Strip steel surface roughness forecasting method suitable for hot galvanizing unit

Abstract

The invention discloses a strip steel surface roughness forecasting method suitable for a hot galvanizing unit, and the method comprises the steps: building a galvanized strip steel surface roughness calculation model, building an optimization objective function, solving an influence coefficient in the galvanized strip steel surface roughness calculation model through on-site actual production data by employing a big data regression method, and carrying out the prediction of the surface roughness of the galvanized strip steel. And finally, a finer calculation model for the surface roughness of the galvanized strip steel is obtained. According to the method, the surface roughness change of the hot-galvanized strip steel is analyzed, the method suitable for forecasting the surface roughness of the hot-galvanized strip steel is established, and the surface roughness of the hot-galvanized strip steel is calculated more accurately.

Description

Technical Field

[0001] The present invention relates to alloying galvanizing technology, and more particularly to a method for predicting the surface roughness of strip steel applicable to a hot-dip galvanizing line. Background Art

[0002] Manufacturing has always been an important pillar of the development of China's national economy and an important guarantee for the prosperity of the country. With the rapid development of China's manufacturing level, improving the quality level of manufactured products has become a top priority. The requirements for the stable rolling of the hot-dip galvanizing temper mill and the control of the strip shape and surface quality after galvanizing are also getting higher and higher. The research on the surface roughness of strip steel after galvanizing mainly uses experimental methods such as metallographic microscope, scanning electron microscope, X-ray diffraction, and glow spectroscopy to observe and study the changes in the microscopic morphology of the substrate surface after galvanizing, and uses experimental methods such as neutral salt spray test and electrochemical corrosion to evaluate the anti-powdering performance and corrosion resistance of the alloying coating under different substrate surface conditions. Analyzing from multiple aspects such as galvanizing time, temperature, substrate roughness, and chemical components in the galvanizing solution, the main influencing factors are obtained through the changes in the surface roughness of the strip steel after galvanizing. With the prolongation of the alloying time and the increase of the alloying temperature, aluminum elements gradually diffuse from the interface between the coating and the substrate to the surface of the coating, and finally are evenly distributed throughout the coating. Iron elements diffuse evenly to the surface of the coating, the alloying rate of the coating increases, the iron-zinc alloy phase is gradually formed and grows towards the surface of the coating, and its morphology gradually changes from strip-columnar to fine granular. Since the coating of the alloying hot-dip galvanized steel sheet is mainly Fe-Zn alloy, it has great brittleness and is prone to coating powdering and peeling, so it greatly limits the processability of the galvanized sheet. Powdering occurs when the Fe-Zn alloy forms and peels off in the form of powder or small particles on or near the surface of the coating during the processing and forming of the alloying galvanized steel sheet. When the coating undergoes powdering, the powder and small particles of the coating fall off, causing the surface of the coating to be uneven, which affects the surface roughness of the galvanized sheet. Then, through big data analysis, the influence of each factor on the surface roughness of the strip steel after galvanizing is accurately obtained, and the influence coefficient is regressed to obtain a prediction model for the surface roughness of the strip steel after galvanizing.

[0003] Currently, the research on the surface roughness of strip steel after galvanizing mainly uses experimental methods such as metallographic microscope, scanning electron microscope, X-ray diffraction, and glow spectroscopy to observe and study the changes in the microscopic morphology of the substrate surface after galvanizing, and uses experimental methods such as neutral salt spray test and electrochemical corrosion to evaluate the anti-powdering performance and corrosion resistance of the alloying coating under different substrate surface conditions.

[0004] Therefore, in actual production research, how to accurately calculate the surface roughness of the strip steel after galvanizing lays a foundation for the prediction of the surface roughness of the strip steel in the hot-dip galvanized strip temper mill and the development of the strip steel surface roughness control technology during the tempering process of the temper mill. Summary of the Invention

[0005] Aiming at the defects existing in the prior art, the purpose of the present invention is to provide a strip surface roughness prediction method applicable to a hot-dip galvanizing unit. By analyzing the change of strip surface roughness after hot-dip galvanizing, a strip surface roughness prediction method suitable for hot-dip galvanized strips is established to calculate the strip surface roughness after hot-dip galvanizing more accurately.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A strip surface roughness prediction method applicable to a hot-dip galvanizing unit:

[0008] Establish a calculation model for the surface roughness of the strip after galvanizing, then establish an optimization objective function, and use the big data regression method to solve the influence coefficients in the calculation model of the surface roughness of the strip after galvanizing through on-site actual production data, and finally obtain a more refined calculation model for the surface roughness of the strip after galvanizing.

[0009] Preferably, the strip surface roughness prediction method specifically includes the following steps:

[0010] S1. Collect data;

[0011] S2. Group the collected data;

[0012] S3. Establish the calculation model for the surface roughness of the strip after galvanizing;

[0013] S4. Introduce the objective function and the working condition influence coefficient;

[0014] S5. Define the initial target value and intermediate variables;

[0015] S6. Given the search step of the working condition influence coefficient;

[0016] S7. Use the calculation model for the surface roughness of the strip after galvanizing to calculate the surface roughness value of the galvanized sheet under the current working condition influence coefficient;

[0017] S8. Calculate the specific value of the objective function under the current condition;

[0018] S9. Define the best working condition influence coefficient;

[0019] S10. Judge whether the inequality holds. If so, go to step S6; if not, go to step S11;

[0020] S11. Output the optimal working condition influence coefficient;

[0021] S12. Substitute the optimal working condition influence coefficient into the calculation model of the surface roughness of the galvanized strip to obtain the surface roughness of the galvanized strip.

[0022] Preferably, in step S1, the data collection specifically includes:

[0023] Collect the equipment and process parameters of the hot-dip galvanizing unit, namely: the surface roughness Ra of the galvanized sheet ge , the surface roughness Ra of the substrate 0 , the incoming material thickness h, and the Al content c in the zinc bath Al .

[0024] Preferably, in step S2, the grouping of the collected data specifically includes:

[0025] Define n groups of input parameters after galvanizing as X = {Ra 0i , h i , c Ali}, and the corresponding n groups of actual strip surface roughnesses {Ra geia} after galvanizing, where i = 1, 2,..., n.

[0026] Preferably, in step S3, establish the calculation model of the surface roughness of the galvanized strip where

[0027] In the formula, β 1 , β 2 are the influence coefficients of the incoming material thickness; is the influence coefficient of the Al content in the zinc bath.

[0028] Preferably, in step S4, introduce the objective function and the working condition influence coefficient

[0029] When F(E) is smaller, it means that the calculated value of the calculation model of the surface roughness of the galvanized strip is closer to the actual value on site. Then, seek a set of optimal working condition influence coefficients such that is the smallest. Then, this set of working condition influence coefficients is the optimal working condition influence coefficient of the calculation model of the surface roughness of the galvanized strip.

[0030] Preferably, in step S5, define the initial target value F 0 , and at the same time define four intermediate variables m 0 , m 1 , m 2 , m 3 , and let m 0 = 0.

[0031] Preferably, in step S6, specifying the search step of the working condition influence coefficient specifically includes the following steps:

[0032] S61. Specify step0 as the search step of β 1 , and let λ 1 = 0.500 + m 0 * step0, and let m 1 = 0;

[0033] S62. Specify step1 as the search step of β 2 , and let λ 2 = 0.001 + m 1 * step1, and let m 2 = 0;

[0034] S63. Specify step2 as the search step of , and let α 2 = 0.001 + m 2 * step2, and let m 3 = 0;

[0035] S64. Specify step3 as the search step of , and let α 2 = 0.001 + m 3 * step3.

[0036] Preferably, in step S9, define the best working condition influence coefficient β 1y , β 2y , and determine whether F < F 0 holds. If so, let F 0 = F, β 1y = β 1 , β 2y = β 2 , then transfer to step S10; if not, directly transfer to step S10.

[0037] Preferably, in step S10, determining whether the inequality holds specifically includes the following steps:

[0038] S101. Determine whether the inequality m 3 ≤ 0.5000 / step3 holds. If so, let m 3 = m 3 + 1, then transfer to step S64; if not, transfer to step S102;

[0039] S102. Determine whether the inequality m 2 ≤ 0.5000 / step2 holds. If so, let m2 = m 2 After adding 1, go to step S63; otherwise, go to step S103;

[0040] S103. Determine whether the inequality m 1 ≤ 0.5000 / step1 holds. If so, let m 1 = m 1 +1 and then go to step S62; otherwise, go to step S104;

[0041] S104. Determine whether the inequality m 0 ≤ 0.5000 / step0 holds. If so, let m 0 = m 0 +1 and then go to step S61; otherwise, go to step S11.

[0042] Preferably, in step S12, substitute the optimal working condition influence coefficients β 1y , β 2y , into the calculation model of the surface roughness of the strip after galvanizing to obtain the surface roughness Ra ge of the strip after galvanizing.

[0043] A method for predicting the surface roughness of a strip applicable to a hot-dip galvanizing unit provided by the present invention combines a theoretical model with on-site actual production data, establishes a calculation model for the surface roughness of the strip after galvanizing that conforms to on-site actual production, calculates the surface roughness of the substrate after hot-dip galvanizing more precisely, and better serves production. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a schematic flowchart of an embodiment of the method for predicting the surface roughness of a strip according to the present invention. DETAILED DESCRIPTION

[0045] In order to better understand the above technical solutions of the present invention, the technical solutions of the present invention will be further described below with reference to the drawings and embodiments.

[0046] A method for predicting the surface roughness of a strip applicable to a hot-dip galvanizing unit provided by the present invention:

[0047] Establish a calculation model for the surface roughness of the strip after galvanizing, then establish an optimization objective function, and use the big data regression method to solve the influence coefficients in the calculation model of the surface roughness of the strip after galvanizing through on-site actual production data, and finally obtain a more refined calculation model for the surface roughness of the strip after galvanizing.

[0048] Combined with Figure 1 as shown, the method for predicting the surface roughness of a strip according to the present invention specifically includes the following steps:

[0049] S1. Collect the equipment and process parameters of the hot-dip galvanizing line, namely: the surface roughness Ra of the galvanized sheet ge , the surface roughness Ra of the substrate 0 , the incoming material thickness h, and the Al content c in the zinc bath Al .

[0050] S2. Group the collected data. Define the n groups of input parameters after galvanizing as X = {Ra 0i , h i , c Ali}, and the corresponding n groups of actual strip surface roughnesses after galvanizing {Ra geia}, where i = 1, 2,..., n.

[0051] S3. Establish a calculation model for the strip surface roughness after galvanizing where

[0052] In the formula, β 1 , β 2 are the influence coefficients of the incoming material thickness; is the influence coefficient of the Al content in the zinc bath.

[0053] S4. Introduce the objective function Influence coefficient of working conditions When F(E) is smaller, it means that the calculated value of the strip surface roughness calculation model after galvanizing is closer to the actual value on site. Then seek a set of optimal influence coefficients of working conditions such that is the smallest. Then this set of influence coefficients of working conditions is the optimal influence coefficient of the strip surface roughness calculation model after galvanizing.

[0054] S5. Define the initial objective value F 0 , and at the same time define four intermediate variables m 0 , m 1 , m 2 , m 3 , and let m 0 = 0.

[0055] S6. Specify the search step size of the influence coefficient of working conditions, specifically including:[[]]

[0056] S61. Specify the search step size step0 of β 1 , and let λ 1 = 0.500 + m 0 * step0, and let m 1 = 0;

[0057] S62. Specify β 2The search step size step1, and let λ 2 = 0.001 + m 1 * step1, let m 2 = 0;

[0058] S63. Given The search step size step2, and let α 2 = 0.001 + m 2 * step2, let m 3 = 0;

[0059] S64. Given The search step size step3, and let α 2 = 0.001 + m 3 * step3.

[0060] S7. Calculate the current working condition influence coefficient Under the condition, the surface roughness value of the galvanized sheet {Ra ge , i = 1, 2,..., n}.

[0061] S8. Calculate the specific value of the objective function under the current condition

[0062] S9. Define the best working condition influence coefficient β 1y , β 2y , And judge whether F < F 0 Holds. If so, let F 0 = F, β 1y = β 1 , β 2y = β 2 , After that, transfer to step S10; if not, directly transfer to step S10.

[0063] S10. Judge whether the inequality holds, which specifically includes the following steps:

[0064] S101. Judge whether the inequality m 3 ≤ 0.5000 / step3 holds. If so, let m 3 = m 3 + 1, and then transfer to step S64. If not, transfer to step S102;

[0065] S102. Judge whether the inequality m 2 ≤ 0.5000 / step2 holds. If so, let m 2 = m 2 + 1, and then transfer to step S63. If not, transfer to step S103;

[0066] S103. Determine whether the inequality m 1 ≤0.5000 / step1 holds. If so, let m 1 =m 1 +1, then go to step S62. If not, go to step S104;

[0067] S104. Determine whether the inequality m 0 ≤0.5000 / step0 holds. If so, let m 0 =m 0 +1, then go to step S61. If not, go to step S11.

[0068] S11. Output the optimal working condition influence coefficient β 1y , β 2y ,

[0069] S12. Substitute the optimal working condition influence coefficient β 1y , β 2y , into the calculation model of the surface roughness of the strip after galvanizing, and calculate the surface roughness Ra ge of the strip after galvanizing.

[0070] Example 1

[0071] Continue to refer to Figure 1 as shown. The method for predicting the surface roughness of the strip in this Example 1 specifically includes the following steps:

[0072] S1. Collect the equipment and process parameters of the hot-dip galvanizing unit, that is: the surface roughness Ra ge of the galvanized sheet = 1.5, the surface roughness Ra 0 of the substrate = 1.6, the incoming material thickness h = 1.4 mm, and the Al content c Al in the zinc bath = 0.12%.

[0073] S2. Group the collected data. Define 200 groups of input parameters after galvanizing as X = {Ra 01 = 1.63, h 1 = 1.412, c Al1 = 0.121}, and the corresponding 200 groups of actual surface roughnesses {Ra geia = 1.51} of the strip after galvanizing, where i = 1.

[0074] S3. Establish a calculation model for the surface roughness of the strip after galvanizing where

[0075] In the formula, β 1 , β2 is the influence coefficient of the incoming material thickness; is the influence coefficient of the Al content in the zinc bath.

[0076] S4. Introduce the objective function The influence coefficient of the working condition When F(E) is smaller, it means that the calculated value of the strip surface roughness calculation model after galvanizing is closer to the actual value on site. Then, find a set of optimal influence coefficients of the working condition such that is the smallest. Then, this set of influence coefficients of the working condition is the optimal influence coefficient of the strip surface roughness calculation model after galvanizing.

[0077] S5. Define the initial objective value F 0 = 0.1. At the same time, define four intermediate variables m 0 , m 1 , m 2 , m 3 , and let m 0 = 0.

[0078] S6. Given the search step of the influence coefficient of the working condition, specifically including:

[0079] S61. Given the search step of β 1 step0 = 0.005, and let λ 1 = 0.500 + m 0 * step0, and let m 1 = 0;

[0080] S62. Given the search step of β 2 step1 = 0.005, and let λ 2 = 0.001 + m 1 * step1, and let m 2 = 0;

[0081] S63. Given the search step of step2 = 0.005, and let α 2 = 0.001 + m 2 * step2, and let m 3 = 0;

[0082] S64. Given the search step of step3 = 0.005, and let α 2 = 0.001 + m 3 * step3.

[0083] S7. Use the strip surface roughness calculation model after galvanizing to calculate the current influence coefficient of the working condition The surface roughness value of the galvanized sheet under the condition {Ra ge = 1.29, i = 1}.

[0084] S8. Calculate the specific value of the objective function under the current condition

[0085] S9. Define the influence coefficient β of the optimal working condition 1y , β 2y , and judge if F = 0.091 < F 0 = 0.1 holds. If so, let F 0 = F, β 1y = 0.06, β 2y = 0.68 2 , then transfer to step S10.

[0086] S10. Judge whether the inequality holds, which specifically includes the following steps:

[0087] S101. Judge if the inequality m 3 = 0 ≤ 0.5000 / 0.005 holds. If so, let m 3 = m 3 + 1, then transfer to step S64;

[0088] S102. Judge if the inequality m 2 = 0 ≤ 0.5000 / 0.005 holds. If so, let m 2 = m 2 + 1, then transfer to step S63;

[0089] S103. Judge if the inequality m 1 = 0 ≤ 0.5000 / 0.005 holds. If so, let m 1 = m 1 + 1, then transfer to step S62;

[0090] S104. Judge if the inequality m 0 = 0 ≤ 0.5000 / 0.005 holds. If so, let m 0 = m 0 + 1, then transfer to step S61.

[0091] S11. Output the influence coefficient β of the optimal working condition 1y = 0.09,

[0092] S12. Substitute the influence coefficient β of the optimal working condition 1y , β 2y , into the calculation model of the surface roughness of the strip after galvanizing to obtain the surface roughness Ra of the strip after galvanizing ge= 1.47.

[0093] It can be seen from the simulation calculation that before optimizing the operating condition coefficients β 1 , β 2 , the deviation between the predicted work roll roughness and the actual work roll roughness is relatively large. After optimizing the coefficients β 1 , β 2 , the work roll roughness of the hot-dip galvanizing temper mill obtained by simulation calculation is basically consistent with the actual work roll roughness, and the error is only ξ 1 = (1.51 - 1.47) / 1.51 = 2.6%, fully meeting the production requirements.

[0094] Example 2

[0095] Continue to refer to Figure 1 as shown, the strip surface roughness prediction method in this Example 2 specifically includes the following steps:

[0096] S1. Collect the equipment and process parameters of the hot-dip galvanizing unit, namely: the surface roughness Ra ge = 1.5 of the galvanized sheet, the surface roughness Ra 0 = 1.6 of the substrate, the incoming material thickness h = 1.4 mm, and the Al content c Al = 0.12% in the zinc bath.

[0097] S2. Group the collected data. Define the 200 groups of input parameters after galvanizing as X = {Ra 01 = 1.55, h 1 = 1.384, c Al1 = 0.123}, and the corresponding 200 groups of actual strip surface roughnesses after galvanizing {Ra geia = 1.47}, where i = 1.

[0098] S3. Establish a calculation model for the strip surface roughness after galvanizing where

[0099] In the formula, β 1 , β 2 are the influence coefficients of the incoming material thickness; is the influence coefficient of the Al content in the zinc bath.

[0100] S4. Introduce the objective function Operating condition influence coefficient When F(E) is smaller, it means that the calculated value of the strip surface roughness calculation model after galvanizing is closer to the actual value on site. Then, seek a set of optimal operating condition influence coefficients such that is the smallest, then the group of working condition influence coefficients is the optimal working condition influence coefficient of the strip surface roughness calculation model after galvanizing.

[0101] S5. Define the initial target value F 0 = 0.1, and at the same time define four intermediate variables m 0 , m 1 , m 2 , m 3 , and let m 0 = 0.

[0102] S6. Given the search step of the working condition influence coefficient, specifically including:

[0103] S61. Given the search step of β 1 step0 = 0.005, and let λ 1 = 0.500 + m 0 * step0, let m 1 = 0;

[0104] S62. Given the search step of β 2 step1 = 0.005, and let λ 2 = 0.001 + m 1 * step1, let m 2 = 0;

[0105] S63. Given the search step of step2 = 0.005, and let α 2 = 0.001 + m 2 * step2, let m 3 = 0;

[0106] S64. Given the search step of step3 = 0.005, and let α 2 = 0.001 + m 3 * step3.

[0107] S7. Use the strip surface roughness calculation model after galvanizing to calculate the surface roughness value of the galvanized sheet under the current working condition influence coefficient {Ra ge = 1.24, i = 1}.

[0108] S8. Calculate the specific value of the objective function under the current condition

[0109] S9. Define the best working condition influence coefficient β 1y , β 2y , and judge F = 0.086 < F 0If =0.1 holds, then let F 0 =F, β 1y =0.07, β 2y =0.72, After that, transfer to step S10.

[0110] S10. Determine whether the inequality holds, which specifically includes the following steps:

[0111] S101. Determine whether the inequality m 3 =0≤0.5000 / 0.005 holds. If so, let m 3 =m 3 +1, and then transfer to step S64;

[0112] S102. Determine whether the inequality m 2 =0≤0.5000 / 0.005 holds. If so, let m 2 =m 2 +1, and then transfer to step S63;

[0113] S103. Determine whether the inequality m 1 =0≤0.5000 / 0.005 holds. If so, let m 1 =m 1 +1, and then transfer to step S62;

[0114] S104. Determine whether the inequality m 0 =0≤0.5000 / 0.005 holds. If so, let m 0 =m 0 +1, and then transfer to step S61.

[0115] S11. Output the optimal working condition influence coefficient β 1y =0.08, β 2y =0.70,

[0116] S12. Substitute the optimal working condition influence coefficient β 1y , β 2y , into the calculation model of the surface roughness of the galvanized strip steel to calculate the surface roughness Ra of the galvanized strip steel ge =1.49.

[0117] It can be seen from the simulation calculation that before the optimization condition coefficients β 1 , β 2 , the deviation between the predicted work roll roughness and the actual work roll roughness is relatively large. After the optimization coefficients β 1 , β 2 , After that, the roughness of the work roll obtained by simulation calculation for the hot-dip galvanizing temper mill is basically consistent with that in practice, and the error is only ξ 1 =(1.49 - 1.47) / 1.49 = 1.3%, fully meeting the production requirements.

[0118] Those of ordinary skill in the art should recognize that the above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. As long as within the scope of the essential spirit of the present invention, changes and modifications to the above-described embodiments will fall within the scope of the claims of the present invention.

Claims

1. A method for predicting the surface roughness of strip steel suitable for hot-dip galvanizing units. Features: A surface roughness calculation model for galvanized steel strip is established, and then an optimization objective function is established. The big data regression method is used to solve the influence coefficient in the surface roughness calculation model for galvanized steel strip through actual on-site production data, and finally a more refined surface roughness calculation model for galvanized steel strip is obtained.

2. The method for predicting the surface roughness of a strip steel applicable to a hot dip galvanizing unit according to claim 1, It is characterized in that The strip steel surface roughness prediction method specifically comprises the following steps: S1. Collect data. S2, grouping the collected data; S3, establishing a calculation model for the surface roughness of the galvanized strip steel; S4, introduce the objective function and working condition influence coefficient; S5. Define initial target value and intermediate variables; S6, a search step length of the given working condition influence coefficient; S7, using the surface roughness calculation model of the galvanized steel strip to calculate the surface roughness value of the galvanized sheet under the influence coefficient of the current working condition; S8, calculating the specific value of the objective function under the current situation; S9, defining the optimal working condition influence coefficient; S10, judging whether the inequality is established, if so, proceeding to step S6, if not, proceeding to step S11; S11, outputting the optimal working condition influence coefficient; S12. Substitute the optimal working condition influence coefficient into the surface roughness calculation model of the galvanized steel strip to calculate the surface roughness of the galvanized steel strip.

3. The method for predicting the surface roughness of a strip steel applicable to a hot dip galvanizing unit according to claim 2, It is characterized in that The step S1, collecting data specifically includes: Collect the equipment and process parameters of the hot-dip galvanizing unit, namely: the surface roughness Ra of the galvanized sheet ge , substrate surface roughness Ra 0 , material thickness h, Al content in zinc liquid c Al .

4. The method for predicting the surface roughness of a strip steel applicable to a hot dip galvanizing unit according to claim 3, It is characterized in that The step S2, grouping the collected data specifically includes: The n input parameters after galvanizing are defined as X = {Ra 0i 、h i 、c Ali }, and the corresponding surface roughness of the actual strip steel of group n after galvanizing {Ra geia }, where i = 1, 2,…, n.

5. The method for predicting the surface roughness of a strip steel applicable to a hot dip galvanizing unit according to claim 4, It is characterized in that In step S3, a calculation model for the surface roughness of the galvanized strip steel is established. in In the formula, β 1 , β 2 is the influence coefficient of incoming material thickness; is the influence coefficient of Al content in zinc liquid.

6. The method for predicting the surface roughness of a strip steel applicable to a hot dip galvanizing unit according to claim 5, It is characterized in that In step S4, the objective function is introduced and working condition influence coefficient When F(E) is smaller, it means that the calculated value of the surface roughness calculation model of the galvanized strip steel is closer to the actual value on site, and a set of optimal working condition influence coefficients is sought. Make minimum, then the influence coefficient of the working condition described in this group That is, it is the optimal working condition influence coefficient of the surface roughness calculation model of the galvanized strip steel.

7. The method for predicting the surface roughness of a strip steel applicable to a hot dip galvanizing unit according to claim 6, It is characterized in that In step S5, the initial target value F is defined 0 , and define four intermediate variables m 0 ,m 1 ,m 2 ,m 3 , and let m 0 =0.

8. The method for predicting the surface roughness of a strip steel applicable to a hot dip galvanizing unit according to claim 7, It is characterized in that In step S6, the search step length of the given operating condition influence coefficient specifically includes the following steps: S61, given β 1 The search step length step0, and let λ 1 =0.500+m 0 *step0, let m 1 =0; S62, given β 2 The search step length step1, and let λ 2 =0.001+m 1 *Step 1, let m 2 =0; S63, given The search step size step2, and let α 2 =0.001+m 2 *Step 2, let m 3 =0; S64, given The search step length step3, and let α 2 =0.001+m 3 *step3.

9. The method for predicting the surface roughness of a strip steel applicable to a hot dip galvanizing unit according to claim 8, It is characterized in that In step S9, the optimal operating condition influence coefficient β is defined. 1y , β 2y , And judge that F<F 0 Is it true? If so, let F 0 =F,β 1y =β 1 , β 2y =β 2 , If yes, then go to step S10; if not, go directly to step S10.

10. The method for predicting the surface roughness of a strip steel applicable to a hot dip galvanizing unit according to claim 9, It is characterized in that In step S10, determining whether the inequality is established specifically includes the following steps: S101, judge the inequality m 3 ≤0.5000 / step3 is true, if so, let m 3 =m 3 If +1, go to step S64, if not, go to step S102; S102, judge the inequality m 2 ≤0.5000 / step2 is true, if so, let m 2 =m 2 If +1, go to step S63, if not, go to step S103; S103, judge the inequality m 1 ≤0.5000 / step1 is true, if so, let m 1 =m 1 If +1, go to step S62, if not, go to step S104; S104, judge the inequality m 0 ≤0.5000 / step0 is true, if so, let m 0 =m 0 After +1, go to step S61, if not, go to step S11.

11. The method for predicting the surface roughness of a strip steel applicable to a hot dip galvanizing unit according to claim 10, It is characterized in that In step S12, the optimal working condition influence coefficient β 1y , β 2y , Substitute the surface roughness calculation model of the galvanized steel strip into the surface roughness calculation model of the galvanized steel strip to obtain the surface roughness Ra of the galvanized steel strip. ge .

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