CN116640906B - Ladle bottom blowing carbon dioxide smelting method and system based on 5G technology - Google Patents
Ladle bottom blowing carbon dioxide smelting method and system based on 5G technology Download PDFInfo
- Publication number
- CN116640906B CN116640906B CN202310926405.7A CN202310926405A CN116640906B CN 116640906 B CN116640906 B CN 116640906B CN 202310926405 A CN202310926405 A CN 202310926405A CN 116640906 B CN116640906 B CN 116640906B
- Authority
- CN
- China
- Prior art keywords
- carbon dioxide
- data
- ladle
- neural network
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 title claims abstract description 298
- 229910002092 carbon dioxide Inorganic materials 0.000 title claims abstract description 149
- 239000001569 carbon dioxide Substances 0.000 title claims abstract description 147
- 238000003723 Smelting Methods 0.000 title claims abstract description 100
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000007664 blowing Methods 0.000 title claims abstract description 64
- 238000005516 engineering process Methods 0.000 title claims abstract description 23
- 238000013528 artificial neural network Methods 0.000 claims abstract description 67
- 230000008569 process Effects 0.000 claims abstract description 50
- 238000012360 testing method Methods 0.000 claims abstract description 35
- 238000012549 training Methods 0.000 claims abstract description 21
- 238000005457 optimization Methods 0.000 claims abstract description 17
- 238000013480 data collection Methods 0.000 claims abstract description 14
- 238000007781 pre-processing Methods 0.000 claims abstract description 14
- 230000008016 vaporization Effects 0.000 claims description 63
- 238000009834 vaporization Methods 0.000 claims description 55
- 229910000831 Steel Inorganic materials 0.000 claims description 22
- 239000010959 steel Substances 0.000 claims description 22
- 239000007788 liquid Substances 0.000 claims description 13
- 238000012937 correction Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 13
- 230000005540 biological transmission Effects 0.000 abstract description 8
- 238000002372 labelling Methods 0.000 abstract 1
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 8
- 238000011161 development Methods 0.000 description 4
- 229910052742 iron Inorganic materials 0.000 description 4
- 239000002893 slag Substances 0.000 description 4
- 238000003756 stirring Methods 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 238000005275 alloying Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000011449 brick Substances 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000006477 desulfuration reaction Methods 0.000 description 1
- 230000023556 desulfurization Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000001151 other effect Effects 0.000 description 1
- 230000001590 oxidative effect Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000007306 turnover Effects 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/10—Analysis or design of chemical reactions, syntheses or processes
-
- 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
- C21C7/06—Deoxidising, e.g. killing
-
- 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
- C21C7/04—Removing impurities by adding a treating agent
- C21C7/072—Treatment with gases
-
- 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
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/90—Programming languages; Computing architectures; Database systems; Data warehousing
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Theoretical Computer Science (AREA)
- Computing Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Organic Chemistry (AREA)
- Metallurgy (AREA)
- Crystallography & Structural Chemistry (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Materials Engineering (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Medical Informatics (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Analytical Chemistry (AREA)
- Treatment Of Steel In Its Molten State (AREA)
Abstract
The invention provides a ladle bottom blowing carbon dioxide smelting method and system based on a 5G technology, comprising the following steps: step S1, data collection: collecting carbon dioxide data and ladle smelting process data through a sensor; step S2, data preprocessing: the carbon dioxide data and the corresponding ladle smelting process data are subjected to connection labeling and are divided into a training set and a testing set; and S3, establishing a model: establishing a neural network; step S4, optimizing a model: training the neural network by using the training set, and testing the neural network by using the testing set; and S5, smelting optimization: the carbon dioxide data is adjusted in the actual smelting process through the neural network, so that the smelting effect is optimized. According to the invention, a model is built to predict the carbon dioxide smelting result, and a 5G digital technology is adopted as an information transmission mode, so that the information transmission time in the smelting process can be greatly reduced, the digital management platform can more conveniently adjust the data of ladle bottom blowing carbon dioxide according to a neural network, and the ladle bottom blowing carbon dioxide smelting effect is optimized.
Description
Technical Field
The invention belongs to the field of metallurgical engineering, and particularly relates to a ladle bottom blowing carbon dioxide smelting method and system based on a 5G technology.
Background
Because of the two restrictions of the iron and steel enterprises in China facing the environment and resources, the low-carbon development mode of the iron and steel industry is actively implemented, and along with the gradual development of the low-carbon economic development mode, a part of iron and steel enterprises are already in CO 2 The technical field is utilized to move in front of other iron and steel enterprises. The ladle bottom blowing carbon dioxide smelting technology is energy-saving and emission-reducing, and CO is reduced 2 The discharge is an effective process.
Ladle bottom blowing CO 2 Compared with the traditional ladle bottom blowing N 2 Or Ar increases the stirring kinetic energy intensity under the condition of unit gas quantity, wherein CO 2 And N 2 Ar are mutually converted, and the conversion ratio is from 0 to 100 percent, which is favorable for alloying. CO during smelting 2 Is a weak oxidizing gas and has the heat absorption or micro-heat release effect, and the physical and chemical characteristics are CO in the metallurgical industry 2 The energy conservation and emission reduction and the high-value utilization of resources provide a new way. At present, the prior art of ladle bottom blowing by partially adopting carbon dioxide exists in China, but because the development of the ladle bottom blowing of the carbon dioxide is relatively late, the ladle bottom blowing parameters of the carbon dioxide still need to be controlled by workers depending on manual experience to achieve better process effect, and the large-scale popularization and application of the ladle bottom blowing of the carbon dioxide are not facilitated. Therefore, there is a need for a method of automatically controlling carbon dioxide parameters during ladle bottom blowing of carbon dioxide.
Disclosure of Invention
Aiming at the technical problems, one of the purposes of one mode of the invention is to provide a ladle bottom blowing carbon dioxide smelting method based on a 5G technology, corresponding data are collected through a sensor in the process of ladle bottom blowing carbon dioxide, a BP neural network model of carbon dioxide ladle bottom blowing is established through the collected data, in the actual production process, a smelting result can be predicted by carbon dioxide parameters through the BP neural network, and finally, the adjustment of the carbon dioxide parameters is realized, so that the best ladle bottom blowing carbon dioxide smelting effect is achieved.
One of the purposes of one mode of the invention is to provide a ladle bottom blowing carbon dioxide smelting system based on a 5G technology, which comprises a vaporization device, a data collection module, a data preprocessing module, a model building module, a model optimizing module, a 5G module and a digital management platform, wherein the 5G digital technology is adopted as an information transmission mode, so that the information transmission time in the smelting process can be greatly reduced, the digital management platform can adjust the data of ladle bottom blowing carbon dioxide according to a BP neural network more conveniently, and the ladle bottom blowing carbon dioxide smelting effect is optimized.
Note that the description of these objects does not prevent the existence of other objects. Not all of the above objects need be achieved in one embodiment of the present invention. Other objects than the above objects can be extracted from the description of the specification, drawings, and claims.
The present invention achieves the above technical object by the following means.
A ladle bottom blowing carbon dioxide smelting method based on a 5G technology comprises the following steps:
step S1, data collection: converting the carbon dioxide liquid into carbon dioxide gas by using a vaporization device, blowing carbon dioxide at the bottom of the ladle, and collecting carbon dioxide data and ladle smelting process data by using a sensor;
step S2, data preprocessing: the carbon dioxide data collected in the step S1 are marked in a connection way with corresponding ladle smelting process data, and are divided into a training set and a testing set;
step S3, establishing a model: establishing a BP neural network with carbon dioxide data as input and ladle smelting process data as output;
step S4, model optimization: training the BP neural network established in the step S3 by using the training set in the step S2, and testing the BP neural network by using the testing set until the BP neural network result accords with the testing set;
s5, smelting optimization: and (3) blowing carbon dioxide at the bottom of the ladle, collecting carbon dioxide data through a sensor, inputting the carbon dioxide data into the BP neural network after optimization in the step (S4) through a 5G module, and if the ladle smelting process data output by the BP neural network does not accord with smelting conditions, adjusting the data of the ladle bottom-blowing carbon dioxide until the ladle smelting process data output by the BP neural network accord with the smelting conditions, and carrying out ladle smelting.
In the above scheme, the step S1 of carbon dioxide data information includes carbon dioxide gas pressure, carbon dioxide gas flow rate and vaporization capacity of the vaporization device.
Further, the vaporization capacity of the vaporization device is as follows:
y=a+bx-w
wherein:
y is the vaporization capacity of the vaporization device, and the unit is Nm 3 /h；
a is a pressure-influencing dimensionless factor;
b is a dimensionless factor of the influence of the liquid CO2 flow;
x is the vaporization amount of carbon dioxide, and the unit is NL/min;
w is the empirical correction factor for pressure and flow. Ranging from 0 to 1050.
In the above scheme, the ladle smelting process data information in step S1 includes the molten steel temperature and the molten steel composition.
In the above scheme, the step S3BP neural network includes an input layer, an hidden layer and an output layer;
the carbon dioxide data is input to the input layer, acts on the output node through the hidden layer, and is subjected to nonlinear transformation to generate ladle smelting process data to be output by the output layer.
In the scheme, the steel liquid surface is required to be fluctuated when the ladle is used for blowing the carbon dioxide in a bottom mode, and tubular flow or splashing cannot be generated.
In the above scheme, the error between the BP neural network result and the test set is less than 3% when the model is optimized in the step S4, and the BP neural network result and the test set are considered to be in line with the test set.
In the above scheme, the steel ladle smelting process data error output by the BP neural network in the step S5 of carbon dioxide smelting is less than 5% and is regarded as meeting smelting conditions.
A ladle bottom blowing carbon dioxide smelting system based on a 5G technology comprises a vaporizing device, a data collection module, a data preprocessing module, a model building module, a model optimizing module, a 5G module and a digital management platform;
the vaporization device is used for converting carbon dioxide liquid into carbon dioxide gas;
the data collection module is used for collecting carbon dioxide data and ladle smelting process data by using a sensor;
the data preprocessing module is used for marking the carbon dioxide data collected by the data collecting module in a connection way with corresponding ladle smelting process data and dividing the data into a training set and a testing set;
the model building module is used for building a BP neural network which is input into carbon dioxide data and output into ladle smelting process data;
the model optimization module is used for training the BP neural network established by the model establishment module by using the training set divided by the data preprocessing module, and testing the BP neural network by using the testing set until the BP neural network result accords with the testing set;
the 5G module is used for transmitting the carbon dioxide data collected by the data collection module to the BP neural network optimized by the model optimization module;
the digital management platform is used for adjusting the data of ladle bottom blowing carbon dioxide according to the ladle smelting process data output by the BP neural network until the ladle smelting process data output by the BP neural network meets smelting conditions.
In the scheme, the vaporization device comprises a low-temperature variable flow pressure control valve, a valve base, a carbon dioxide adding device, a pressure gauge, a pressure reducing valve, a pressure control relief valve, an outlet valve, a carbon dioxide vaporization module, a vaporization module base and a supporting member;
the vaporizing device base is arranged at the top of the supporting component;
the carbon dioxide vaporization module is arranged at the top of the vaporization module base;
the pressure control relief valve is arranged on the valve base;
the low-temperature variable flow pressure control valve, the carbon dioxide adding device, the pressure gauge, the pressure reducing valve, the pressure control relief valve and the outlet valve are connected in sequence;
the low-temperature variable flow pressure control valve is connected with the carbon dioxide vaporization module through a carbon dioxide adding device.
Compared with the prior art, the invention has the beneficial effects that:
according to one mode of the invention, the BP neural network of the ladle bottom blowing carbon dioxide is established, so that the carbon dioxide parameters can be adjusted in real time, and the smelting effect is finally optimized, so that the ladle bottom blowing meets the smelting task requirement.
According to one mode of the invention, the ladle bottom blowing carbon dioxide smelting system based on the 5G technology adopts the 5G digital technology as an information transmission mode, so that the information transmission time in the smelting process can be greatly reduced, the digital management platform can adjust the ladle bottom blowing carbon dioxide data according to the BP neural network more conveniently, and the ladle bottom blowing carbon dioxide smelting effect is optimized.
Note that the description of these effects does not hinder the existence of other effects. One embodiment of the present invention does not necessarily have all of the above effects. Effects other than the above are obvious and can be extracted from the description of the specification, drawings, claims, and the like.
Drawings
Fig. 1 is a schematic structural view of an embodiment of the present invention.
Fig. 2 is a schematic view of a vaporization apparatus according to an embodiment of the present invention.
In the figure: 1. a low-temperature variable flow pressure control valve; 2. a valve base; 3. a carbon dioxide addition device; 4. a pressure gauge; 5. a pressure reducing valve; 6. a pressure-controlled relief valve; 7. an outlet valve; 8. a carbon dioxide vaporization module; 9. a vaporization module base; 10. and a support member.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "front", "rear", "left", "right", "upper", "lower", "axial", "radial", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element in question must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. 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.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
Fig. 1 shows a preferred embodiment of the ladle bottom blowing carbon dioxide smelting method based on the 5G technology, which comprises the following steps:
step S1, data collection: converting the carbon dioxide liquid into carbon dioxide gas by using a vaporization device, blowing carbon dioxide at the bottom of the ladle, and collecting carbon dioxide data and ladle smelting process data by using a sensor;
step S2, data preprocessing: the carbon dioxide data collected in the step S1 are marked in a connection way with corresponding ladle smelting process data, and are divided into a training set and a testing set;
step S3, establishing a model: establishing a BP neural network which is input into carbon dioxide data and output into ladle smelting process data, and constructing an initial weight of an hidden layer by the BP neural network through a thermodynamic calculation model and a kinetic calculation model;
step S4, model optimization: training the BP neural network established in the step S3 by using the training set in the step S2, and testing the BP neural network by using the testing set until the BP neural network result accords with the testing set;
s5, smelting optimization: and (3) blowing carbon dioxide at the bottom of the ladle, collecting carbon dioxide data through a sensor, inputting the carbon dioxide data into the BP neural network after optimization in the step (S4) through a 5G module, and if the ladle smelting process data output by the BP neural network does not accord with smelting conditions, adjusting the data of the ladle bottom-blowing carbon dioxide until the ladle smelting process data output by the BP neural network accord with the smelting conditions, and carrying out ladle smelting.
The step S1 carbon dioxide data information comprises carbon dioxide gas pressure, carbon dioxide gas flow and vaporization capacity of the vaporization device.
According to this embodiment, preferably, the vaporization capacity of the vaporization device is:
y=a+bx-w
wherein:
y is the vaporization capacity of the vaporization device, and the unit is Nm 3 And/h, in the range of 50Nm 3 /h-200Nm 3 /h；
a is a pressure influence dimensionless factor, and the range is 55-72;
b is a dimensionless factor of the influence of the liquid CO2 flow, and the range is 0.3-2.1;
x is the vaporization amount of carbon dioxide, the unit is NL/min, and the range is 50NL/min-1200NL/min;
w is a dimensionless correction coefficient ranging from 0 to 1050.
The ladle smelting process data information in the step S1 comprises molten steel temperature and molten steel components. The temperature of the molten steel is detected by a temperature sensor, and in the production process of the molten steel components, a first-line worker takes a sample, and the sample is sent to a laboratory for detection.
The step S3BP neural network comprises an input layer, an implicit layer and an output layer;
the carbon dioxide data is input to the input layer, acts on the output node through the hidden layer, and is subjected to nonlinear transformation to generate ladle smelting process data to be output by the output layer.
The ladle bottom blowing of carbon dioxide requires fluctuation of the molten steel surface and cannot generate tubular flow or splash.
According to the present embodiment, it is preferable that the gas flow rate at the time of ladle bottom blowing is as follows with reference to table 1:
when the strong stirring is carried out, the visual slag layer is more than or equal to 300mm, and the reference mixed blowing flow is more than or equal to 200NL/min, so that the purpose is deoxidization alloying and desulfurization. When weak stirring is carried out, the visual slag layer is 100-300mm, the reference mixed blowing flow is 20-200NL/min, and the purpose is molten steel static waiting time. When in soft blowing, the visual slag layer should be that molten steel is not exposed, the reference mixed blowing flow rate should be 20-200NL/min, and the purpose of the method is to purify molten steel. The flow and the pressure can not be seen only by adjusting the bottom blowing, and the flow and the pressure are determined according to the ventilation condition of the air brick and the fluctuation degree of the liquid level of steel.
And when the model is optimized in the step S4, the error between the BP neural network result and the test set is less than 3 percent, and the BP neural network result and the test set are considered to be in line with the test set.
In the above scheme, the steel ladle smelting process data error output by the BP neural network in the step S5 of carbon dioxide smelting is less than 5% and is regarded as meeting smelting conditions.
According to the embodiment, the ladle is preferably a turnover ladle, so that good air permeability of the ladle is ensured, and the ladle is clean and free of residual steel and residues. When the ladle is selected, the thickness of the slag line is ensured to meet the requirement of the refractory regulation.
According to the invention, by establishing the BP neural network for ladle bottom blowing carbon dioxide, the carbon dioxide parameters can be adjusted in real time, and the smelting effect is finally optimized, so that the ladle bottom blowing meets the smelting task requirement. Meanwhile, a 5G digital technology is adopted as an information transmission mode, so that the information transmission time in the smelting process can be greatly reduced, the digital management platform can adjust the data of ladle bottom blowing carbon dioxide according to the BP neural network more conveniently, and the ladle bottom blowing carbon dioxide smelting effect is optimized.
Example 2
A ladle bottom blowing carbon dioxide smelting system based on a 5G technology comprises a vaporizing device, a data collection module, a data preprocessing module, a model building module, a model optimizing module, a 5G module and a digital management platform;
the vaporization device is used for converting carbon dioxide liquid into carbon dioxide gas;
the data collection module is used for collecting carbon dioxide data and ladle smelting process data by using a sensor;
the data preprocessing module is used for marking the carbon dioxide data collected by the data collecting module in a connection way with corresponding ladle smelting process data and dividing the data into a training set and a testing set;
the model building module is used for building a BP neural network which is input into carbon dioxide data and output into ladle smelting process data;
the model optimization module is used for training the BP neural network established by the model establishment module by using the training set divided by the data preprocessing module, and testing the BP neural network by using the testing set until the BP neural network result accords with the testing set;
and the 5G module is used for transmitting the carbon dioxide data collected by the data collection module to the BP neural network optimized by the model optimization module.
According to this embodiment, preferably, the process that the 5G module transmits the carbon dioxide data collected by the data collecting module to the BP neural network optimized by the model optimizing module is:
receiving a call instruction sent by a terminal, wherein the call instruction carries a service identifier to be processed;
obtaining a target 5G service execution instruction according to the service identifier to be processed;
determining a target 5G service providing module according to the target 5G service executing instruction;
and sending a service instruction to the target 5G service providing module, wherein the service instruction carries the target 5G service executing instruction and access information corresponding to the terminal, and the service instruction is used for instructing the target 5G service providing module to determine target 5G information according to the target 5G service executing instruction and send the target 5G information to the receiving terminal based on the access information.
The digital management platform is used for adjusting the data of ladle bottom blowing carbon dioxide according to the ladle smelting process data output by the BP neural network until the ladle smelting process data output by the BP neural network meets smelting conditions.
As shown in fig. 2, in the above scheme, the vaporization device comprises a low-temperature variable flow pressure control valve 1, a valve base 2, a carbon dioxide adding device 3, a pressure gauge 4, a pressure reducing valve 5, a pressure control release valve 6, an outlet valve 7, a carbon dioxide vaporization module 8, a vaporization module base 9 and a supporting member 10;
the vaporizing device base 9 is arranged on the top of the supporting member 10;
the carbon dioxide vaporization module 8 is arranged at the top of the vaporization module base 9;
the valve base 2 is arranged at the top of the carbon dioxide vaporization module 8;
the low-temperature variable flow pressure control valve 1, the carbon dioxide adding device 3, the pressure gauge 4, the pressure reducing valve 5, the pressure control relief valve 6 and the outlet valve 7 are arranged on the valve base 2;
the carbon dioxide adding device 3 is connected with one end of the low-temperature variable flow pressure control valve 1, and the other end of the low-temperature variable flow pressure control valve 1 is connected with the input end of the carbon dioxide vaporization module 8;
the output end of the carbon dioxide vaporization module 8 is connected with one end of the pressure gauge 4, the other end of the pressure gauge 4 is connected with one end of the pressure reducing valve 5, the other end of the pressure reducing valve 5 is connected with one end of the pressure control relief valve 6, and the other end of the pressure control relief valve 6 is connected with the outlet valve 7; the outlet valve 7 is connected with a ladle bottom blowing pipeline.
The liquid carbon dioxide enters the carbon dioxide vaporization device through the low-temperature variable flow pressure control valve 1, and the pressure of the carbon dioxide vaporization device needs to be regulated due to the large pressure fluctuation of the liquid carbon dioxide. If the pressure increases stepwise, exceeding the upper limit of the pressure range, the pressure relief valve 6 should be opened to relieve the pressure.
According to this embodiment, preferably, the bottom blowing operation control of the vaporizing device can be adjusted in the operation room or the outside, the maximum blowing pressure is 2.4MPa, the minimum pressure is 0.2MPa, and the vaporization amount is 50-200Nm 3 /h。
It should be understood that although the present disclosure has been described in terms of various embodiments, not every embodiment is provided with a separate technical solution, and this description is for clarity only, and those skilled in the art should consider the disclosure as a whole, and the technical solutions in the various embodiments may be combined appropriately to form other embodiments that will be understood by those skilled in the art.
The above list of detailed descriptions is only specific to practical embodiments of the present invention, and they are not intended to limit the scope of the present invention, and all equivalent embodiments or modifications that do not depart from the spirit of the present invention should be included in the scope of the present invention.
Claims (7)
1. The ladle bottom blowing carbon dioxide smelting method based on the 5G technology is characterized by comprising the following steps of:
step S1, data collection: converting the carbon dioxide liquid into carbon dioxide gas by using a vaporization device, blowing carbon dioxide at the bottom of the ladle, and collecting carbon dioxide data and ladle smelting process data by using a sensor; the carbon dioxide data information comprises carbon dioxide gas pressure, carbon dioxide gas flow and vaporization capacity of the vaporization device;
the vaporization capacity of the vaporization device is as follows:
y=a+bx-w
wherein:
y is the vaporization capacity of the vaporization device, and the unit is Nm 3 /h；
a is a pressure-influencing dimensionless factor;
b is liquid CO 2 Flow affects dimensionless factors;
x is the vaporization amount of carbon dioxide, and the unit is NL/min;
w is an empirical correction factor of pressure and flow, and ranges from 0 to 1050;
the ladle smelting process data information comprises molten steel temperature and molten steel components;
step S2, data preprocessing: the carbon dioxide data collected in the step S1 are marked in a connection way with corresponding ladle smelting process data, and are divided into a training set and a testing set;
step S3, establishing a model: establishing a BP neural network with carbon dioxide data as input and ladle smelting process data as output;
step S4, model optimization: training the BP neural network established in the step S3 by using the training set in the step S2, and testing the BP neural network by using the testing set until the BP neural network result accords with the testing set, so as to obtain an optimized BP neural network;
s5, smelting optimization: and (3) blowing carbon dioxide at the bottom of the ladle, collecting carbon dioxide data through a sensor, inputting the carbon dioxide data into the BP neural network after optimization in the step (S4) through a 5G module, and if the ladle smelting process data output by the BP neural network does not accord with smelting conditions, adjusting the data of the ladle bottom-blowing carbon dioxide until the ladle smelting process data output by the BP neural network accord with the smelting conditions, and carrying out ladle smelting.
2. The ladle bottom blowing carbon dioxide smelting method based on the 5G technology according to claim 1, wherein the BP neural network of the step S3 comprises an input layer, an implicit layer and an output layer;
the carbon dioxide data is input to the input layer, acts on the output node through the hidden layer, and is subjected to nonlinear transformation to generate ladle smelting process data to be output by the output layer.
3. The method for smelting ladle bottom-blown carbon dioxide based on the 5G technology according to claim 1, wherein the ladle bottom-blown carbon dioxide requires fluctuation of the molten steel surface and cannot generate tubular flow or splash.
4. The ladle bottom blowing carbon dioxide smelting method based on the 5G technology according to claim 1, wherein the BP neural network output result and the test set error of less than 3% in the step S4 model optimization are regarded as conforming to the test set.
5. The ladle bottom blowing carbon dioxide smelting method based on the 5G technology according to claim 1, wherein the ladle smelting process data output by the BP neural network during the step S5 carbon dioxide smelting has an error of less than 5% and is considered to be in accordance with smelting conditions.
6. A system for using the ladle bottom blowing carbon dioxide smelting method based on the 5G technology as claimed in any one of claims 1 to 5, which is characterized by comprising a vaporization device, a data collection module, a data preprocessing module, a model building module, a model optimizing module, a 5G module and a digital management platform;
the vaporization device is used for converting carbon dioxide liquid into carbon dioxide gas;
the data collection module is used for collecting carbon dioxide data and ladle smelting process data by using a sensor;
the data preprocessing module is used for marking the carbon dioxide data collected by the data collecting module in a connection way with corresponding ladle smelting process data and dividing the data into a training set and a testing set;
the model building module is used for building a BP neural network which is input into carbon dioxide data and output into ladle smelting process data;
the model optimization module is used for training the BP neural network established by the model establishment module by using the training set divided by the data preprocessing module, and testing the BP neural network by using the testing set until the BP neural network result accords with the testing set;
the 5G module is used for transmitting the carbon dioxide data collected by the data collection module to the BP neural network optimized by the model optimization module;
the digital management platform is used for adjusting the data of ladle bottom blowing carbon dioxide according to the ladle smelting process data output by the BP neural network until the ladle smelting process data output by the BP neural network meets smelting conditions.
7. The ladle bottom blowing carbon dioxide smelting system based on the 5G technology according to claim 6, wherein the vaporizing device comprises a low-temperature variable flow pressure control valve (1), a valve base (2), a carbon dioxide adding device (3), a pressure gauge (4), a pressure reducing valve (5), a pressure control relief valve (6), an outlet valve (7), a carbon dioxide vaporizing module (8), a vaporizing module base (9) and a supporting member (10);
the vaporization module base (9) is arranged at the top of the supporting member (10);
the carbon dioxide vaporization module (8) is arranged at the top of the vaporization module base (9);
the valve base (2) is arranged at the top of the carbon dioxide vaporization module (8);
the low-temperature variable flow pressure control valve (1), the carbon dioxide adding device (3), the pressure gauge (4), the pressure reducing valve (5), the pressure control relief valve (6) and the outlet valve (7) are arranged on the valve base (2);
the carbon dioxide adding device (3) is connected with one end of the low-temperature variable flow pressure control valve (1), and the other end of the low-temperature variable flow pressure control valve (1) is connected with the input end of the carbon dioxide vaporization module (8);
the output end of the carbon dioxide vaporization module (8) is connected with one end of the pressure gauge (4), the other end of the pressure gauge (4) is connected with one end of the pressure reducing valve (5), the other end of the pressure reducing valve (5) is connected with one end of the pressure control relief valve (6), and the other end of the pressure control relief valve (6) is connected with the outlet valve (7); the outlet valve (7) is connected with a ladle bottom blowing pipeline.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310926405.7A CN116640906B (en) | 2023-07-27 | 2023-07-27 | Ladle bottom blowing carbon dioxide smelting method and system based on 5G technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310926405.7A CN116640906B (en) | 2023-07-27 | 2023-07-27 | Ladle bottom blowing carbon dioxide smelting method and system based on 5G technology |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116640906A CN116640906A (en) | 2023-08-25 |
| CN116640906B true CN116640906B (en) | 2023-10-20 |
Family
ID=87619203
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310926405.7A Active CN116640906B (en) | 2023-07-27 | 2023-07-27 | Ladle bottom blowing carbon dioxide smelting method and system based on 5G technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116640906B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113435114A (en) * | 2021-06-23 | 2021-09-24 | 马鞍山钢铁股份有限公司 | Method for accurately controlling nitrogen content of CV-RH-CC process path steel |
| CN113512622A (en) * | 2020-04-10 | 2021-10-19 | 北京凯德恒源科技发展有限公司 | Converter smelting overall process end point carbon dynamic control method based on gas analysis |
| CN113919206A (en) * | 2021-08-23 | 2022-01-11 | 南京钢铁股份有限公司 | Narrow composition control method for steelmaking alloying |
| CN113962050A (en) * | 2021-09-16 | 2022-01-21 | 中冶赛迪技术研究中心有限公司 | Oxygen scheduling calculation method combining production consumption prediction and pipe network calculation |
| CN114854929A (en) * | 2022-05-07 | 2022-08-05 | 北京科技大学 | Converter blowing CO 2 Method for dynamically predicting molten steel components and temperature in real time |
| CN115522012A (en) * | 2022-08-25 | 2022-12-27 | 邯郸钢铁集团有限责任公司 | Method for controlling TSC (thyristor switched capacitor) stage oxygen blowing amount of large converter |
| CN115927784A (en) * | 2022-11-30 | 2023-04-07 | 北京科技大学 | Based on CO 2 Dynamic mixed blowing converter steelmaking end point control method |
| CN115933567A (en) * | 2022-12-27 | 2023-04-07 | 山东钢铁集团永锋临港有限公司 | Intelligent smelting and steelmaking process |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111353656B (en) * | 2020-03-23 | 2021-05-07 | 大连理工大学 | Steel enterprise oxygen load prediction method based on production plan |
-
2023
- 2023-07-27 CN CN202310926405.7A patent/CN116640906B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113512622A (en) * | 2020-04-10 | 2021-10-19 | 北京凯德恒源科技发展有限公司 | Converter smelting overall process end point carbon dynamic control method based on gas analysis |
| CN113435114A (en) * | 2021-06-23 | 2021-09-24 | 马鞍山钢铁股份有限公司 | Method for accurately controlling nitrogen content of CV-RH-CC process path steel |
| CN113919206A (en) * | 2021-08-23 | 2022-01-11 | 南京钢铁股份有限公司 | Narrow composition control method for steelmaking alloying |
| CN113962050A (en) * | 2021-09-16 | 2022-01-21 | 中冶赛迪技术研究中心有限公司 | Oxygen scheduling calculation method combining production consumption prediction and pipe network calculation |
| CN114854929A (en) * | 2022-05-07 | 2022-08-05 | 北京科技大学 | Converter blowing CO 2 Method for dynamically predicting molten steel components and temperature in real time |
| CN115522012A (en) * | 2022-08-25 | 2022-12-27 | 邯郸钢铁集团有限责任公司 | Method for controlling TSC (thyristor switched capacitor) stage oxygen blowing amount of large converter |
| CN115927784A (en) * | 2022-11-30 | 2023-04-07 | 北京科技大学 | Based on CO 2 Dynamic mixed blowing converter steelmaking end point control method |
| CN115933567A (en) * | 2022-12-27 | 2023-04-07 | 山东钢铁集团永锋临港有限公司 | Intelligent smelting and steelmaking process |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116640906A (en) | 2023-08-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108342540B (en) | A kind of steel mill RH refining equipment automatic control system | |
| Lin et al. | Consideration of green intelligent steel processes and narrow window stability control technology on steel quality | |
| CN111304406B (en) | Ladle bottom argon blowing control method and device | |
| CN110157848A (en) | A kind of bottom blowing flow dynamics control method of top and bottom combined blown converter | |
| CN115522012B (en) | Method for controlling TSC stage oxygen blowing amount of large converter | |
| CN116640906B (en) | Ladle bottom blowing carbon dioxide smelting method and system based on 5G technology | |
| CN110846459A (en) | Converter and smelting method thereof | |
| CN111020118B (en) | RH endpoint temperature prediction method based on particle swarm optimization case reasoning | |
| CN108148948A (en) | A kind of efficient single-mouth refining furnace and special steel smelting process | |
| CN115927784B (en) | Based on CO 2 Converter steelmaking end point control method by dynamic mixed blowing | |
| CN113961865A (en) | Method for accurately controlling addition amount of temperature regulator in TSC (thyristor switched capacitor) stage of large converter | |
| CN103194574B (en) | Dynamic regulation method of VOD refined end point carbon content prediction model | |
| CN115074491B (en) | Automatic control method for circulation gas of RH furnace | |
| CN107164597A (en) | It is a kind of to detect the one-touch automatic method for making steel of converter without furnace gas without sublance | |
| CN208517458U (en) | A kind of LF furnace dynamic bottom blowing CO2- Ar purifier system | |
| CN214422680U (en) | RH refining furnace ascending pipe internal side blowing CO2-Ar refining device | |
| CN110991772B (en) | Efficient furnace protection method for predicting final slag viscosity model of converter | |
| CN208055390U (en) | Automatic carburetting device in a kind of molten steel tapping process | |
| CN108424995A (en) | A kind of ultra-low phosphoretic steel and its production method | |
| CN110530425A (en) | It is a kind of for determining the evaluating apparatus and method of soft mixing effect | |
| CN218345520U (en) | Ventilating device for leaching reaction | |
| CN113778035B (en) | Intelligent crude tin smelting system | |
| CN102071278B (en) | Method and device for automatically regulating speed of converter fan | |
| CN114262767B (en) | RH furnace oxygen lance control method based on large forest algorithm | |
| CN112501391B (en) | Dynamic powder spraying device system of RH refining furnace and application method of dynamic powder spraying device system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |