CN117363838A - Smelting control method and system for continuous dephosphorization and sulfur, electronic equipment and medium - Google Patents
Smelting control method and system for continuous dephosphorization and sulfur, electronic equipment and medium Download PDFInfo
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- 238000003723 Smelting Methods 0.000 title claims abstract description 386
- 238000000034 method Methods 0.000 title claims abstract description 110
- 229910052717 sulfur Inorganic materials 0.000 title claims abstract description 66
- 239000011593 sulfur Substances 0.000 title claims abstract description 65
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 title claims abstract description 62
- 239000000463 material Substances 0.000 claims abstract description 121
- 238000006477 desulfuration reaction Methods 0.000 claims abstract description 55
- 230000023556 desulfurization Effects 0.000 claims abstract description 55
- 238000012549 training Methods 0.000 claims description 60
- 238000005275 alloying Methods 0.000 claims description 38
- 238000013528 artificial neural network Methods 0.000 claims description 35
- 238000007670 refining Methods 0.000 claims description 25
- 238000004590 computer program Methods 0.000 claims description 11
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- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 38
- 239000002893 slag Substances 0.000 description 21
- 229910052742 iron Inorganic materials 0.000 description 19
- 229910000831 Steel Inorganic materials 0.000 description 17
- 239000010959 steel Substances 0.000 description 17
- 229910052698 phosphorus Inorganic materials 0.000 description 13
- 239000011574 phosphorus Substances 0.000 description 12
- 239000012452 mother liquor Substances 0.000 description 11
- PXHVJJICTQNCMI-UHFFFAOYSA-N Nickel Chemical compound [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 description 10
- 238000009847 ladle furnace Methods 0.000 description 10
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 9
- 235000008733 Citrus aurantifolia Nutrition 0.000 description 9
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- 235000019738 Limestone Nutrition 0.000 description 3
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- 229910000519 Ferrosilicon Inorganic materials 0.000 description 2
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 description 2
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Classifications
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21C—PROCESSING OF PIG-IRON, e.g. REFINING, MANUFACTURE OF WROUGHT-IRON OR STEEL; TREATMENT IN MOLTEN STATE OF FERROUS ALLOYS
- C21C7/00—Treating molten ferrous alloys, e.g. steel, not covered by groups C21C1/00 - C21C5/00
- C21C7/04—Removing impurities by adding a treating agent
- C21C7/064—Dephosphorising; Desulfurising
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21C—PROCESSING OF PIG-IRON, e.g. REFINING, MANUFACTURE OF WROUGHT-IRON OR STEEL; TREATMENT IN MOLTEN STATE OF FERROUS ALLOYS
- C21C7/00—Treating molten ferrous alloys, e.g. steel, not covered by groups C21C1/00 - C21C5/00
- C21C7/0006—Adding metallic additives
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21C—PROCESSING OF PIG-IRON, e.g. REFINING, MANUFACTURE OF WROUGHT-IRON OR STEEL; TREATMENT IN MOLTEN STATE OF FERROUS ALLOYS
- C21C7/00—Treating molten ferrous alloys, e.g. steel, not covered by groups C21C1/00 - C21C5/00
- C21C7/0075—Treating in a ladle furnace, e.g. up-/reheating of molten steel within the ladle
-
- C—CHEMISTRY; METALLURGY
- C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
- C22C—ALLOYS
- C22C33/00—Making ferrous alloys
- C22C33/04—Making ferrous alloys by melting
- C22C33/06—Making ferrous alloys by melting using master alloys
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21C—PROCESSING OF PIG-IRON, e.g. REFINING, MANUFACTURE OF WROUGHT-IRON OR STEEL; TREATMENT IN MOLTEN STATE OF FERROUS ALLOYS
- C21C2300/00—Process aspects
- C21C2300/06—Modeling of the process, e.g. for control purposes; CII
-
- C—CHEMISTRY; METALLURGY
- C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
- C22C—ALLOYS
- C22C38/00—Ferrous alloys, e.g. steel alloys
- C22C38/001—Ferrous alloys, e.g. steel alloys containing N
-
- C—CHEMISTRY; METALLURGY
- C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
- C22C—ALLOYS
- C22C38/00—Ferrous alloys, e.g. steel alloys
- C22C38/02—Ferrous alloys, e.g. steel alloys containing silicon
-
- C—CHEMISTRY; METALLURGY
- C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
- C22C—ALLOYS
- C22C38/00—Ferrous alloys, e.g. steel alloys
- C22C38/04—Ferrous alloys, e.g. steel alloys containing manganese
-
- C—CHEMISTRY; METALLURGY
- C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
- C22C—ALLOYS
- C22C38/00—Ferrous alloys, e.g. steel alloys
- C22C38/18—Ferrous alloys, e.g. steel alloys containing chromium
- C22C38/40—Ferrous alloys, e.g. steel alloys containing chromium with nickel
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
- F27D2019/0028—Regulation
- F27D2019/0075—Regulation of the charge quantity
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- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Materials Engineering (AREA)
- Metallurgy (AREA)
- Organic Chemistry (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
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- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
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- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Refinement Of Pig-Iron, Manufacture Of Cast Iron, And Steel Manufacture Other Than In Revolving Furnaces (AREA)
Abstract
The invention provides a smelting control method, a smelting control system, electronic equipment and a smelting control medium for continuous dephosphorization and desulfurization, wherein the smelting control method comprises the following steps: in each smelting stage of the continuous dephosphorization and sulfur smelting process, initial smelting data and internal control result data are obtained; inputting the initial smelting data and the internal control result data into a smelting prediction model to obtain smelting material data output by the smelting prediction model; and controlling the interlocking bin to feed materials into the smelting furnace based on smelting material data. According to the scheme provided by the invention, the initial smelting data and the internal control result data of each smelting stage of the continuous dephosphorization and sulfur smelting process are input into the smelting prediction model, so that accurate smelting material data can be obtained, and further, the interlocking bin is controlled to feed materials into the smelting furnace based on the smelting material data, so that automatic control of smelting materials and automatic feeding in the continuous dephosphorization and sulfur smelting process is realized, and compared with a traditional manual control mode, the accuracy and reliability are higher.
Description
Technical Field
The invention relates to the technical field of steel smelting, in particular to a smelting control method, a system, electronic equipment and a medium for continuous dephosphorization and sulfur.
Background
In the steel smelting process, dephosphorization and desulfurization processes are often involved, and dephosphorization and desulfurization of molten steel are one of the basic reactions in the steelmaking process and also an essential metallurgical reaction. In practical application, dephosphorization and desulfurization steps are required to be carried out separately due to the difference of smelting process conditions.
In the related art, dephosphorization and desulfurization processes generally control the types and amounts of materials required in each smelting stage manually by related workers, and because of the manual control mode, the precision of the materials is difficult to ensure, and human errors are easy to occur. Therefore, the traditional dephosphorization and dephosphorization smelting links have the problem of lower accuracy and reliability.
Disclosure of Invention
The invention provides a smelting control method, a system, electronic equipment and a medium for continuous dephosphorization and desulfurization, which are used for solving the defects of lower accuracy and reliability of the traditional dephosphorization and dephosphorization smelting links in the prior art.
In a first aspect, the present invention provides a smelting control method for continuous dephosphorization and desulfurization, the method being performed by a controller, the controller being connected to an interlocking silo; the method comprises the following steps:
in each smelting stage of the continuous dephosphorization and sulfur smelting process, initial smelting data and internal control result data are obtained;
inputting the initial smelting data and the internal control result data into a smelting prediction model to obtain smelting material data output by the smelting prediction model; the smelting prediction model is obtained by training a neural network based on initial smelting data samples, internal control result data samples and smelting material data samples of a plurality of smelting stages;
and controlling the interlocking bin to feed materials into the smelting furnace based on the smelting material data.
According to the smelting control method for continuous dephosphorization and sulfur provided by the invention, the smelting prediction model comprises the following steps: an input layer, a hidden layer, and an output layer;
the input layer is used for receiving and transmitting initial smelting data and internal control result data of each smelting stage to the hidden layer;
the hidden layer is used for extracting characteristics of the initial smelting data and the internal control result data, and generating and transmitting smelting material data to the output layer;
the output layer is used for outputting the smelting material data.
According to the smelting control method for continuous dephosphorization and desulfurization provided by the invention, the smelting prediction model is obtained through training the following process, and comprises the following steps:
obtaining smelting key data in a smelting data pool;
preprocessing the smelting key data to establish a training sample set; the training sample set comprises initial smelting data samples, internal control result data samples and smelting material data samples of a plurality of smelting stages;
and carrying out band progressive training on the pre-established neural network based on the training sample set to obtain a smelting prediction model.
According to the smelting control method for continuous dephosphorization and desulfurization provided by the invention, the continuous dephosphorization and desulfurization smelting process comprises a dephosphorization pretreatment stage, a desulfurization alloying stage and a furnace refining stage;
the band progressive training of the pre-established neural network based on the training sample set comprises the following steps:
inputting an initial smelting data sample, an internal control result data sample and a smelting material data sample in the dephosphorization pretreatment stage into a pre-established neural network for training;
inputting an internal control result data sample output by the dephosphorization pretreatment stage, an internal control result data sample of the desulfurization alloying stage and a smelting material data sample into a pre-established neural network for training;
and inputting the internal control result data sample output by the desulfurization alloying stage, the internal control result data sample of the furnace refining stage and the smelting material data sample into a pre-established neural network for training.
According to the smelting control method for continuous dephosphorization and sulfur provided by the invention, smelting material data comprise material types and material amounts;
based on the smelting material data, controlling the interlocking bin to feed materials into the smelting furnace, comprising:
according to the material types, controlling a target discharge port of the interlocking bin to be opened;
and controlling the discharging amount of the target discharging hole according to the material amount.
According to the smelting control method for continuous dephosphorization and desulfurization provided by the invention, the continuous dephosphorization and desulfurization smelting process comprises a dephosphorization pretreatment stage, a desulfurization alloying stage and a furnace refining stage;
the initial smelting data of the desulfurization alloying stage is determined based on the internal control result data output by the dephosphorization pretreatment stage; initial smelting data of the furnace refining stage is determined based on output internal control result data of the desulfurization alloying stage.
In a second aspect, the present invention also provides a smelting control system for continuous dephosphorization and desulfurization, comprising:
the interlocking bin is arranged close to a smelting furnace used in the continuous dephosphorization and sulfur smelting process;
the controller is connected with the interlocking bin and is used for acquiring initial smelting data and internal control result data in each smelting stage of the continuous dephosphorization and sulfur smelting process; inputting the initial smelting data and the internal control result data into a smelting prediction model to obtain smelting material data output by the smelting prediction model; the smelting prediction model is obtained by training a neural network based on initial smelting data samples, internal control result data samples and smelting material data samples of a plurality of smelting stages; and controlling the interlocking bin to feed materials into the smelting furnace based on the smelting material data.
According to the smelting control system for continuous dephosphorization and desulfurization provided by the invention, the interlocking bin comprises a first bin and a second bin, and the smelting furnace comprises an AOD furnace and an LF furnace;
the first bin is arranged close to the AOD furnace and is used for feeding the AOD furnace; the second bin is arranged close to the LF furnace and is used for feeding the LF furnace.
In a third aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing a smelting control method for continuously dephosphorizing sulfur as described in any one of the above when executing the program.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a smelting control method for continuous dephosphorization as described in any one of the above.
According to the smelting control method, system, electronic equipment and medium for continuous dephosphorization and sulfur, the initial smelting data and the internal control result data of each smelting stage of the continuous dephosphorization and sulfur smelting process are input into the smelting prediction model, so that accurate smelting material data can be obtained, and further, based on the smelting material data, the feeding of the interlocking bin into the smelting furnace can be controlled, and therefore automatic control of smelting material and automatic feeding in the continuous dephosphorization and sulfur smelting process is achieved, and compared with a traditional manual control mode, the accuracy and reliability are higher.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a smelting control method for continuous dephosphorization and sulfur removal provided by an embodiment of the invention;
FIG. 2 is a schematic illustration of a process route in a continuous dephosphorization process;
FIG. 3 is a schematic structural diagram of a smelting control system for continuous dephosphorization and sulfur removal provided by an embodiment of the invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While embodiments of the present invention are illustrated in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
It should be understood that although the terms "first," "second," "third," etc. may be used herein to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the invention. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
The embodiment relates to the field of steel smelting, namely the field of steelmaking, and can be particularly applied to dephosphorization and smelting control scenes of each smelting stage in the dephosphorization process. In the related art, the dephosphorization and dephosphorization processes are usually carried out separately due to the fact that different smelting equipment is needed and obvious differences exist in smelting conditions, workers are needed to feed materials timely and manually in the smelting process, materials are controlled according to experience, the smelting process is time-consuming and labor-consuming, the material accuracy is difficult to guarantee, and the accuracy and reliability of dephosphorization and dephosphorization links are low.
Accordingly, the present invention provides a solution to the above-mentioned problems, and details of the smelting control method, system, electronic device and medium for continuous dephosphorization and sulfur provided by the present invention are described below with reference to fig. 1 to 4.
Referring to fig. 1, an embodiment of the present invention provides a smelting control method for continuous dephosphorization and desulfurization, which is performed by a controller, and the controller is connected with an interlocking bunker; the method specifically comprises the following steps:
step 110: and acquiring initial smelting data and internal control result data at each smelting stage of the continuous dephosphorization and sulfur smelting process.
In this embodiment, the continuous dephosphorization and the sulfur removal process can be realized by performing continuous dephosphorization and sulfur removal through an AOD (Argon Oxygen Decarburization, argon oxygen refining) Furnace and performing fine adjustment by matching with an LF (Ladle Furnace) Furnace.
In practical application, two interlocking bins can be arranged, namely an AOD furnace and an LF furnace are respectively arranged correspondingly, so that the automatic feeding function in the reaction process is realized.
In some embodiments, the continuous dephosphorization and desulfurization process may specifically include three smelting stages, namely a dephosphorization pretreatment stage, a desulfurization alloying stage, and a furnace refining stage.
The dephosphorization pretreatment stage mainly removes the phosphorus content in the molten iron mother liquor, synchronously removes a small amount of sulfur, and simultaneously adds partial metal ores or semi-finished products which are not easy to oxidize to perform heat balance and alloying. In the dephosphorization pretreatment stage, molten iron mother liquor can be added according to the charging amount, the initial sulfur and phosphorus content of the molten iron mother liquor which is charged into the furnace can be sampled and detected, and the AOD furnace is utilized to pretreat the high-phosphorus and high-sulfur molten iron mother liquor. The dephosphorization pretreatment stage needs to clean slag as much as possible, and the molten iron mother liquor component after dephosphorization pretreatment is taken as the initial component of the desulfurization alloying stage.
The desulfurization alloying stage is integrated with oxidation and reduction, and is used for temperature measurement and sampling, slag skimming operation is not carried out in the desulfurization alloying stage, slag flowing can be carried out when temperature measurement and sampling are carried out according to the needs, and the internal control requirement of the steel grade is basically met after the process is finished.
And in the furnace refining stage, lime can be supplemented according to the desulfurization effect and alloying component deviation so as to further desulfurize and realize alloy fine adjustment.
It can be understood that the initial smelting data of each smelting stage can be determined according to the components and the content of the main material after the completion of the previous smelting stage, and the internal control result data is the attribute data of the main material after the completion of the current smelting stage.
Step 120: inputting the initial smelting data and the internal control result data into a smelting prediction model to obtain smelting material data output by the smelting prediction model; the smelting prediction model is obtained by training the neural network based on initial smelting data samples, internal control result data samples and smelting material data samples of a plurality of smelting stages.
In this embodiment, through the pre-trained smelting prediction model, after initial smelting data and internal control result data of the current smelting stage are input in the continuous dephosphorization and sulfur smelting process, smelting material data of the current smelting stage can be directly determined, so that an automatic control function of the material data is realized, and compared with a mode of controlling the material according to experience of a worker, the material control precision is higher.
Step 130: and controlling the interlocking bin to feed materials into the smelting furnace based on smelting material data.
In this embodiment, after confirming smelting materials data, can control the interlocking feed bin and add the material to the smelting furnace according to smelting materials data to realize automatic material loading function, reduced materials and reinforced process lazy to the staff, and the smelting process is more accurate and reliable.
In one embodiment, the smelting prediction model may specifically include: an input layer, a hidden layer, and an output layer.
The input layer is used for receiving and transmitting initial smelting data and internal control result data of each smelting stage to the hidden layer.
The hidden layer is used for extracting characteristics of initial smelting data and internal control result data, and generating and transmitting smelting material data to the output layer.
The output layer is used for outputting smelting material data.
In this embodiment, the structure architecture of the smelting prediction model is built based on neural network training, and a BP (Back Propagation) neural network may be adopted, specifically a three-layer BP neural network including an input layer, a hidden layer and an output layer may be adopted, where the hidden layer may be built by a smelting logic algorithm, so that feature extraction and feature analysis of input layer data may be implemented, and thus smelting material data to be output may be obtained.
In one embodiment, the smelting prediction model may be specifically obtained through training of the following process, including:
firstly, obtaining smelting key data in a smelting data pool.
In this embodiment, the automatic learning may be performed through an AI algorithm, and a smelting data pool is established based on accumulation of sample data required for completing training of a smelting prediction model in the large data pool, and smelting key data may be extracted from the smelting data pool before training, for example, initial smelting data, internal control result data and smelting material data in each smelting stage in a continuous desulfurization and phosphorus process may be extracted as smelting key data.
Secondly, preprocessing smelting key data, and establishing a training sample set; the training sample set comprises initial smelting data samples, internal control result data samples and smelting material data samples of a plurality of smelting stages.
It can be understood that the preprocessing process of the smelting key data mainly ensures the validity and the integrity of the data, the preprocessing process can be processing operations such as data rejection, data complement and the like, for example, invalid data in the smelting key data can be rejected, blank data in the smelting key data can be complemented, and the data accuracy of a training sample set established later can be improved through the preprocessing operation.
And thirdly, performing band progressive training on a pre-established neural network based on a training sample set to obtain a smelting prediction model.
In practical application, the neural network can be trained in a supervised training or unsupervised training mode, and the trained smelting prediction model is finally obtained by continuously updating and adjusting model parameters.
In some embodiments, before the trained smelting prediction model is put into use, the trained smelting prediction model can be verified through a verification sample set, the verified smelting prediction model is tested through a test sample set, and finally the qualified smelting prediction model can be put into use.
In one embodiment, the continuous dephosphorization and sulfur smelting process specifically comprises a dephosphorization pretreatment stage, a desulfurization alloying stage and a furnace refining stage.
Based on a training sample set, carrying out band progressive training on a pre-established neural network, and specifically comprising the following steps:
and inputting an initial smelting data sample, an internal control result data sample and a smelting material data sample in the dephosphorization pretreatment stage into a pre-established neural network for training.
And inputting the internal control result data sample output in the dephosphorization pretreatment stage, the internal control result data sample in the desulfurization alloying stage and the smelting material data sample into a pre-established neural network for training.
And inputting an internal control result data sample output in the desulfurization alloying stage, an internal control result data sample in the furnace refining stage and a smelting material data sample into a pre-established neural network for training.
In this embodiment, taking the BP neural network training process as an example, in the model training link, the current stage sampling data and the last stage smelting result can be used as the input data of the second stage input layer to perform band type progressive to complete the BP neural network cycle training.
It can be understood that in the band progressive training process, the internal control result data of the previous smelting stage can be used as an initial smelting data sample of the current smelting stage, so that the relevance of data in the training process is ensured.
In one embodiment, the smelting charge data may include, in particular, a charge type and a charge amount.
Based on smelting material data, control the feeding of interlocking feed bin to smelting furnace, specifically include:
according to the material types, controlling a target discharge port of the interlocking bin to be opened;
and controlling the discharging amount of the target discharging hole according to the material amount.
In some embodiments, the interlocking bin can set up a plurality of bins, and different bins are used for storing different kinds of materials, and each bin is equipped with the discharge gate, before the blowing, the discharge gate of each bin can be closed through the bin gate, when the ejection of compact, can control the bin gate to open.
In practical application, can deposit the position door of bin that corresponds kind material according to the control of material kind and open to the discharge gate of this position of bin is opened, according to the volume of material, can control the blowing volume of discharge gate, specifically can control the blowing volume of discharge gate through control door aperture, blowing speed and blowing flow.
In one embodiment, the continuous dephosphorization and sulfur smelting process specifically comprises a dephosphorization pretreatment stage, a desulfurization alloying stage and a furnace refining stage.
The initial smelting data of the desulfurization alloying stage is determined based on the internal control result data output by the dephosphorization pretreatment stage; initial smelting data of the furnace refining stage is determined based on output internal control result data of the desulfurization alloying stage.
In this embodiment, the dephosphorization pretreatment stage, the desulfurization alloying stage and the furnace refining stage are performed according to a sequential smelting order, so that the smelting result of the previous smelting stage can provide a data basis for determining the initial smelting data of the next smelting stage.
In a specific implementation, the molten iron mother liquor can be flexibly selected according to cost, for example, various hot feed sources such as blast furnace molten iron, submerged arc furnace chromium molten iron, intermediate frequency furnace impurity water and the like can be selected, the phosphorus content in the molten iron mother liquor is less than or equal to 0.150%, the sulfur content is less than or equal to 0.450%, a trace deviation is allowed, and the molten iron mother liquor is fed in red as far as possible so as to ensure heat in the furnace.
In the dephosphorization pretreatment stage, the slag is mainly limestone and magnesium balls, sintering ore is added in the dephosphorization process, nickel-containing iron blocks and other alloy elements which are not easy to oxidize are added according to heat, and coke can be added before steel addition due to insufficient heat source; slag formation control is carried out according to the final slag basicity R=2.5-3.0, the phosphorus content is less than or equal to 0.020%, the temperature is controlled according to 1650 ℃, and the carbon content is more than 2.0%; the pressure of the air gun is controlled to be 0.60-0.75MPa, the pressure of the top gun is controlled to be 1.0-1.2MPa, and the gun position is more than or equal to 1.5m; after slag flowing operation, a bypass is opened to remove slag by nitrogen, and slag is scraped off; this stage can complete the dephosphorization reaction and perform preliminary alloying.
In practical application, the initial smelting data of the dephosphorization pretreatment stage mainly comprise weight, component, temperature and other data of molten iron mother liquor, and the internal control result data are final slag alkalinity, one-shot phosphorus content and other data.
In the desulfurization alloying stage, slag in the oxidation process is mainly lime, nitrogen-oxygen partial pressure ratio blowing is carried out according to the carbon content in the smelting process, alloy elements such as ferrochrome and the like are added in the smelting process, the air gun pressure is controlled to be 1.55-1.65MPa, and the top gun pressure is controlled to be 0.9MPa; stopping oxygen and stirring for 1min by using pure inert gas before the reduction period, further utilizing residual oxygen for decarbonization, controlling the pressure of an air gun at 0.70-0.85MPa, adding a reducing agent, fluorite, manganese and other high-purity alloys, controlling the alkalinity of final slag according to R=1.7-2.0, reducing the temperature to be less than or equal to 1680 ℃, and adding continuous casting or steel rolling for head and tail cutting for cooling if the temperature is higher; the desulfurization reaction is completed in the stage, the alloying of molten steel is completed, meanwhile, the stage can directly carry out continuous casting backwater operation of the same steel grade, and slag flowing operation can be carried out according to the desulfurization effect and the slag quantity.
In practical application, the initial smelting data of the desulfurization alloying stage mainly comprise the weight, the components, the temperature and other data of the pretreated molten iron mother liquor, the internal control result data are the final slag alkalinity and other data, and the smelting material data comprise the types and the consumption of alloy elements.
In the refining stage in the furnace, lime supplementing operation can be performed according to the desulfurization effect and alloying component deviation so as to further desulfurize and conduct alloy fine adjustment, if the reduction temperature is higher, continuous casting or steel rolling head and tail cutting can be supplemented to cool, the refining time is controlled to be 3-5min, and then tapping is completed.
In practical application, initial smelting data in the furnace refining stage are data such as weight, components, temperature and the like of main materials after molten steel alloying, internal control result data are control target data after refining, and smelting material data comprise data such as lime material, fine-tuning alloy types and consumption and the like.
Referring to fig. 2, in the continuous dephosphorization and sulfur process of the embodiment, the process route adopted is links such as blast furnace molten iron, intermediate frequency furnace water, submerged arc furnace chromium molten iron and the like, after being proportioned, the molten iron is sent into an AOD furnace for smelting, an LF furnace for fine tuning, a VD (Vacuum Degassing) furnace for Degassing (optional links), and a continuous casting machine for processing and the like. Compared with the traditional converter or electric furnace multi-set equipment production process, the continuous treatment of one set of equipment on phosphorus and sulfur is realized, the smelting production flow is reduced, the high-cost production of the multi-set equipment is avoided, and the continuous dephosphorization and sulfur production and smelting are realized.
The implementation flow of the smelting control method for continuous dephosphorization and sulfur provided in this embodiment is specifically described below by taking smelting low-carbon low-phosphorus low-sulfur austenitic steel S30400 as an example.
In this embodiment, the low carbon low phosphorus low sulfur austenitic steel S30400 comprises the following chemical components in percentage by mass: 0.045% or less of C or less than 0.065%,0.45% or less of Si or less than 0.60%,1.05% or less of Mn or less than 1.20%, P or less than 0.030%, S or less than 0.005%,18.00% or less of Cr or less than 18.30%,8.00% or less of Ni or less than 8.10%, N or less than 0.075% or less, and the tapping amount of an AOD furnace or less than 150t.
The smelting control process specifically comprises the following steps:
firstly, adding 2t of magnesium balls into an AOD furnace, paving 2t of coke, and baking by shaking the furnace back and forth.
And secondly, hot feeding molten iron mother liquor, namely adding blast furnace molten iron according to the weight and nickel content of the molten nickel, and adding medium-frequency miscellaneous material water (scrap steel, nickel-containing iron blocks and the like) to 100-105 tons.
Thirdly, starting converting, controlling the pressure of an air gun to be 0.60-0.75MPa, converting nitrogen and oxygen in a ratio of 1:1, controlling the pressure of a top gun to be 1.0-1.2MPa, controlling the gun position to be more than or equal to 1.5m, and accumulating the top-bottom re-blowing oxygen to be about 4500Nm 3 The method comprises the steps of carrying out a first treatment on the surface of the Adding a small amount of limestone or sinter in batches, wherein each time is less than or equal to 500Kg, the limestone is completed according to 68.5Kg/t, adjusting high-gun-position slag to quickly form foam slag, observing the shape and color of flame at a furnace mouth, and controlling the process temperature according to the temperature less than or equal to 1650 ℃; pouring the furnace to measure the temperature and sample, wherein the phosphorus content is 0.031%, and the temperature is controlled at 1659 ℃; and synchronously opening a bypass of the air gun, blowing away slag surface by using nitrogen, and cleaning slag.
Fourthly, lime is added for 3-4t after the furnace is started, top lance decarburization is carried out, the pressure of an air lance is controlled to be 1.55-1.65MPa, nitrogen and oxygen are blown in steps according to the decarburization area in the proportion of 1:1, 3:1 and 5:1, the pressure of the top lance is controlled to be 0.9MPa, and the total oxygen blowing amount at the top and the bottom is accumulated to be about 5500Nm 3 The method comprises the steps of carrying out a first treatment on the surface of the The process temperature is controlled to be about 1680 ℃, lime and high-carbon ferrochrome are added in batches, the lime is finished according to 47.95kg/t, and the high-carbon ferrochrome is finished according to 308.22 kg/t; stirring for 1min with pure inert gas, and then pouring into a furnace for temperature measurement and sampling, wherein the carbon content is 0.087%, the sulfur content is 0.118%, and the temperature is 1673 ℃.
Fifth step, the furnace is started to blow oxygen 485Nm 3 Stirring with pure inert gas for 1min, transferring nitrogen gas for reduction, adding fluorite, electrolytic manganese and ferrosilicon for reduction, wherein the fluorite quantity=17% of lime consumption is added, the electrolytic manganese is completed according to 8.85kg/t, the ferrosilicon is added according to 14.52kg/t according to the oxidation condition of chromium element, transferring argon gas for denitrification after 3min of reduction, and the reduction time is prolongedFinishing alloying in not less than 6 min; and (3) pouring the furnace to measure the temperature and sample, wherein the carbon content is 0.041%, the sulfur content is 0.0085%, the temperature is 1673 ℃, and the slag flowing operation is carried out according to the condition of reducing the residual steel and the slag quantity.
And sixthly, starting the furnace, adding 550Kg of lime, 300Kg of fluorite and other alloy elements, fine-tuning, and refining for 3min to finish tapping.
And in each smelting stage, the current initial smelting data and the internal control result data containing production feeding information are accurately input into a smelting prediction model, the smelting material data of the current smelting stage is calculated by utilizing multiple linear regression and a loss function, and real-time control parameters are output to control the smelting process.
And finally tapping 146.5t, wherein the molten steel comprises the following chemical components in percentage by mass: 0.042% of C, 0.48% of Si, 1.07% of Mn, 0.028% of P, 0.0038% of S, 18.15% of Cr, 8.05% of Ni and 0.045% of N, and meets the requirements of smelting technology, thus obtaining the low-carbon low-phosphorus low-sulfur austenitic steel.
According to the smelting control method for continuous dephosphorization and sulfur provided by the embodiment, the smelting prediction model can realize automatic and accurate control of feeding in each smelting stage while ensuring the smelting process requirements and realizing continuous dephosphorization and sulfur, and is suitable for smelting scenes of high alloy steel such as stainless steel and the like, and the accuracy and stability of the continuous dephosphorization and sulfur smelting process are improved.
Based on the same general inventive concept, the invention also protects a smelting control system for continuous dephosphorization and sulfur, and the smelting control system for continuous dephosphorization and sulfur provided by the invention is described below, and the smelting control system for continuous dephosphorization and sulfur described below and the smelting control method for continuous dephosphorization and sulfur described above can be referred to correspondingly.
Referring to fig. 3, the smelting control system for continuous dephosphorization and sulfur provided by the embodiment of the invention specifically includes:
an interlocking bin 210 arranged near a smelting furnace used in the continuous dephosphorization and desulfurization smelting process;
the controller 220 is connected with the interlocking bin 210 and is used for acquiring initial smelting data and internal control result data in each smelting stage of the continuous dephosphorization and sulfur smelting process; inputting the initial smelting data and the internal control result data into a smelting prediction model to obtain smelting material data output by the smelting prediction model; the smelting prediction model is obtained by training a neural network based on initial smelting data samples, internal control result data samples and smelting material data samples of a plurality of smelting stages; based on the smelting material data, the interlocking bunker 210 is controlled to feed materials into the smelting furnace.
In one embodiment, the interlocking bin comprises a first bin and a second bin, and the smelting furnace comprises an AOD furnace and an LF furnace;
the first bin is arranged close to the AOD furnace and is used for feeding the AOD furnace; the second feed bin is arranged close to the LF furnace and is used for feeding the LF furnace.
In one embodiment, the smelting prediction model in the controller 220 specifically includes: an input layer, a hidden layer, and an output layer;
the input layer is used for receiving and transmitting initial smelting data and internal control result data of each smelting stage to the hidden layer;
the hidden layer is used for extracting characteristics of initial smelting data and internal control result data, and generating and transmitting smelting material data to the output layer;
the output layer is used for outputting smelting material data.
In one embodiment, the smelting prediction model in the controller 220 may be specifically obtained by training the following process, including:
obtaining smelting key data in a smelting data pool;
preprocessing smelting key data, and establishing a training sample set; the training sample set comprises initial smelting data samples, internal control result data samples and smelting material data samples of a plurality of smelting stages;
and carrying out band progressive training on the pre-established neural network based on the training sample set to obtain a smelting prediction model.
In one embodiment, the continuous dephosphorization and sulfur smelting process specifically comprises a dephosphorization pretreatment stage, a desulfurization alloying stage and a furnace refining stage;
the controller 220 may specifically implement band progressive training of the pre-established neural network based on the training sample set by:
inputting an initial smelting data sample, an internal control result data sample and a smelting material data sample in the dephosphorization pretreatment stage into a pre-established neural network for training;
inputting an internal control result data sample output in the dephosphorization pretreatment stage, an internal control result data sample in the desulfurization alloying stage and a smelting material data sample into a pre-established neural network for training;
and inputting an internal control result data sample output in the desulfurization alloying stage, an internal control result data sample in the furnace refining stage and a smelting material data sample into a pre-established neural network for training.
In one embodiment, the smelting charge data includes a charge type and a charge amount;
the controller 220 can specifically control the interlocking bin to charge the smelting furnace based on the smelting material data through the following process:
according to the material types, controlling a target discharge port of the interlocking bin to be opened;
and controlling the discharging amount of the target discharging hole according to the material amount.
In one embodiment, the continuous dephosphorization and sulfur smelting process comprises a dephosphorization pretreatment stage, a desulfurization alloying stage and a furnace refining stage;
the initial smelting data of the desulfurization alloying stage is determined based on the internal control result data output by the dephosphorization pretreatment stage; initial smelting data of the furnace refining stage is determined based on output internal control result data of the desulfurization alloying stage.
In summary, according to the smelting control device for continuous dephosphorization and sulfur provided by the embodiment, by inputting the initial smelting data and the internal control result data of each smelting stage of the continuous dephosphorization and sulfur smelting process into the smelting prediction model, accurate smelting material data can be obtained, and further based on the smelting material data, the feeding of the interlocking bin into the smelting furnace can be controlled, so that the automatic control of the smelting material and the automatic feeding in the continuous dephosphorization and sulfur smelting process is realized, and compared with the traditional manual control mode, the accuracy and the reliability are higher.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
As shown in fig. 4, the electronic device may include: processor 310, communication interface (Communications Interface) 320, memory 330 and communication bus 340, wherein processor 310, communication interface 320, memory 330 accomplish communication with each other through communication bus 340. The processor 310 may call logic instructions in the memory 330 to perform the smelting control method for continuous dephosphorization provided in the above embodiments, the method comprising: in each smelting stage of the continuous dephosphorization and sulfur smelting process, initial smelting data and internal control result data are obtained; inputting the initial smelting data and the internal control result data into a smelting prediction model to obtain smelting material data output by the smelting prediction model; the smelting prediction model is obtained by training a neural network based on initial smelting data samples, internal control result data samples and smelting material data samples of a plurality of smelting stages; and controlling the interlocking bin to feed materials into the smelting furnace based on smelting material data.
Further, the logic instructions in the memory 330 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of executing the smelting control method of continuous dephosphorization provided in the above embodiments, the method including: in each smelting stage of the continuous dephosphorization and sulfur smelting process, initial smelting data and internal control result data are obtained; inputting the initial smelting data and the internal control result data into a smelting prediction model to obtain smelting material data output by the smelting prediction model; the smelting prediction model is obtained by training a neural network based on initial smelting data samples, internal control result data samples and smelting material data samples of a plurality of smelting stages; and controlling the interlocking bin to feed materials into the smelting furnace based on smelting material data.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the smelting control method of continuous dephosphorization provided by the above embodiments, the method comprising: in each smelting stage of the continuous dephosphorization and sulfur smelting process, initial smelting data and internal control result data are obtained; inputting the initial smelting data and the internal control result data into a smelting prediction model to obtain smelting material data output by the smelting prediction model; the smelting prediction model is obtained by training a neural network based on initial smelting data samples, internal control result data samples and smelting material data samples of a plurality of smelting stages; and controlling the interlocking bin to feed materials into the smelting furnace based on smelting material data.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; 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 technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The smelting control method for continuous dephosphorization and desulfurization is characterized in that the method is executed by a controller, and the controller is connected with an interlocking bin; the method comprises the following steps:
in each smelting stage of the continuous dephosphorization and sulfur smelting process, initial smelting data and internal control result data are obtained;
inputting the initial smelting data and the internal control result data into a smelting prediction model to obtain smelting material data output by the smelting prediction model; the smelting prediction model is obtained by training a neural network based on initial smelting data samples, internal control result data samples and smelting material data samples of a plurality of smelting stages;
and controlling the interlocking bin to feed materials into the smelting furnace based on the smelting material data.
2. The smelting control method for continuous dephosphorization and sulfur according to claim 1, wherein the smelting prediction model comprises: an input layer, a hidden layer, and an output layer;
the input layer is used for receiving and transmitting initial smelting data and internal control result data of each smelting stage to the hidden layer;
the hidden layer is used for extracting characteristics of the initial smelting data and the internal control result data, and generating and transmitting smelting material data to the output layer;
the output layer is used for outputting the smelting material data.
3. The smelting control method for continuous dephosphorization and sulfur according to claim 1, wherein the smelting prediction model is obtained by training the following process, comprising:
obtaining smelting key data in a smelting data pool;
preprocessing the smelting key data to establish a training sample set; the training sample set comprises initial smelting data samples, internal control result data samples and smelting material data samples of a plurality of smelting stages;
and carrying out band progressive training on the pre-established neural network based on the training sample set to obtain a smelting prediction model.
4. The smelting control method for continuous dephosphorization and sulfur according to claim 3, wherein the continuous dephosphorization and sulfur smelting process comprises a dephosphorization pretreatment stage, a desulfurization alloying stage and a furnace refining stage;
the band progressive training of the pre-established neural network based on the training sample set comprises the following steps:
inputting an initial smelting data sample, an internal control result data sample and a smelting material data sample in the dephosphorization pretreatment stage into a pre-established neural network for training;
inputting an internal control result data sample output by the dephosphorization pretreatment stage, an internal control result data sample of the desulfurization alloying stage and a smelting material data sample into a pre-established neural network for training;
and inputting the internal control result data sample output by the desulfurization alloying stage, the internal control result data sample of the furnace refining stage and the smelting material data sample into a pre-established neural network for training.
5. The smelting control method for continuous dephosphorization and sulfur according to claim 1, wherein the smelting material data includes a kind of material and an amount of material;
based on the smelting material data, controlling the interlocking bin to feed materials into the smelting furnace, comprising:
according to the material types, controlling a target discharge port of the interlocking bin to be opened;
and controlling the discharging amount of the target discharging hole according to the material amount.
6. The smelting control method for continuous dephosphorization and sulfur according to claim 1, wherein the continuous dephosphorization and sulfur smelting process comprises a dephosphorization pretreatment stage, a desulfurization alloying stage and a furnace refining stage;
the initial smelting data of the desulfurization alloying stage is determined based on the internal control result data output by the dephosphorization pretreatment stage; initial smelting data of the furnace refining stage is determined based on output internal control result data of the desulfurization alloying stage.
7. A smelting control system for continuous dephosphorization and desulfurization, comprising:
the interlocking bin is arranged close to a smelting furnace used in the continuous dephosphorization and sulfur smelting process;
the controller is connected with the interlocking bin and is used for acquiring initial smelting data and internal control result data in each smelting stage of the continuous dephosphorization and sulfur smelting process; inputting the initial smelting data and the internal control result data into a smelting prediction model to obtain smelting material data output by the smelting prediction model; the smelting prediction model is obtained by training a neural network based on initial smelting data samples, internal control result data samples and smelting material data samples of a plurality of smelting stages; and controlling the interlocking bin to feed materials into the smelting furnace based on the smelting material data.
8. The continuous dephosphorization and sulfur smelting control system according to claim 7, wherein the interlocking bin comprises a first bin and a second bin, and the smelting furnace comprises an AOD furnace and an LF furnace;
the first bin is arranged close to the AOD furnace and is used for feeding the AOD furnace; the second bin is arranged close to the LF furnace and is used for feeding the LF furnace.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the smelting control method for continuous dephosphorization according to any one of claims 1 to 6 when executing the program.
10. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements a smelting control method for continuous dephosphorization according to any one of claims 1 to 6.
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2023
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