required that the rolling process fluctuations of all cast steel plates produced in the same furnace should not be too large.

[0091] The k-th contract, the i-th steel type The temperature difference index between the finishing and final rolling of the m-th billet rolled steel plate in the furnace is compared with that of the m-th billet. It is related to the difference in final rolling temperature of any other ordered steel plate in the same heat. This is the penalty coefficient for the temperature difference in final rolling. When the temperature is less than 20℃ and the rolling mode adopts controlled final rolling... The value is 1 when the experimental temperature is greater than 20℃ or the rolling mode is only controlled rolling temperature. The value is 0. It is required that the rolling process fluctuations of all cast steel plates produced in the same furnace should not be too large.

[0092] The strength grades of products produced by hot delivery and hot packaging include 7 strength grades: 235MPa, 265MPa, 295MPa, 345MPa, 355MPa, 390MPa, and 420MPa. The toughness grades include 4 thermal grades: B (20℃), C (0℃), D (-20℃), and E (-40℃).

[0093] In step S200, the total number of heats required for all customized steel grades is determined. Based on the influence of various parameters in the heat position sequence data on the heat grouping order, the characteristic value of the heat grouping order for each customized steel grade during the casting period is determined. The heat grouping order characteristic values ​​are then sorted to determine the heat grouping order for each customized steel grade during the casting period. For example... Figure 2 This is a schematic diagram of the grouping of each heat in the N-heat continuous casting process of the present invention, as proposed in Embodiment 1 of the present invention.

[0094] The process of determining the total number of heats required to smelt all custom steel grades includes:

[0095] Group all I customized steel types from K order contracts into a set C = {g_t} ki};g_t ki Let N be the smelting symbol for the i-th steel grade in the k-th order contract; the total number of furnaces required to smelt all i-th customized steel grades in the k order contracts is N.

[0096]

[0097] Among them, t ki Let represent the order weight of the i-th steel grade in the k-th order contract; The number of steelmaking furnaces required for the i-th steel grade; ω is the integer with rounding and greater than 0; ω is the rated output of the converter.

[0098] When the sum of the theoretical weights of the continuously cast billets grouped into a certain furnace is less than the minimum tapping rate ω of that furnace.min When this happens, excess material is added to the furnace until the minimum steel output ω is reached. min ;

[0099] N is a positive integer not exceeding the maximum number of casting furnaces in continuous casting, and N≤100.

[0100] Based on the influence of various parameters in the furnace position sequence dataset on the grouping order of each furnace, the characteristic values ​​of the grouping order of each furnace during the casting period for any custom steel grade are determined. The calculation method is as follows:

[0101]

[0102] in:

[0103] To ensure the original furnace position sequence for all customized steel grades, adjacent furnaces must have the same or similar chemical composition.

[0104]

[0105] The effect of the content of each alloying element on the grouping sequence of each furnace batch;

[0106] To mitigate the impact of delivery cycles on the grouping sequence of each heat during the casting period, it is required that the delivery dates of all order contracts during the continuous casting period be the same or similar.

[0107] To assess the impact of manufacturing costs on the grouping sequence of each heat, the required selling price of ordered steel grades must be greater than or equal to the selling price of surplus steel without contracts.

[0108] Let the silicon content in the steel be the i-th steel grade from k contracts.

[0109] Let be the chromium content of the i-th steel grade in k contracts;

[0110] Let the molybdenum content be the molybdenum content of the i-th steel grade in k contracts;

[0111] Let vanadium content be the vanadium content of the i-th steel grade in k contracts;

[0112] Let the manganese content be the value of the i-th steel grade in k contracts.

[0113] Let the nickel content be the value of the i-th steel grade in k contracts.

[0114] Let the copper content of the i-th steel grade in k contracts be denoted by ;

[0115] Let the carbon content of the i-th steel grade in k contracts be denoted as .

[0116] Let be the niobium content of the i-th steel grade in k contracts;

[0117] Let represent the titanium content of the i-th steel grade in k contracts.

[0118] In accordance with The order of heats for the i-th steel grade ordered under the k-th contract is determined by an incremental or decremental method, denoted as . Specific calculation methods include:

[0119] (1) Calculate the steel output markers for all i orders out of K orders during the casting period. Corresponding characteristic values ​​of the grouping sequence of each furnace The comparison shows that the largest number of all custom steel grades in all orders is... The grouping sequence of each heat during the casting period for this steel grade is determined as the first grouping sequence within this casting period, denoted as .

[0120] (2) Calculation

[0121] The calculated value above is denoted as: δ 11 δ 12 δ 13 ,...,δ 1i , …, δ 21 δ 22 δ 23 ,...,δ 2i , …, δ k,1 δ k,2 , …, δ ki-1 δ K1 δ K2 , …, δ KI Compare δ 11 δ 12 δ 13 ,...,δ 1i , …, δ 21 δ 22 δ 23 ,...,δ 2i , …, δ k1 δ k2 , …, δ ki-1 δ K1 δ K2 , …, δKI Size, minimum δ min δ min The corresponding original position order is *g_t mj The grouping order of the j-th steel grade in the m-th order during the casting period is determined as the second smelting sequence, denoted as .

[0122] (3) Calculation

[0123] The calculated value above is denoted as: δ * 11 δ * 12 δ * 13 ,...,δ * 1i , …, δ * 21 δ * 22 δ * 23 ,...,δ * 2i , …, δ * m1 δ * m2 , …, δ * mj-1 δ * mj+1 , …, δ * mi-1 δ * mi+1 , …, δ * K1 δ * K2 , …, δ * K,I Compare δ * 11 δ * 12 δ * 13 ,...,δ * 1i , …, δ * 21 δ * 22 δ * 23 ,...,δ *2i , …, δ * m1 δ * m2 , …, δ * mj-1 δ * mj+1 , …, δ * mi-1 δ * mi+1 , …, δ * K1 δ * K2 , …, δ * KI The value of δ is obtained. * min δ * min The corresponding original position order is *g_t rs The grouping sequence of each furnace during the casting period is determined as the 3rd smelting sequence, denoted as...

[0124] (4) Iteratively calculate the above process to determine the grouping order of each heat of N heats of molten steel for K orders and I steel grades during the casting period.

[0125] In step S300, the total number of continuously cast billets required for all customized steel grades is determined. Based on the in-furnace billet dataset, all billets corresponding to the customized steel grades are matched to the grouping sequence of each heat cycle. The billet grouping position characteristic values ​​within each smelting heat cycle are determined according to the continuity of the rolling process. The billet grouping position characteristic values ​​are then sorted to determine the positional order of each billet. Figure 3 This is a schematic diagram illustrating the sequence of M billets within a specific heat batch during the casting period, as proposed in Embodiment 1 of the present invention.

[0126] The process of determining the total number of continuously cast billets required for all custom steel grades includes: collecting all continuously cast billets corresponding to all I custom steel grades in K order contracts; Let m be the number of continuously cast billets for the i-th steel grade in the k-th order contract; let M be the total number of continuously cast billets required for all I custom steel grades in the k order contracts; M satisfies the condition that M is greater than or equal to the theoretical number of billets calculated from the order contract weight and dimensions, and

[0127] The calculation method for M and the conditions that must be met are as follows:

[0128]

[0129] in, The total number of actual continuously cast billets required for the i-th customized steel grade in the k-th order contract; Let i be the output quantity of the i-th customized steel grade in the k-th contract;

[0130] ρ θ The weight loss coefficient for converting the output of the i-th customized steel grade in the k-th contract into the weight of the cast billet, where 0 < ρ θ <1;

[0131] 7.85 is the density of molten steel, in g / cm³. 3 ;

[0132] The width of the billet corresponding to the i-th customized steel grade in the k-th contract;

[0133] The length of the billet corresponding to the i-th customized steel grade in the k-th contract;

[0134] The thickness of the billet corresponding to the i-th customized steel grade in the k-th contract;

[0135] The number of smelting furnaces for the i-th customized steel grade in the k-th contract;

[0136] ξ is the length loss coefficient for segmented cutting of the continuously cast billet.

[0137] Following the principle of increasing or decreasing rolling dimensions and rolling process differences, the billets corresponding to all customized steel grades in the collected order contracts are matched to the completed heat batch grouping Ψ*gt. The order is based on the strength grades from 235MPa to 265MPa to 295MPa to 345MPa to 355MPa to 390MPa to 420MPa, and the toughness grades from B (20℃) to C (0℃) to D (-20℃) to E (-40℃). ki -x pl Inside the furnace.

[0138] The process of determining the characteristic values ​​of the billet assembly position in each smelting furnace during the casting period according to the continuity of the rolling process includes:

[0139] Determine the characteristic value of the assembly sequence of m billets in the furnace for the i-th customized steel grade of the k-th contract. The calculation method is as follows:

[0140]

[0141] During the casting period, all continuously cast billets must have the same thickness and width, while the width and thickness of rolled steel plates can be different.

[0142] Calculation of the continuous casting process In the group Characteristic values ​​of the in-furnace billet assembly sequence corresponding to steel grade The steel grades were determined according to the ascending order principle. All cast billets in furnace number l in the first The order of billet assembly within the furnace in each grouping sequence. Each billet in the [number]th [stage / section]... The order of positions is The steel grade was determined as follows All cast billets with a furnace quantity of l are grouped in the continuous casting sequence as follows: The location.

[0143] Calculation of the continuous casting process In the group Sequential characteristic values ​​of steel grades corresponding to cast billets The steel grades were determined according to the ascending order principle. All cast billets in furnace number l in the first The order of billet assembly within the furnace in each grouping sequence. Each billet in the [number]th [stage / section]... The order of billet assembly in the furnace during grouping is as follows: The steel grade was determined as follows All cast billets with a furnace quantity of l are grouped in the continuous casting sequence as follows: The location.

[0144] When the sum of the theoretical weights of all cast billets is not greater than the first... Maximum steel output of the furnace When the output is not less than the minimum output of the furnace, the M billets will be matched to the casting period in the following grouping order: Within N furnace cycles. Otherwise, the above calculation process terminates.

[0145] Repeat the above process to determine the steel grade under hot delivery and charging conditions. All billets with a furnace number of l are grouped in the continuous casting sequence as follows: The furnace sequence position is determined to match all the custom steel billets required in the order to each furnace.

[0146] This invention, based on multiple highly relevant factors such as the elements C, Si, Mn, Cr, Ni, Mo, Nb, V, and Ti contained in the smelted steel, order weight, smelting cost of customized steel, delivery time, as well as the product strength and toughness grades, steel plate thickness and width specifications, and rolling process during the rolling process, constructs a data model-driven grouping model for the hot-feeding and hot-charging continuous casting process of low-alloy medium-thick plates, to achieve a more scientific and reasonable grouping of heats in the continuous casting process of low-alloy medium-thick plates.

[0147] This invention can improve the continuity of chemical composition between furnaces during the casting period of low alloy medium-thick plates under hot delivery and charging conditions, reduce chemical composition fluctuations, reduce the number of continuous casting billets and mixed casting billets between furnaces, and avoid "insufficient" or "excessive" composition of continuous casting billets and mixed casting billets.

[0148] Embodiment 1 of this invention proposes a data model-driven method for grouping furnace batches in continuous casting of low-alloy medium-thick plates. Considering key data such as order weight, smelting cost index of customized steel grades, and delivery time difference index, a characteristic value model for the furnace batch grouping sequence in the continuous casting process is constructed. Based on the characteristic values, the furnace batch sequence position of each customized steel grade during the casting period is determined. In the rolling process, a characteristic value model for the in-furnace billet grouping sequence is established, integrating key data elements such as the width and thickness of the rolled steel plate, strength and toughness grades, and rolling process. This model determines the sequential position of all billets in the order during the casting period, effectively avoiding large jumps in quality grade, size specifications, and rolling process parameters during the hot-charged continuous casting billet rolling process, thereby improving the continuity and stability of production.

[0149] Embodiment 1 of this invention proposes a data model-driven furnace grouping method for low-alloy medium-thick plate continuous casting, which can be applied to actual production. It can consider multiple highly relevant information such as the elements C, Si, Mn, Cr, Ni, Mo, Nb, V, and Ti contained in the steel grade, order weight, smelting cost of customized steel grades, delivery time, as well as the product strength and toughness grade, steel plate thickness and width specifications, and rolling cycle during the rolling process. It constructs a grouping model for the hot-delivery and hot-charging low-alloy medium-thick plate continuous casting process, replacing the relatively crude furnace grouping method for low-alloy medium-thick plate continuous casting process that relies on manual experience.

[0150] Under hot charging and hot delivery process conditions, this invention can improve the continuity of the rolling process for products with strength of 235MPa-420MPa, reduce rolling process jumps, improve equipment utilization, significantly improve production line efficiency and product quality control capabilities, and significantly enhance the ability to customize personalized medium and heavy plate products on a large scale.

[0151] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that the elements inherent in a process, method, article, or apparatus that includes a list of elements are included. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Additionally, portions of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0152] While specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art can make other modifications or variations based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A data model-driven method for grouping low-alloy medium-thick plate continuous casting furnaces, characterized in that, Includes the following steps: Construct a dataset of furnace position sequence and in-furnace billet assembly data in the continuous casting process of low alloy medium-thick plates under hot charging and hot delivery conditions. Determine the total number of heats required for all custom steel grades, and determine the characteristic value of the heat grouping order of any custom steel grade during the casting period based on the influence of each parameter in the heat position sequence data on the grouping order of each heat. The grouping sequence characteristic value of each heat is used to determine the grouping sequence of each heat during the casting period for any custom steel grade. The characteristic value of the grouping order of each heat of any customized steel grade during the casting period is determined by analyzing the influence of various parameters in the heat position sequence dataset on the grouping order of each heat. The method for determining it is as follows: in: The effect of the content of each alloying element on the grouping sequence of each furnace batch; The impact of delivery cycle on the grouping sequence of each heat during the casting period; The impact of manufacturing costs on the grouping sequence of each furnace batch; Let the silicon content in the steel be the i-th steel grade from k contracts. Let be the chromium content of the i-th steel grade in k contracts; Let the molybdenum content be the molybdenum content of the i-th steel grade in k contracts; Let vanadium content be the vanadium content of the i-th steel grade in k contracts; Let the manganese content be the value of the i-th steel grade in k contracts. Let the nickel content be the value of the i-th steel grade in k contracts. Let the copper content of the i-th steel grade in k contracts be denoted by ; Let the carbon content of the i-th steel grade in k contracts be denoted as . Let be the niobium content of the i-th steel grade in k contracts; Let the titanium content be the value of the i-th steel grade in k contracts. The process of determining the grouping sequence of each heat for any custom steel grade within the casting period by sorting the grouping sequence characteristic values ​​of each heat includes: sorting the grouping sequence characteristic values ​​of each heat The order of increasing or decreasing determines the grouping sequence of the heats for the i-th steel grade ordered under the k-th contract during the casting period, denoted as . Iteration based on the characteristic values ​​of each furnace batch grouping order. The process of determining the grouping order of each heat of the i-th steel grade ordered under the k-th contract during the casting period by increasing or decreasing order; and determining the grouping order of each heat of N heats of molten steel for K orders and I steel grades during the casting period. Determine the total number of continuously cast billets required for all customized steel grades. Match all billets corresponding to customized steel grades into the grouping sequence of each heat according to the in-furnace billet grouping dataset. Determine the billet grouping position feature values ​​in each smelting heat during the casting period according to the continuity of the rolling process. Sort the billet grouping position feature values ​​to determine the position order of each billet.

2. The data model-driven grouping method for continuous casting furnaces of low-alloy medium-thick plates according to claim 1, characterized in that, The process of constructing the heat position sequence data set in the grouping of the low alloy medium and heavy plate continuous casting process is as follows: the data set required for the heat position sequence is constructed by using the content of each alloy element in the smelting steel grade, the order weight, the smelting cost index of the customized steel grade, and the delivery date. The data set required for the furnace position sequence is: in, For the i-th order weight plan of the k-th contract; The delivery date difference index for the i-th steel grade in the k-th contract; Let i be the manufacturing cost index for the i-th customized steel grade in the k-th contract; The weight percentage of alloying elements for the i-th customized steel grade in the k-th contract; The influence of alloy composition on the characteristic value of furnace grouping sequence during the casting period in the continuous casting process; The impact of contract delivery date differences on the characteristic value of furnace grouping sequence during the continuous casting process; The influence of the customized steel grade smelting cost index on the characteristic value of the furnace grouping sequence during the continuous casting process.

3. The data model-driven grouping method for continuous casting furnaces of low-alloy medium-thick plates according to claim 2, characterized in that, The process of constructing the in-furnace billet data set in the continuous casting process of low alloy medium and heavy plates is as follows: the data set required for the in-furnace billet is constructed using the custom steel plate width difference index, custom steel plate thickness difference index, custom steel plate strength, custom steel plate toughness, finishing mill start temperature and finishing mill finish temperature. The set of in-furnace billet assembly data is as follows: in, For the k-th contract and the i-th steel type The m-th billet in a furnace; where k, i, and m are all positive integers; The steel delivery mark for the i-th steel type ordered in the k-th contract; For the k-th contract and the i-th steel type The width difference index of the rolled steel plate of the m-th billet in a furnace; This is the width difference penalty coefficient; For the k-th contract and the i-th steel type The thickness difference index of the rolled steel plate of the m-th billet in a furnace; This is the thickness difference penalty coefficient; For the k-th contract and the i-th steel type The yield strength variation index of the rolled steel plate of the m-th billet in the furnace; This is the penalty coefficient for differences in intensity levels; For the k-th contract and the i-th steel type The toughness grade difference index of the rolled steel plate of the m-th billet in the furnace; This is the penalty coefficient for differences in toughness levels; For the k-th contract and the i-th steel type The temperature difference index between the finishing and initial rolling of the m-th billet in a furnace; This is the penalty coefficient for the difference in rolling temperature. For the k-th contract and the i-th steel type The temperature difference index between the finishing and final rolling of the m-th billet in a furnace; This is the penalty coefficient for the difference in final rolling temperature.

4. The data model-driven grouping method for continuous casting furnaces of low-alloy medium-thick plates according to claim 2, characterized in that, The process of determining the total number of heats required to smelt all custom steel grades includes: All I customized steel types in K order contracts are grouped into a set. ; Let N be the smelting symbol for the i-th steel grade in the k-th order contract; the total number of furnaces required to smelt all i-th customized steel grades in the k order contracts is N. in, Let represent the order weight of the i-th steel grade in the k-th order contract; The number of steelmaking furnaces required for the i-th steel grade; ω is an integer greater than 0 and rounded up; ω is the rated output of the converter.

5. The data model-driven grouping method for continuous casting furnaces of low-alloy medium-thick plates according to claim 3, characterized in that, All continuously cast billets have the same thickness and width during the casting period.

6. The data model-driven grouping method for continuous casting furnaces of low-alloy medium-thick plates according to claim 3, characterized in that, The process of determining the total number of continuously cast billets required for all custom steel grades includes: Collect all continuously cast billets corresponding to all I customized steel grades in K order contracts; The total number of continuously cast billets required for all I customized steel grades in K order contracts is M; the calculation method for M and the conditions for satisfying it are as follows: ； in, Let i be the output quantity of the i-th customized steel grade in the k-th contract; The weight loss coefficient is used to convert the output of the i-th customized steel grade in the k-th contract into the weight of the cast billet. ; This refers to the density of molten steel, expressed in g / cm³. 3 ; The width of the billet corresponding to the i-th customized steel grade in the k-th contract; The length of the billet corresponding to the i-th customized steel grade in the k-th contract; The thickness of the billet corresponding to the i-th customized steel grade in the k-th contract; The number of smelting furnaces for the i-th customized steel grade in the k-th contract; This is the length loss coefficient for segmented cutting of the continuously cast billet.

7. The data model-driven grouping method for continuous casting furnaces of low-alloy medium-thick plates according to claim 6, characterized in that, The process of determining the characteristic values ​​of the billet assembly position in each smelting furnace during the casting period according to the continuity of the rolling process includes: Determine the characteristic value of the assembly sequence of m billets in the furnace for the i-th customized steel grade of the k-th contract. The calculation method is as follows: 。 8. The data model-driven grouping method for continuous casting furnaces of low-alloy medium-thick plates according to claim 7, characterized in that, The process of sorting the positional feature values ​​of the billet assembly to determine the positional order of each billet includes: calculating the in-furnace assembly sequence feature values ​​corresponding to all customized steel grades in any group during continuous casting; determining the in-furnace assembly sequence position of all billets of any steel grade in the continuous casting grouping sequence according to a preset order; and iterating through the steps of calculating the in-furnace assembly sequence feature values ​​corresponding to all customized steel grades in any group during continuous casting and determining the in-furnace assembly sequence position of all billets of any steel grade in the continuous casting grouping sequence according to a preset order to obtain the positional order of each billet.

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