CN110885912B - Automatic steelmaking method and system based on data analysis - Google Patents
Automatic steelmaking method and system based on data analysis Download PDFInfo
- Publication number
- CN110885912B CN110885912B CN201911124804.1A CN201911124804A CN110885912B CN 110885912 B CN110885912 B CN 110885912B CN 201911124804 A CN201911124804 A CN 201911124804A CN 110885912 B CN110885912 B CN 110885912B
- Authority
- CN
- China
- Prior art keywords
- smelting
- raw material
- material information
- steelmaking
- data analysis
- 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
Images
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
- C21C5/00—Manufacture of carbon-steel, e.g. plain mild steel, medium carbon steel or cast steel or stainless steel
- C21C5/28—Manufacture of steel in the converter
-
- 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
- C21C5/00—Manufacture of carbon-steel, e.g. plain mild steel, medium carbon steel or cast steel or stainless steel
- C21C5/28—Manufacture of steel in the converter
- C21C5/30—Regulating or controlling the blowing
-
- 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
- C21C5/00—Manufacture of carbon-steel, e.g. plain mild steel, medium carbon steel or cast steel or stainless steel
- C21C5/28—Manufacture of steel in the converter
- C21C5/42—Constructional features of converters
- C21C5/46—Details or accessories
- C21C5/466—Charging device for converters
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P10/00—Technologies related to metal processing
- Y02P10/20—Recycling
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P10/00—Technologies related to metal processing
- Y02P10/25—Process efficiency
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Manufacturing & Machinery (AREA)
- Materials Engineering (AREA)
- Metallurgy (AREA)
- Organic Chemistry (AREA)
- Carbon Steel Or Casting Steel Manufacturing (AREA)
Abstract
The application provides an automatic steelmaking method based on data analysis, which comprises the following steps: collecting raw material information before converter steelmaking, wherein the raw material information comprises scrap steel quality, molten iron quality, carbon content and slagging material proportion; inquiring a smelting operation mode and a smelting result corresponding to the raw material information in a smelting database according to the raw material information, and comparing different smelting operation modes with different smelting results to obtain an optimal smelting operation mode; the optimal smelting operation mode comprises qualified terminal components, qualified temperature and lowest cost; and generating a control instruction of the steelmaking equipment by utilizing the optimal smelting operation mode, and carrying out steelmaking by the steelmaking equipment according to the control instruction. According to the method and the device, the raw material information in the converter is collected in advance, and the optimal smelting operation mode of the raw material information is obtained in a data analysis and comparison mode, so that the purpose of automatically smelting steel is achieved, the automatic smelting level is improved, the molten steel quality is improved, and the labor intensity of operators is reduced.
Description
Technical Field
The application relates to the technical field of steel making, in particular to an automatic steel making method and system based on data analysis.
Background
Converter steelmaking (converter steelmaking) is to use molten iron, scrap steel and ferroalloy as main raw materials, and the steelmaking process is completed in a converter by means of heat generated by physical heat of molten iron and chemical reaction among molten iron components without external energy. However, the current converter steelmaking technology still depends on manual control, and oxygen lance position, slag adding amount, slag adding time, smelting emphasis and the like are judged by manual according to experience.
However, the manual experience method judges the end point of the converter by visually observing the characteristics of the flame barrier lake, the shape, the texture, the stroboflash and the like of the converter, and in the actual smelting process, the judgment is carried out by the manual experience, so that the inaccurate judgment (multiple times of converter reversing and reblowing) is easy to cause the waste of raw materials and energy, the steel-making efficiency is influenced, the steel-making cost is increased, and in addition, the accidents such as splashing, material overflowing and the like are more easily caused, and the safety accidents are caused.
Content of application
In view of the above disadvantages of the prior art, the present application aims to provide an automatic steelmaking method and system based on data analysis, which are used to solve the problems of low steelmaking efficiency, high cost and incapability of ensuring safe production caused by relying on manual steelmaking in the prior art.
To achieve the above and other related objects, there is provided in a first aspect of the present application an automatic steelmaking method based on data analysis, including:
collecting raw material information before converter steelmaking, wherein the raw material information comprises scrap steel quality, molten iron quality, carbon content and slagging material proportion;
inquiring a smelting operation mode and a smelting result corresponding to the raw material information in a smelting database according to the raw material information, and comparing different smelting operation modes with different smelting results to obtain an optimal smelting operation mode; the optimal smelting operation mode comprises qualified terminal components, qualified temperature and lowest cost;
and generating a control instruction of the steelmaking equipment by utilizing the optimal smelting operation mode, and carrying out steelmaking by the steelmaking equipment according to the control instruction.
In certain embodiments of the first aspect, the smelting database comprises different raw material information and different smelting results corresponding to different smelting operation modes, wherein the different smelting operation modes comprise batch slag amount adding amount and adding time, oxygen lance oxygen blowing time, lance position, oxygen blowing intensity, and deoxidation alloying agent adding amount and time; the different smelting results include molten iron temperature, carbon content, sulfur content, and phosphorus content.
In certain embodiments of the first aspect, the step of querying a smelting operation mode and a smelting result corresponding to the raw material information in a smelting database according to the raw material information, and comparing different smelting operation modes with different smelting results to obtain an optimal smelting operation mode includes:
establishing a data analysis model formed by different smelting results corresponding to the raw material information in different smelting operation modes in a smelting database by utilizing a support vector machine algorithm;
obtaining raw material information to be analyzed and inputting the raw material information to the data analysis model to obtain a corresponding smelting operation mode and a smelting result;
and outputting an optimal smelting operation mode according to the performance examination with qualified terminal components, qualified temperature and lowest cost.
In certain embodiments of the first aspect, the steelmaking apparatus comprises an automatic feed control system, a timing system, and an automatic lance; the automatic feeding control system is used for weighing auxiliary materials to be added according to control signals and automatically adding the auxiliary materials into the converter, wherein the auxiliary materials comprise slag materials and deoxidation alloying agents; the timing system is used for controlling the adding time of auxiliary materials and the oxygen blowing time of the oxygen lance according to the time of the control signal; the automatic oxygen lance is used for controlling the oxygen blowing intensity and the oxygen blowing position according to the control signal.
In certain embodiments of the first aspect, the automatic charging control system comprises:
the weighing device is used for weighing the weight of the auxiliary materials to be added;
and the feeding device is used for adding the weighed auxiliary materials into the converter.
In a second aspect of the present application, there is provided an automatic steelmaking system based on data analysis, comprising:
the raw material acquisition module is used for acquiring raw material information before converter steelmaking, wherein the raw material information comprises scrap steel quality, molten iron quality, carbon content and slagging material proportion;
the data analysis module is used for inquiring the smelting operation mode and the smelting result corresponding to the raw material information in the smelting database according to the raw material information, and comparing different smelting operation modes with different smelting results to obtain an optimal smelting operation mode; the optimal smelting operation mode comprises qualified terminal components, qualified temperature and lowest cost;
and the operation execution module is used for generating a control instruction of the steelmaking equipment by using the optimal smelting operation mode, and the steelmaking equipment performs steelmaking according to the control instruction.
In certain embodiments of the second aspect, the smelting database comprises different raw material information and different smelting results corresponding to different smelting operation modes, wherein the different smelting operation modes comprise slag amount batch addition amount and addition time, oxygen lance oxygen blowing time, lance position, oxygen blowing intensity, and deoxidation alloying agent addition amount and time; the different smelting results include molten iron temperature, carbon content, sulfur content, and phosphorus content.
In certain embodiments of the second aspect, the data analysis module further comprises:
the data analysis model is used for establishing a data analysis model formed by different smelting results corresponding to the raw material information in different smelting operation modes in a smelting database by utilizing a support vector machine algorithm;
the data analysis unit is used for acquiring raw material information to be analyzed and inputting the raw material information to the data analysis model to obtain a corresponding smelting operation mode and a corresponding smelting result;
and the optimal judging unit is used for outputting an optimal smelting operation mode according to the performance examination of qualified terminal components, qualified temperature and lowest cost.
In certain embodiments of the second aspect, the steelmaking apparatus comprises an automatic charging control system, a timing system, and an automatic lance; the automatic feeding control system is used for weighing auxiliary materials to be added according to control signals and automatically adding the auxiliary materials into the converter, wherein the auxiliary materials comprise slag materials and deoxidation alloying agents; the timing system is used for controlling the adding time of auxiliary materials and the oxygen blowing time of the oxygen lance according to the time of the control signal; the automatic oxygen lance is used for controlling the oxygen blowing intensity and the oxygen blowing position according to the control signal.
In certain embodiments of the second aspect, the automatic charging control system comprises:
the weighing device is used for weighing the weight of the auxiliary materials to be added;
and the feeding device is used for adding the weighed auxiliary materials into the converter.
As described above, the automatic steelmaking method and system based on data analysis of the present application have the following beneficial effects:
according to the method, the optimal smelting operation mode of the raw material information is obtained by acquiring the scrap steel quality, the molten iron quality, the carbon content and the slag-forming material ratio of the raw material information in the converter in advance and adopting a data analysis and comparison mode, so that the purpose of automatically smelting steel is achieved, on one hand, the automatic smelting level is improved, the molten steel quality is improved, and the labor intensity of operators is reduced; on the other hand, the end point control is accurate according to the optimal smelting operation mode, the automatic control level of the converter is improved, and the smelting efficiency is improved on the premise of keeping the cost unchanged.
Drawings
FIG. 1 is a schematic flow chart of an automatic steelmaking method based on data analysis according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating a step S2 of the automatic steelmaking method based on data analysis according to the embodiment of the present application;
FIG. 3 is a block diagram of an automated steelmaking system based on data analysis according to an embodiment of the present application;
FIG. 4 is a block diagram of a data analysis module in an automatic steelmaking system based on data analysis according to an embodiment of the present application;
fig. 5 is a detailed block diagram of an automatic steelmaking system based on data analysis according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present application is provided for illustrative purposes, and other advantages and capabilities of the present application will become apparent to those skilled in the art from the present disclosure.
In the following description, reference is made to fig. 1 to 5, which illustrate several embodiments of the present application. The following detailed description is not to be taken in a limiting sense, and the scope of embodiments of the present application is defined only by the claims of the issued patent. Spatially relative terms, such as "upper," "lower," "left," "right," "lower," "below," "lower," "above," "upper," and the like, may be used herein to facilitate describing one element or feature's relationship to another element or feature as illustrated in the figures.
Although the terms first, second, etc. may be used herein to describe various elements in some instances, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, the first preset threshold may be referred to as a second preset threshold, and similarly, the second preset threshold may be referred to as a first preset threshold, without departing from the scope of the various described embodiments. The first preset threshold and the preset threshold are both described as one threshold, but they are not the same preset threshold unless the context clearly indicates otherwise. Similar situations also include a first volume and a second volume.
Furthermore, as used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context indicates otherwise, it should be further understood that the terms "comprises" and "comprising" indicate the presence of the stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, items, species, and/or groups. A; b; c; a and B; a and C; b and C; A. b and C "are only exceptions to this definition should be done when combinations of elements, functions, steps or operations are inherently mutually exclusive in some manner.
Referring to fig. 1, a schematic flow chart of an automatic steelmaking method based on data analysis according to an embodiment of the present application includes:
step S1, collecting raw material information before converter steelmaking, wherein the raw material information comprises scrap steel quality, molten iron quality, carbon content and slagging material proportion;
wherein, the scrap steel is the steel waste which does not become the product in the production process, the slag former comprises lime, dolomite, light burned dolomite and the like, and the mass of the scrap steel, the mass of molten iron, the carbon content and the proportion of the slag former are respectively 5-30%: 70% -90%: 3% -4.5%: 2% -6%, which is not described in detail herein.
Step S2, inquiring a smelting operation mode and a smelting result corresponding to the raw material information in a smelting database according to the raw material information, and comparing different smelting operation modes with different smelting results to obtain an optimal smelting operation mode; the optimal smelting operation mode comprises qualified terminal components, qualified temperature and lowest cost;
the smelting database comprises different raw material information and different smelting results corresponding to different smelting operation modes, wherein the different smelting operation modes comprise batch slag amount adding amount and adding time, oxygen lance oxygen blowing time, lance position, oxygen blowing strength and addition amount and time of a deoxidation alloying mixture; the different smelting results include molten iron temperature, carbon content, sulfur content, and phosphorus content.
And S3, generating a control instruction of the steelmaking equipment by using the optimal smelting operation mode, and carrying out steelmaking by the steelmaking equipment according to the control instruction.
The steelmaking equipment comprises an automatic feeding control system, a timing system and an automatic oxygen lance; the automatic feeding control system is used for weighing auxiliary materials to be added according to control signals and automatically adding the auxiliary materials into the converter, wherein the auxiliary materials comprise slag materials and deoxidation alloying agents; the timing system is used for controlling the adding time of auxiliary materials and the oxygen blowing time of the oxygen lance according to the time of the control signal; the automatic oxygen lance is used for controlling the oxygen blowing intensity and the oxygen blowing position according to the control signal.
The automatic charging control system comprises: the weighing device is used for weighing the weight of the auxiliary materials to be added; and the feeding device is used for adding the weighed auxiliary materials into the converter.
In some embodiments, the control commands are divided into several control commands according to different steelmaking devices to be controlled, for example, for an automatic charging control system, the charging command includes the type and weight of the material to be charged; for the timing system, except for normal timing, time length control related to the work of various steelmaking devices is generated; and aiming at the automatic oxygen lance, the oxygen blowing intensity of the automatic oxygen lance is controlled, the oxygen blowing intensity of a controller is also required, and the like, so that smelting is carried out according to an optimal smelting operation mode, and the quality and the efficiency of steel making are improved.
It should be noted that, although the conventional sublance detection method, furnace gas analysis method, sampling detection method, intelligent end point prediction and other methods, for example, the sublance detection method has high accuracy, but can only be used in a converter with more than 120 tons, which is difficult to meet the requirements of medium and small steel mills, and meanwhile, the sublance probe cannot be continuously measured, and belongs to consumables and needs to be replaced periodically, which increases the steel-making cost. The mass spectrometry used by the furnace gas analysis method has high temperature hit rate and strong process control capability, but has strict requirements on production and smelting standards, high price and difficult equipment maintenance. The sampling detection method depends on the molten steel scooped out from the converter as a sample representative, and the molten steel is sent to a laboratory for detection, and a series of problems also exist, for example, the sample representative problem, the sample is not representative due to the fact that a pool is poor during sampling or the problem of slag exists; the real-time problem, the sampling, sample grinding and testing need longer time, and the carbon content of the molten steel can not be judged; the cost problem, the sampling and testing of the sampler needs to increase the cost by 0.3 yuan/ton; the safety problem, the accident that has the splash when the sample, the potential safety hazard is great. The intelligent end point forecasting method utilizes actually acquired converting data in the steelmaking process, has better real-time performance in principle, but the existing methods have the problems of cost increase or untimely data acquisition and the like in order to acquire accurate data.
Compared with the prior art, in the embodiment, the optimal smelting operation mode of the raw material information is obtained by acquiring the scrap steel quality, the molten iron quality and the carbon content of the raw material information in the converter and the slag-forming material ratio in advance and adopting a data analysis and comparison mode, so that the purpose of automatically smelting steel is realized, on one hand, the automatic smelting level is improved, the molten steel quality is improved, and the labor intensity of operators is reduced; on the other hand, the end point control is accurate according to the optimal smelting operation mode, the automatic control level of the converter is improved, and the smelting efficiency is improved on the premise of keeping the cost unchanged.
Referring to fig. 2, a schematic flow chart of step S2 in the automatic steelmaking method based on data analysis according to the embodiment of the present application is shown, which is detailed as follows:
step S201, establishing a data analysis model formed by different smelting results corresponding to raw material information in different smelting operation modes in a smelting database by using a support vector machine algorithm;
for example, the smelting database includes historical smelting data, the historical smelting data includes specific raw material information before converter steelmaking each time, one raw material information corresponds to different smelting operation modes, after smelting is finished, each smelting operation mode corresponds to a group of output data, and a support vector machine algorithm (classification algorithm) is adopted to classify data accumulated by the historical smelting data, so that a decision-making and classification data analysis model is obtained.
In another more specific example the classification algorithm includes, the algorithm model built using unsupervised machine learning includes, but is not limited to: k-mean algorithm, BIRCH algorithm, DBSCAN algorithm, CURE algorithm, CLARANS algorithm, etc.
Step S202, obtaining raw material information to be analyzed, inputting the raw material information to the data analysis model to obtain a corresponding smelting operation mode and a corresponding smelting result;
for example, model raw material information is analyzed by inputting data, and a plurality of corresponding smelting operation modes and smelting results are obtained.
And S203, outputting an optimal smelting operation mode according to the performance examination of qualified end-point components, qualified temperature and lowest cost.
The end point components comprise carbon, phosphorus and the like, the component threshold is set according to requirements, and the components are qualified when meeting the threshold; for example, if the temperature meets the corresponding threshold value, the smelting operation mode is qualified, and if the end point component and the temperature are both qualified, the lowest cost of which smelting operation mode is the optimal smelting operation mode.
In some embodiments, as shown in fig. 5, raw material information (raw material conditions) is acquired and input to a data analysis center (including a data processing module, that is, a data analysis module) to perform data analysis and matching on different smelting results (actual effects) of different conditions and different operation modes in a historical database to obtain an optimal smelting operation mode, so that steel making is performed according to the optimal smelting operation mode, end point components can be accurately controlled, and automatic, accurate and streamlined converter steel making is realized.
Referring to fig. 3, a block diagram of an automatic steelmaking system based on data analysis according to an embodiment of the present application includes:
the raw material collecting module 1 is used for collecting raw material information before converter steelmaking, wherein the raw material information comprises scrap steel quality, molten iron quality, carbon content and slagging material proportion;
the data analysis module 2 is used for inquiring the smelting operation mode and the smelting result corresponding to the raw material information in the smelting database according to the raw material information, and comparing different smelting operation modes with different smelting results to obtain an optimal smelting operation mode; the optimal smelting operation mode comprises qualified terminal components, qualified temperature and lowest cost;
and the operation execution module 3 is used for generating a control instruction of the steelmaking equipment by using the optimal smelting operation mode, and the steelmaking equipment performs steelmaking according to the control instruction.
The smelting database comprises different raw material information and different smelting results corresponding to different smelting operation modes, wherein the different smelting operation modes comprise batch addition amount and addition time of slag amount, oxygen lance oxygen blowing time, lance position, oxygen blowing strength and addition amount and time of a deoxidation alloying agent; the different smelting results include molten iron temperature, carbon content, sulfur content, and phosphorus content.
Referring to fig. 4, a block diagram of a data analysis module in an automatic steelmaking system based on data analysis according to an embodiment of the present application includes:
the data analysis model 21 is used for establishing a data analysis model formed by different smelting results corresponding to the raw material information in different smelting operation modes in a smelting database by utilizing a support vector machine algorithm;
the data analysis unit 22 is used for acquiring raw material information to be analyzed and inputting the raw material information to the data analysis model to obtain a corresponding smelting operation mode and a corresponding smelting result;
and the optimal judging unit 23 is used for outputting an optimal smelting operation mode according to the performance examination of qualified terminal components, qualified temperature and lowest cost.
The steelmaking equipment comprises an automatic feeding control system, a timing system and an automatic oxygen lance; the automatic feeding control system is used for weighing auxiliary materials to be added according to control signals and automatically adding the auxiliary materials into the converter, wherein the auxiliary materials comprise slag materials and deoxidation alloying agents; the timing system is used for controlling the adding time of auxiliary materials and the oxygen blowing time of the oxygen lance according to the time of the control signal; the automatic oxygen lance is used for controlling the oxygen blowing intensity and the oxygen blowing position according to the control signal.
The automatic charging control system comprises: the weighing device is used for weighing the weight of the auxiliary materials to be added; and the feeding device is used for adding the weighed auxiliary materials into the converter.
It should be noted that, since the automatic steel-making system based on data analysis and the automatic steel-making method based on data analysis are in a one-to-one correspondence relationship, please refer to the above automatic steel-making method for technical details and technical effects corresponding to the system, which is not described herein in detail.
In summary, the optimal smelting operation mode of the raw material information is obtained by acquiring the scrap steel quality, the molten iron quality, the carbon content and the slag-forming material ratio of the raw material information in the converter in advance and adopting a data analysis and comparison mode, so that the purpose of automatically smelting steel is realized, on one hand, the automatic smelting level is improved, the molten steel quality is improved, and the labor intensity of operators is reduced; on the other hand, the end point control is accurate according to the optimal smelting operation mode, the automatic control level of the converter is improved, and the smelting efficiency is improved on the premise of keeping the cost unchanged. Therefore, the application effectively overcomes various defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the application. Any person skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical concepts disclosed in the present application shall be covered by the claims of the present application.
Claims (4)
1. An automatic steelmaking process based on data analysis, said process comprising the steps of:
collecting raw material information before converter steelmaking, wherein the raw material information comprises scrap steel quality, molten iron quality, carbon content and slagging material proportion;
establishing a data analysis model formed by different smelting results corresponding to the raw material information in different smelting operation modes in a smelting database by utilizing a support vector machine algorithm; obtaining raw material information to be analyzed and inputting the raw material information to the data analysis model to obtain a corresponding smelting operation mode and a smelting result; outputting an optimal smelting operation mode according to performance examination with qualified terminal components, qualified temperature and lowest cost, wherein the optimal smelting operation mode comprises qualified terminal components, qualified temperature and lowest cost; the smelting database comprises different raw material information and different smelting results corresponding to different smelting operation modes, wherein the different smelting operation modes comprise batch addition amount and addition time of slag amount, oxygen lance oxygen blowing time, lance position, oxygen blowing strength and addition amount and time of a deoxidation alloying agent; the different smelting results comprise molten iron temperature, carbon content, sulfur content and phosphorus content;
generating a control instruction of steelmaking equipment by using the optimal smelting operation mode, and steelmaking by the steelmaking equipment according to the control instruction; the steelmaking equipment comprises an automatic feeding control system, a timing system and an automatic oxygen lance; the automatic feeding control system is used for weighing auxiliary materials to be added according to control signals and automatically adding the auxiliary materials into the converter, wherein the auxiliary materials comprise slag materials and deoxidation alloying agents; the timing system is used for controlling the adding time of auxiliary materials and the oxygen blowing time of the oxygen lance according to the time of the control signal; the automatic oxygen lance is used for controlling the oxygen blowing intensity and the oxygen blowing position according to the control signal.
2. The automatic steelmaking process based on data analysis of claim 1 wherein the automatic charging control system includes: the weighing device is used for weighing the weight of the auxiliary materials to be added; and the feeding device is used for adding the weighed auxiliary materials into the converter.
3. An automated steelmaking system based on data analysis, the system comprising:
the raw material acquisition module is used for acquiring raw material information before converter steelmaking, wherein the raw material information comprises scrap steel quality, molten iron quality, carbon content and slagging material proportion;
the data analysis module is used for inquiring the smelting operation mode and the smelting result corresponding to the raw material information in the smelting database according to the raw material information, and comparing different smelting operation modes with different smelting results to obtain an optimal smelting operation mode; the optimal smelting operation mode comprises qualified terminal components, qualified temperature and lowest cost; the smelting database comprises different raw material information and different smelting results corresponding to different smelting operation modes, wherein the different smelting operation modes comprise batch addition amount and addition time of slag amount, oxygen lance oxygen blowing time, lance position, oxygen blowing strength and addition amount and time of a deoxidation alloying agent; the different smelting results comprise molten iron temperature, carbon content, sulfur content and phosphorus content; the data analysis module further comprises:
the data analysis model is used for establishing a data analysis model formed by different smelting results corresponding to the raw material information in different smelting operation modes in a smelting database by utilizing a support vector machine algorithm;
the data analysis unit is used for acquiring raw material information to be analyzed and inputting the raw material information to the data analysis model to obtain a corresponding smelting operation mode and a corresponding smelting result;
the optimal judging unit is used for outputting an optimal smelting operation mode according to performance examination of qualified terminal components, qualified temperature and lowest cost;
the operation execution module is used for generating a control instruction of the steelmaking equipment by utilizing the optimal smelting operation mode, and the steelmaking equipment performs steelmaking according to the control instruction; the steelmaking equipment comprises an automatic feeding control system, a timing system and an automatic oxygen lance; the automatic feeding control system is used for weighing auxiliary materials to be added according to control signals and automatically adding the auxiliary materials into the converter, wherein the auxiliary materials comprise slag materials and deoxidation alloying agents; the timing system is used for controlling the adding time of auxiliary materials and the oxygen blowing time of the oxygen lance according to the time of the control signal; the automatic oxygen lance is used for controlling the oxygen blowing intensity and the oxygen blowing position according to the control signal.
4. The automatic steel making system based on data analysis of claim 3, wherein said automatic charging control system comprises: the weighing device is used for weighing the weight of the auxiliary materials to be added; and the feeding device is used for adding the weighed auxiliary materials into the converter.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911124804.1A CN110885912B (en) | 2019-11-18 | 2019-11-18 | Automatic steelmaking method and system based on data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911124804.1A CN110885912B (en) | 2019-11-18 | 2019-11-18 | Automatic steelmaking method and system based on data analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110885912A CN110885912A (en) | 2020-03-17 |
| CN110885912B true CN110885912B (en) | 2021-10-15 |
Family
ID=69747738
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911124804.1A Active CN110885912B (en) | 2019-11-18 | 2019-11-18 | Automatic steelmaking method and system based on data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110885912B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113515861A (en) * | 2021-07-12 | 2021-10-19 | 安徽绿洲危险废物综合利用有限公司 | Casting system for smelting regenerated copper plate |
| CN113981167B (en) * | 2021-11-08 | 2022-08-05 | 北京科技大学 | Multi-mode smelting method based on molten iron grading system |
| CN114317884B (en) * | 2022-01-07 | 2023-01-20 | 河北科技大学 | Method for regulating and controlling content of residual elements for smelting full-scrap steel in electric arc furnace |
| CN115125363B (en) * | 2022-06-09 | 2023-10-20 | 北京同创信通科技有限公司 | Intelligent recognition system and method for detecting ingredients in scrap steel hopper |
| CN115074480A (en) * | 2022-08-16 | 2022-09-20 | 张家港广大特材股份有限公司 | Method and system for improving processing quality of steel-making production |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104630410A (en) * | 2015-02-10 | 2015-05-20 | 东北大学 | Real-time dynamic converter steelmaking quality prediction method based on data analysis |
| CN108004368A (en) * | 2016-11-01 | 2018-05-08 | 北京明诚技术开发有限公司 | Intelligent automatic method for making steel and device |
| CN108018393A (en) * | 2016-11-01 | 2018-05-11 | 北京明诚技术开发有限公司 | Intelligent automatic steelmaking system and method |
-
2019
- 2019-11-18 CN CN201911124804.1A patent/CN110885912B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104630410A (en) * | 2015-02-10 | 2015-05-20 | 东北大学 | Real-time dynamic converter steelmaking quality prediction method based on data analysis |
| CN108004368A (en) * | 2016-11-01 | 2018-05-08 | 北京明诚技术开发有限公司 | Intelligent automatic method for making steel and device |
| CN108018393A (en) * | 2016-11-01 | 2018-05-11 | 北京明诚技术开发有限公司 | Intelligent automatic steelmaking system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110885912A (en) | 2020-03-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110885912B (en) | Automatic steelmaking method and system based on data analysis | |
| CN108676955B (en) | A kind of BOF Steelmaking Endpoint carbon content and temprature control method | |
| CN109447346B (en) | Converter oxygen consumption prediction method based on gray prediction and neural network combined model | |
| CN104630410B (en) | A kind of pneumatic steelmaking quality real-time dynamic forecast method based on data parsing | |
| CN102399933B (en) | Automatic control method for converter blowing low-carbon steel oxygen lance | |
| CN109359723A (en) | Based on the converter terminal manganese content prediction technique for improving regularization extreme learning machine | |
| EP2935633B1 (en) | Method and device for predicting, controlling and/or regulating steelworks processes | |
| CN101832967B (en) | Method for continuously predicting carbon content of converter molten pool | |
| CN106198408A (en) | Method that in casting, alloying component on-line checking and visualizing control and device thereof | |
| CN111893237A (en) | Method for predicting carbon content and temperature of molten pool of converter steelmaking in whole process in real time | |
| CN109252009A (en) | BOF Steelmaking Endpoint manganese content prediction technique based on regularization extreme learning machine | |
| CN104419799A (en) | Method for predicting carbon content of high-carbon steel on line during converter smelting | |
| CN101592650B (en) | Method for continuously measuring carbon content of molten steel in electric steelmaking furnace | |
| CN110991772B (en) | Efficient furnace protection method for predicting final slag viscosity model of converter | |
| CN103276136A (en) | Converter-steelmaking molten steel phosphorus-determination method based on sublance system | |
| Xin et al. | Modeling of LF refining process: A review | |
| CN115927784B (en) | Based on CO 2 Converter steelmaking end point control method by dynamic mixed blowing | |
| CN111518981B (en) | Method and system for forecasting phosphorus content of converter endpoint | |
| CN101271095B (en) | Method for detecting scrap steel constituent in stainless steel | |
| CN101592651B (en) | Method for measuring carbon content of molten steel in electric steelmaking furnace | |
| CN113362903A (en) | Method for intelligently adding lime in TSC (thyristor switched capacitor) stage of large converter | |
| US20240158876A1 (en) | Method for evaluating energy efficiency of electric arc furnace steelmaking | |
| CN116386774A (en) | Steelmaking alloy element yield prediction method and prediction terminal | |
| Yang et al. | Research of digital platform and process guidance model in EAF steelmaking process | |
| CN117708708B (en) | Quality rating and recommending method for ferroalloy for steel making |
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 |