CN117217023A - Acid washing process sorting method based on raw material characteristics - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 74
- 239000002253 acid Substances 0.000 title claims abstract description 34
- 239000002994 raw material Substances 0.000 title claims abstract description 14
- 238000005406 washing Methods 0.000 title claims description 9
- 238000005554 pickling Methods 0.000 claims abstract description 109
- 230000008569 process Effects 0.000 claims abstract description 49
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims abstract description 30
- UQSXHKLRYXJYBZ-UHFFFAOYSA-N Iron oxide Chemical compound [Fe]=O UQSXHKLRYXJYBZ-UHFFFAOYSA-N 0.000 claims abstract description 26
- 238000005098 hot rolling Methods 0.000 claims abstract description 14
- 229910052742 iron Inorganic materials 0.000 claims abstract description 14
- 238000013178 mathematical model Methods 0.000 claims abstract description 12
- 238000004519 manufacturing process Methods 0.000 claims abstract description 10
- 230000009471 action Effects 0.000 claims abstract description 6
- 229910000831 Steel Inorganic materials 0.000 claims description 31
- 239000010959 steel Substances 0.000 claims description 31
- 239000011572 manganese Substances 0.000 claims description 16
- 229910004298 SiO 2 Inorganic materials 0.000 claims description 5
- 238000010438 heat treatment Methods 0.000 claims description 5
- 238000004140 cleaning Methods 0.000 claims description 4
- PWHULOQIROXLJO-UHFFFAOYSA-N Manganese Chemical compound [Mn] PWHULOQIROXLJO-UHFFFAOYSA-N 0.000 claims description 3
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 claims description 3
- 229910052710 silicon Inorganic materials 0.000 claims description 3
- 239000010703 silicon Substances 0.000 claims description 3
- 229910045601 alloy Inorganic materials 0.000 abstract description 4
- 239000000956 alloy Substances 0.000 abstract description 4
- 230000009286 beneficial effect Effects 0.000 abstract 1
- 238000013461 design Methods 0.000 description 7
- 238000013528 artificial neural network Methods 0.000 description 5
- 238000005097 cold rolling Methods 0.000 description 3
- 238000004090 dissolution Methods 0.000 description 2
- 239000012535 impurity Substances 0.000 description 2
- 239000003112 inhibitor Substances 0.000 description 2
- 235000008373 pickled product Nutrition 0.000 description 2
- 238000010187 selection method Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 229910000967 As alloy Inorganic materials 0.000 description 1
- 238000005422 blasting Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000004886 process control Methods 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
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- Cleaning And De-Greasing Of Metallic Materials By Chemical Methods (AREA)
Abstract
The invention provides a pickling process sorting method based on raw material characteristics, which comprises the following steps: constructing a pickling difficulty grading mathematical model Q; according to the relative thickness of the iron oxide scale and SiO under the action of coiling temperature 2 And FeSiO 4 Relative thickness of scale affected by layer, h c Constructing mathematical models of the relative thickness of the iron oxide scale under different hot rolling processes for the relative thickness of the iron oxide scale influenced by the Mn-rich layer, and constructing an acid pickling difficulty level and an acid pickling process database of an acid pickling unit according to experimental data and on-site production practice; and (3) calculating a Q value, bringing the Q value into a database, judging the pickling difficulty level of the Q value, and selecting a pickling process matched with the Q value. The method fully considers the influence of alloy components, a hot rolling process and the thickness of the iron scale on the pickling process difficulty, re-ranks the pickling difficulty according to the reference factors, selects a more suitable pickling process, and is beneficial to the rapid adjustment of the pickling process by workers.
Description
Technical Field
The invention relates to the technical field of acid cleaning, in particular to a method for matching an acid cleaning process based on characteristics of an oxide layer on the surface of a raw material and dissolution behavior of the oxide layer, and particularly relates to a method for selecting an acid cleaning process based on characteristics of the raw material.
Background
At present, a shallow groove turbulent pickling technology is generally adopted by modern steel cold rolling enterprises to produce pickled products, and because the components and raw material production process design of the existing pickling line products are more complex, the pickling process design is required to be carried out on each pickled product steel in the actual production process so as to ensure the surface quality after pickling.
The product of the pickling line is changed along with the continuous change of market demands, and the alloy components and hot rolling production processes of various products are greatly different, so that the structure and distribution of the hot rolling surface oxide layer are greatly different, and a greater challenge is provided for the pickling process design of different products, and if the pickling process design is not matched with the dissolution behavior of the oxide layer, a large number of surface defects are generated, so that the efficient and stable production of the pickling line is not facilitated.
The current pickling process design generally adopts product target strength and manual experience to carry out classification design, evaluates and classifies the pickling difficulty of the product according to field technicians, and matches corresponding pickling process parameters in a secondary server according to the classification in the production process. Along with the continuous improvement of the structural difference of the oxide layers on the hot rolled surfaces of various products, the traditional evaluation and classification methods only depend on the target strength of the products, so that the good matching of the pickling process cannot be realized.
The invention provides a blasting-pickling combined type descaling process optimization method, which is disclosed in patent document CN202110129463.8, and designs a plurality of blasting-pickling combined type descaling schemes based on different strip steel running speeds; determining the amount of acid required for each of said shot blasting-pickling compound descaling schemes; constructing a cost function based on the running speed of the strip steel and the acid consumption, and respectively calculating the descaling cost corresponding to each shot blasting-pickling combined type descaling scheme through the cost function; and determining the strip steel running speed corresponding to the shot blasting and acid pickling combined type descaling scheme with the lowest descaling cost, and taking the strip steel running speed as the optimal strip steel running speed to realize strip steel descaling. The invention optimizes the pickling process by constructing a cost function according to the running speed of the strip steel and the acid quantity, but does not consider the differences of iron scales under different alloy compositions and hot rolling processes.
The invention discloses an automatic control method for pickling hot-rolled strip steel, which is disclosed in patent document CN115386886A, wherein an adjustable control program module for increasing pickling strength is added on the basis of an automatic L1 system of a pickling line, a manual setting and modifying key for setting a pickling strength coefficient n is added on an operation screen picture, and pickling strength is increased by controlling one or more operations of increasing pickling temperature, increasing pickling flow, increasing pickling solution concentration, reducing pickling inhibitor concentration and increasing pickling accelerator concentration; the pickling strength is reduced by controlling one or more of reducing the pickling temperature, reducing the pickling flow, reducing the acid concentration, increasing the pickling inhibitor concentration, and reducing the pickling accelerator concentration. The method does not describe the numerical source or theoretical basis of the pickling intensity coefficient n, and simultaneously, the method of the technological parameters matched by the scheme is that when n is the maximum value, all technological parameters use the model to allow the maximum value, when n is the minimum value, all technological parameters use the model to allow the minimum value, and the other conditions adopt an interpolation method to determine the technological parameters.
The invention provides an intelligent control method for an acid pickling process section in a cold rolling unit, which is disclosed in patent document CN201510347034.2, namely an intelligent control method and an intelligent control system for an acid pickling process section in a cold rolling unit, wherein a control host reads strip steel data, and the trained artificial neural network model for setting the acid pickling process parameters is utilized for setting the acid pickling process parameters for strip steel; the acid liquor concentration PLC collects the acid liquor process parameters in real time, and the control host invokes the trained acid liquor temperature control artificial neural network model, the acid liquor concentration control artificial neural network model and the acid pump flow control artificial neural network model to respectively control the acid liquor temperature, the acid liquor concentration and the acid pump flow, so that the automatic setting and control of the acid liquor process parameters are realized. The patent optimizes the current pickling data according to an artificial neural network, but still considers the influence of the characteristics of the hot rolled raw material on the oxide layer.
In conclusion, since the pickling behavior of the oxide layer on the hot-rolled surface is closely related to influencing factors such as alloy components, hot-rolling process, phase and thickness of the oxide layer and the like, the pickling difficulties of different steel grades need to be reevaluated and classified, so that the exploration of a pickling process control method based on the influencing factors has great significance.
Disclosure of Invention
According to the technical problems, the acid washing process selection method based on the characteristics of the raw materials is provided.
The invention adopts the following technical means:
a pickling process sorting method based on raw material characteristics comprises the following steps:
(1) Constructing a grading mathematical model of pickling difficulty:
Q＝mh t ×n lnω(Si)+4 (1)
wherein Q is an acid washing difficulty coefficient for grading strip steel; m is the pickling process coefficient and is a constant; h is a t N is the influence factor of the Si-rich layer of the strip steel on the withdrawal and straightening scale breaking and scale stripping, and is a constant; omega (Si) is the mass percentage of Si element content in the strip steel;
(2) Constructing mathematical model types of relative thickness of iron oxide scale under different hot rolling processes:
h t ＝h a +h b +h c (2)
wherein h is a The relative thickness of the iron oxide scale under the action of coiling temperature is as follows:
wherein T is a The coiling temperature of the strip steel; a, a 1 、a 2 、a 3 、a 4 、a 5 The growth coefficient of the iron scale affected by temperature is constant; h is a 1 H 2 Is a constant;
wherein h is b Is SiO 2 And FeSiO 4 Relative thickness of scale affected by layer:
wherein T is b B is the heating temperature of the hot rolled strip 1 The growth coefficient of the iron scale affected by silicon element is constant;
wherein h is c Relative thickness of scale affected by Mn-rich layer:
h c ＝c 1 ×ω(Mn) (5)
wherein, c 1 Is the growth coefficient of the iron scale affected by manganese element, is constant, and c 1 <0; omega (Mn) is the mass percentage of Mn element content in the strip steel;
(3) According to experimental data and on-site production practice, establishing a pickling difficulty level and a pickling process database of a used pickling line; and (3) calculating a Q value according to formulas (1), (2), (3), (4) and (5), bringing the Q value into a database, judging the pickling difficulty level of the Q value, and selecting a pickling process matched with the Q value.
Preferably, in formula (1), m=0.42 and n=2.21.
Preferably, in formula (3), a 1 ＝0.15、a 2 ＝0.057、a 3 ＝0.80、a 4 ＝0.267、a 5 ＝0.75、h 1 ＝15、h 2 ＝20。
Preferably, in formula (4), b 1 ＝0.026。
Preferably, in formula (5), c 1 ＝-104.2。
Preferably, the acid washing machine set is a three-section type shallow groove turbulent acid washing and five-section type rinsing acid washing machine set.
Preferably, five pickling difficulty levels are divided into the database, Q is more than or equal to 0 and less than 15, Q is more than or equal to 15 and less than 30, Q is more than or equal to 30 and less than or equal to 45, Q is more than or equal to 45 and less than or equal to 50, and Q is more than or equal to 50, and the corresponding pickling processes are S 0 、S 1 、S 2 、S 3 、S 4 。
Compared with the prior art, the invention has the following advantages:
according to the pickling process sorting method based on the raw material characteristics, influences of alloy components, a hot rolling process and the thickness of iron scales on the pickling process difficulty are fully considered, the pickling difficulty is rated again according to the reference factors, a more suitable pickling process is selected, and the pickling process can be adjusted quickly by workers.
For the reasons, the invention can be widely popularized in the fields of pickling and the like.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The described embodiments are only some, but not all, embodiments of the invention. The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A pickling process sorting method based on raw material characteristics comprises the following steps:
(1) Constructing a grading mathematical model of pickling difficulty:
Q＝mh t ×n lnω(Si)+4 (1)
wherein Q is an acid washing difficulty coefficient for grading strip steel; m is the pickling process coefficient and is a constant; h is a t The relative thickness of the scale of the strip steel is that n is the shadow of the Si-rich layer of the strip steel on the scale breaking by tension straightening and the scale strippingA response factor, which is a constant; omega (Si) is the mass percentage of Si element content in the strip steel;
(2) Constructing mathematical model types of relative thickness of iron oxide scale under different hot rolling processes:
h t ＝h a +h b +h c (2)
wherein h is a The relative thickness of the iron oxide scale under the action of coiling temperature is as follows:
wherein T is a The coiling temperature of the strip steel; a, a 1 、a 2 、a 3 、a 4 、a 5 The growth coefficient of the iron scale affected by temperature is constant; h is a 1 H 2 Is a constant;
wherein h is b Is SiO 2 And FeSiO 4 Relative thickness of scale affected by layer:
wherein T is b B is the heating temperature of the hot rolled strip 1 The growth coefficient of the iron scale affected by silicon element is constant;
wherein h is c Relative thickness of scale affected by Mn-rich layer:
h c ＝c 1 ×ω(Mn) (5)
wherein, c 1 Is the growth coefficient of the iron scale affected by manganese element, is constant, and c 1 <0; omega (Mn) is the mass percentage of Mn element content in the strip steel;
the reference values of the relevant parameters in the formulas (1) to (5) are shown in table 1.
Table 1 related model parameters
(3) According to experimental data and on-site production practice, establishing a pickling difficulty level and a pickling process database of a used pickling line; and (3) calculating a Q value according to formulas (1), (2), (3), (4) and (5), bringing the Q value into a database, judging the pickling difficulty level of the Q value, and selecting a pickling process matched with the Q value.
The pickling lines adopted in the following embodiments are three-section type shallow groove turbulent pickling and five-section type rinsing pickling lines. The corresponding relation between the pickling difficulty level and the pickling process in the database is shown in table 2.
Table 2 matching table of pickling difficulty and pickling process
Example 1
The hot rolled strip steel comprises the following chemical components in percentage by mass: 0.10% of C, 1% of Si, 1.20% of Mn, less than or equal to 0.020% of P, less than or equal to 0.0050% of S, and the balance of Fe and other unavoidable impurities. The hot rolling heating temperature is 1200 ℃ and the coiling temperature is 600 ℃.
(1) Constructing a grading mathematical model type of pickling difficulty:
Q＝mh t ×n lnω(Si)+4 ＝0.42×h t ×2.21 2 ＝2.05h t (1)
h in t Is the relative thickness of the iron scale.
(2) Constructing mathematical model types of relative thickness of iron oxide scale under different hot rolling processes:
h t ＝h a +h b +h c (2)
wherein h is a The relative thickness of the iron oxide scale under the action of coiling temperature is as follows:
h a ＝[(600-500)×0.057+15]×0.8＝16.56 (3)
wherein h is b Is SiO 2 And FeSiO 4 Relative thickness of scale affected by layer:
h b ＝(1200-1173)×0.026×2＝1.404 (4)
h c the relative thickness of the scale, which is affected by the Mn-rich layer, is expressed by the formula 10:
h c ＝-104.2×1.2％＝-1.25 (5)
substituting the calculated result into the formula (1) to obtain the pickling difficulty Q=34.26, wherein the pickling process adopts S 2 。
Example 2
The hot rolled strip steel comprises the following chemical components in percentage by mass: 0.13% of C, 0.1% of Si, 1.15% of Mn, less than or equal to 0.020% of P, less than or equal to 0.0050% of S, and the balance of Fe and other unavoidable impurities. The hot rolling heating temperature was 1150℃and the coiling temperature was 630 ℃.
(1) Constructing a grading mathematical model type of pickling difficulty:
Q＝mh t ×n lnω(Si)+4 ＝0.42×h t ×2.21＝0.93h t (1)
h in t Is the relative thickness of the iron scale.
(2) Constructing mathematical model types of relative thickness of iron oxide scale under different hot rolling processes:
h t ＝h a +h b +h c (2)
h a the relative thickness of the iron oxide scale under the action of coiling temperature is as follows:
h a ＝[(630-500)×0.057+15]×0.8＝17.928 (3)
h b is SiO 2 And FeSiO 4 Relative thickness of scale affected by layer:
h b ＝0 (4)
h c relative thickness of scale affected by Mn-rich layer:
h c ＝-104.2×1.15％＝-1.198 (5)
substituting the calculated result into the formula (1) to obtain the pickling difficulty Q=15.56, wherein the pickling process adopts S 1 。
According to the embodiment, the selection method can divide the products with similar pickling difficulties into the same group and adopts similar pickling process parameters, so that on-site technicians can be facilitated to quickly adjust the pickling process to cover the production capacity of the elevator group, and meanwhile, the pickling difficulty can be calculated in advance and matched with the pickling process in the process of producing the all-new product, so that the research and development of the new product and the industrialization efficiency are greatly improved.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (7)
1. The acid cleaning process sorting method based on the characteristics of raw materials is characterized by comprising the following steps of:
(1) Constructing a grading mathematical model of pickling difficulty:
Q＝mh t ×n lnω(Si)+4 (1)
wherein Q is an acid washing difficulty coefficient for grading strip steel; m is the pickling process coefficient and is a constant; h is a t N is the influence factor of the Si-rich layer of the strip steel on the withdrawal and straightening scale breaking and scale stripping, and is a constant; omega (Si) is the mass percentage of Si element content in the strip steel;
(2) Constructing mathematical model types of relative thickness of iron oxide scale under different hot rolling processes:
h t ＝h a +h b +h c (2)
wherein h is a The relative thickness of the iron oxide scale under the action of coiling temperature is as follows:
wherein T is a The coiling temperature of the strip steel; a, a 1 、a 2 、a 3 、a 4 、a 5 The growth coefficient of the iron scale affected by temperature is constant; h is a 1 H 2 Is a constant;
wherein h is b Is SiO 2 And FeSiO 4 Relative thickness of scale affected by layer:
wherein T is b B is the heating temperature of the hot rolled strip 1 The growth coefficient of the iron scale affected by silicon element is constant;
wherein h is c Relative thickness of scale affected by Mn-rich layer:
h c ＝c 1 ×ω(Mn) (5)
wherein, c 1 Is the growth coefficient of the iron scale affected by manganese element, is constant, and c 1 <0; omega (Mn) is the mass percentage of Mn element content in the strip steel;
(3) According to experimental data and on-site production practice, establishing a pickling difficulty level and a pickling process database of a used pickling line; and (3) calculating a Q value according to formulas (1), (2), (3), (4) and (5), bringing the Q value into a database, judging the pickling difficulty level of the Q value, and selecting a pickling process matched with the Q value.
2. The method of claim 1, wherein in formula (1), m=0.42 and n=2.21.
3. The method of claim 1, wherein in formula (3), a 1 ＝0.15、a 2 ＝0.057、a 3 ＝0.80、a 4 ＝0.267、a 5 ＝0.75、h 1 ＝15、h 2 ＝20。
4. The method for selecting a pickling process based on the characteristics of a raw material according to claim 1, wherein in the formula (4), b 1 ＝0.026。
5. The method of claim 1, wherein in equation (5), c 1 ＝-104.2。
6. The method for selecting a pickling process based on raw material characteristics according to claim 1, wherein the used pickling line is a three-section type shallow tank turbulent pickling line and a five-section type rinsing pickling line.
7. The method of claim 6, wherein the database is divided into five pickling difficulty levels, Q is more than or equal to 0 and less than 15, Q is more than or equal to 15 and less than or equal to 30, Q is more than or equal to 30 and less than or equal to 45, Q is more than or equal to 45 and less than or equal to 50, and the corresponding pickling processes are S respectively 0 、S 1 、S 2 、S 3 、S 4 。
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