CN113122669A - Blast furnace taphole state monitoring method and system - Google Patents
Blast furnace taphole state monitoring method and system Download PDFInfo
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B7/00—Blast furnaces
- C21B7/24—Test rods or other checking devices
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- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B7/00—Blast furnaces
- C21B7/12—Opening or sealing the tap holes
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The invention discloses a method for monitoring the state of a blast furnace taphole, which comprises the following steps of S1: acquiring image data of a blast furnace taphole to obtain a blast furnace taphole image; s2: sequentially carrying out interception, morphological opening operation, Gaussian blur, K-Means color clustering, gray level image conversion, threshold segmentation and morphological operation on the basis of the blast furnace taphole image to obtain a blast furnace molten iron characteristic image; s3: analyzing image data based on the characteristic image of the blast furnace molten iron to obtain analysis data; s4: and calculating based on the analysis data to respectively obtain the size, the depth, the piercing time, the iron output and the stemming filling amount of the blast furnace taphole. The invention has high accuracy and short running time by intelligently monitoring the state of the blast furnace taphole, and can meet the requirements of safe and stable production on the blast furnace ironmaking production site; the invention has the advantages of more scientific monitoring mode and more accurate monitoring result; the monitoring process is automatic, manual operation is not needed, and personnel redundancy is reduced; the safety of the operation area in front of the furnace is improved.
Description
Technical Field
The invention belongs to the field of metallurgical blast furnace ironmaking, and particularly relates to a method and a system for monitoring the state of a blast furnace taphole.
Background
The development of the steel industry is one of the important pillars of the national development, and the large-scale blast furnace is used as the important production equipment for steel making and has no function of replacing. The intelligent monitoring of the state of the blast furnace taphole has very important correlation to the production quality and the service life of the blast furnace.
At present, whether the blast furnace taphole is pierced is comprehensively judged mainly through observation of operators and combination of abundant experience, but the judgment mode is rough, and the work in a working area in front of the furnace is dangerous. Therefore, the method provides a method for realizing intelligent monitoring of the state of the blast furnace taphole by integrating and processing image data through a visual sensor by using a C + + and Open CV library and a Haikangwei secondary development SDK library and providing an intelligent algorithm.
Disclosure of Invention
The technical purpose of the invention is to provide a method and a system for monitoring the state of a blast furnace taphole so as to obtain the technical effect of automatically monitoring the blast furnace taphole.
In order to solve the problems, the technical scheme of the invention is as follows:
a method for monitoring the state of a blast furnace taphole comprises the following steps:
s1: acquiring image data of a blast furnace taphole to obtain a blast furnace taphole image;
s2: sequentially carrying out interception, morphological opening operation, Gaussian blur, K-Means color clustering, gray level image conversion, threshold segmentation and morphological operation on the basis of the blast furnace taphole image to obtain a blast furnace molten iron characteristic image;
s3: analyzing image data based on the blast furnace molten iron characteristic image to obtain analysis data, wherein the analysis data comprises molten iron areas and corresponding time parameters in each frame of blast furnace molten iron characteristic image;
s4: and calculating based on the analysis data to respectively obtain the size of the blast furnace taphole, the depth of the blast furnace taphole, the piercing time of the blast furnace taphole, the tapping amount of the blast furnace taphole and the stemming filling amount of the blast furnace taphole, thereby monitoring the blast furnace taphole in real time.
Wherein step S2 specifically includes the following steps
S21: carrying out molten iron area interception based on the blast furnace taphole image to obtain a molten iron area image;
s22: corroding and expanding in sequence based on the molten iron area image to obtain a molten iron area fuzzy image;
s23: performing Gaussian blur processing based on the fuzzy image of the molten iron area to filter interference factors and obtain a Gaussian image of the molten iron area;
s24: obtaining a molten iron region segmentation image by segmenting through a K-means clustering algorithm based on the molten iron region Gaussian image;
s25: performing gray level conversion based on the molten iron area segmentation image to obtain a molten iron area gray level image;
s26: performing threshold segmentation based on the gray level image of the molten iron area, reserving image information of the molten iron area, and filtering interference image information in the environment to obtain a binary image of the molten iron area;
s27: and expanding and corroding the molten iron area binary image to obtain a blast furnace molten iron characteristic image.
Wherein step S3 specifically includes the following steps
S31: accumulating white pixel points in each frame of blast furnace molten iron characteristic image to obtain molten iron area in each frame of blast furnace molten iron characteristic image, and storing;
s32: and acquiring and storing the time parameters in each frame of blast furnace molten iron characteristic image from each frame of blast furnace molten iron characteristic image.
Wherein step S4 specifically includes the following steps
S41: obtaining the size of a blast furnace taphole based on the area of molten iron;
s42: comparing threshold values based on the area of molten iron, judging whether the blast furnace taphole is pierced, and calculating to obtain the piercing time of the blast furnace taphole based on time parameters;
s43: calculating the tapping amount of a blast furnace tapping hole based on the area of molten iron and the corresponding time parameter;
s44: obtaining the depth of a blast furnace taphole based on the punch-through time and the position value of the displacement encoder;
s45: and calculating to obtain the stemming filling amount of the blast furnace taphole based on the size of the blast furnace taphole and the depth of the blast furnace taphole.
A blast furnace taphole state monitoring system is applied to the blast furnace taphole state monitoring method meeting any one of the above requirements, and comprises an image acquisition module, an image processing module, an image analysis module and a detection module;
the image acquisition module is used for acquiring image data of a blast furnace taphole to obtain a blast furnace taphole image;
the image processing module is used for sequentially carrying out interception, morphological opening operation, Gaussian blur, K-Means color clustering, gray level image conversion, threshold segmentation and morphological operation on the blast furnace taphole image to obtain a blast furnace molten iron characteristic image;
the image analysis module is used for carrying out image data analysis on the blast furnace molten iron characteristic image to obtain analysis data, and the analysis data comprises molten iron areas and corresponding time parameters in each frame of blast furnace molten iron characteristic image;
the detection module is used for calculating the analysis data to obtain the size of the blast furnace taphole, the depth of the blast furnace taphole, the piercing time of the blast furnace taphole, the tapping amount of the blast furnace taphole and the stemming filling amount of the blast furnace taphole, so that the blast furnace taphole is monitored in real time.
Specifically, the image acquisition module comprises a camera, a video recorder and a switch;
the camera and the video recorder are arranged 10 to 15 meters away from the external blast furnace taphole, and are matched for acquiring image data at the external blast furnace taphole;
the exchanger is respectively connected with the camera and the video recorder through signals and is used for receiving image data and uploading the image data to the image processing module.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects:
by intelligently monitoring the state of the blast furnace taphole, the method provided by the invention has high accuracy and short running time, and can meet the requirements of safe and stable production on the blast furnace ironmaking production site; the invention has the advantages of more scientific monitoring mode and more accurate monitoring result; the monitoring process is automatic, manual operation is not needed, and personnel redundancy is reduced; the safety of the operation area in front of the furnace is improved.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.
FIG. 1 is a schematic flow chart illustrating a method for monitoring the state of a taphole of a blast furnace according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a sign extraction process according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a data analysis process according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart illustrating a process of obtaining monitoring parameters according to an embodiment of the present invention;
FIG. 5 is a schematic structural view of a blast furnace taphole status monitoring system according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
For the sake of simplicity, the drawings only schematically show the parts relevant to the present invention, and they do not represent the actual structure as a product. In addition, in order to make the drawings concise and understandable, components having the same structure or function in some of the drawings are only schematically illustrated or only labeled. In this document, "one" means not only "only one" but also a case of "more than one".
The method and system for monitoring the state of the tap hole of the blast furnace according to the present invention will be described in detail with reference to the accompanying drawings and specific embodiments. Advantages and features of the present invention will become apparent from the following description and from the claims.
Example 1
Referring to fig. 1 to 4, the present embodiment provides a method for monitoring a state of a taphole of a blast furnace, including the following steps:
s1: acquiring image data of a blast furnace taphole to obtain a blast furnace taphole image;
s2: sequentially carrying out interception, morphological opening operation, Gaussian blur, K-Means color clustering, gray level image conversion, threshold segmentation and morphological operation on the basis of the blast furnace taphole image to obtain a blast furnace molten iron characteristic image;
s3: analyzing image data based on the blast furnace molten iron characteristic image to obtain analysis data, wherein the analysis data comprises molten iron areas and corresponding time parameters in each frame of blast furnace molten iron characteristic image;
s4: and calculating based on the analysis data to respectively obtain the size of the blast furnace taphole, the depth of the blast furnace taphole, the piercing time of the blast furnace taphole, the tapping amount of the blast furnace taphole and the stemming filling amount of the blast furnace taphole, thereby monitoring the blast furnace taphole in real time.
Referring to fig. 1, the present embodiment will now be described in detail, and a preliminary step is first required before step S1. Selecting a suitable visual sensor to acquire image data, which includes the selection of a camera, a video recorder, and an exchange, and then, it is necessary to build a software system applied to this embodiment.
After the preparation work is completed, step S1 is started, in step S1, the camera and the video recorder are matched, that is, the image data of the camera and the video recorder at the same time are bound for convenient storage and processing, the collected blast furnace taphole image is uploaded through the exchange, and then step S2 is performed.
Referring to fig. 1 and 2, the step S2 performs feature extraction on the received image data through the Open CV library and the C + + language, and since the image data is received continuously in real time in this embodiment, the following steps are described as each frame of the blast furnace taphole image. Is particularly subdivided into
And step S21, carrying out molten iron area interception on the uploaded blast furnace taphole image to obtain a molten iron area image. The step greatly reduces the subsequent image processing amount, reduces the interference of the surrounding environment and reduces the time delay of the embodiment.
Subsequently, the process proceeds to step S22, where the morphological opening operation is performed on the molten iron region image in step S21, and erosion and re-expansion are performed first to obtain a molten iron region blurred image. This step can blur the image and filter out interference factors such as sparks.
Then, the process goes to step S23, where gaussian blurring processing is performed on step S22 to perform two-degree blurring processing, so as to filter out interference factors and obtain a gaussian image of the molten iron area.
And secondly, in step S24, effectively segmenting the Gaussian image of the molten iron region in step S23 by adopting a K-Means color clustering method, and maximally reserving color information to obtain a molten iron region segmentation image. The K-Means color clustering method is a color segmentation method of a K-Means clustering algorithm, is a clustering analysis algorithm of iterative solution, is an unsupervised clustering algorithm, and is relatively simple to realize and good in clustering effect.
Subsequently, the process proceeds to step S25, where the molten iron region division image of step S24 is converted from the BGR chart to the grayscale chart, thereby obtaining a molten iron region grayscale image. Therefore, the subsequent processing amount is greatly reduced, and the subsequent threshold segmentation operation is facilitated.
And then, in step S26, performing threshold segmentation on the gray level image of the molten iron region in step S25, where the threshold is preset, so as to retain image information of the molten iron region and filter interference image information in an environment, and obtain a binary image of the molten iron region, where the molten iron region is a white pixel and the interference image is a black pixel.
And finally, in step S27, performing morphological closing operation on the binary image of the molten iron region in step S26, and performing expansion and corrosion to obtain a blast furnace molten iron characteristic image convenient for contour extraction.
Referring to fig. 3, the step S3 is to analyze the image data of the blast furnace molten iron characteristic image to obtain the analysis data, and includes the following steps in detail
First, in step S31, the white pixels in the characteristic image of the blast furnace molten iron in step S27 are accumulated to obtain the molten iron area of the current frame, and the molten iron area is stored.
Meanwhile, in step S32, the time parameters of the current characteristic image of the molten iron in the blast furnace are read, and the time parameters of each frame of image are read and stored by using the seaway-based secondary development SDK library. And in the storage process, the molten iron area data and the time parameter are correlated.
Referring to FIG. 4, in step S4, the analysis data in step S3 are calculated to obtain various parameters of the taphole of the blast furnace, so as to monitor the taphole of the blast furnace in real time, and the detailed subdivision includes the following steps
First, in step S41, the current size of the taphole of the blast furnace is obtained based on the molten iron area of the current frame in step S31.
Next, in step S42, the molten iron area of each frame is compared with a preset area threshold, if the molten iron area exceeds the preset threshold and reaches a certain number of frames, it is determined that the blast furnace taphole is pierced, and the piercing time of the blast furnace taphole is calculated by combining the time parameter of step S32.
In step S43, based on the molten iron area of the current frame in step S31 and the time parameter in step S32, the tapping amount of the taphole of the blast furnace can be calculated, specifically, Q is K is S, where Q is the tapping amount of the taphole of the blast furnace, K is an image pixel conversion coefficient, and S is the molten iron area of the current frame in step S31; t is the time parameter of step S32.
Then, in step S44, the blast furnace taphole depth is obtained based on the piercing time and the position value of the displacement encoder in step S42 by using the OPC protocol as an input variable of the PLC.
The OPC (ole for Process control) technology is to establish an interface standard for communication between applications of the industrial control system, and to establish a unified data access specification between the industrial control device and the control software, and this example completes communication with the PLC through the OPC server.
In this example, the PLC is S7-300 PLC used in a Bao Steel 3 blast furnace, and is used for controlling tapping machine equipment, specifically, a displacement encoder is provided in the tapping machine for calculating the length of the drill rod of the tapping machine, and the tapping hole depth of the blast furnace can be measured by inputting the piercing time in step S42.
Finally, in step S45, the stemming filling amount of the blast furnace taphole is calculated based on the size of the blast furnace taphole obtained in step S41 and the depth of the blast furnace taphole obtained in step S44. The concrete formula is W ═ A × D, wherein W is the stemming filling amount of the blast furnace taphole, A is the size of the blast furnace taphole obtained in step S41, and D is the depth of the blast furnace taphole obtained in step S44. The real-time monitoring of the blast furnace taphole can be realized through the parameters.
Example 2
Referring to fig. 2, the present embodiment provides a system for monitoring a state of a taphole of a blast furnace based on embodiment 1, the system adopts a method for monitoring a state of a taphole of a blast furnace as required in any one of embodiments 1, and specifically includes an image acquisition module, an image processing module, an image analysis module and a detection module, referring to fig. 5;
the image acquisition module is used for acquiring image data of a blast furnace taphole to obtain a blast furnace taphole image;
the image processing module is used for sequentially carrying out interception, morphological opening operation, Gaussian blur, K-Means color clustering, gray level image conversion, threshold segmentation and morphological operation on the blast furnace taphole image to obtain a blast furnace molten iron characteristic image;
the image analysis module is used for carrying out image data analysis on the blast furnace molten iron characteristic image to obtain analysis data, and the analysis data comprises molten iron areas and corresponding time parameters in each frame of blast furnace molten iron characteristic image;
the detection module is used for calculating the analysis data to obtain the size of the blast furnace taphole, the depth of the blast furnace taphole, the piercing time of the blast furnace taphole, the tapping amount of the blast furnace taphole and the stemming filling amount of the blast furnace taphole, so that the blast furnace taphole is monitored in real time.
Specifically, the image acquisition module comprises a camera, a video recorder and a switch.
Selecting a camera: the method is applied to complex industrial scenes, and equipment needs to have stability and support long-time work; selecting 400W pixels by using the pixels; POE power supply is selected as the power supply mode, so that field wiring can be reduced; the network supply mode selects network cable transmission, and signals are stable; the camera is required to have a zoom function and support secondary development. The network camera preferably produced by Haikangwei vision has the following model: DS-2CD5A47 EFWD-IZS.
Selecting a video recorder: the video recorder needs to be matched with the camera, so that data storage is facilitated; the memory is selected to be 2T or more, and can store more than one month of image data; the device needs to support secondary development. The network hard disk video recorder preferably adopts Haikangwei video production, and has the model: DS-7604N-F1V 2.
Selecting a switch: because camera, video recorder and computer need carry out the information interaction, need choose for use the switch, the switch needs to choose for use POE power supply type, reduces the scene and walks the line, for the camera power supply. The invention selects a four-way POE exchanger produced by Haikangwei vision, and the model is as follows: DS-3E 0105P-E.
Specifically, the camera, the video recorder and the video recorder are arranged at a position 10-15 meters away from the external blast furnace taphole, and the camera and the video recorder are matched and used for acquiring complete image data at the external blast furnace taphole. The exchanger is respectively in signal connection with the camera and the video recorder and is used for receiving image data and uploading the image data to the image processing module, and the development of functions such as equipment initialization, local parameter setting, connection and receiving timeout time, reconnection setting, login and the like is completed through a Haokangwei video secondary development SDK library and a corresponding program, so that the camera and the video recorder are in communication with a PC and data interaction is realized.
The embodiment can achieve real-time reading, the development of the functions of starting preview and real-time data callback is completed through the Haokangwei secondary development SDK library and the combination of corresponding programs, the embodiment adopts a mode of calling the sub-stream codes, the calculation amount is reduced, and the real-time performance is improved. The obtained sub-stream code video data can be previewed on line through a Visual Studio platform, and the acquisition of the image data of the blast furnace taphole is realized.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments. Even if various changes are made to the present invention, it is still within the scope of the present invention if they fall within the scope of the claims of the present invention and their equivalents.
Claims (6)
1. A method for monitoring the state of a blast furnace taphole is characterized by comprising the following steps:
s1: acquiring image data of a blast furnace taphole to obtain a blast furnace taphole image;
s2: sequentially carrying out interception, morphological opening operation, Gaussian blur, K-Means color clustering, gray level image conversion, threshold segmentation and morphological operation on the basis of the blast furnace taphole image to obtain a blast furnace molten iron characteristic image;
s3: analyzing image data based on the blast furnace molten iron characteristic image to obtain analysis data, wherein the analysis data comprises molten iron areas and corresponding time parameters in each frame of blast furnace molten iron characteristic image;
s4: and calculating based on the analysis data to respectively obtain the size of the blast furnace taphole, the depth of the blast furnace taphole, the piercing time of the blast furnace taphole, the tapping amount of the blast furnace taphole and the stemming filling amount of the blast furnace taphole, thereby monitoring the blast furnace taphole in real time.
2. The method for monitoring the status of a blast furnace taphole according to claim 1, wherein the step S2 specifically comprises the following steps
S21: carrying out molten iron area interception based on the blast furnace taphole image to obtain a molten iron area image;
s22: corroding and expanding the molten iron area image in sequence to obtain a molten iron area fuzzy image;
s23: performing Gaussian blur processing on the fuzzy image of the molten iron area to filter interference factors and obtain a Gaussian image of the molten iron area;
s24: obtaining a molten iron region segmentation image by segmenting through a K-means clustering algorithm based on the molten iron region Gaussian image;
s25: performing gray level conversion based on the molten iron area segmentation image to obtain a molten iron area gray level image;
s26: performing threshold segmentation based on the gray level image of the molten iron area, reserving image information of the molten iron area, and filtering interference image information in the environment to obtain a binary image of the molten iron area;
s27: and expanding and corroding the molten iron region binary image to obtain a blast furnace molten iron characteristic image.
3. The method for monitoring the status of a blast furnace taphole according to claim 2, wherein the step S3 specifically comprises the following steps
S31: accumulating white pixel points in each frame of blast furnace molten iron characteristic image to obtain the molten iron area in each frame of blast furnace molten iron characteristic image, and storing the molten iron area;
s32: and acquiring the time parameter in each frame of the blast furnace molten iron characteristic image from each frame of the blast furnace molten iron characteristic image, and storing the time parameter.
4. The method for monitoring the status of a blast furnace taphole according to claim 3, wherein the step S4 specifically comprises the following steps
S41: obtaining the size of the blast furnace taphole based on the area of the molten iron;
s42: comparing the threshold value based on the area of the molten iron, judging whether the blast furnace taphole is pierced, and calculating to obtain the piercing time of the blast furnace taphole based on the time parameter;
s43: calculating the tapping amount of the blast furnace tapping hole based on the area of the molten iron and the corresponding time parameter;
s44: obtaining the depth of the blast furnace taphole based on the punch-through time and the position value of a displacement encoder;
s45: and calculating the stemming filling amount of the blast furnace taphole based on the size of the blast furnace taphole and the depth of the blast furnace taphole.
5. A blast furnace taphole state monitoring system applying the blast furnace taphole state monitoring method according to any one of claims 1 to 4, characterized by comprising an image acquisition module, an image processing module, an image analysis module and the detection module;
the image acquisition module is used for acquiring image data of a blast furnace taphole to obtain a blast furnace taphole image;
the image processing module is used for sequentially carrying out interception, morphological opening operation, Gaussian blur, K-Means color clustering, gray level image conversion, threshold segmentation and morphological operation on the blast furnace taphole image to obtain a blast furnace molten iron characteristic image;
the image analysis module is used for carrying out image data analysis on the blast furnace molten iron characteristic image to obtain analysis data, and the analysis data comprises molten iron areas and corresponding time parameters in each frame of blast furnace molten iron characteristic image;
the detection module is used for calculating the analysis data to obtain the size of the blast furnace taphole, the depth of the blast furnace taphole, the piercing time of the blast furnace taphole, the tapping amount of the blast furnace taphole and the stemming filling amount of the blast furnace taphole, so that the blast furnace taphole is monitored in real time.
6. The system for monitoring the status of a blast furnace taphole according to claim 5, wherein the image acquisition module comprises a camera, a video recorder and a switch;
the camera and the video recorder are arranged 10-15 meters away from an external blast furnace taphole, and are matched for acquiring image data at the external blast furnace taphole;
the switch is respectively in signal connection with the camera and the video recorder and is used for receiving image data and uploading the image data to the image processing module.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115125340A (en) * | 2022-06-23 | 2022-09-30 | 抚顺新钢铁有限责任公司 | Iron frequency information acquisition system |
| CN115612765A (en) * | 2022-10-14 | 2023-01-17 | 合肥视展光电科技有限公司 | Real-time detection control method and system for state of blast furnace taphole |
| CN115652003A (en) * | 2022-09-06 | 2023-01-31 | 中南大学 | Blast furnace taphole plugging time online monitoring method and system based on two-stage classification |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007248148A (en) * | 2006-03-14 | 2007-09-27 | Nippon Steel Corp | Blast furnace tapping temperature measuring system, blast furnace tapping temperature measuring method, and computer program |
| CN105678295A (en) * | 2016-01-04 | 2016-06-15 | 武汉科技大学 | Method for real-time monitoring gas heating furnace flame on the basis of ROI average image analysis |
| CN106435074A (en) * | 2016-10-26 | 2017-02-22 | 上海宝九和耐火材料有限公司 | Treatment method for tapping hole of blast furnace |
| WO2019132495A1 (en) * | 2017-12-26 | 2019-07-04 | 주식회사 포스코 | Device for monitoring tapping amount of molten iron |
| CN110073008A (en) * | 2016-12-12 | 2019-07-30 | 株式会社Posco | Blast furnace iron notch temperature measuring device |
| CN110218824A (en) * | 2019-06-05 | 2019-09-10 | 青岛大学 | A kind of blast furnace slag centrifugal granulation droplet measurement system and method based on image recognition |
-
2021
- 2021-04-22 CN CN202110437293.XA patent/CN113122669B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007248148A (en) * | 2006-03-14 | 2007-09-27 | Nippon Steel Corp | Blast furnace tapping temperature measuring system, blast furnace tapping temperature measuring method, and computer program |
| CN105678295A (en) * | 2016-01-04 | 2016-06-15 | 武汉科技大学 | Method for real-time monitoring gas heating furnace flame on the basis of ROI average image analysis |
| CN106435074A (en) * | 2016-10-26 | 2017-02-22 | 上海宝九和耐火材料有限公司 | Treatment method for tapping hole of blast furnace |
| CN110073008A (en) * | 2016-12-12 | 2019-07-30 | 株式会社Posco | Blast furnace iron notch temperature measuring device |
| WO2019132495A1 (en) * | 2017-12-26 | 2019-07-04 | 주식회사 포스코 | Device for monitoring tapping amount of molten iron |
| CN110218824A (en) * | 2019-06-05 | 2019-09-10 | 青岛大学 | A kind of blast furnace slag centrifugal granulation droplet measurement system and method based on image recognition |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115125340A (en) * | 2022-06-23 | 2022-09-30 | 抚顺新钢铁有限责任公司 | Iron frequency information acquisition system |
| CN115652003A (en) * | 2022-09-06 | 2023-01-31 | 中南大学 | Blast furnace taphole plugging time online monitoring method and system based on two-stage classification |
| CN115652003B (en) * | 2022-09-06 | 2024-01-09 | 中南大学 | Blast furnace iron notch blocking time on-line monitoring method and system based on two-stage classification |
| CN115612765A (en) * | 2022-10-14 | 2023-01-17 | 合肥视展光电科技有限公司 | Real-time detection control method and system for state of blast furnace taphole |
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