A method for selecting pickling process based on raw material characteristics

Abstract

The present invention provides a method for selecting pickling processes based on raw material characteristics, comprising: constructing a mathematical model Q for pickling difficulty classification; according to the relative thickness of the oxide scale under the effect of the coiling temperature, the relative thickness of the oxide scale affected by the SiO2 and FeSiO4 layers, h c A mathematical model of the relative thickness of iron oxide scale under different hot rolling processes is constructed for the relative thickness of iron oxide scale affected by the Mn-rich layer. According to experimental data and on-site production practice, a database of the pickling difficulty level and pickling process of the pickling unit used is established; the Q value is calculated, the Q value is brought into the database, the pickling difficulty level of the Q value is determined, and a pickling process adapted to it is selected. The present invention fully considers the influence of alloy composition, hot rolling process and iron oxide scale thickness on the pickling process difficulty, re-rates the pickling difficulty based on the above reference factors, and selects a more suitable pickling process, which is conducive to the staff to quickly adjust the pickling process.

Description

Technical Field

[0001] The present invention relates to the field of pickling technology, in particular to a method for matching pickling processes based on the characteristics of a raw material surface oxide layer and its dissolution behavior, and in particular to a method for selecting a pickling process based on raw material characteristics. Background Art

[0002] At present, modern steel cold rolling enterprises generally use shallow trough turbulent pickling technology to produce pickled products. Since the composition of pickling unit products and the raw material production process design are relatively numerous and complex, in the actual production process, the pickling process design must be carried out for each pickling product steel to ensure its surface quality after pickling.

[0003] As the market demand for pickling unit products continues to change, the alloy composition and hot rolling production process of various products vary greatly, resulting in large differences in the structure and distribution of the hot-rolled surface oxide layer, posing greater challenges to the pickling process design of different products. If the pickling process design does not match the dissolution behavior of the oxide layer, a large number of surface defects will be generated, which is not conducive to the efficient and stable production of the pickling unit.

[0004] At present, the pickling process design generally adopts the product target strength combined with manual experience for classification design, and the pickling difficulty of the product is evaluated and classified by on-site technicians. In the production process, the corresponding pickling process parameters are matched in the secondary server according to the classification. With the increasing differences in the structure of the oxide layer on the hot-rolled surface of various products, the traditional evaluation and classification method that only relies on the product target strength cannot achieve a good match for the pickling process.

[0005] Patent document CN202110129463.8 "A shot blasting-pickling composite descaling process optimization method" provides a shot blasting-pickling composite descaling process optimization method. Based on different strip running speeds, multiple shot blasting-pickling composite descaling schemes are designed; the amount of acid required for each of the shot blasting-pickling composite descaling schemes is determined; a cost function is constructed based on the strip running speed and the amount of acid used, and the descaling cost corresponding to each of the shot blasting-pickling composite descaling schemes is calculated through the cost function; the strip running speed corresponding to the shot blasting-pickling composite descaling scheme with the lowest descaling cost is determined, and it is used as the optimal strip running speed to achieve strip descaling. The invention optimizes the pickling process by constructing a cost function based on the strip running speed and the amount of acid used, but has not yet considered the differences in iron oxide scales under different alloy compositions and hot rolling processes.

[0006] Patent document CN115386886A "An automatic control method for pickling hot-rolled strip steel" discloses an automatic control method for pickling hot-rolled strip steel. Based on the basic automation L1 system of the pickling unit, the method adds an adjustable control program module for pickling intensity, adds manual setting and modification buttons for setting the pickling intensity coefficient n on the operation screen, and increases the pickling intensity by controlling one or more operations of increasing the pickling temperature, increasing the pickling flow, increasing the acid concentration, reducing the pickling inhibitor concentration, and increasing the pickling accelerator concentration; and reduces the pickling intensity by controlling one or more operations of lowering the pickling temperature, reducing the pickling flow, reducing the acid concentration, increasing the pickling inhibitor concentration, and reducing the pickling accelerator concentration. The method described in this invention does not explain the numerical source or theoretical basis of the pickling intensity coefficient n. At the same time, the method of matching process parameters in this scheme is that when n is the maximum value, all process parameters use the maximum value allowed by the model, and when n is the minimum value, all process parameters use the minimum value allowed by the model. In other cases, the interpolation method is used to determine the process parameters. This process design method has the problems of insufficient control accuracy of process parameters and fails to cover the independent design of each parameter for the characteristics of the oxide layer.

[0007] Patent document CN201510347034.2 "An intelligent control method and system for the pickling process section in a cold rolling unit" This invention proposes an intelligent control method for the pickling process section in a cold rolling unit. The control host reads the strip data and uses the trained artificial neural network model for setting the pickling process parameters to set the pickling process parameters for the strip; the acid concentration PLC collects the pickling process parameters in real time, and the control host calls the trained acid temperature control artificial neural network model, acid concentration control artificial neural network model and acid pump flow control artificial neural network model to control the acid temperature, acid concentration and acid pump flow respectively, so as to realize the automatic setting and control of the pickling process parameters. Although this patent can optimize the current pickling data based on the artificial neural network, it still considers the influence of the characteristics of hot-rolled raw materials on the oxide layer.

[0008] In summary, since the pickling behavior of the hot-rolled surface oxide layer is closely related to influencing factors such as alloy composition, hot rolling process, physical phase and thickness of the oxide layer, the pickling difficulty of different steel grades needs to be re-evaluated and classified. Therefore, it is of great significance to explore a pickling process control method based on the above-mentioned influencing factors. Summary of the invention

[0009] In view of the above technical problems, a pickling process selection method based on raw material characteristics is provided.

[0010] The technical means adopted by the present invention are as follows:

[0011] A method for selecting a pickling process based on raw material characteristics, comprising:

[0012] (1) Construct a mathematical model for pickling difficulty classification:

[0013] Q=mh t ×n lnω(Si)+4 (1)

[0014] Where Q is the pickling difficulty coefficient used for strip steel grade classification; m is the pickling process coefficient, which is a constant; h t is the relative thickness of the strip iron scale, n is the influence factor of the Si-rich layer of the strip on the tensile leveling and descaling and stripping of the iron scale, which is a constant; ω(Si) is the mass percentage of the Si element content in the strip;

[0015] (2) Construct a mathematical model of the relative thickness of iron oxide scale under different hot rolling processes:

[0016] h t =h a +h b +h c (2)

[0017] Among them, h a The relative thickness of the oxide scale under the effect of coiling temperature:

[0018]

[0019] Where, T a is the coiling temperature of the strip; a1, a2, a3, a4, a5 are the growth coefficients of the oxide scale affected by temperature, which are constants; h1 and h2 are constants;

[0020] Among them, h b The relative thickness of the iron oxide scale affected by the SiO2 and FeSiO4 layers:

[0021]

[0022] Where, T b is the heating temperature of the hot-rolled strip, b1 is the growth coefficient of the oxide scale affected by silicon, which is a constant;

[0023] Among them, h c Relative thickness of iron oxide scale affected by Mn-rich layer:

[0024] h c =c1×ω(Mn) (5)

[0025] Wherein, c1 is the growth coefficient of the oxide scale affected by manganese, which is a constant and c1<0; ω(Mn) is the mass percentage of the Mn element content in the strip;

[0026] (3) Based on experimental data and on-site production practice, a database of pickling difficulty levels and pickling processes for the pickling units used is established; the Q value is calculated according to formulas (1), (2), (3), (4), and (5), and the Q value is brought into the database to determine the pickling difficulty level of the Q value and select a pickling process that is suitable for it.

[0027] Preferably, in formula (1), m=0.42, n=2.21.

[0028] Preferably, in formula (3), a1=0.15, a2=0.057, a3=0.80, a4=0.267, a5=0.75, h1=15, h2=20.

[0029] Preferably, in formula (4), b1=0.026.

[0030] Preferably, in formula (5), c1=-104.2.

[0031] Preferably, the pickling unit used is a three-stage shallow trough turbulent pickling plus a five-stage rinsing pickling unit.

[0032] Preferably, the database is divided into five pickling difficulty levels, namely 0≤Q<15, 15≤Q<30, 30≤Q<45, 45≤Q<50, Q≥50, and the corresponding pickling processes are S0, S1, S2, S3, and S4, respectively.

[0033] Compared with the prior art, the present invention has the following advantages:

[0034] The present invention provides a pickling process selection method based on raw material characteristics, which fully considers the influence of alloy composition, hot rolling process and iron oxide thickness on the difficulty of the pickling process, re-rates the pickling difficulty based on the above reference factors, and selects a more suitable pickling process, which is conducive to the staff to quickly adjust the pickling process.

[0035] Based on the above reasons, the present invention can be widely promoted in the fields of pickling and the like. DETAILED DESCRIPTION

[0036] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] A method for selecting a pickling process based on raw material characteristics, comprising:

[0038] (1) Construct a mathematical model for pickling difficulty classification:

[0039] Q=mh t ×n lnω(Si)+4 (1)

[0040] Where Q is the pickling difficulty coefficient used for strip steel grade classification; m is the pickling process coefficient, which is a constant; h t is the relative thickness of the strip iron scale, n is the influence factor of the Si-rich layer of the strip on the tensile leveling and descaling and stripping of the iron scale, which is a constant; ω(Si) is the mass percentage of the Si element content in the strip;

[0041] (2) Construct a mathematical model of the relative thickness of iron oxide scale under different hot rolling processes:

[0042] h t =h a +h b +h c (2)

[0043] Among them, h a The relative thickness of the oxide scale under the effect of coiling temperature:

[0044]

[0045] Where, T a is the coiling temperature of the strip; a1, a2, a3, a4, a5 are the growth coefficients of the oxide scale affected by temperature, which are constants; h1 and h2 are constants;

[0046] Among them, h b The relative thickness of the iron oxide scale affected by the SiO2 and FeSiO4 layers:

[0047]

[0048] Where, T b is the heating temperature of the hot-rolled strip, b1 is the growth coefficient of the oxide scale affected by silicon, which is a constant;

[0049] Among them, h c Relative thickness of iron oxide scale affected by Mn-rich layer:

[0050] h c =c1×ω(Mn) (5)

[0051] Wherein, c1 is the growth coefficient of the oxide scale affected by manganese, which is a constant and c1<0; ω(Mn) is the mass percentage of the Mn element content in the strip;

[0052] The reference values ​​of the relevant parameters in formulas (1) to (5) are shown in Table 1.

[0053] Table 1. Relevant model parameters

[0054]

[0055] (3) Based on experimental data and on-site production practice, a database of pickling difficulty levels and pickling processes for the pickling units used is established; the Q value is calculated according to formulas (1), (2), (3), (4), and (5), and the Q value is brought into the database to determine the pickling difficulty level of the Q value and select a pickling process that is suitable for it.

[0056] The pickling units used in the following embodiments are all three-stage shallow trough turbulent pickling plus five-stage rinsing pickling units. The corresponding relationship between the pickling difficulty level and the pickling process in the database is shown in Table 2.

[0057] Table 2 Pickling difficulty and pickling process matching table

[0058]

[0059] Example 1

[0060] The chemical composition of the hot rolled steel strip is as follows: C 0.10%, Si 1%, Mn 1.20%, P≤0.020%, S≤0.0050%, and the balance is Fe and other inevitable impurities. The hot rolling heating temperature is 1200°C and the coiling temperature is 600°C.

[0061] (1) Construct a mathematical model for pickling difficulty classification:

[0062] Q=mh t ×n lnω(Si)+4 =0.42×h t ×2.21 2 =2.05h t (1)

[0063] Where h t is the relative thickness of iron oxide scale.

[0064] (2) Construct a mathematical model of the relative thickness of iron oxide scale under different hot rolling processes:

[0065] h t =h a +h b +h c (2)

[0066] Among them, h a The relative thickness of the oxide scale under the effect of coiling temperature:

[0067] ha =[(600-500)×0.057+15]×0.8=16.56 (3)

[0068] Among them, h b The relative thickness of the iron oxide scale affected by the SiO2 and FeSiO4 layers:

[0069] h b =(1200-1173)×0.026×2=1.404 (4)

[0070] h c is the relative thickness of the iron oxide scale affected by the Mn-rich layer, and its expression is formula 10:

[0071] h c =-104.2×1.2%=-1.25 (5)

[0072] Substituting the above calculation results into formula (1), it can be obtained that the pickling difficulty Q=34.26, and the pickling process adopts S2.

[0073] Example 2

[0074] The chemical composition of the hot-rolled strip is calculated by mass percentage as follows: C 0.13%, Si 0.1%, Mn 1.15%, P≤0.020%, S≤0.0050%, and the balance is Fe and other inevitable impurities. The hot rolling heating temperature is 1150°C and the coiling temperature is 630°C.

[0075] (1) Construct a mathematical model for pickling difficulty classification:

[0076] Q=mh t ×n lnω(Si)+4 =0.42×h t ×2.21=0.93h t (1)

[0077] Where h t is the relative thickness of iron oxide scale.

[0078] (2) Construct a mathematical model of the relative thickness of iron oxide scale under different hot rolling processes:

[0079] h t =h a +h b +h c (2)

[0080] h a The relative thickness of the oxide scale under the effect of coiling temperature:

[0081] h a=[(630-500)×0.057+15]×0.8=17.928 (3)

[0082] h b The relative thickness of the iron oxide scale affected by the SiO2 and FeSiO4 layers:

[0083] h b =0 (4)

[0084] h c Relative thickness of iron oxide scale affected by Mn-rich layer:

[0085] h c = -104.2 × 1.15% = -1.198 (5)

[0086] Substituting the above calculation results into formula (1), it can be obtained that the pickling difficulty Q=15.56, and the pickling process adopts S1.

[0087] It can be seen from the above embodiments that by adopting the selection method described in the present invention, products with similar pickling difficulty can be divided into the same group and similar pickling process parameters can be adopted, which is conducive to on-site technicians to quickly adjust the pickling process to improve the coverage of the unit production capacity. At the same time, in the process of producing new products, the pickling difficulty can be calculated in advance and its pickling process can be matched, which greatly improves the research and development and industrialization efficiency of new products.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A pickling process selection method based on raw material characteristics, characterized in that: include: (1) Construct a mathematical model for pickling difficulty classification: Q=mh t ×n lnω(Si)+4 (1) Where Q is the pickling difficulty coefficient used for strip steel grade classification; m is the pickling process coefficient, which is a constant; h t is the relative thickness of the strip iron scale, n is the influence factor of the Si-rich layer of the strip on the tensile leveling and descaling and stripping of the iron scale, which is a constant; ω(Si) is the mass percentage of the Si element content in the strip; (2) Construct a mathematical model of the relative thickness of iron oxide scale under different hot rolling processes: h t =h a +h b +h c (2) Among them, h a The relative thickness of the oxide scale under the effect of coiling temperature: Where, T a is the coiling temperature of the strip; a1, a2, a3, a4, a5 are the growth coefficients of the oxide scale affected by temperature, which are constants; h1 and h2 are constants; Among them, h b The relative thickness of the iron oxide scale affected by the SiO2 and FeSiO4 layers: Where, T b is the heating temperature of the hot-rolled strip, b1 is the growth coefficient of the oxide scale affected by silicon, which is a constant; Among them, h c Relative thickness of iron oxide scale affected by Mn-rich layer: h c =c1×ω(Mn) (5) Wherein, c1 is the growth coefficient of the oxide scale affected by manganese, which is a constant and c1<0; ω(Mn) is the mass percentage of the Mn element content in the strip; (3) Based on experimental data and on-site production practice, a database of pickling difficulty levels and pickling processes for the pickling units used is established; the Q value is calculated according to formulas (1), (2), (3), (4), and (5), and the Q value is brought into the database to determine the pickling difficulty level of the Q value and select a pickling process that is suitable for it.

2. A pickling process selection method based on raw material characteristics according to claim 1, characterized in that: In formula (1), m=0.42, n=2.

21.

3. A pickling process selection method based on raw material characteristics according to claim 1, characterized in that: In formula (3), a1=0.15, a2=0.057, a3=0.80, a4=0.267, a5=0.75, h1=15, h2=20.

4. A pickling process selection method based on raw material characteristics according to claim 1, characterized in that: In formula (4), b1=0.

026.

5. The pickling process selection method based on raw material characteristics according to claim 1 is characterized in that: In formula (5), c1 = -104.

2.

6. The pickling process selection method based on raw material characteristics according to claim 1 is characterized in that: The pickling unit used is a three-stage shallow trough turbulent pickling plus a five-stage rinsing pickling unit.

7. A method for selecting pickling processes based on raw material characteristics according to claim 6, characterized in that: The database is divided into five pickling difficulty levels, namely 0≤Q<15, 15≤Q<30, 30≤Q<45, 45≤Q<50, Q≥50, and the corresponding pickling processes are S0, S1, S2, S3, and S4, respectively.

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