Finishing rolling temperature control process analysis method

Abstract

The invention relates to a finish rolling temperature control process analysis method, and belongs to the technical field of hot rolling production methods in the metallurgical industry. According to the technical scheme, a finish rolling temperature curve is recognized and judged, the finish rolling temperature defects are classified into head temperature anomaly, overall temperature anomaly and tail temperature anomaly according to an analysis method, and according to different defect types, the final rolling temperature defects are classified into head temperature anomaly, overall temperature anomaly and tail temperature anomaly. And the model self-learning parameters, the rolling speed, the cooling water state between racks, the rough rolling outlet temperature and the finish rolling inlet temperature are analyzed, and the reason causing the finish rolling temperature control deviation is found out in the control process. The method has the beneficial effects that analysis and deviation correction are carried out from the control process, then the finish rolling temperature hit rate and the mechanical property of a hot-rolled product are improved, the practicability is wide, and the application prospect is wide.

Description

Technical Field

[0001] This invention relates to an analysis method for the final rolling temperature control process, belonging to the technical field of hot rolling production methods in the metallurgical industry. Background Technology

[0002] In hot rolling processes, the final rolling temperature is a crucial parameter that significantly impacts various aspects of steel. A suitable final rolling temperature can improve the tensile strength, yield strength, and elongation of the steel, while maintaining good hardness. This is because the final rolling temperature determines the extent of static recrystallization and static recovery of the strip after finishing. If the final rolling temperature is low, the deformation bands and numerous dislocations generated during finishing rolling are retained. During subsequent continuous cooling, these dislocations act as ferrite nucleation points, effectively refining the ferrite grain size, thereby improving the material's yield and tensile strength, and enhancing the steel's toughness.

[0003] Therefore, ensuring the precision of final rolling temperature control is crucial not only for guaranteeing product performance and quality to meet user requirements but also for maintaining the output of hot-rolled production. Ensuring accurate final rolling temperature control and identifying the causes of control deviations require analysis of process data to pinpoint the errors and implement corrective measures. Currently, there is no standardized method for analyzing the final rolling temperature control process, demanding a high level of expertise from technical personnel. Consequently, when control deviations occur, it is often difficult to pinpoint the root cause and effectively correct the issues, impacting not only product quality but also production schedule and output.

[0004] Patent CN118371541B discloses a method for controlling the final rolling temperature of hot-rolled strip. Based on the measured average temperature values ​​of different lengths of the strip head and the first and second deviation update values ​​between these values ​​and the target and back-calculated head temperatures, a current and historical first temperature compensation value are calculated. Based on these values ​​and a first gain coefficient, a first temperature compensation update value for the next strip is calculated. Temperature compensation weighting coefficients are assigned to multiple stand intervals based on the proportion of actual water spray volume lower than theoretical water spray volume exceeding a predetermined threshold for target inter-stand cooling pipe groups. Based on these weighting coefficients, the first temperature compensation update value is divided into second temperature compensation update values ​​corresponding to each stand interval. This effective calculation of temperature compensation values ​​ensures the accuracy and stability of the final rolling temperature control of the strip.

[0005] Patent CN118122791A discloses a control method to improve the final rolling temperature hit rate of strip steel. It adopts methods such as optimizing strip threading speed, finishing rolling acceleration, intermediate slab thickness, interstand cooling water and heating temperature to eliminate the influence of slab temperature change and rolling speed change on strip steel temperature, thereby controlling the strip steel process temperature within the target range.

[0006] Patent CN117960799A discloses a final rolling temperature control method, which realizes feedforward and feedback adjustment of the final rolling temperature based on the deviation between the predicted value and the target value of the final rolling temperature of the target section, thereby improving the accuracy of the final rolling temperature calculation and reducing the deviation between the finished product temperature and the target temperature in stages.

[0007] Most of the above technologies are improvements and optimizations to the final rolling temperature control method, but they have not studied or elaborated on the analysis method of the final rolling temperature control process, and cannot effectively identify the problems in the control process. Summary of the Invention

[0008] The purpose of this invention is to provide a method for analyzing the final rolling temperature control process. By fully considering the relationship between the model self-learning parameters, rolling speed, inter-stand cooling water status, roughing mill exit temperature, finishing mill inlet temperature, and final rolling temperature during the final rolling temperature control process, this method identifies the causes of deviations in the final rolling temperature control process. This effectively solves the problems of the lack of a standard method for final rolling temperature analysis and the incomplete and inadequate analysis in the prior art. It enables analysis and correction from the control process, thereby improving the accuracy of the final rolling temperature and the mechanical properties of hot-rolled products. This method has wide applicability and broad application prospects, effectively solving the aforementioned problems existing in the background art.

[0009] The technical solution of this invention is: a method for analyzing the final rolling temperature control process, comprising the following steps:

[0010] S101. Obtain the final rolling temperature curve and determine whether the curve is valid;

[0011] S102. Determine the type of final rolling temperature defect;

[0012] S103. Obtain relevant control information for the rolled piece and execute subsequent steps according to the defect type;

[0013] S104-1, Analyze the overall temperature anomaly;

[0014] S104-2, Analysis of abnormal head temperature;

[0015] S104-3. Analyze the abnormal temperature at the tail end.

[0016] In step S101, the set of final rolling temperature curve data is denoted as T, where T = {T1, T2, ..., T}. i ,…,TN}, where N is the number of data samples for the final rolling temperature curve; the final rolling temperature curve data is processed, and S data points at the beginning and end of the curve are removed. The processed data set is denoted as t, t={t1,t2,…,t i ,…,t n}, where n is the number of data items in data set t, n = N - 2 * S;

[0017] In step S101, the method for determining whether the curve is valid includes: calculating the average value t of the data set t. ave The maximum value t max and minimum value t min The curve is considered valid if all conditions are met; otherwise, the curve is considered invalid.

[0018] The conditions for determining whether a curve is valid include:

[0019] (1) t max -t min >3℃;

[0020] (2) t max -t ave >2℃;

[0021] (3) t ave -t min >2℃;

[0022] (4) t max <1000℃;

[0023] (5) t max >600℃;

[0024] (6) t max -t min <100℃.

[0025] In step S102, the types of final rolling temperature defects include head temperature abnormality, overall temperature abnormality, and tail temperature abnormality. The determination method is carried out according to the following steps:

[0026] Obtain the target value for final rolling temperature control, denoted as T. targ Determine the average value t of the final rolling temperature curve ave With target value T targ The deviation, if |t ave -T targ If the temperature exceeds 15℃, the final rolling temperature defect type is determined to be an overall temperature abnormality; otherwise, continue the judgment.

[0027] Calculate the average of the first 50 data points in dataset t, denoted as H. ave If |H ave -Ttarg If the temperature is >20℃, the final rolling temperature defect type is determined to be abnormal head temperature; otherwise, it is determined to be normal head temperature.

[0028] Calculate the average of the last 50 data points in dataset t, denoted as T. ave If |T ave -T targ If the temperature exceeds 20℃, the final rolling temperature defect type is determined to be abnormal tail temperature; otherwise, it is determined to be normal tail temperature.

[0029] In step S104-1, the specific steps are as follows:

[0030] Based on the slab number of the rolled piece, the corresponding cooling efficiency self-learning coefficient zlht and self-learning update number zlht_cnt are retrieved from the historical database. If zlht = 1.0 and zlht_cnt = 0, it is determined that the overall temperature of the final rolling is abnormal due to the first production of this steel grade and specification.

[0031] Compare the actual acceleration of the rolled piece with the maximum and minimum acceleration values ​​set by the model. If the actual acceleration of the rolled piece is equal to the maximum acceleration value set by the model, and the actual final rolling temperature is less than the target value for final rolling temperature control, it is determined that the acceleration has reached the upper limit and the final rolling temperature control is too low. Otherwise, if the actual acceleration of the rolled piece is equal to the minimum acceleration value set by the model, and the actual final rolling temperature is greater than the target value for final rolling temperature control, it is determined that the acceleration has reached the lower limit and the final rolling temperature control is too high.

[0032] Compare the average finishing mill inlet temperature of the current rolled piece with that of the previous rolled piece. If the deviation between the average finishing mill inlet temperature of the current rolled piece and the average finishing mill inlet temperature of the previous rolled piece is ≥20℃, it is determined that the deviation in the finishing mill inlet temperature causes fluctuations in the rolling speed, which in turn affects the final rolling temperature control.

[0033] The roughing mill exit temperature was analyzed, and the roughing mill exit temperature data was processed and calculated.

[0034] Specifically, the first and last three data points of the roughing mill exit temperature data are removed, and the remaining data is denoted as set R, R = {R1, R2, ..., R...} i ,…,R m}, where m is the number of data points, calculate the maximum roughing mill exit temperature R. max Minimum value R min Average R ave and standard deviation R std ;

[0035] in

[0036] If R max -R min >50℃ or Rstd If the temperature is >20℃, it is determined that the intermediate billet temperature fluctuates greatly, affecting the final rolling temperature control.

[0037] In step S104-2, the specific steps are as follows:

[0038] Based on the slab number of the rolled piece, the corresponding cooling efficiency self-learning coefficient zlht and self-learning update number zlht_cnt are retrieved from the historical database. If zlht = 1.0 and zlht_cnt = 0, it is determined that the abnormal head temperature of the final rolling temperature is caused by the first production of this steel grade and specification.

[0039] Compare the average finishing mill inlet temperature of the current rolled piece with that of the previous rolled piece. If the deviation between the average finishing mill inlet temperature of the current rolled piece and the average finishing mill inlet temperature of the previous rolled piece is ≥20℃, it is determined that the deviation in the finishing mill inlet temperature causes fluctuations in the rolling speed, which in turn affects the final rolling temperature control.

[0040] Compare the measured average temperature at the finish mill inlet head of the current rolled piece with the calculated value of the finish mill inlet head temperature;

[0041] The head of the finishing mill entrance is located 1-2 meters from the head. The average measured temperature at the head of the finishing mill entrance is the average of all sampled values ​​within 1-2 meters. The calculated temperature at the head of the finishing mill entrance is obtained by the model based on the measured value of the high temperature gauge at the roughing mill exit. If the difference between the two is greater than 20℃, it is judged that the abnormal temperature detection at the finishing mill entrance affects the final rolling temperature control.

[0042] Compare the deviation between the set and actual cooling water settings of the finishing mill at the strip head, in percentage of opening. Number of cooling water stands = number of finishing mills - 1;

[0043] Specifically, the setpoint value of the inter-rack cooling water flow rate is obtained when the strip head is 1-10 meters, denoted as ISC_S, where ISC_S = {ISC_S1, ISC_S2, ..., ISC_S} i ,…,ISC_S m}, where m is the number of cooling water units between racks, ISC_S i ISC_S is the set of setpoints for the cooling water between the i-th racks in the strip head from 1 to 10 meters. i ={ISC_S i1 ,ISC_S i2 ,…,ISC_S ij ,…,ISC_S i10}, where ISC_S ij This is the set value of the cooling water between the i-th racks at the j-th meter of the strip;

[0044] Obtain the actual value of the inter-stand cooling water flow rate when the strip head is 1-10 meters, denoted as ISC_A, where ISC_A = {ISC_A1, ISC_A2, ..., ISC_A} i ,…,ISC_A m}, where m is the number of cooling water units between racks, ISC_A i ISC_A represents the set of actual values ​​of cooling water between the i-th racks at the strip head, from 1 to 10 meters. i ={ISC_A i1 ,ISC_A i2 ,…,ISC_A ij ,…,ISC_A i10}, where ISC_A ij This represents the actual value of the cooling water between the i-th racks at the j-th meter of the strip.

[0045] Calculate the deviation between the set and actual cooling water settings of the finishing mill at the head of the strip. The deviation between the set and actual cooling water settings of the i-th inter-stand at the j-th meter of the strip is denoted as ISC_D. ij ISC_D ij =|ISC_S ij -ISC_A ij Therefore, the average deviation between the setting and the actual value of the cooling water strip head between the i-th racks is denoted as ISC_DA. i ,

[0046] Determine the cooling water deviation between all racks ISC_DA i There exists any ISC_DA i If the deviation is greater than 10%, it is determined that the actual cooling water between the stands does not match the setting, resulting in a deviation in the final rolling temperature control.

[0047] In step S104-3, the specific steps are as follows:

[0048] The maximum speed and steel-throwing speed of the workpiece during the finishing rolling process are obtained. The steel-throwing speed is the speed at which the workpiece leaves the last stand of the finishing mill. If the maximum speed minus the steel-throwing speed is greater than 5 m / s, it is determined that the steel-throwing speed is set too low, resulting in abnormal tail temperature control.

[0049] Analyze the roughing mill exit temperature data to obtain the maximum and minimum roughing mill exit temperatures within 10 meters of the tail of the rolled piece. If the maximum value minus the minimum value is greater than 20℃, it is determined that the temperature fluctuation at the tail of the intermediate billet is large, resulting in abnormal temperature control at the tail of the final rolling.

[0050] The beneficial effects of this invention are: by fully considering the relationship between the model self-learning parameters, rolling speed, interstand cooling water status, roughing mill exit temperature, finishing mill inlet temperature and final rolling temperature in the final rolling temperature control process, the causes of final rolling temperature control deviations are identified from the control process. This effectively solves the problem that there is no standard method for final rolling temperature analysis in the prior art, and the analysis is not comprehensive or thorough. It realizes analysis and correction from the control process, thereby improving the final rolling temperature hit rate and mechanical properties of hot-rolled products. It has wide applicability and broad application prospects. Attached Figure Description

[0051] Figure 1 This is a flowchart of the method of the present invention;

[0052] Figure 2 This is a final rolling temperature curve of the rolled piece 1 provided in an embodiment of the present invention;

[0053] Figure 3 This is a finishing mill inlet temperature curve of the rolled piece 1 provided in an embodiment of the present invention;

[0054] Figure 4 This is a temperature curve of the finishing mill inlet of the rolled piece 2 provided in an embodiment of the present invention. Detailed Implementation

[0055] To make the purpose, technical solutions, and advantages of the invention's embodiments clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only a small part of the embodiments of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention.

[0056] A method for analyzing the final rolling temperature control process includes the following steps:

[0057] S101. Obtain the final rolling temperature curve and determine whether the curve is valid;

[0058] S102. Determine the type of final rolling temperature defect;

[0059] S103. Obtain relevant control information for the rolled piece and execute subsequent steps according to the defect type;

[0060] S104-1, Analyze the overall temperature anomaly;

[0061] S104-2, Analysis of abnormal head temperature;

[0062] S104-3. Analyze the abnormal temperature at the tail end.

[0063] In step S101, the set of final rolling temperature curve data is denoted as T, where T = {T1, T2, ..., T}. i ,…,T N}, where N is the number of data samples for the final rolling temperature curve; the final rolling temperature curve data is processed, and S data points at the beginning and end of the curve are removed. The processed data set is denoted as t, t={t1,t2,…,t i ,…,t n}, where n is the number of data items in data set t, n = N - 2 * S;

[0064] In step S101, the method for determining whether the curve is valid includes: calculating the average value t of the data set t. ave The maximum value t max and minimum value t min The curve is considered valid if all conditions are met; otherwise, the curve is considered invalid.

[0065] The conditions for determining whether a curve is valid include:

[0066] (1) t max -t min >3℃;

[0067] (2) t max -t ave >2℃;

[0068] (3) t ave -t min >2℃;

[0069] (4) t max <1000℃;

[0070] (5) t max >600℃;

[0071] (6) t max -t min <100℃.

[0072] In step S102, the types of final rolling temperature defects include head temperature abnormality, overall temperature abnormality, and tail temperature abnormality. The determination method is carried out according to the following steps:

[0073] Obtain the target value for final rolling temperature control, denoted as T. targ Determine the average value t of the final rolling temperature curve ave With target value T targ The deviation, if |t ave -T targ If the temperature exceeds 15℃, the final rolling temperature defect type is determined to be an overall temperature abnormality; otherwise, continue the judgment.

[0074] Calculate the average of the first 50 data points in dataset t, denoted as H. ave If |H ave -T targ If the temperature is >20℃, the final rolling temperature defect type is determined to be abnormal head temperature; otherwise, it is determined to be normal head temperature.

[0075] Calculate the average of the last 50 data points in dataset t, denoted as T. ave If |T ave -T targ If the temperature exceeds 20℃, the final rolling temperature defect type is determined to be abnormal tail temperature; otherwise, it is determined to be normal tail temperature.

[0076] In step S104-1, the specific steps are as follows:

[0077] Based on the slab number of the rolled piece, the corresponding cooling efficiency self-learning coefficient zlht and self-learning update number zlht_cnt are retrieved from the historical database. If zlht = 1.0 and zlht_cnt = 0, it is determined that the overall temperature of the final rolling is abnormal due to the first production of this steel grade and specification.

[0078] Compare the actual acceleration of the rolled piece with the maximum and minimum acceleration values ​​set by the model. If the actual acceleration of the rolled piece is equal to the maximum acceleration value set by the model, and the actual final rolling temperature is less than the target value for final rolling temperature control, it is determined that the acceleration has reached the upper limit and the final rolling temperature control is too low. Otherwise, if the actual acceleration of the rolled piece is equal to the minimum acceleration value set by the model, and the actual final rolling temperature is greater than the target value for final rolling temperature control, it is determined that the acceleration has reached the lower limit and the final rolling temperature control is too high.

[0079] Compare the average finishing mill inlet temperature of the current rolled piece with that of the previous rolled piece. If the deviation between the average finishing mill inlet temperature of the current rolled piece and the average finishing mill inlet temperature of the previous rolled piece is ≥20℃, it is determined that the deviation in the finishing mill inlet temperature causes fluctuations in the rolling speed, which in turn affects the final rolling temperature control.

[0080] The roughing mill exit temperature was analyzed, and the roughing mill exit temperature data was processed and calculated.

[0081] Specifically, the first and last three data points of the roughing mill exit temperature data are removed, and the remaining data is denoted as set R, R = {R1, R2, ..., R...} i ,…,R m}, where m is the number of data points, calculate the maximum roughing mill exit temperature R. max Minimum value R min Average R ave and standard deviation R std ;

[0082] in

[0083] If R max -R min >50℃ or R std If the temperature is >20℃, it is determined that the intermediate billet temperature fluctuates greatly, affecting the final rolling temperature control.

[0084] In step S104-2, the specific steps are as follows:

[0085] Based on the slab number of the rolled piece, the corresponding cooling efficiency self-learning coefficient zlht and self-learning update number zlht_cnt are retrieved from the historical database. If zlht = 1.0 and zlht_cnt = 0, it is determined that the abnormal head temperature of the final rolling temperature is caused by the first production of this steel grade and specification.

[0086] Compare the average finishing mill inlet temperature of the current rolled piece with that of the previous rolled piece. If the deviation between the average finishing mill inlet temperature of the current rolled piece and the average finishing mill inlet temperature of the previous rolled piece is ≥20℃, it is determined that the deviation in the finishing mill inlet temperature causes fluctuations in the rolling speed, which in turn affects the final rolling temperature control.

[0087] Compare the measured average temperature at the finish mill inlet head of the current rolled piece with the calculated value of the finish mill inlet head temperature;

[0088] The head of the finishing mill entrance is located 1-2 meters from the head. The average measured temperature at the head of the finishing mill entrance is the average of all sampled values ​​within 1-2 meters. The calculated temperature at the head of the finishing mill entrance is obtained by the model based on the measured value of the high temperature gauge at the roughing mill exit. If the difference between the two is greater than 20℃, it is judged that the abnormal temperature detection at the finishing mill entrance affects the final rolling temperature control.

[0089] Compare the deviation between the set and actual cooling water settings of the finishing mill at the strip head, in percentage of opening. Number of cooling water stands = number of finishing mills - 1;

[0090] Specifically, the setpoint value of the inter-rack cooling water flow rate is obtained when the strip head is 1-10 meters, denoted as ISC_S, where ISC_S = {ISC_S1, ISC_S2, ..., ISC_S} i ,…,ISC_S m}, where m is the number of cooling water units between racks, ISC_S i ISC_S is the set of setpoints for the cooling water between the i-th racks in the strip head from 1 to 10 meters. i ={ISC_S i1 ,ISC_S i2 ,…,ISC_S ij ,…,ISC_S i10}, where ISC_S ij This is the set value of the cooling water between the i-th racks at the j-th meter of the strip;

[0091] Obtain the actual value of the inter-stand cooling water flow rate when the strip head is 1-10 meters, denoted as ISC_A, where ISC_A = {ISC_A1, ISC_A2, ..., ISC_A} i ,…,ISC_A m}, where m is the number of cooling water units between racks, ISC_A i ISC_A represents the set of actual values ​​of cooling water between the i-th racks at the strip head, from 1 to 10 meters. i ={ISC_A i1 ,ISC_A i2 ,…,ISC_A ij ,…,ISC_A i10}, where ISC_A ij This represents the actual value of the cooling water between the i-th racks at the j-th meter of the strip.

[0092] Calculate the deviation between the set and actual cooling water settings of the finishing mill at the head of the strip. The deviation between the set and actual cooling water settings of the i-th inter-stand at the j-th meter of the strip is denoted as ISC_D. ij ISC_D ij =|ISC_S ij -ISC_A ij Therefore, the average deviation between the setting and the actual value of the cooling water strip head between the i-th racks is denoted as ISC_DA. i ,

[0093] Determine the cooling water deviation between all racks ISC_DA i There exists any ISC_DA i If the deviation is greater than 10%, it is determined that the actual cooling water between the stands does not match the setting, resulting in a deviation in the final rolling temperature control.

[0094] In step S104-3, the specific steps are as follows:

[0095] The maximum speed and steel-throwing speed of the workpiece during the finishing rolling process are obtained. The steel-throwing speed is the speed at which the workpiece leaves the last stand of the finishing mill. If the maximum speed minus the steel-throwing speed is greater than 5 m / s, it is determined that the steel-throwing speed is set too low, resulting in abnormal tail temperature control.

[0096] Analyze the roughing mill exit temperature data to obtain the maximum and minimum roughing mill exit temperatures within 10 meters of the tail of the rolled piece. If the maximum value minus the minimum value is greater than 20℃, it is determined that the temperature fluctuation at the tail of the intermediate billet is large, resulting in abnormal temperature control at the tail of the final rolling.

[0097] Example:

[0098] A hot rolling production line includes 2 roughing mills and 7 finishing mills, denoted as R1-R2 and F1-F7 respectively.

[0099] Obtain the final rolling temperature curve data of rolled piece 1, denoted as T, where T = {T1, T2, ..., T}. i ,…,T N}, where N is the number of data points in the final rolling temperature curve;

[0100] The final rolling temperature curve data is sampled every 1 meter. The slab ID is 24C01561E50 and the length is 903 meters. Therefore, the number of data points N = 914.

[0101] The final rolling temperature curve data is processed by removing S data points from the beginning and end of the curve, preferably S = 5. Therefore, the processed data set is denoted as t, t = {t1, t2, ..., t}. i ,…,t n}, where n = N - 2 * S = 904;

[0102] To determine if a curve is valid, calculate the average value t of the data set t. ave =880.8℃, maximum value t max =911.2℃ and minimum value t min =859.1℃;

[0103] The conditions for the curve to be valid are evaluated one by one.

[0104] 1. t max -t min =911.2℃-859.1℃=52.1℃>3℃, satisfying condition 1;

[0105] 2. t max -t ave =911.2℃-880.8℃=30.4℃>2℃, satisfying condition 2;

[0106] 3. t ave -t min =880.8℃-859.1℃=21.7℃>2℃, satisfying condition 3;

[0107] 4. t max =911.2℃ < 1000℃, satisfying condition 4;

[0108] 5. t min =859.1℃>600℃, satisfying condition 5;

[0109] 6. t max -t min =911.2℃-859.1℃=52.1℃<100℃, satisfying condition 6;

[0110] Therefore, since conditions 1-6 are all satisfied, the curve is deemed valid, and the analysis can continue.

[0111] Determine the type of temperature defect in the final rolling process;

[0112] The target value for the final rolling temperature control of workpiece 1 is obtained as T. targ =870℃, determine the average value t of the final rolling temperature curve ave With target value T targ The deviation, |t ave -T targ |=|880.8℃-870℃|=10.8℃, which is no greater than 15℃, therefore the overall temperature is judged to be normal;

[0113] Calculate the average of the first 50 data points in dataset t. Judgment | H ave -T targ |=|904.3℃-870℃|=34.3℃>20℃, therefore it is determined to be an abnormal head temperature; calculate the average of the last 50 data points in data set t. Judgment | T ave -T targ |=|864.7℃-870℃|=5.3℃, which is no more than 20℃, therefore the tail temperature is judged to be normal;

[0114] Obtain relevant control information for the rolled piece and execute subsequent steps based on the defect type.

[0115] The slab number of rolled piece 1 is 24C01561E50, the steel class is 16, the thickness layer is 6, and the coil usage mode is no coil.

[0116] Based on the determined defect type as abnormal head temperature, step 104-2 is executed;

[0117] Based on the slab number 24C01561E50 of the rolled piece, the corresponding cooling efficiency self-learning coefficient zlht and self-learning update number zlht_cnt were retrieved from the historical database. The retrieved values ​​were zlht = 1.043 and zlht_cnt = 1098. Therefore, it was determined that this steel grade and specification was not produced for the first time. The following analysis will continue.

[0118] Compare the average finishing entry temperature of the current rolled piece with that of the previous rolled piece. The current rolled piece is called rolled piece 1, and the previous rolled piece is called rolled piece 2. The average finishing entry temperature of rolled piece 1 is calculated to be 1051.4℃, and the average finishing entry temperature of rolled piece 2 is calculated to be 1006.3℃. The finishing entry temperature deviation = |1051.4℃-1006.3℃| = ​​45.1℃ > 20℃. Therefore, it is determined that the finishing entry temperature deviation causes the rolling speed fluctuation, which in turn affects the final rolling temperature control.

[0119] This invention identifies and judges the final rolling temperature curve, and classifies the final rolling temperature defects into head temperature abnormalities, overall temperature abnormalities, and tail temperature abnormalities according to the analysis method. For different defect types, the invention analyzes process data such as model self-learning parameters, rolling speed, interstand cooling water status, roughing mill exit temperature, and finishing mill inlet temperature, and finds the causes of final rolling temperature control deviations from the control process.

[0120] This invention fully considers the relationship between model self-learning parameters, rolling speed, inter-stand cooling water status, roughing mill exit temperature, finishing mill inlet temperature, and final rolling temperature during the final rolling temperature control process. It effectively solves the problem of existing technologies lacking a standard method for final rolling temperature analysis and resulting in incomplete and superficial analysis. It achieves analysis and correction from the control process perspective, thereby improving the final rolling temperature accuracy and mechanical properties of hot-rolled products. This invention has wide applicability and broad application prospects.

[0121] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

[0122] The preferred embodiments of the present invention have been described above. It should be noted that the present invention is not limited to the specific embodiments described above. The devices and structures not described in detail should be understood to be implemented in the ordinary way in the art. Any simple modifications, equivalent changes and modifications made by any person skilled in the art to the above embodiments based on the technical essence of the present invention without departing from the scope of the technical solution of the present invention shall still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for analyzing the final rolling temperature control process, characterized in that... Includes the following steps: S101. Obtain the final rolling temperature curve and determine whether the curve is valid; S102. Determine the type of final rolling temperature defect; S103. Obtain relevant control information for the rolled piece and execute subsequent steps according to the defect type; S104-1, Analyze the overall temperature anomaly; S104-2, Analysis of abnormal head temperature; S104-3. Analyze the abnormal temperature at the tail end.

2. The method for analyzing the final rolling temperature control process according to claim 1, characterized in that: In step S101, the set of final rolling temperature curve data is denoted as T, where T = {T1, T2, ..., T}. i ,…,T N }, where N is the number of data samples for the final rolling temperature curve; the final rolling temperature curve data is processed, and S data points at the beginning and end of the curve are removed. The processed data set is denoted as t, t={t1,t2,…,t i ,…,t n }, where n is the number of data in data set t, n = N - 2 * S.

3. The method for analyzing the final rolling temperature control process according to claim 1, characterized in that: In step S101, the method for determining whether the curve is valid includes: calculating the average value t of the data set t. ave The maximum value t max and minimum value t min The curve is considered valid if all conditions are met; otherwise, the curve is considered invalid.

4. The method for analyzing the final rolling temperature control process according to claim 3, characterized in that: The conditions for determining whether a curve is valid include: (1)、t max -t min >3℃; (2)、t max -t ave >2℃; (3)、t ave -t min >2℃; (4)、t max <1000℃； (5)、t max >600℃； (6)、t max -t min <100℃。 5. The method for analyzing the final rolling temperature control process according to claim 1, characterized in that: In step S102, the types of final rolling temperature defects include head temperature abnormality, overall temperature abnormality, and tail temperature abnormality. The determination method is carried out according to the following steps: Obtain the target value for final rolling temperature control, denoted as T. targ Determine the average value t of the final rolling temperature curve ave With target value T targ The deviation, if |t ave -T targ If the temperature exceeds 15℃, the final rolling temperature defect type is determined to be an overall temperature abnormality; otherwise, continue the judgment. Calculate the average of the first 50 data points in dataset t, denoted as H. ave If |H ave -T targ If the temperature is >20℃, the final rolling temperature defect type is determined to be abnormal head temperature; otherwise, it is determined to be normal head temperature. Calculate the average of the last 50 data points in dataset t, denoted as T. ave If |T ave -T targ If the temperature exceeds 20℃, the final rolling temperature defect type is determined to be abnormal tail temperature; otherwise, it is determined to be normal tail temperature.

6. The method for analyzing the final rolling temperature control process according to claim 1, characterized in that: In step S104-1, the specific steps are as follows: Based on the slab number of the rolled piece, the corresponding cooling efficiency self-learning coefficient zlht and self-learning update number zlht_cnt are retrieved from the historical database. If zlht = 1.0 and zlht_cnt = 0, it is determined that the overall temperature of the final rolling is abnormal due to the first production of this steel grade and specification. Compare the actual acceleration of the rolled piece with the maximum and minimum acceleration values ​​set by the model. If the actual acceleration of the rolled piece is equal to the maximum acceleration value set by the model, and the actual final rolling temperature is less than the target value for final rolling temperature control, it is determined that the acceleration has reached the upper limit and the final rolling temperature control is too low. Otherwise, if the actual acceleration of the rolled piece is equal to the minimum acceleration value set by the model, and the actual final rolling temperature is greater than the target value for final rolling temperature control, it is determined that the acceleration has reached the lower limit and the final rolling temperature control is too high. Compare the average finishing mill inlet temperature of the current rolled piece with that of the previous rolled piece. If the deviation between the average finishing mill inlet temperature of the current rolled piece and the average finishing mill inlet temperature of the previous rolled piece is ≥20℃, it is determined that the deviation in the finishing mill inlet temperature causes fluctuations in the rolling speed, which in turn affects the final rolling temperature control. The roughing mill exit temperature was analyzed, and the roughing mill exit temperature data was processed and calculated. Specifically, the first and last three data points of the roughing mill exit temperature data are removed, and the remaining data is denoted as set R, R = {R1, R2, ..., R...} i ,…,R m }, where m is the number of data points, calculate the maximum roughing mill exit temperature R. max Minimum value R min Average R ave and standard deviation R std ; in If R max -R min >50℃ or R std If the temperature is >20℃, it is determined that the intermediate billet temperature fluctuates greatly, affecting the final rolling temperature control.

7. The method for analyzing the final rolling temperature control process according to claim 1, characterized in that: In step S104-2, the specific steps are as follows: Based on the slab number of the rolled piece, the corresponding cooling efficiency self-learning coefficient zlht and self-learning update number zlht_cnt are retrieved from the historical database. If zlht = 1.0 and zlht_cnt = 0, it is determined that the abnormal head temperature of the final rolling temperature is caused by the first production of this steel grade and specification. Compare the average finishing mill inlet temperature of the current rolled piece with that of the previous rolled piece. If the deviation between the average finishing mill inlet temperature of the current rolled piece and the average finishing mill inlet temperature of the previous rolled piece is ≥20℃, it is determined that the deviation in the finishing mill inlet temperature causes fluctuations in the rolling speed, which in turn affects the final rolling temperature control. Compare the measured average temperature at the finish mill inlet head of the current rolled piece with the calculated value of the finish mill inlet head temperature; The head of the finishing mill entrance is located 1-2 meters from the head. The average measured temperature at the head of the finishing mill entrance is the average of all sampled values ​​within 1-2 meters. The calculated temperature at the head of the finishing mill entrance is obtained by the model based on the measured value of the high temperature gauge at the roughing mill exit. If the difference between the two is greater than 20℃, it is judged that the abnormal temperature detection at the finishing mill entrance affects the final rolling temperature control. Compare the deviation between the set and actual cooling water settings of the finishing mill at the strip head, in percentage of opening. Number of cooling water stands = number of finishing mills - 1; Specifically, the setpoint value of the inter-rack cooling water flow rate is obtained when the strip head is 1-10 meters, denoted as ISC_S, where ISC_S = {ISC_S1, ISC_S2, ..., ISC_S} i ,…,ISC_S m }, where m is the number of cooling water units between racks, ISC_S i ISC_S is the set of setpoints for the cooling water between the i-th racks in the strip head from 1 to 10 meters. i ={ISC_S i1 ,ISC_S i2 ,…,ISC_S ij ,…,ISC_S i10 }, where ISC_S ij This is the set value of the cooling water between the i-th racks at the j-th meter of the strip; Obtain the actual value of the inter-stand cooling water flow rate when the strip head is 1-10 meters, denoted as ISC_A, where ISC_A = {ISC_A1, ISC_A2, ..., ISC_A} i ,…,ISC_A m }, where m is the number of cooling water units between racks, ISC_A i ISC_A represents the set of actual values ​​of cooling water between the i-th racks at the strip head, from 1 to 10 meters. i ={ISC_A i1 ,ISC_A i2 ,…,ISC_A ij ,…,ISC_A i10 }, where ISC_A ij This represents the actual value of the cooling water between the i-th racks at the j-th meter of the strip. Calculate the deviation between the set and actual cooling water settings of the finishing mill at the head of the strip. The deviation between the set and actual cooling water settings of the i-th inter-stand at the j-th meter of the strip is denoted as ISC_D. ij ISC_D ij =|ISC_S ij -ISC_A ij Therefore, the average deviation between the setting and the actual value of the cooling water strip head between the i-th racks is denoted as ISC_DA. i , Determine the cooling water deviation between all racks ISC_DA i There exists any ISC_DA i If the deviation is greater than 10%, it is determined that the actual cooling water between the stands does not match the setting, resulting in a deviation in the final rolling temperature control.

8. The method for analyzing the final rolling temperature control process according to claim 1, wherein the hot rolling process includes: step S104-3, specifically the following steps: The maximum speed and steel-throwing speed of the workpiece during the finishing rolling process are obtained. The steel-throwing speed is the speed at which the workpiece leaves the last stand of the finishing mill. If the maximum speed minus the steel-throwing speed is greater than 5 m / s, it is determined that the steel-throwing speed is set too low, resulting in abnormal tail temperature control. Analyze the roughing mill exit temperature data to obtain the maximum and minimum roughing mill exit temperatures within 10 meters of the tail of the rolled piece. If the maximum value minus the minimum value is greater than 20℃, it is determined that the temperature fluctuation at the tail of the intermediate billet is large, resulting in abnormal temperature control at the tail of the final rolling.

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