CN111079537A - Method, system, machine readable medium and equipment for identifying smelting working condition of converter - Google Patents
Method, system, machine readable medium and equipment for identifying smelting working condition of converter Download PDFInfo
- Publication number
- CN111079537A CN111079537A CN201911127453.XA CN201911127453A CN111079537A CN 111079537 A CN111079537 A CN 111079537A CN 201911127453 A CN201911127453 A CN 201911127453A CN 111079537 A CN111079537 A CN 111079537A
- Authority
- CN
- China
- Prior art keywords
- flame
- converter smelting
- furnace mouth
- smelting
- image
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- 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
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Manufacturing & Machinery (AREA)
- Materials Engineering (AREA)
- Metallurgy (AREA)
- Organic Chemistry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Carbon Steel Or Casting Steel Manufacturing (AREA)
Abstract
本发明公开了一种转炉冶炼工况的识别系统，所述识别系统包括：图像获取模块，用于获取炉口火焰图像；所述炉口火焰图像包括转炉冶炼化渣阶段的火焰图像；图像处理模块，用于基于所述炉口火焰图像得到炉口火焰的纹理特征，还用于基于所述炉口火焰图像判断是否存在氧枪的轮廓特征；实时判别模块，用于根据所述炉口火焰的纹理特征判断化渣情况，还用于根据是否存在所述氧枪的轮廓特征对转炉冶炼终点进行第一次预测。本发明通过冶炼化渣过程中软硬火的判断和冶炼终点氧枪轮廓，火焰轮廓的判断，基于gabor滤波提取边缘特性和纹理特性，能够基于视觉原理，准确判断冶炼过程中化渣良好与否，能够精确实时的判断冶炼终点。
The invention discloses an identification system for converter smelting working conditions. The identification system comprises: an image acquisition module, which is used for acquiring a furnace mouth flame image; the furnace mouth flame image includes a flame image of a converter smelting slag stage; image processing The module is used to obtain the texture feature of the furnace mouth flame based on the furnace mouth flame image, and is also used to judge whether there is the outline feature of the oxygen lance based on the furnace mouth flame image; the real-time discrimination module is used for according to the furnace mouth flame. The texture feature of the slag is used to judge the slag situation, and it is also used to predict the converter smelting end point for the first time according to whether there is the contour feature of the oxygen lance. The invention can accurately judge whether the slag is good in the smelting process by judging the soft and hard fire in the process of smelting and smelting, and judging the outline of the oxygen lance and the flame at the end of the smelting, and extracting edge characteristics and texture characteristics based on gabor filtering. , can accurately judge the smelting end point in real time.
Description
Technical Field
The application relates to the technical field of converter smelting, in particular to a method, a system, a machine readable medium and equipment for identifying converter smelting conditions.
Background
China is the largest iron and steel producing country in the world, and the converter steelmaking capacity accounts for more than 80% of the total steelmaking capacity. Converter steelmaking is a very complicated process, comprises a periodic heating, carbon reduction and impurity removal process and a very complicated multi-element, multi-phase and high-temperature reaction, and is a very key technology for identifying and responding in time to the working condition in the smelting process without difference.
The slagging process in the smelting process is one of important indexes of working conditions and is closely related to the removal of impurity elements in the smelting process. Generally, slag and molten steel are in direct contact in a smelting process, and participate in physicochemical reactions and heat transfer processes between the slag and the molten steel. By adjusting the slag composition and the properties and quantity thereof, the oxidation and reduction processes of individual elements in the metal, such as the oxidation and reduction of silicon, manganese and phosphorus, desulfurization and deoxidation, can be controlled. The slag can also absorb non-metallic inclusions in the molten steel and prevent the molten steel from absorbing gases (hydrogen and nitrogen).
Meanwhile, the end point control at the last stage of smelting is also a very important ring in the working condition indexes, which means that the carbon content and the temperature of molten steel are controlled to meet the requirements of leaving a port, and the quality of the final molten steel is directly related. The accurate real-time prediction and judgment of the smelting end point have important significance for improving the production efficiency, improving the steel quality and saving the cost. Since the advent of the steel-making method of the self-rotating furnace, the common methods for the end point control of the steel-making of the converter mainly include an artificial experience method, a chemical analysis method, a static end point control and the like.
The intensity of carbon-oxygen reaction in converter steelmaking and the temperature of molten steel can be reflected by the flame at the furnace mouth. Therefore, the manual experience method is mainly used for judging the steelmaking end point by manually observing the flame and spark at the intersection. The problems of low hit rate, high labor intensity of workers and the like exist. The chemical analysis method mainly comprises sublance detection and sampling detection methods, the sublance detection can accurately predict the steelmaking endpoint of the converter, but the used detection equipment needs to work in a high-temperature and corrosive environment for a long time, the gas calibration period is short, the sampling head is frequently replaced, the use and maintenance cost of the equipment is high, and the equipment is difficult to popularize and use in the steelmaking converter industry. In addition, the converter is generally used in a converter with more than 120t, and the current situation that the converter is mainly used in small and medium steel mills in China is difficult to meet. The measurement time of the sampling detection method cannot meet the real-time requirement, and the splashing accident exists during sampling, and the production cost of steel is increased. Common modeling methods in process control can be basically divided into three major categories: white box model (mechanism model), black box model (statistical model), grey box model (model combining mechanism and statistics). Because a mechanism model of a complex process is difficult to establish, the traditional optimization control technology based on an accurate mathematical model is often difficult to be applied in actual production.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention provides a method, a system, a machine readable medium and a device for identifying converter smelting conditions, which are used to solve the shortcomings of the prior art.
In order to achieve the above objects and other related objects, the present invention provides an identification system for smelting conditions of a converter, the identification system comprising:
the image acquisition module is used for acquiring a flame image of the fire hole; the flame image at the furnace mouth comprises a flame image at a converter smelting slagging stage;
the image processing module is used for obtaining the texture characteristics of the flame at the furnace mouth based on the flame image at the furnace mouth and also used for judging whether the contour characteristics of the oxygen lance exist based on the flame image at the furnace mouth;
and the real-time judging module is used for judging the slagging condition according to the textural features of the flame at the furnace mouth and also used for predicting the smelting end point of the converter for the first time according to whether the contour features of the oxygen lance exist or not.
Optionally, the flame image at the furnace mouth further includes a flame at the last stage of converter smelting, and the real-time discrimination module performs second prediction on the converter smelting end point according to the contour feature of the flame at the last stage of converter smelting.
Optionally, the texture characteristic and the contour characteristic of the fire hole flame image are obtained through a Gabor filter, and the contour characteristic of the oxygen lance is detected through the Gabor filter.
Optionally, the identification system further includes a preprocessing module, configured to preprocess the fire door flame image, where the preprocessing includes at least graying.
Alternatively, if the contour characteristics of the lance appear, the converter smelting steel is close to the end point.
Alternatively, when the flame shrinks at the end of the converter smelting, the converter smelting steel reaches the end point.
Optionally, the hardness of the flame is judged according to the texture features of the flame image at the furnace mouth, and the slagging condition is judged according to the hardness of the flame; when the flame at the furnace mouth is soft, the slag melting condition is good; when the flame at the furnace mouth becomes hard, the slagging condition is not good.
In order to achieve the above objects and other related objects, the present invention provides a method for identifying a smelting condition of a converter, the method comprising:
acquiring a flame image of a furnace mouth; the flame image at the furnace mouth comprises a flame image at a converter smelting slagging stage;
obtaining the texture characteristics of the fire hole flame based on the fire hole flame image,
judging whether the profile characteristics of the oxygen lance exist or not based on the furnace mouth flame image;
judging the slagging condition according to the texture characteristics of the flame at the furnace mouth;
and carrying out first prediction on the smelting end point of the converter according to whether the profile characteristics of the oxygen lance exist or not.
Optionally, the fire hole flame image further comprises a flame at the end of converter smelting, and the end point of converter smelting is predicted for the second time according to the contour feature of the flame at the end of converter smelting.
Optionally, the texture characteristic and the contour characteristic of the fire hole flame image are obtained through a Gabor filter, and the contour characteristic of the oxygen lance is detected through the Gabor filter.
Optionally, the identification method further includes: and preprocessing the flame image of the fire door, wherein the preprocessing at least comprises graying processing.
Alternatively, if the contour characteristics of the lance appear, the converter smelting steel is close to the end point.
Alternatively, when the flame shrinks at the end of the converter smelting, the converter smelting steel reaches the end point.
Optionally, the hardness of the flame is judged according to the texture features of the flame image at the furnace mouth, and the slagging condition is judged according to the hardness of the flame; when the flame at the furnace mouth is soft, the slag melting condition is good; when the flame at the furnace mouth becomes hard, the slagging condition is not good.
To achieve the above and other related objects, the present invention provides a storage medium storing a computer program which, when executed by a processor, performs the method.
To achieve the above and other related objects, the present invention provides an apparatus comprising: a processor and a memory;
the memory is used for storing computer programs, and the processor is used for executing the computer programs stored by the memory so as to enable the terminal to execute the method.
As mentioned above, the method, the system, the machine readable medium and the equipment for identifying the smelting working condition of the converter have the following beneficial effects:
according to the invention, through the judgment of soft and hard fire in the smelting and slagging process and the judgment of the smelting end point oxygen lance profile and flame profile, the edge characteristic and the texture characteristic are extracted based on gabor filtering, whether slagging is good or not in the smelting process can be accurately judged based on a visual principle, and the smelting end point can be accurately judged in real time.
Drawings
FIG. 1 is a schematic block diagram of a converter smelting condition identification system according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for identifying smelting conditions of a converter in an embodiment of the invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
As shown in fig. 1, a converter smelting condition identification system is a visual converter smelting condition identification system, and the identification system comprises an image acquisition module 11, an image processing module 12 and a real-time discrimination module 13;
the image acquisition module 11 is used for acquiring a flame image of a furnace mouth; wherein the flame image at the furnace mouth comprises a flame image at a converter smelting slagging stage. Specifically, the images of the fire hole flames are acquired frame by frame in a video mode. The images of flames can be directly acquired by an industrial camera which is fixed and supported by a tripod, and a better visual orientation is obtained by adjusting the height and the rotation degree of the tripod.
The image processing module 12 is used for obtaining the texture characteristics of the fire hole flame based on the fire hole flame image and also used for judging whether the contour characteristics of the oxygen lance exist based on the fire hole flame image;
the texture of a flame is characterized in that the flame has a different characteristic in color spatial distribution and combination, i.e., a difference in texture, from the color analog of the background color.
Specifically, the texture features of the fire door flame image can be obtained through a Gabor filter, the frequency and the direction of the Gabor filter are similar to the visual system of human, and the Gabor filter is suitable for texture representation and discrimination. The Gabor feature relies primarily on the Gabor kernel to window the signal in the frequency domain, thereby enabling the description of the signal's local frequency information.
Judging whether the oxygen lance contour exists or not, and first carrying out contour detection, wherein the contour detection means that the influence of the background, the texture inside the target and noise interference is ignored in a digital image containing the target and the background, and a certain technology and a certain method are adopted to realize the process of extracting the target contour. In this embodiment, contour detection can be performed by a Gabor filter to determine whether or not the lance contour is present in the flame image.
And the real-time judging module 13 is used for judging the slagging condition according to the textural features of the flame at the furnace mouth and also used for predicting the smelting end point of the converter for the first time according to whether the contour features of the oxygen lance exist or not.
Generally speaking, whether the slagging condition is good or not in the smelting process can be judged by the hardness of flame: the slag is well melted and can be uniformly covered on the surface of the molten steel, and the gas is discharged with resistance, so the flame is soft; if the slag is not melted well or is agglomerated, the molten steel liquid level cannot be covered well, the resistance is small when gas is discharged, and the flame is strong, namely the flame is hard. When the amount of slag is large, the resistance is also large when gas is discharged, and the flame is soft.
Therefore, the specific standard of judging the hardness of the flame according to the textural features of the flame at the furnace mouth and judging the slagging condition according to the hardness of the flame is as follows: when the flame at the furnace mouth is soft, the slag melting condition is good; when the flame at the furnace mouth becomes hard, the slagging condition is not good.
The soft and hard conditions of the flame are judged through the textural features of the flame:
the method comprises the steps of collecting a plurality of flame texture characteristics, and then performing parameter optimization modeling based on the flame texture characteristics to obtain a model, wherein the hardness of flame can be distinguished through the model.
When a new flame texture feature is collected, the softness and hardness of the flame can be judged based on the model. Generally, the flame texture changes disorderly, and is soft if the turbulence is large; otherwise it is hard.
In the embodiment, the converter smelting end point is firstly predicted through the profile characteristics of the oxygen lance, namely when the oxygen lance profile appears in the flame image, the converter smelting end point can be preliminarily judged to be close to the end point.
In order to further determine the converter steelmaking end point, it is necessary to perform accurate determination. The specific method comprises the following steps: when the flame profile shrinks at the end of the converter smelting, the converter smelting steel reaches the end point. In particular, a Gabor filter may be employed to extract the profile of the flame.
In an embodiment, the method further comprises preprocessing the fire door flame image, wherein the preprocessing at least comprises graying processing. The grayed image obtained by the graying processing is input to the Gabor filter.
In one embodiment, the method further provides a step of guiding the image processed by the image processing module into a real-time judging module to judge the current working condition.
Specifically, when the hard fire texture judgment result is 0 and the soft fire texture judgment result is 1, and when the output result is 0, the condition that the slag is formed in the converter at present is poor; when the output result is 1, the condition of the slag in the converter is good at present.
Specifically, when the smelting end point is judged for the first time, the condition that the contour of the oxygen lance does not appear is taken as 0, the contour of the oxygen lance appears as 1, and when the output result is 0, the condition that the smelting end point is not close to the smelting end point is indicated; when the output result is 1, the fact that the smelting end point is approached currently is meant.
Specifically, the smelting end point is judged for the second time, the flame profile is not contracted into 0, the flame profile begins to be contracted into 1, and when the output result is 0, the fact that the smelting end point is not reached currently is meant; when the output result is 1, the end point of smelting is about to be reached currently.
TABLE 1 real-time discrimination of the smelting conditions of the modules
In conclusion, outputting 0 in the slagging process represents that the slagging condition is poor; output 1 represents good slagging. When the smelting end point is judged, 0 and 0 are output to represent that the smelting end point is not reached or is not close to the smelting end point; outputs 1 and 0 represent that the smelting end point is approached but not reached currently; outputs 1 and 1 represent that the smelting end point of the converter is about to be reached currently.
The invention provides a vision-based identification system for converter smelting conditions, which judges whether the current slagging is good or not by the hardness and softness of flame in a converter smelting slagging stage; the smelting end point is preliminarily judged according to the appearance of the outline of the oxygen lance, and the smelting end point of the converter is accurately predicted according to the contraction and rarefaction of the flame at the last stage of the smelting of the converter. Meanwhile, edge characteristics and texture characteristics are extracted through gabor filtering so as to replace manual judgment of real-time smelting working conditions. The method avoids subjective errors caused by artificial observation, realizes accurate judgment of the smelting working condition of the converter, improves the production efficiency and reduces the smelting production cost.
As shown in fig. 2, a method for identifying smelting conditions of a converter includes:
s1, acquiring a flame image of the fire door; the flame image at the furnace mouth comprises a flame image at a converter smelting slagging stage;
s2 obtaining the texture characteristics of the fire hole flame based on the fire hole flame image,
s3, judging whether the contour characteristics of the oxygen lance exist or not based on the furnace mouth flame image;
s4, judging the slagging condition according to the texture characteristics of the flame at the furnace mouth;
s5, according to whether the contour characteristics of the oxygen lance exist, the first prediction is carried out on the smelting end point of the converter.
In an embodiment, the mouth flame image further includes a flame at the end of converter smelting, and the end point of converter smelting is predicted for the second time according to the contour characteristics of the flame at the end of converter smelting.
In one embodiment, the texture characteristic and the contour characteristic of the fire hole flame image are obtained through a Gabor filter, and the contour characteristic of the oxygen lance is detected through the Gabor filter.
In one embodiment, the identification method further comprises: and preprocessing the flame image of the fire door, wherein the preprocessing at least comprises graying processing.
In one embodiment, if the profile characteristics of the lance occur, the converter smelting steel approaches the end point.
In one embodiment, the end of converter smelting is reached when the flame shrinks at the end of the converter smelting.
In one embodiment, the hardness of the flame is judged according to the texture features of the flame image at the fire hole, and the slagging condition is judged according to the hardness of the flame; when the flame at the furnace mouth is soft, the slag melting condition is good; when the flame at the furnace mouth becomes hard, the slagging condition is not good.
Since the embodiment of the method portion corresponds to the embodiment of the apparatus portion, please refer to the description of the embodiment of the apparatus portion for the content of the embodiment of the method portion, which is not repeated here.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may comprise any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, etc.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. 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 spirit of the present invention be covered by the claims of the present invention.
Claims (16)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911127453.XA CN111079537B (en) | 2019-11-18 | 2019-11-18 | Methods, systems, machine-readable media and equipment for identifying converter smelting conditions |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911127453.XA CN111079537B (en) | 2019-11-18 | 2019-11-18 | Methods, systems, machine-readable media and equipment for identifying converter smelting conditions |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111079537A true CN111079537A (en) | 2020-04-28 |
| CN111079537B CN111079537B (en) | 2023-09-26 |
Family
ID=70311255
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911127453.XA Active CN111079537B (en) | 2019-11-18 | 2019-11-18 | Methods, systems, machine-readable media and equipment for identifying converter smelting conditions |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111079537B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115600378A (en) * | 2022-09-20 | 2023-01-13 | 中冶赛迪工程技术股份有限公司（Cn） | Oxygen lance control method and system, electronic equipment and storage medium |
| CN117094971A (en) * | 2023-08-22 | 2023-11-21 | 中冶赛迪信息技术(重庆)有限公司 | A method, device, electronic equipment and medium for detecting the softness and hardness of a flame |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102206727A (en) * | 2011-05-31 | 2011-10-05 | 湖南镭目科技有限公司 | Converter steelmaking endpoint determination method and system, control method and control system |
| CN102399933A (en) * | 2010-09-07 | 2012-04-04 | 鞍钢股份有限公司 | A method for automatic control of oxygen lance in converter blowing low carbon steel |
| CN202193799U (en) * | 2011-05-31 | 2012-04-18 | 湖南镭目科技有限公司 | Converter steelmaking end point judging system and control system |
| CN102968644A (en) * | 2012-11-21 | 2013-03-13 | 长春工业大学 | Method for predicting smelting finishing point of argon-oxygen refined iron alloy |
| WO2015045766A1 (en) * | 2013-09-30 | 2015-04-02 | Ｊｆｅスチール株式会社 | Control device and control method for converter furnace blowing equipment |
| CN105925750A (en) * | 2016-05-13 | 2016-09-07 | 南阳理工学院 | Steelmaking end point prediction method based on neural networks |
| CN106148637A (en) * | 2015-04-10 | 2016-11-23 | 南京理工大学 | The pneumatic steelmaking carbon content dynamic detection system of high stable |
| CN205845067U (en) * | 2016-04-27 | 2016-12-28 | 云南昆钢集团电子信息工程有限公司 | The pneumatic steelmaking blowing state recognition system of static nature is moved based on image |
| CN106846305A (en) * | 2017-01-11 | 2017-06-13 | 华北电力大学 | A kind of boiler combustion stability monitoring method based on many characteristics of image of flame |
| WO2018014752A1 (en) * | 2016-07-22 | 2018-01-25 | 唐山正丰钢铁有限公司 | Production process for dephosphorization outside of furnace of molten steel |
| CN108647407A (en) * | 2018-04-24 | 2018-10-12 | 北京科技大学 | A kind of pneumatic steelmaking flue gas analysis carbon determination method |
| CN109711345A (en) * | 2018-12-27 | 2019-05-03 | 南京林业大学 | A flame image recognition method, device and storage medium thereof |
-
2019
- 2019-11-18 CN CN201911127453.XA patent/CN111079537B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102399933A (en) * | 2010-09-07 | 2012-04-04 | 鞍钢股份有限公司 | A method for automatic control of oxygen lance in converter blowing low carbon steel |
| CN102206727A (en) * | 2011-05-31 | 2011-10-05 | 湖南镭目科技有限公司 | Converter steelmaking endpoint determination method and system, control method and control system |
| CN202193799U (en) * | 2011-05-31 | 2012-04-18 | 湖南镭目科技有限公司 | Converter steelmaking end point judging system and control system |
| CN102968644A (en) * | 2012-11-21 | 2013-03-13 | 长春工业大学 | Method for predicting smelting finishing point of argon-oxygen refined iron alloy |
| WO2015045766A1 (en) * | 2013-09-30 | 2015-04-02 | Ｊｆｅスチール株式会社 | Control device and control method for converter furnace blowing equipment |
| CN106148637A (en) * | 2015-04-10 | 2016-11-23 | 南京理工大学 | The pneumatic steelmaking carbon content dynamic detection system of high stable |
| CN205845067U (en) * | 2016-04-27 | 2016-12-28 | 云南昆钢集团电子信息工程有限公司 | The pneumatic steelmaking blowing state recognition system of static nature is moved based on image |
| CN105925750A (en) * | 2016-05-13 | 2016-09-07 | 南阳理工学院 | Steelmaking end point prediction method based on neural networks |
| WO2018014752A1 (en) * | 2016-07-22 | 2018-01-25 | 唐山正丰钢铁有限公司 | Production process for dephosphorization outside of furnace of molten steel |
| CN106846305A (en) * | 2017-01-11 | 2017-06-13 | 华北电力大学 | A kind of boiler combustion stability monitoring method based on many characteristics of image of flame |
| CN108647407A (en) * | 2018-04-24 | 2018-10-12 | 北京科技大学 | A kind of pneumatic steelmaking flue gas analysis carbon determination method |
| CN109711345A (en) * | 2018-12-27 | 2019-05-03 | 南京林业大学 | A flame image recognition method, device and storage medium thereof |
Non-Patent Citations (11)
| Title |
|---|
| RAO, JP 等: "Research and Application of New Oxygen Lance For BOF Steelmaking", ADVANCED MATERIALS AND STRUCTURES * |
| 何涛焘;田陆;文华北;刘卓明;冯正金;周存波;: "基于炉口火焰信息的转炉炼钢终点预报系统", 中国冶金, no. 02, pages 40 - 43 * |
| 刘辉等: "基于灰度差分统计的火焰图像纹理特征提取", 《控制工程》 * |
| 刘辉等: "基于灰度差分统计的火焰图像纹理特征提取", 《控制工程》, no. 02, 20 March 2013 (2013-03-20), pages 213 - 218 * |
| 尤佳等: "通过转炉炉口火焰纹理分析判断氧气顶吹转炉吹炼终点", 《北京科技大学学报》 * |
| 尤佳等: "通过转炉炉口火焰纹理分析判断氧气顶吹转炉吹炼终点", 《北京科技大学学报》, vol. 22, no. 6, 31 December 2000 (2000-12-31), pages 524 - 528 * |
| 李帷韬等: "基于Gabor滤波器和潜在语义分析的回转窑烧成状态识别研究", 《第22届中国过程控制会议论文》 * |
| 李帷韬等: "基于Gabor滤波器和潜在语义分析的回转窑烧成状态识别研究", 《第22届中国过程控制会议论文》, 31 August 2011 (2011-08-31), pages 1501 - 1502 * |
| 罗莉萍等: "《转炉炼钢实训指导书》", 30 April 2016, pages: 107 - 108 * |
| 赵琦等: "光强与图像信息在转炉炼钢终点判断中的应用", 《仪器仪表学报》 * |
| 赵琦等: "光强与图像信息在转炉炼钢终点判断中的应用", 《仪器仪表学报》, vol. 26, 31 August 2005 (2005-08-31), pages 575 - 580 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115600378A (en) * | 2022-09-20 | 2023-01-13 | 中冶赛迪工程技术股份有限公司（Cn） | Oxygen lance control method and system, electronic equipment and storage medium |
| CN115600378B (en) * | 2022-09-20 | 2025-07-08 | 中冶赛迪工程技术股份有限公司 | Oxygen lance control method, system, electronic equipment and storage medium |
| CN117094971A (en) * | 2023-08-22 | 2023-11-21 | 中冶赛迪信息技术(重庆)有限公司 | A method, device, electronic equipment and medium for detecting the softness and hardness of a flame |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111079537B (en) | 2023-09-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112017145B (en) | Efficient automatic slag skimming method and system for molten iron pretreatment | |
| CN105956618B (en) | Converter steelmaking blowing state identification system and method based on image dynamic and static characteristics | |
| CN106017691B (en) | Non-contact molten metal solution temperature continuous detection method and system | |
| CN102206727A (en) | Converter steelmaking endpoint determination method and system, control method and control system | |
| CN101403678B (en) | Metallic phase automatic detection method for stainless steel casting blank foreign matter | |
| CN111128313A (en) | Method and system for detecting FeO content of sinter | |
| CN112862769A (en) | On-line intelligent monitoring method and system for blast furnace slag-iron ratio | |
| CN111079537B (en) | Methods, systems, machine-readable media and equipment for identifying converter smelting conditions | |
| CN106670449A (en) | Molten iron slagging-off measurement and control method | |
| CN116402813B (en) | Neural network-based copper converter converting copper-making period end point judging method | |
| CN116240328B (en) | Converter steelmaking end point control method, system, device, equipment, medium and product | |
| CN113718082A (en) | Prediction system and method for judging steelmaking end point temperature of converter based on flame image | |
| CN104392213B (en) | A kind of image information state recognition system suitable for fusion process | |
| CN109252009A (en) | BOF Steelmaking Endpoint manganese content prediction technique based on regularization extreme learning machine | |
| CN205845067U (en) | The pneumatic steelmaking blowing state recognition system of static nature is moved based on image | |
| CN202193799U (en) | Converter steelmaking end point judging system and control system | |
| CN112458231B (en) | Converter slag discharge detection method and system | |
| CN103267498B (en) | Iron ore roughness automatic digital measures of quantization method | |
| CN116721089A (en) | Method and system for measuring fluctuation behavior of smelting liquid level of nonferrous metal molten pool | |
| CN116363096A (en) | A method, device, medium and electronic equipment for detecting slag overflow in a steelmaking converter | |
| CN113063474A (en) | Slag liquid level real-time detection method, device, equipment and storage medium | |
| CN114623935B (en) | Ladle accurate casting accurate stopping model application method based on infrared thermal imager imaging data | |
| CN109521730B (en) | Online calculation method for slag surface in ladle | |
| CN113592760B (en) | Converter endpoint carbon content prediction method based on flame image texture features | |
| CN113255741A (en) | Thermal image tracking and control method for high-temperature smelting soft stirring 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 |