Method and system for tracing reasons of abnormal fluctuation of liquid level of crystallizer

Abstract

The invention relates to a crystallizer liquid level abnormal fluctuation reason tracing method and system, and belongs to the technical field of ferrous metallurgy continuous casting. The method comprises the steps that continuous casting production data are collected, and a crystallizer liquid level fluctuation index is calculated; determining a slag entrapment defect index, counting the number of crystallizer liquid level fluctuation indexes greater than or equal to the slag entrapment defect index on each continuous casting billet, establishing a judgment rule of slag entrapment severity, and obtaining a plurality of frequency bands corresponding to crystallizer liquid level fluctuation according to the collected crystallizer liquid level height and a discrete wavelet transform method; each frequency band comprises a frequency Rw and an amplitude Ra; when a peak value with the amplitude Ra larger than + / -3mm appears in the frequency band, abnormal fluctuation is judged; and the casting parameters corresponding to the frequency of the abnormal fluctuation of the liquid level of the crystallizer when slag entrapment occurs and the casting parameters corresponding to the frequency of the normal liquid level fluctuation are compared and analyzed, a judgment rule for the reason of the abnormal fluctuation of the liquid level of the crystallizer is established, and the reason of the abnormal fluctuation of the liquid level is traced.

Description

Technical Field

[0001] This invention relates to the field of continuous casting technology in iron and steel metallurgy, and in particular to a method and system for tracing the causes of abnormal fluctuations in the liquid level of a crystallizer. Background Technology

[0002] Automotive steel sheets are representative of high-quality sheet metal products, and their surface quality requirements are quite stringent. Automotive steel sheets are mainly produced through continuous casting, hot rolling, and / or cold rolling processes. During continuous casting, slag entrapment at the surface of the liquid crystallizer can reduce the surface quality of the automotive steel sheets.

[0003] In the continuous casting process, existing detection technologies cannot identify slag entrapment defects on the surface of continuously cast billets. Only after the billets are rolled into plates can surface linear defects caused by slag entrapment on the crystallizer surface be identified using a hot-rolled surface inspection system. Due to the low accuracy of identifying abnormal fluctuations in the crystallizer surface, steel companies machine-clean all continuously cast billets before sending them for rolling to meet customers' "zero tolerance" requirements for defects in steel coils. However, machine cleaning of continuously cast billets reduces metal yield. Therefore, simultaneously achieving the determination and tracing of abnormal fluctuations in the crystallizer surface, predicting slag entrapment defects on the surface of continuously cast billets, and performing pre-rolling treatment on defective billets are of great significance for improving the quality of continuously cast billets.

[0004] Currently, the amplitude of liquid level fluctuations in crystallizers is commonly used to analyze and evaluate the actual situation of liquid level fluctuations. However, its identification accuracy is low, making it difficult to identify the problem accurately. Furthermore, it cannot trace the cause of the fluctuations. Summary of the Invention

[0005] Based on the above analysis, the present invention aims to provide a method for tracing the cause of abnormal fluctuations in the liquid level of a crystallizer, in order to solve the problems of difficulty in accurately identifying abnormal fluctuations in the liquid level and the inability to trace the cause.

[0006] On the one hand, the present invention provides a method for tracing the cause of abnormal fluctuations in the liquid level of a crystallizer, comprising the following steps:

[0007] S1: Collect continuous casting production data and calculate the crystallizer liquid level fluctuation index;

[0008] S2: Compare and analyze the maximum value of the crystallizer liquid level fluctuation index calculated for each continuous casting billet, and the judgment result of the corresponding slag entrapment defect on the hot-rolled plate, so as to determine the critical threshold of the liquid level fluctuation index corresponding to the occurrence of slag entrapment defect in the continuous casting billet, i.e., the slag entrapment defect index.

[0009] S3: Count the number of crystallizer liquid level fluctuation indices that are greater than or equal to the slag entrapment defect index on each continuous casting billet, and compare and analyze the number of slag entrapment defects on the corresponding hot-rolled plate to establish a judgment rule for the severity of slag entrapment on the continuous casting billet.

[0010] S4: Based on the collected liquid level height in the crystallizer, calculate the crystallizer fluctuation height, and then obtain multiple frequency bands corresponding to the liquid level fluctuation in the crystallizer using the discrete wavelet transform method. Each frequency band includes frequency Rw and amplitude Ra. When a peak value with amplitude Ra greater than ±3mm appears in the frequency band, it is judged as an abnormal fluctuation.

[0011] S5: Compare and analyze the casting parameters corresponding to the frequency of abnormal fluctuations in the liquid level of the crystallizer when slag entrapment occurs with the casting parameters corresponding to the frequency of normal liquid level fluctuations, establish rules for determining the causes of abnormal fluctuations in the liquid level of the crystallizer, and trace the causes of abnormal fluctuations in the liquid level.

[0012] Furthermore, in step S4, the calculation formula for the discrete wavelet transform analysis method is as follows:

[0013]

[0014] In the formula, W t (a,b) represents the discrete wavelet transform of the liquid level fluctuation in the crystallizer; f(t) represents the liquid level fluctuation data in the crystallizer; ψ a,b (t) represents the wavelet basis function, a is the size factor, b is the translation factor, and t is the time node.

[0015] Furthermore, in step S4, the wavelet basis function is selected as the Sym5 wavelet basis function, and the maximum frequency after discrete wavelet transform is 5.0 Hz.

[0016] Furthermore, in step S4, the process parameters during crystallizer slag entrapment and normal casting include continuous casting billet production time, continuous casting billet width, crystallizer liquid level height, stopper rod position, casting speed, nozzle immersion depth, stopper rod argon flow rate, stopper rod argon back pressure, tundish top nozzle argon flow rate, tundish top nozzle argon back pressure, plate-to-plate argon flow rate, plate-to-plate argon back pressure, tundish tonnage, torque of the inner arc side drive rollers in different sector segments, and continuous casting billet reduction.

[0017] Furthermore, in step S5, based on the liquid level frequency and amplitude calculated by the discrete wavelet transform analysis method, the casting parameters corresponding to the abnormal fluctuation of the liquid level in the crystallizer and the normal fluctuation are compared to determine the rules for judging the cause of abnormal fluctuation of the liquid level in the crystallizer.

[0018] In step S5, the specific rules for determining the cause of abnormal fluctuations in the crystallizer liquid level are as follows:

[0019] When 0 < Rw < 0.1 Hz, the reason for the abnormal fluctuation of the liquid level is the change in casting speed, continuous casting slab width, tundish tonnage or submerged nozzle immersion depth; if there is no change in casting speed, continuous casting slab width, and tundish tonnage, the reason for the abnormal fluctuation of the liquid level is determined to be the shedding of nodules on the tip or body of the stopper rod, the inner wall of the upper nozzle or the submerged nozzle.

[0020] When 0.1 Hz ≤ Rw ≤ 0.4 Hz, the reason for the abnormal fluctuation of the liquid level is the unstable bulging between rolls.

[0021] When 0.4 Hz < Rw ≤ 1.0 Hz, the reason for the abnormal fluctuation of the liquid level is that the stopper rod, tundish upper nozzle or submerged nozzle are severely coked.

[0022] When 1.0 Hz < Rw ≤ 5.0 Hz, the reason for the abnormal fluctuation of the liquid level is the frequency, which is the natural frequency of the mold vibration.

[0023] Furthermore, in step S1, the mold liquid level fluctuation index = the maximum value of the slope of the mold liquid level multiplied by the maximum value of the amplitude of the mold liquid level, J index = K max ×ΔL;

[0024] J index is the mold liquid level fluctuation index; K max is the maximum value of the liquid level slope within the T time period; △L is the maximum value of the amplitude of the mold liquid level within the T time period.

[0025] Furthermore, the time sliding window T should satisfy 3 s ≤ T ≤ 5 s.

[0026] Even further, taking each continuous casting slab as a unit, extract the maximum value J of the liquid level fluctuation index during the casting time of the slab max ;

[0027] J max = max(J1, J2...J n )

[0028] Extract the number of times that the liquid level fluctuation index is greater than or equal to the slag entrainment defect index during the production time of a slab;

[0029]

[0030] In the formula, P is the sampling point corresponding to J index ; P tot is the total number of sampling points; J slag is the slag entrainment defect index, mm 2 / s.

[0031] Even further, the determination rule for the severity of slag entrainment in continuous casting slabs:

[0032] When J max < J slag it is determined as a normal billet and not machined cleaned;

[0033] When J slag ≤ J max ≤ J slag + 30 mm 2 / s and N ≤ 5, it is determined as mild slag entrainment and not machined cleaned;

[0034] When J slag ≤ J max ≤ J slag + 30 mm 2 / s and 5 < N, it is determined as mild slag entrainment and machined cleaned once;

[0035] When J slag + 30 mm 2 / s < J max ≤ J slag + 50 mm 2 / s and N ≤ 5, it is determined as moderate slag entrainment and not machined cleaned;

[0036] When J slag + 30 mm 2 / s < J max ≤ J slag + 50 mm 2 / s and 5 < N ≤ 15, it is determined as moderate slag entrainment and machined cleaned once;

[0037] When J slag + 30 mm 2 / s < J max ≤ J slag + 50 mm 2 / s and 15 < N, it is determined as moderate slag entrainment and machined cleaned twice;

[0038] When J slag + 50 mm<着 2 / s < J max and N ≤ 5, it is determined as severe slag entrainment and not machined cleaned;

[0039] When J slag + 50 mm 2 / s < J max and 5 < N ≤ 15, it is determined as severe slag entrainment and machined cleaned once;

[0040] When J slag + 50 mm 2 / s < J max and 15 < N, it is determined as severe slag entrainment and machined cleaned twice;

[0041] On the other hand, the present invention provides a system for tracing the causes of abnormal fluctuations in the liquid level of a crystallizer, which is used to implement the method described in the present invention.

[0042] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0043] 1. This invention uses the discrete wavelet transform method to analyze the time-frequency characteristics of crystallizer liquid level fluctuations, enabling the tracing of the causes of abnormal liquid level fluctuations and the optimization of process parameters, thus guiding production. This method comprehensively and multidimensionally solves the problems of abnormal liquid level fluctuations and slag entrapment in crystallizers, achieving stable control of crystallizer liquid level fluctuations and improving the quality of continuously cast billets, thereby reducing quality complaints and objections from users.

[0044] 2. This invention provides a method for identifying abnormal fluctuations in the liquid level of a crystallizer. It collects continuous casting production data, calculates the liquid level fluctuation index of the crystallizer, extracts the maximum value of the liquid level fluctuation index during the billet production time, and establishes a rule for determining the severity of slag entrainment in the continuous casting billet. This method can accurately identify slag entrainment defects and provide guidance for the pre-rolling treatment of the continuous casting billet.

[0045] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0046] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0047] Figure 1 This is a flowchart of the method for determining the degree of slag entrapment on the liquid surface of the crystallizer and the pre-rolling treatment of the continuously cast billet according to the present invention.

[0048] Figure 2 This is a flowchart of the method for tracing the causes of abnormal fluctuations in the liquid level of the crystallizer according to the present invention;

[0049] Figure 3 The figure shows the comparative analysis results of the crystallizer liquid level fluctuation index and the slag curling defect on the hot-rolled plate in Example 1.

[0050] Figure 4 This is a block diagram of the system structure of the present invention. Detailed Implementation

[0051] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which constitute a part of the present invention and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0052] In the continuous casting process, existing detection technologies cannot identify slag entrapment defects on the surface of continuously cast billets. Only after the billets are rolled into plates can surface linear defects caused by slag entrapment on the crystallizer surface be identified using a hot-rolled surface inspection system. Due to the low accuracy of identifying abnormal fluctuations in the crystallizer surface, steel companies machine-clean all continuously cast billets before sending them for rolling to meet customers' "zero tolerance" requirements for defects in steel coils. However, machine cleaning of continuously cast billets reduces metal yield. Therefore, achieving the ability to determine and trace the causes of abnormal fluctuations in the crystallizer surface, as well as predicting slag entrapment defects on the surface of continuously cast billets and performing pre-rolling treatment on defective billets, is of great significance for improving the quality of continuously cast billets.

[0053] Currently, the amplitude of liquid level fluctuations in crystallizers is commonly used to analyze and evaluate the actual situation of liquid level fluctuations. However, its identification accuracy is low, making it difficult to identify the problem accurately. Furthermore, it cannot trace the cause of the fluctuations.

[0054] Therefore, the present invention provides a method for tracing the cause of abnormal fluctuations in the liquid level of a crystallizer, comprising the following steps:

[0055] S1: Collect continuous casting production data and calculate the crystallizer liquid level fluctuation index;

[0056] S2: Compare and analyze the maximum value of the crystallizer liquid level fluctuation index calculated for each continuous casting billet, and the judgment result of the corresponding slag entrapment defect on the hot-rolled plate, so as to determine the critical threshold of the liquid level fluctuation index corresponding to the occurrence of slag entrapment defect in the continuous casting billet, i.e., the slag entrapment defect index.

[0057] S3: Count the number of crystallizer liquid level fluctuation indices that are greater than or equal to the slag entrapment defect index on each continuous casting billet, and compare and analyze the number of slag entrapment defects on the corresponding hot-rolled plate to establish a judgment rule for the severity of slag entrapment on the continuous casting billet.

[0058] S4: The discrete wavelet transform method is used to determine the crystallizer level fluctuation and corresponding process parameters when slag entrapment and normal liquid level fluctuation occur, and to obtain the frequency and amplitude information of each casting parameter when the crystallizer liquid level fluctuates abnormally and normally.

[0059] S5: Compare and analyze the casting parameters corresponding to the frequency of abnormal fluctuations in the crystallizer liquid level when slag entrapment occurs with the casting parameters corresponding to the frequency of normal liquid level fluctuations, establish rules for determining the causes of abnormal fluctuations in the crystallizer liquid level, and trace the causes of abnormal liquid level fluctuations.

[0060] It should be noted that this invention uses the discrete wavelet transform method to analyze the time-frequency characteristics of crystallizer liquid level fluctuations, enabling the tracing of the causes of abnormal liquid level fluctuations and the optimization of process parameters; thus guiding production. This method comprehensively and multidimensionally solves the problems of abnormal liquid level fluctuations and slag entrapment in the crystallizer, achieving stable control of crystallizer liquid level fluctuations and improving the quality of continuously cast billets, thereby reducing quality complaints and objections from users.

[0061] This invention provides a method for identifying abnormal fluctuations in the liquid level of a crystallizer. It collects continuous casting production data, calculates the liquid level fluctuation index of the crystallizer, extracts the maximum value of the liquid level fluctuation index during the billet production time, and establishes a rule for judging the severity of slag entrapment in the continuous casting billet. This method can accurately identify slag entrapment defects and provide guidance for the pre-rolling treatment of the continuous casting billet.

[0062] Specifically, in step S1, the continuous casting production data includes the continuous casting billet production time, continuous casting billet width, crystallizer liquid level height, stopper rod position, casting speed, tundish tonnage, torque of the inner arc side drive rollers of different sector segments, and continuous casting billet reduction. Taking the continuous casting billet as the unit, the above process parameters are matched with the billet number, casting machine flow number, furnace number, and steel grade according to the billet production time.

[0063] It should be noted that in this invention, the width of the continuously cast billet is equal to the width of the crystallizer; the stopper rod position refers to the change in the position of the stopper rod during the casting process. When nodules fall off, the stopper rod position curve will change abruptly. The torque of the drive roller on the inner arc side of different sector segments and the reduction of the continuously cast billet are used to evaluate whether the billet bulges.

[0064] Specifically, in step S1, the crystallizer liquid level fluctuation index = the maximum slope of the crystallizer liquid level multiplied by the maximum amplitude of the crystallizer liquid level, J index =K max ×ΔL.

[0065] J index K is the crystallizer liquid level fluctuation index. max ΔL is the maximum value of the liquid level slope within the time period T; ΔL is the maximum value of the crystallizer liquid level amplitude within the time period T.

[0066] Specifically, the formula for calculating the slope of the crystallizer liquid level is as follows:

[0067] In the formula, K is the slope of the liquid level in the crystallizer, mm / s; L n+1 The value of the crystallizer liquid level at the previous time, in mm; L n ΔT represents the actual liquid level in the crystallizer at the later time, in mm; ΔT is the casting time interval, in s, ΔT = 0.01~0.1s.

[0068] Specifically, in step S1, the liquid level difference is grouped using a time sliding window, and the maximum value K of the liquid level slope within the time interval T is extracted, with ΔT as the sliding time step. max =max(K1, K2...K) n ).

[0069] It should be noted that ΔT is the casting time interval, which is also the slip time step. In this invention, ΔT is controlled to be between 0.01 and 0.1 s. If ΔT is less than 0.01 s, the amount of data calculation becomes too large, affecting the calculation efficiency. If ΔT is greater than 0.1 s, data is easily missed, resulting in inaccurate calculation results.

[0070] Specifically, in step S1, the maximum amplitude of the crystallizer liquid level is calculated by using T as the time sliding window and ΔT as the sliding time step, to find the maximum value L of the liquid level within the time period T. max =max(L1, L2…L) n The minimum value of the liquid level L min =min(L1, L2…L) n The minimum liquid level is calculated by subtracting the maximum liquid level from the minimum liquid level within the group, i.e., ΔL = L. max -L min ;

[0071] In the formula, L max The maximum liquid level within time interval T, in mm; L min The minimum liquid level within time interval T, in mm.

[0072] It should be noted that in order to obtain a continuous and complete crystallizer level waveform, that is, the waveform change from trough to peak or from peak to trough, the time sliding window (T) should satisfy 3s≤T≤5s.

[0073] Specifically, taking each continuously cast billet as a unit, the maximum value J of the liquid level fluctuation index during the billet production time is extracted. max ;

[0074] J max =max(J1, J2...J n )

[0075] Extract the J values ​​that are greater than or equal to the slag contamination defect index during the production time of a slab. index The number of;

[0076]

[0077] In the formula, P is related to J index Corresponding sampling point; P tot J represents the total number of sampling points; slag The defect index for slag contamination is expressed in mm. 2 / s.

[0078] Preferably, in step S2, the types of slag rolling defects on the hot-rolled sheet include edge slag rolling, middle slag rolling, small slag rolling on the upper and lower surfaces of the hot-rolled sheet, and edge peeling and head peeling caused by steelmaking reasons.

[0079] It should be noted that different steel grades have different names for the linear slag rolling defects and peeling defects on the hot-rolled sheet. Here, it only refers to the defects caused by mold powder slag rolling.

[0080] Specifically, the value of the slag rolling defect index of the hot-rolled sheet corresponding to determining whether the continuous casting billet has slag rolling defects is J slag , J slag The value range is 20 - 40 mm 2 / s.

[0081] It should be noted that by comparing and analyzing the maximum value of the mold liquid level fluctuation index calculated for each continuous casting billet and the surface inspection situation of the slag rolling defects on the hot-rolled sheet, it can be given in a range according to the probability of slag rolling occurrence. For example, 20 mm 2 / s ≤ J slag < 40 mm 2 / s. The smaller the value of J slag , the higher the accuracy rate, but the false alarm rate is large; the larger the value of J slag , the lower the accuracy rate of accuracy rate, but the missed alarm rate is large.

[0082] The determination rule for the severity of the slag rolling defect of the continuous casting billet and the treatment of the casting billet before rolling is: for each continuous casting billet, combined with the size of J max during the production time of the whole continuous casting billet and the number N of J index ≥ J slag counted for the length of each continuous casting billet, comprehensively determine whether the current continuous casting billet needs to be subjected to machine cleaning operation.

[0083] Specifically, the determination rule for the severity of the slag rolling of the continuous casting billet:

[0084] When J max < J slag , it is determined as a normal casting billet and not subjected to machine cleaning;

[0085] When J slag ≤ J max ≤ J slag +30 mm 2 / s and N ≤ 5, it is determined as mild slag rolling and not subjected to machine cleaning;

[0086] When J slag ≤ J max ≤ J slag +30 mm 2 / s and 5 < N, it is determined as mild slag rolling and subjected to machine cleaning once;

[0087] When J slag +30 mm 2 / s < J max ≤ J slag +50 mm 2 / s, when N ≤ 5, it is judged as moderate slag entrainment and not machine-cleaned;

[0088] When J slag +30 mm 2 / s < J max ≤ J slag +50 mm 2 / s, when 5 < N ≤ 15, it is judged as moderate slag entrainment and machine-cleaned once;

[0089] When J slag +30 mm 2 / s < J max ≤ J slag +50 mm 2 / s, when 15 < N, it is judged as moderate slag entrainment and machine-cleaned twice; <00​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​During the continuous casting process, due to various reasons such as the entrainment of mold powder, defects such as cracks, scabs, slag inclusions, and bubbles will occur on the surface of the slab. If these defects are not treated, they will be magnified and extended in the subsequent rolling process, resulting in the final products (such as steel plates and strip steel) becoming waste or defective products. The purpose of "mechanical cleaning" is to remove these defective surface layers before entering the heating furnace and rolling mill to ensure the surface quality of the slab. However, a large number of mechanical cleaning operations on the cast slab will lead to low metal yield and increased production costs.

[0095] Specifically, in step S4, the calculation formula of the discrete wavelet transform analysis method is:

[0096]

[0097] In the formula, W t (a, b) is the discrete wavelet transform of the mold liquid level fluctuation; f(t) is the mold liquid level fluctuation data; ψ a,b (t) is the wavelet basis function, a is the scale factor; b is the translation factor; t is the time node.

[0098] Specifically, in step S4, the selected type of wavelet basis function is the Sym5 wavelet basis function, and the maximum value of the frequency after discrete wavelet transform is 5.0 Hz.

[0099] In step S4, the process parameters during mold liquid level slag entrainment and normal casting include the continuous casting billet production time, continuous casting billet width, mold liquid level height, stopper rod position, casting speed, submerged entry nozzle immersion depth, stopper rod argon flow rate, stopper rod argon back pressure, tundish upper nozzle argon flow rate, tundish upper nozzle argon back pressure, inter-slab argon flow rate, inter-slab argon back pressure, tundish tonnage, torque of the drive rolls on the inner arc side of different segment sectors, and continuous casting billet reduction.

[0100] In step S5, based on the liquid level frequency and amplitude information calculated by the discrete wavelet transform analysis method, by comparing the casting parameters corresponding to the abnormal and normal fluctuations of the mold liquid level, the determination rule for the cause of the abnormal fluctuation of the mold liquid level is determined.

[0101] In step S5, the determination rule for the cause of the abnormal fluctuation of the mold liquid level is specifically:

[0102] When 0 < Rw < 0.1 Hz, the cause of the abnormal liquid level fluctuation is a change in the casting speed, continuous casting billet width, tundish tonnage, or submerged entry nozzle immersion depth; if the casting speed, continuous casting billet width, and tundish tonnage do not change, the cause of the abnormal liquid level fluctuation is determined to be the shedding of nodules on the tip or body of the stopper rod, the inner wall of the upper nozzle, or the submerged entry nozzle.

[0103] When 0.1 Hz ≤ Rw ≤ 0.4 Hz, the cause of the abnormal liquid level fluctuation is unstable bulging between the rolls;

[0104] When 0.4 Hz < Rw ≤ 1.0 Hz, the reason for the abnormal liquid level fluctuation is severe nodulation of the stopper rod, the upper nozzle of the tundish or the submerged entry nozzle.

[0105] When 1.0 Hz < Rw ≤ 5.0 Hz, the reason for the abnormal liquid level fluctuation is the frequency, which is the natural frequency of the mold vibration.

[0106] It should be noted that when 0 < Rw < 0.1 Hz, there are mainly two reasons for the abnormal liquid level fluctuation. On the first hand, first check whether it is due to changes in the casting speed, the width of the continuous casting billet, the tonnage of the tundish or the immersion depth of the nozzle. On the second hand, if the liquid level fluctuation is not caused by the above reasons, then it is the fluctuation caused by the shedding of the nodule, which can be identified according to whether there is a drastic change (jump) in the stopper rod position curve; and then adjust, change or clean the nodule on the head or body of the stopper rod, the inner wall of the upper nozzle or the submerged entry nozzle.

[0107] When 0.1 Hz ≤ Rw ≤ 0.4 Hz, the reason for the abnormal liquid level fluctuation is the unstable bulging between the rolls. The occurrence of the bulging phenomenon can be identified according to the abnormal torque of the driving rolls on the inner arc side of different segment sectors and the abnormal reduction of the continuous casting billet, and then corresponding adjustments can be made.

[0108] When 0.4 Hz < Rw ≤ 1.0 Hz, the reason for the abnormal liquid level fluctuation is severe nodulation at the stopper rod, the upper nozzle of the tundish or the submerged entry nozzle. Whether the stopper rod is nodulated can be identified according to the abnormality of the argon flow rate and the argon back pressure of the stopper rod. Whether the upper nozzle of the tundish is nodulated can be identified according to the abnormality of the argon flow rate and the argon back pressure of the upper nozzle. Whether the submerged entry nozzle is nodulated can be identified according to the abnormality of the argon flow rate between the plates and the argon back pressure between the plates. Adjust or control the above parameters to reduce the coefficient of nodulation.

[0109] When 1.0 Hz < Rw ≤ 5.0 Hz, this frequency is the natural characteristic frequency of the mold's own vibration, which belongs to the natural fluctuation signal during the normal operation of the equipment and is not the abnormal fluctuation of the mold liquid level caused by external factors such as process parameter deviation and equipment failure. Therefore, in the tracing of the cause of the abnormal liquid level and the process control, the fluctuations corresponding to this frequency range can be excluded, and no special treatment measures need to be taken for the frequencies in this interval.

[0110] The present invention also provides a system for tracing the cause of abnormal fluctuation of the mold liquid level, including the method described in the present invention.

[0111] The system includes a data processing module, a slag entrainment discrimination module, a machine cleaning guidance module and a module for tracing the cause of abnormal liquid level.

[0112] The data processing module is used to calculate the mold liquid level fluctuation index and obtain the maximum value J of the liquid level fluctuation index during the production time of the continuous casting billet.max It is used to analyze the liquid level height and corresponding process parameters of the crystallizer based on the discrete wavelet transform method, and to obtain the frequency information of the liquid surface fluctuation in the crystallizer.

[0113] The slag entrapment discrimination module is used to compare the crystallizer liquid level fluctuation index and the slag entrapment defect index to determine whether slag entrapment has occurred on the crystallizer liquid level.

[0114] The machine cleaning guidance module is used to execute the judgment rules of the severity of slag entrapment in continuous casting billets and the pre-rolling billet treatment, and to provide pre-rolling treatment guidance for billets.

[0115] The liquid level anomaly cause tracing module is used to execute the determination rules for the cause of abnormal fluctuations in the crystallizer liquid level and trace the cause of the abnormal liquid level.

[0116] The system also includes a process parameter adjustment module, which is used to optimize process parameters in real time after tracing the cause of abnormal liquid level in the crystallizer.

[0117] Specifically, the data processing module collects the liquid level height, obtains the slope and amplitude of the crystallizer liquid level based on the liquid level height, and then calculates the crystallizer liquid level fluctuation index based on the slope and amplitude of the crystallizer liquid level. The crystallizer liquid level fluctuation index = the maximum slope of the crystallizer liquid level multiplied by the maximum amplitude of the crystallizer liquid level. index =K max ×ΔL;

[0118] J index K is the crystallizer liquid level fluctuation index. max ΔL is the maximum value of the liquid level slope within the time period T; ΔL is the maximum value of the crystallizer liquid level amplitude within the time period T.

[0119] Specifically, the data processing module takes each continuously cast billet as a unit and extracts the maximum value J of the liquid level fluctuation index during the billet production time based on the calculated crystallizer liquid level fluctuation index. max ;

[0120] J max =max(J1, J2...J n ).

[0121] The data processing module calculates the crystallizer fluctuations based on the collected crystallizer liquid level height, and then obtains the frequency Rw and amplitude Ra corresponding to the crystallizer liquid level fluctuations using the discrete wavelet transform method. The calculation formula for the discrete wavelet transform analysis method is as follows:

[0122]

[0123] In the formula, W t(a, b) is the discrete wavelet transform of the mold liquid level fluctuation; f(t) is the mold liquid level fluctuation data; ψ a,b (t) is the wavelet basis function, a is the scale factor; b is the translation factor; t is the time node. The selected type of wavelet basis function is the Sym5 wavelet basis function, and the maximum value of the frequency after discrete wavelet transform is 5.0 Hz.

[0124] Specifically, the slag entrainment discrimination module extracts the number of J where the mold liquid level fluctuation index is greater than or equal to the slag entrainment defect index during the production time of one slab according to the mold liquid level fluctuation index and the slag entrainment defect index. index of them;

[0125]

[0126] In the formula, P is the sampling point corresponding to J index ; P tot is the total number of sampling points; J slag is the slag entrainment defect index, mm 2 / s; J slag ranges from 20 to 40 mm 2 / s.

[0127] Specifically, the determination rule for the severity of slag entrainment adopted by the machine cleaning guidance module is:

[0128] When J max < J slag , it is determined as a normal slab and not machine-cleaned;

[0129] When J slag ≤ J max ≤ J slag + 30 mm 2 / s and N ≤ 5, it is determined as mild slag entrainment and not machine-cleaned;

[0130] When J slag ≤ J max ≤ J slag + 30 mm 2 / s and 5 < N, it is determined as mild slag entrainment and machine-cleaned once;

[0131] When J slag + 30 mm 2 / s < J max ≤ J slag + 50 mm 2 / s and N ≤ 5, it is determined as moderate slag entrainment and not machine-cleaned; <>

[0132] When J slag + 30 mm 2 / s < J max ≤ J slag + 50 mm2 / s, when 5 < N ≤ 15, it is determined as moderate slag entrainment, and the machine is cleaned once;

[0133] When J slag +30mm 2 / s < J max ≤ J slag +50mm 2 / s, when 15 < N, it is determined as moderate slag entrainment, and the machine is cleaned twice;

[0134] When J slag +50mm 2 / s < J <00001​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

[0144] A computer-readable storage medium includes computer instructions that, when executed on a computer, cause the computer to execute a system for identifying and tracing abnormal fluctuations in crystallizer liquid level.

[0145] To more clearly describe the present invention, the following embodiments and comparative examples are provided for further illustration.

[0146] Example 1

[0147] The process for producing ultra-low carbon steel (C content 0.0035%) at a steel plant is as follows: converter → RH refining furnace → continuous casting. The slab continuous casting machine is a two-strand arc-shaped casting machine with a crystallizer casting cross-section of 1600mm × 220mm, adjustable online. Electromagnetic sensors are used to collect crystallizer liquid level fluctuations. The electromagnetic level sensor is from VUHz in the Czech Republic, model SH7-S10, with a response time of 0.1s. The sensor is mounted on the outer arc side back plate of the crystallizer, directly behind the submerged entry nozzle. The measurement area is 800mm wide and 100mm high along the casting direction from the upper edge of the crystallizer downwards. Nine heats of molten steel were cast in this cycle, producing a total of 86 slabs.

[0148] The sampling time for the production process data was 0.1s, and the total casting time was 32257.8s. The calculation process is as follows:

[0149] (1) Using ΔT = 0.1s as the time step and T = 3.0s as the time slip window, calculate the crystallizer liquid level fluctuation index J for each continuously cast billet. index And the maximum value J of the liquid level fluctuation index. max ;

[0150] (2) Statistical analysis of J on each continuously cast billet index ≥J slag The number of [determinants]; determining the critical threshold of the liquid level fluctuation index corresponding to the occurrence of slag entrapment defects in continuously cast billets, i.e., the slag entrapment defect index J. slag =20mm 2 / s.

[0151] (3) The model calculation results are judged by comparing the severity of slag entrapment in continuous casting billets and the judgment rules for pre-rolling billet treatment.

[0152] The predicted results of the slag entrapment degree of the continuously cast billet in this casting are shown in Table 1.

[0153] The number of hot-rolled slabs with slag inclusion defects in this casting batch is 45 (the actual number measured by the surface inspection system or surface inspection instrument). The model calculates that the number of continuous casting slabs with slag inclusion defects corresponding to the hot-rolled slabs is 43 (the number predicted by the identification method described in this invention is 11+12+20=43). Therefore, the accuracy rate of slag inclusion defect prediction is 43 / 45*100=95.6% (accuracy rate of slag inclusion defect prediction = number of slabs whose prediction results are consistent with the hot-rolled surface inspection results / total number of hot-rolled slag inclusion defects ×100).

[0154] Under the same casting parameters, since it was impossible to accurately determine abnormal fluctuations in the crystallizer level, Comparative Example 1 used a crystallizer level fluctuation exceeding ±5mm as the criterion. The predicted number of continuously cast billets with slag entrapment risk was 33, while the corresponding number of hot-rolled plates with slag entrapment defects was 50 (actually measured by a surface inspection system or instrument). The accuracy rate of slag entrapment defect prediction in Comparative Example 1 was 33 / 50*100 = 66%.

[0155] Under the same casting parameters, since it is impossible to accurately determine abnormal fluctuations in the liquid level of the crystallizer, all continuous casting billets produced in Comparative Example 1 under the same casting parameters were cleaned by machine once, with a machine cleaning ratio of 100%.

[0156] Compared with Comparative Example 1, the number of machine-cleaned billets in Example 1 was 7, with a ratio of 7 / 86*100=8.1%. The proportion of machine-cleaned billets was reduced by 91.9%, which significantly improved the metal yield.

[0157] Table 1. Severity of slag contamination defects in continuously cast billets and pre-rolling billet treatment.

[0158]

[0159] The continuous casting billet width is 1600mm; the casting speed is 1.4m / min; the tundish nozzle immersion depth is 160mm; the stopper rod argon flow rate is 3NL / min, and the stopper rod argon back pressure is 0.55bar; the tundish top nozzle argon flow rate is 3NL / min, and the tundish top nozzle argon back pressure is 0.66bar; the inter-plate argon flow rate is 10NL / min, and the inter-plate argon back pressure is 0.58bar; the ladle tonnage is 320t, and the tundish tonnage is... The casting depth is 70t; the torques of the drive rollers on the inner arc side of sector sections 1 to 11 are 33.4, 31.2, 30.6, 35.0, 35.9, 34.0, 38.8, 36.2, 28.6, 34.6, and 36.8 N·m, respectively; the reduction of the continuously cast billet is 0.30, 0.30, 0.31, 0.33, 0.31, 0.30, 0.32, 0.32, 0.35, 0.34, and 0.35 mm, respectively. The superheat of the tundish is 24℃. The cooling water flow rate of the narrow face of the crystallizer is 520 NL / min, the cooling water flow rate of the wide face is 4650 NL / min, and the secondary cooling water flow rate of the crystallizer is 350 NL / min.

[0160] Discrete wavelet transform was used to analyze and determine the crystallizer level height when slag entrapment occurred and the crystallizer level height under normal casting conditions, as well as the corresponding process parameters. The causes of abnormal crystallizer level fluctuations during this casting process included changes in the tonnage of the tundish during ladle replacement and blockage of the submerged entry nozzle (see Table 2). Stable control of crystallizer level fluctuations was achieved by timely adjustment of process parameters. In contrast, Comparative Example 1 used a crystallizer level fluctuation exceeding ±5mm as the criterion, which could not accurately determine the abnormal crystallizer level fluctuations and their causes.

[0161] Table 2. Judgment of Abnormal Fluctuations in Crystallizer Liquid Level, Tracing of Abnormal Causes, and Optimization of Process Parameters

[0162]

[0163]

[0164] Example 2

[0165] Example 2 is largely the same as Example 1 in its preparation process, except that in Example 2, the process for producing semi-peritectic steel (C content 0.091%) at a steel plant is as follows: converter → CAS refining furnace → continuous casting. The casting cross-section of the crystallizer is 1100mm × 230mm. The crystallizer liquid level acquisition frequency is 10Hz. This casting run involves 10 heats of molten steel, producing a total of 80 billets.

[0166] The sampling time for the production process data was 0.1s, and the total casting time was 34587.5s.

[0167] Using a time step of ΔT = 0.1 s and a time sliding window of T = 3.0 s, the maximum values ​​of the crystallizer liquid level fluctuation index and the extraction liquid level fluctuation index were calculated for each continuously cast billet. Based on the severity of slag contamination defects in the billets and the judgment rules for pre-rolling billet treatment, the corresponding machine cleaning operations for continuously cast billets under different degrees of slag contamination are shown in Table 3. The number of hot-rolled plates with slag contamination defects in this casting batch was 30 coils (actually measured by a surface inspection system or instrument). The predicted number of slag-contaminated billets corresponding to the hot-rolled plates with slag contamination defects was 29 coils. Therefore, the accuracy rate of slag contamination defect prediction was 29 / 30*100 = 96.7%.

[0168] Under the same casting parameters, since it was impossible to accurately determine abnormal fluctuations in the crystallizer liquid level, Comparative Example 2 used a crystallizer liquid level fluctuation exceeding ±5mm as the judgment criterion, predicting that the number of continuously cast billets with slag entrapment risk was 20, while the number of hot-rolled plates corresponding to this casting batch with slag entrapment defects was 33 (actually measured by a surface inspection system or surface inspection instrument). The accuracy rate of slag entrapment defect prediction in Comparative Example 2 was 20 / 33*100 = 60.6%.

[0169] Under the same casting parameters, since it was impossible to accurately determine abnormal fluctuations in the liquid level of the crystallizer, all continuous casting billets produced in Comparative Example 2 under the same casting parameters were cleaned by machine once, with a machine cleaning ratio of 100%.

[0170] Compared with Comparative Example 2, the number of machine-cleaned billets in Example 2 was 12, and the proportion was 12 / 80*100=15%. The proportion of machine-cleaned billets was reduced by 85%, which greatly improved the metal yield.

[0171] Table 3. Severity of slag contamination defects in continuously cast billets and pre-rolling billet treatment.

[0172]

[0173] The continuous casting billet width is 1100mm; the casting speed is 1.5m / min; the tundish nozzle immersion depth is 140mm; the stopper rod argon flow rate is 4NL / min, and the stopper rod argon back pressure is 0.41bar; the tundish top nozzle argon flow rate is 4NL / min, and the tundish top nozzle argon back pressure is 0.35bar; the interplate argon flow rate is 10NL / min, and the interplate argon back pressure is 0.36bar; the ladle tonnage is 320t, and the tundish tonnage is 70t; The torques of the drive rollers on the inner arc side of sections 1 to 11 are 19.3, 22.4, 19.6, 20.0, 21.0, 22.9, 28.6, 19.7, 18.1, 15.6, and 14.8 N·m, respectively; the reduction of the continuously cast billet is 0.30 mm, 0.30 mm, 0.30 mm, 0.32 mm, 0.30 mm, 0.30 mm, 0.31 mm, 0.31 mm, 1.39 mm, and 1.38 mm, respectively. The superheat of the tundish is 28℃. The cooling water flow rate of the narrow face of the crystallizer is 460 NL / min, the cooling water flow rate of the wide face is 3200 NL / min, and the secondary cooling water flow rate of the crystallizer is 340 NL / min.

[0174] Discrete wavelet transform was used to analyze and determine the crystallizer level height when slag entrapment occurred and the crystallizer level height under normal casting conditions, as well as the corresponding process parameters. The cause of the abnormal fluctuation in the crystallizer level during this casting process was a bulging phenomenon in the continuous casting machine (see Table 4), and the crystallizer level fluctuation was stabilized by timely adjustment of process parameters. In contrast, Comparative Example 2 used a crystallizer level fluctuation exceeding ±5mm as the judgment criterion, which could not accurately determine the abnormal fluctuation in the crystallizer level and its cause.

[0175] Table 4. Judgment of Abnormal Fluctuations in Crystallizer Liquid Level, Tracing of Abnormal Causes, and Optimization of Process Parameters

[0176]

[0177] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for tracing the cause of abnormal fluctuations in the liquid level of a crystallizer, characterized in that, It includes the following steps: S1: Collect continuous casting production data and calculate the mold liquid level fluctuation index; S2: Compare and analyze the maximum value of the mold liquid level fluctuation index calculated for each continuous casting slab and the judgment result of the slag entrainment defect on the corresponding hot-rolled coil, so as to determine the critical threshold of the liquid level fluctuation index corresponding to the occurrence of slag entrainment defect in the continuous casting slab, that is, the slag entrainment defect index; S3: Count the number of mold liquid level fluctuation indexes greater than or equal to the slag entrainment defect index for each continuous casting slab and the number of slag entrainment defects on the corresponding hot-rolled coil for comparative analysis, and establish a judgment rule for the severity of slag entrainment in the continuous casting slab; S4: Calculate the mold liquid level fluctuation according to the collected mold liquid level height, and then obtain multiple frequency bands corresponding to the mold liquid level fluctuation by the discrete wavelet transform method. Each frequency band includes a frequency Rw and an amplitude Ra; when a peak value with an amplitude Ra greater than ±3 mm appears in the frequency band, it is judged as an abnormal fluctuation; S5: Compare and analyze the casting parameters corresponding to the abnormal fluctuation frequency of the mold liquid level during slag entrainment with the casting parameters corresponding to the normal liquid level fluctuation frequency, establish a judgment rule for the cause of the abnormal fluctuation of the mold liquid level, and trace the cause of the abnormal fluctuation of the liquid level.

2. The method for tracing the cause of abnormal fluctuations in crystallizer liquid level according to claim 1, characterized in that, In step S4, the calculation formula of the discrete wavelet transform analysis method is: In the formula, W t (a,b) represents the discrete wavelet transform of the liquid level fluctuation in the crystallizer; f(t) represents the liquid level fluctuation data in the crystallizer; ψ a,b (t) represents the wavelet basis function, a is the size factor, b is the translation factor, and t is the time node.

3. The method for tracing the cause of abnormal fluctuations in the liquid level of a crystallizer according to claim 1, characterized in that, In step S4, the selected type of wavelet basis function is the Sym5 wavelet basis function, and the maximum value of the frequency after discrete wavelet transform is 5.0 Hz.

4. The method for tracing the cause of abnormal fluctuations in the liquid level of a crystallizer according to claim 1, characterized in that, In step S4, the process parameters during the occurrence of slag entrainment in the mold liquid level and normal casting include the production time of the continuous casting slab, the width of the continuous casting slab, the mold liquid level height, the stopper rod position, the casting speed, the submerged nozzle immersion depth, the stopper rod argon flow rate, the stopper rod argon back pressure, the tundish upper nozzle argon flow rate, the tundish upper nozzle argon back pressure, the inter-slab argon flow rate, the inter-slab argon back pressure, the tundish tonnage, the torque of the inner arc side driving rolls in different segment sectors, and the reduction of the continuous casting slab.

5. The method for tracing the cause of abnormal fluctuations in crystallizer liquid level according to claim 1, characterized in that, In step S5, based on the frequency and amplitude corresponding to the mold liquid level fluctuation calculated by the discrete wavelet transform analysis method, by comparing the casting parameters corresponding to the abnormal fluctuation and normal fluctuation conditions of the mold liquid level, determine the judgment rule for the cause of the abnormal fluctuation of the mold liquid level; In step S5, the judgment rule for the cause of the abnormal fluctuation of the mold liquid level is specifically: When 0 < Rw < 0.1 Hz, the reason for the abnormal fluctuation of the liquid level is that the casting speed, the width of the continuous casting slab, the tundish tonnage or the submerged nozzle immersion depth changes; if the casting speed, the width of the continuous casting slab, and the tundish tonnage do not change, the reason for the abnormal fluctuation of the liquid level is judged to be the shedding of nodules on the rod head or rod body of the stopper rod, the inner wall of the upper nozzle or the submerged nozzle; When 0.1 Hz ≤ Rw ≤ 0.4 Hz, the reason for the abnormal fluctuation of the liquid level is the unstable bulging between the rolls; When 0.4 Hz < Rw ≤ 1.0 Hz, the reason for the abnormal fluctuation of the liquid level is that the stopper rod, the tundish upper nozzle or the submerged nozzle is severely nodulated; When 1.0 Hz < Rw ≤ 5.0 Hz, the reason for the abnormal fluctuation of the liquid level is the frequency, which is the natural frequency of the mold vibration.

6. The method for tracing the cause of abnormal fluctuations in the liquid level of a crystallizer according to claim 1, characterized in that, In step S1, the crystallizer liquid level fluctuation index = the maximum slope of the crystallizer liquid level multiplied by the maximum amplitude of the crystallizer liquid level, J index =K max ×ΔL; J index K is the crystallizer liquid level fluctuation index. max ΔL is the maximum value of the liquid level slope within the time period T; ΔL is the maximum value of the crystallizer liquid level amplitude within the time period T.

7. The method for tracing the cause of abnormal fluctuations in the liquid level of a crystallizer according to claim 6, characterized in that, The time sliding window T should satisfy 3 s ≤ T ≤ 5 s.

8. The method for tracing the cause of abnormal fluctuations in the liquid level of a crystallizer according to claim 1, characterized in that, For each continuously cast billet, extract the maximum value J of the liquid level fluctuation index during the billet production time. max ; J max =max(J1,J2...J n ) Extract the number of liquid level fluctuation indexes greater than or equal to the slag entrainment defect index during the production time of a slab; In the formula, P is related to J index Corresponding sampling point; P tot J represents the total number of sampling points; slag The defect index for slag contamination is expressed in mm. 2 / s.

9. The method for tracing the cause of abnormal fluctuations in the liquid level of a crystallizer according to claim 1, characterized in that, Rules for determining the severity of slag contamination in continuously cast billets: When J max <J slag At that time, it was determined to be a normal casting billet and was not machine cleaned; When J slag ≤J max ≤J slag +30mm 2 When the density is / s and N≤5, it is judged as slight slag entrainment and should not be machine cleaned; When J slag ≤ J max ≤ J slag + 30 mm 2 / s and when N < 5, it is determined as mild slag entrainment and cleaned once by mechanical cleaning; When J slag +30mm 2 / s <J max ≤J slag +50mm 2 When the density is / s and N≤5, it is judged as moderate slag entrainment and does not require machine cleaning; When J slag +30 mm 2 / s < J max ≤ J slag +50 mm 2 / s, when 5 < N ≤ 15, it is determined as moderate slag entrainment and the machine is cleaned once; When J slag +30 mm 2 / s < J max ≤ J slag +50 mm 2 / s, when 15 < N, it is judged as moderate slag entrainment and the machine is cleaned twice; When J slag +50mm 2 / s <J max When N≤5, it is judged as heavy slag entrainment and not machine cleaning is required; When J slag +50 mm 2 / s < J max , when 5 < N ≤ 15, it is judged as severe slag entrainment and the machine is cleaned once; When J slag +50 mm 2 / s < J max , and when 15 < N, it is determined as severe slag entrainment and the machine is cleaned twice.

10. A system for tracing the causes of abnormal fluctuations in the liquid level of a crystallizer, characterized in that, Used to implement the method according to any one of claims 1-9.

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