CA2491646A1 - Method for online measurement of molten phases - Google Patents
Method for online measurement of molten phases Download PDFInfo
- Publication number
- CA2491646A1 CA2491646A1 CA002491646A CA2491646A CA2491646A1 CA 2491646 A1 CA2491646 A1 CA 2491646A1 CA 002491646 A CA002491646 A CA 002491646A CA 2491646 A CA2491646 A CA 2491646A CA 2491646 A1 CA2491646 A1 CA 2491646A1
- Authority
- CA
- Canada
- Prior art keywords
- image data
- standard
- characterizing
- molten
- line
- 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.)
- Abandoned
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
- G06T7/001—Industrial image inspection using an image reference approach
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
- B22D2/00—Arrangement of indicating or measuring devices, e.g. for temperature or viscosity of the fused mass
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B22—CASTING; POWDER METALLURGY
- B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
- B22D2/00—Arrangement of indicating or measuring devices, e.g. for temperature or viscosity of the fused mass
- B22D2/001—Arrangement of indicating or measuring devices, e.g. for temperature or viscosity of the fused mass for the slag appearance in a molten metal stream
-
- 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
- C21C5/4673—Measuring and sampling devices
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D21/00—Arrangements of monitoring devices; Arrangements of safety devices
- F27D21/0028—Devices for monitoring the level of the melt
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D21/00—Arrangements of monitoring devices; Arrangements of safety devices
- F27D21/02—Observation or illuminating devices
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/20—Metals
- G01N33/205—Metals in liquid state, e.g. molten metals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
- G06T7/41—Analysis of texture based on statistical description of texture
- G06T7/42—Analysis of texture based on statistical description of texture using transform domain methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- 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
- C21C5/52—Manufacture of steel in electric furnaces
- C21C2005/5288—Measuring or sampling devices
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27D—DETAILS OR ACCESSORIES OF FURNACES, KILNS, OVENS, OR RETORTS, IN SO FAR AS THEY ARE OF KINDS OCCURRING IN MORE THAN ONE KIND OF FURNACE
- F27D19/00—Arrangements of controlling devices
- F27D2019/0006—Monitoring the characteristics (composition, quantities, temperature, pressure) of at least one of the gases of the kiln atmosphere and using it as a controlling value
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30136—Metal
-
- 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
Abstract
A method for identifying and quantifying information about molten phases, including slags, fluxes, metal and matte using a multivariate image analysis approach. Using this procedure, the properties of molten phases such as disruption of slag, the size of bare metal, partial solidification of slag, and temperature of slag can be accurately determined within a reasonable computation time. Moreover, this method can be implemented as an online measurement tool of molten phases.
Description
_1_ Online Measurement of Molten Phases Technical Field The present invention is directed to identifying and quantifying information from molten phases, including slags, fluxes, metal, and matte. Using a method based upon principal components analysis of image data taken from the surface of molten phases.
Background Art Multivariate image processing provides a reliable method for extracting information from image data. This method has been successfully applied for image processing in several applications, such as satellite image data and the medical area. However, there is no prior application of this method for online measurements of molten phases.
Availability of a reliable real time measurement of a process is an important factor for developing any control system. In the case of high temperature molten phases processing such as steel making, due to the extreme conditions, it is difficult and costly to carry out real time ~0 measurements. Currently, several methods for gathering information of molten phases, such as detection of the relative surface areas of molten phases and assessment of whether the phases are fully molten, rely on visual observations by human operators. Therefore, there is a clear need for more reliable online measurement of molten phases.
_2_ An obj ect of this invention is to delineate and quantify online information about molten phases within a reasonable computation time for detecting the relative surface areas of molten phases, determining whether the phases are fully molten, and predicting the temperature of the phases. Since the computation time is significantly fast, the method can be used as an online measurement device and integrated into a control system.
Disclosure of the Invention In accordance with the invention, there is provided a method of characterizing molten phases using principal components analysis of image data taken from the surface of molten phases. The method developed involves (a) developing a standard and (b) using the standard to identify and quantify an online image data. For purpose of standard development, the procedure developed consists of the following steps:
(i) taking a digital image of the surface of molten phases, (ii) performing principal component analysis of the image, and (iii) judging the standard values of the principal components, based on the knowledge of the molten phases properties, which will be used to determine the properties of online images. In using a standard to identify and quantify an online image data, the following steps are carried out: (a) taking a digital image of the surface of molten phases, (b) performing principal component analysis on the image, (c) comparing this analysis with standard values of the principal components to determine the properties of the images, and (d) quantifying the considered properties of the image.
Description of Drawings Figure 1 depicts a schematic diagram of the online measurement of molten phases. Basically, the system consists of three main parts, i.e.
molten phases being measured, a digital camera for taking image data, and a computer for processing the image data;
Figure 2 shows an example of an RGB image taken from molten phases;
Figure 3 shows a schematics diagram of the principal component analysis procedure;
Figure 4 depicts an example of the first two principal components plot (tl versus tz) from the image in Figure 2;
Figure 5 is a plot correlating of predicted bare metal area, presented together with inert gas flowrate injected from the bottom of vessel, as a function of gas inj ection time; and Figure 6 is a plot correlating the temperature of the bath and the average second principal component, tv, for slag properties.
Best Mode for Carr i~ng Out the Invention A schematic depiction of an online measurement system of molten phases.is generally indicated by reference numeral 20 in Figure 1. As shown in the figure, this system 20 is applied to measuring molten phases in a vessel 22 and includes a digital camera 24 for taking image data, and a computer 26 for processing the image data.
The very first step for measuring the properties of molten phases, such as disruption of a slag surface, partial solidification of a slag phase, or temperature of the slag, is capturing image data of the slag surface using the digital camera 24 in RGB (Red-Green-Blue) format. The RGB
format is a common way to represent high-resolution colour images, which each pixel is specified by three values-one each for the red, green, and blue (RGB) components of the pixel's colour. In a colour image of Figure 2, the white areas of the image correspond to bare metal, yellow areas correspond to thin slag, brown areas correspond to fluid slag, and black areas correspond to solidified slag. Such an image may be schematically represented as a stack of three congruent n x m pixel images. Mathematically, the image can be viewed as a matrix, Im, with dimension n x m x 3, as shown in Figure 3. Such an image taken from the surface of a steel making ladle is visually represented in Figure 2. Digital image data are transmitted into the process computer 26 to determine the properties of the molten phases based on the information captured by the image data.
In processing the captured image data of molten phases, principal component analysis or PCA is used. PCA is a multivariate statistical procedure applied to a set of variables, which are highly correlated, with the purpose of revealing its principal components (or score vectors).
The principal components are linear combinations of the original variables, which are independent of each other and that capture most of the information in the original variables into its first few principal components [Jackson, 1991].
Multivariate statistical methods, e.g. principal component analysis (PCA) and partial least squares (PLS), have been successfully used for multivariate image analysis [Esbensen et al., 1989; Geladi et al., 1989;
Grahn et al., 1989; Bharati and MacGegor, 1998]. Using these approaches, a set of highly dimensioned and highly correlated data can be projected into a set of un-correlated data with a reduction in dimensionality. In this invention the PCA approach is used to evaluate the image of molten phases.
For simplifying the problem, the three-way matrix Im~",~,,X3~ of Figure 3 is unfolded into an extended two-way matrix X~~",max3)~ as illustrated in Figure 3.
Im urafold~ x 1 (n x m x 3) (mn x 3) The unfolded image matrix, X, is decomposed by performing principal component analysis [Jackson, 1991]. The relation between the original matrix and its principal component is given by the following equation:
X =~tlpT +E=TPT +E (2) where: X is an unfolded version of Im; T is a score matrix; P is a loading matrix; and E is a residual matrix.
a By assuming that all information in the image is retained in the first two principal components, i.e. tl and tz, then X matrix can be approximated by:
a (3) f=I
The score vectors, t;, are linear combinations of the variables (columns) in the data matrix X that explain the greatest variation in the multivariate data. These vectors have a property of orthogonality with respect to each other. Loading vectors, p;, are the eigenvectors-in descending order-of the variance-covariance structure (XTX) in the data matrix. These vectors have a property of orthonormality with respect to each other (i.e. PTP = I;
where I is the identity matrix). Based on the property of the score and loading vectors, the value of score matrix, T, can be obtained by multiplying X by P [Geladi et al., 1989]:
1 s T=XF (4) Following the assumption that all information in the image is retained in the first two principal components, the combination of the first two score vectors (tl and t2) would be almost identical with these pixels [Bharati and MacGregor, 1998], as shown mathematically in equation (3). Therefore, ' the combination of these principal components can be used to extract information from (or to discriminate materials in) the considered image. In addition, the average of the pixel intensities at each wavelength is _7_ represented by tl, whilst the contrast or difference among the pixel intensities at various wavelengths is represented by tz [Bharati and MacGregor, 1998]. In accordance with the invention, the average value of tl or tz may be used to characterize the property of an image, such as to determine the temperature.
The image data from the image presented in Figure 2 was unfolded by using the procedure given in Figure 3 to give matrix X. Analyzing the principal component of matrix X using a standard procedure of PCA [e.g.
Jackson, 1991 ] gives values of loading vector, p;, and eigenvalues presented in Table 1. All computation for this report is performed in a high-level computer language, i.e. MATLABTM Version 6 and MATLABTM Image Processing Toolbox Version 3.
Table 1. Loading vectors and eigenvalues of the image presented in Figure 3.
Scow 1 2 3 Loading 0.7002 -0.5738 -0.4247 vector p,6189 0.1915 0.7617 0.3558 0.7963 ' -0.4893 Eigenvalue 0.2458 0.0387 0.0081 Total variance, % 84 13.23 ~ 2.77 As shown in Table 1, the cumulative of total variance of the first two principal components is 97.23% (84.00% and 13.23 %, respectively).
Therefore, it is reasonable to assume that the majority of information in _g_ the considered imaged is retained in the first two principal components;
the combination of these principal components can be used to extract information from (or to discriminate materials in) the image and then, only the first two principal components are used in the subsequent analyses. The loading vectors for these two principal components are pl =~0.70020.6I890.355~ and p~ =~-0.57380.19150.'796.
A scatter plot of the first two score vectors (tl versus tz) is presented in Figure 4. The figure has 3110400 score combinations plotted, one for each of the 2160 x 1440 pixel locations in the original image. It is interesting to note that there were several overlaps of points in the figure due to the large number of pixels to be plotted into the graph and similar features in the original image yielded similar score vector combination.
By projecting the values of the first two principal components (tl and tz) of the pixels to the corresponding image, the information in the original image that is explained by the combination values of tl and tz can be identified.. The results from this process can be used to delineate the pixel class. Using the combination values of tl and tz, and combined with information representing an area by one pixel, the area of an object under consideration in the image can be determined. The results from this process can be used to delineate the pixel class that is given in Table 2. By using this approach, if the represented area of one-pixel is known, then the total area under consideration can be determined by multiplying the area of one-pixel with the number of points at a same group in Figure 4. For example, using this approach to calculate the area of a spout eye or bare metal area observed in the steel making ladle of in Figure 2 gives a value of 1.764 m2.
Table 2. Mapping of the first two principal components to information in original image.
t, t~ Original Image 1.1475 to 1.2634 0.2995 to 0.5322 Eye (white) 0.6138 to 1.1475 -0.2245 to 0.2995 Thin slag (yellow) 0.0790 to 0.6138 -0.3356 to -0.1998 Fluid slag and ladle wall (brown) Figure 5 shows an example of predicted bare metal area, presented together with inert gas flowrate as a function of gas injection time. As clearly shown in the figure, the area of bare metal is a function of inert gas flowrate. Clearly from the preceding discussion, the method according to the invention can be used to delineate the surface properties, such as disruption of slag or bare metal and partial solidification of slags and to quantify the surface attributes in term of its area.
Since the second principal component, tz, represents the contrast or difference among the pixel intensities at various wavelengths [Bharati and MacGregor, 1998], the average value of the second principal component is used to quantify the temperature of the bath. The relationship between temperature and intensitywill also be a function ofthe reflecting properties of the material, which in part is a function of ladle chemistry.
Figure 6 shows a correlation between temperature of the bath and the average second principal component, t2, for various slag grades. As shown in Figure 6, there is a good indication that the temperature of the bath can be represented by the average value of the second principal component, tz.
Hence, it can be concluded that the temperature of molten phases, a including slags, fluxes, metal, and matte can be determined using the average value of t2.
r In order to apply the image processing results as a real time measurement data, it is important to be able to process the image in a reasonable period of time. In the present work, the processing time for measuring the bare metal area is a few seconds. Therefore, it can be concluded that the computation 'speed is adequate for an online measurement system. The calculations were performed on an IBMTM compatible Pentium III/800 MHz personal computer with 250 MHz RAM running in a WindowsTM
2000 environment and using MATLABTM Version 6 and MATLABTM
,Image Processing Toolbox Version 3.
Background Art Multivariate image processing provides a reliable method for extracting information from image data. This method has been successfully applied for image processing in several applications, such as satellite image data and the medical area. However, there is no prior application of this method for online measurements of molten phases.
Availability of a reliable real time measurement of a process is an important factor for developing any control system. In the case of high temperature molten phases processing such as steel making, due to the extreme conditions, it is difficult and costly to carry out real time ~0 measurements. Currently, several methods for gathering information of molten phases, such as detection of the relative surface areas of molten phases and assessment of whether the phases are fully molten, rely on visual observations by human operators. Therefore, there is a clear need for more reliable online measurement of molten phases.
_2_ An obj ect of this invention is to delineate and quantify online information about molten phases within a reasonable computation time for detecting the relative surface areas of molten phases, determining whether the phases are fully molten, and predicting the temperature of the phases. Since the computation time is significantly fast, the method can be used as an online measurement device and integrated into a control system.
Disclosure of the Invention In accordance with the invention, there is provided a method of characterizing molten phases using principal components analysis of image data taken from the surface of molten phases. The method developed involves (a) developing a standard and (b) using the standard to identify and quantify an online image data. For purpose of standard development, the procedure developed consists of the following steps:
(i) taking a digital image of the surface of molten phases, (ii) performing principal component analysis of the image, and (iii) judging the standard values of the principal components, based on the knowledge of the molten phases properties, which will be used to determine the properties of online images. In using a standard to identify and quantify an online image data, the following steps are carried out: (a) taking a digital image of the surface of molten phases, (b) performing principal component analysis on the image, (c) comparing this analysis with standard values of the principal components to determine the properties of the images, and (d) quantifying the considered properties of the image.
Description of Drawings Figure 1 depicts a schematic diagram of the online measurement of molten phases. Basically, the system consists of three main parts, i.e.
molten phases being measured, a digital camera for taking image data, and a computer for processing the image data;
Figure 2 shows an example of an RGB image taken from molten phases;
Figure 3 shows a schematics diagram of the principal component analysis procedure;
Figure 4 depicts an example of the first two principal components plot (tl versus tz) from the image in Figure 2;
Figure 5 is a plot correlating of predicted bare metal area, presented together with inert gas flowrate injected from the bottom of vessel, as a function of gas inj ection time; and Figure 6 is a plot correlating the temperature of the bath and the average second principal component, tv, for slag properties.
Best Mode for Carr i~ng Out the Invention A schematic depiction of an online measurement system of molten phases.is generally indicated by reference numeral 20 in Figure 1. As shown in the figure, this system 20 is applied to measuring molten phases in a vessel 22 and includes a digital camera 24 for taking image data, and a computer 26 for processing the image data.
The very first step for measuring the properties of molten phases, such as disruption of a slag surface, partial solidification of a slag phase, or temperature of the slag, is capturing image data of the slag surface using the digital camera 24 in RGB (Red-Green-Blue) format. The RGB
format is a common way to represent high-resolution colour images, which each pixel is specified by three values-one each for the red, green, and blue (RGB) components of the pixel's colour. In a colour image of Figure 2, the white areas of the image correspond to bare metal, yellow areas correspond to thin slag, brown areas correspond to fluid slag, and black areas correspond to solidified slag. Such an image may be schematically represented as a stack of three congruent n x m pixel images. Mathematically, the image can be viewed as a matrix, Im, with dimension n x m x 3, as shown in Figure 3. Such an image taken from the surface of a steel making ladle is visually represented in Figure 2. Digital image data are transmitted into the process computer 26 to determine the properties of the molten phases based on the information captured by the image data.
In processing the captured image data of molten phases, principal component analysis or PCA is used. PCA is a multivariate statistical procedure applied to a set of variables, which are highly correlated, with the purpose of revealing its principal components (or score vectors).
The principal components are linear combinations of the original variables, which are independent of each other and that capture most of the information in the original variables into its first few principal components [Jackson, 1991].
Multivariate statistical methods, e.g. principal component analysis (PCA) and partial least squares (PLS), have been successfully used for multivariate image analysis [Esbensen et al., 1989; Geladi et al., 1989;
Grahn et al., 1989; Bharati and MacGegor, 1998]. Using these approaches, a set of highly dimensioned and highly correlated data can be projected into a set of un-correlated data with a reduction in dimensionality. In this invention the PCA approach is used to evaluate the image of molten phases.
For simplifying the problem, the three-way matrix Im~",~,,X3~ of Figure 3 is unfolded into an extended two-way matrix X~~",max3)~ as illustrated in Figure 3.
Im urafold~ x 1 (n x m x 3) (mn x 3) The unfolded image matrix, X, is decomposed by performing principal component analysis [Jackson, 1991]. The relation between the original matrix and its principal component is given by the following equation:
X =~tlpT +E=TPT +E (2) where: X is an unfolded version of Im; T is a score matrix; P is a loading matrix; and E is a residual matrix.
a By assuming that all information in the image is retained in the first two principal components, i.e. tl and tz, then X matrix can be approximated by:
a (3) f=I
The score vectors, t;, are linear combinations of the variables (columns) in the data matrix X that explain the greatest variation in the multivariate data. These vectors have a property of orthogonality with respect to each other. Loading vectors, p;, are the eigenvectors-in descending order-of the variance-covariance structure (XTX) in the data matrix. These vectors have a property of orthonormality with respect to each other (i.e. PTP = I;
where I is the identity matrix). Based on the property of the score and loading vectors, the value of score matrix, T, can be obtained by multiplying X by P [Geladi et al., 1989]:
1 s T=XF (4) Following the assumption that all information in the image is retained in the first two principal components, the combination of the first two score vectors (tl and t2) would be almost identical with these pixels [Bharati and MacGregor, 1998], as shown mathematically in equation (3). Therefore, ' the combination of these principal components can be used to extract information from (or to discriminate materials in) the considered image. In addition, the average of the pixel intensities at each wavelength is _7_ represented by tl, whilst the contrast or difference among the pixel intensities at various wavelengths is represented by tz [Bharati and MacGregor, 1998]. In accordance with the invention, the average value of tl or tz may be used to characterize the property of an image, such as to determine the temperature.
The image data from the image presented in Figure 2 was unfolded by using the procedure given in Figure 3 to give matrix X. Analyzing the principal component of matrix X using a standard procedure of PCA [e.g.
Jackson, 1991 ] gives values of loading vector, p;, and eigenvalues presented in Table 1. All computation for this report is performed in a high-level computer language, i.e. MATLABTM Version 6 and MATLABTM Image Processing Toolbox Version 3.
Table 1. Loading vectors and eigenvalues of the image presented in Figure 3.
Scow 1 2 3 Loading 0.7002 -0.5738 -0.4247 vector p,6189 0.1915 0.7617 0.3558 0.7963 ' -0.4893 Eigenvalue 0.2458 0.0387 0.0081 Total variance, % 84 13.23 ~ 2.77 As shown in Table 1, the cumulative of total variance of the first two principal components is 97.23% (84.00% and 13.23 %, respectively).
Therefore, it is reasonable to assume that the majority of information in _g_ the considered imaged is retained in the first two principal components;
the combination of these principal components can be used to extract information from (or to discriminate materials in) the image and then, only the first two principal components are used in the subsequent analyses. The loading vectors for these two principal components are pl =~0.70020.6I890.355~ and p~ =~-0.57380.19150.'796.
A scatter plot of the first two score vectors (tl versus tz) is presented in Figure 4. The figure has 3110400 score combinations plotted, one for each of the 2160 x 1440 pixel locations in the original image. It is interesting to note that there were several overlaps of points in the figure due to the large number of pixels to be plotted into the graph and similar features in the original image yielded similar score vector combination.
By projecting the values of the first two principal components (tl and tz) of the pixels to the corresponding image, the information in the original image that is explained by the combination values of tl and tz can be identified.. The results from this process can be used to delineate the pixel class. Using the combination values of tl and tz, and combined with information representing an area by one pixel, the area of an object under consideration in the image can be determined. The results from this process can be used to delineate the pixel class that is given in Table 2. By using this approach, if the represented area of one-pixel is known, then the total area under consideration can be determined by multiplying the area of one-pixel with the number of points at a same group in Figure 4. For example, using this approach to calculate the area of a spout eye or bare metal area observed in the steel making ladle of in Figure 2 gives a value of 1.764 m2.
Table 2. Mapping of the first two principal components to information in original image.
t, t~ Original Image 1.1475 to 1.2634 0.2995 to 0.5322 Eye (white) 0.6138 to 1.1475 -0.2245 to 0.2995 Thin slag (yellow) 0.0790 to 0.6138 -0.3356 to -0.1998 Fluid slag and ladle wall (brown) Figure 5 shows an example of predicted bare metal area, presented together with inert gas flowrate as a function of gas injection time. As clearly shown in the figure, the area of bare metal is a function of inert gas flowrate. Clearly from the preceding discussion, the method according to the invention can be used to delineate the surface properties, such as disruption of slag or bare metal and partial solidification of slags and to quantify the surface attributes in term of its area.
Since the second principal component, tz, represents the contrast or difference among the pixel intensities at various wavelengths [Bharati and MacGregor, 1998], the average value of the second principal component is used to quantify the temperature of the bath. The relationship between temperature and intensitywill also be a function ofthe reflecting properties of the material, which in part is a function of ladle chemistry.
Figure 6 shows a correlation between temperature of the bath and the average second principal component, t2, for various slag grades. As shown in Figure 6, there is a good indication that the temperature of the bath can be represented by the average value of the second principal component, tz.
Hence, it can be concluded that the temperature of molten phases, a including slags, fluxes, metal, and matte can be determined using the average value of t2.
r In order to apply the image processing results as a real time measurement data, it is important to be able to process the image in a reasonable period of time. In the present work, the processing time for measuring the bare metal area is a few seconds. Therefore, it can be concluded that the computation 'speed is adequate for an online measurement system. The calculations were performed on an IBMTM compatible Pentium III/800 MHz personal computer with 250 MHz RAM running in a WindowsTM
2000 environment and using MATLABTM Version 6 and MATLABTM
,Image Processing Toolbox Version 3.
Claims (8)
1. A method of identifying and quantifying information from a molten phase product having an exposed surface area, the method comprising the steps of a) developing a standard for on-line evaluation of digital images and b) performing said evaluation on-line, in which the standard is developed using the following steps:
i) taking a digital image of an exposed surface area of a molten phase product to produce a standard image data;
ii) performing principal component analysis on the standard image data to define score vectors t1 and t2 characterizing the standard image data;
iii) correlating values of the score vectors t1 and t2 with characterizing properties of the molten phase product to define standard values of t1 and t2;
and the evaluation is performed using the following steps:
iv) taking a.digital image of an exposed surface area of a molten phase product to produce on-line image data;
v) performing principal component analysis on the on-line image data to define score vectors t1 and t2 characterizing the on-line image data;
vi) assigning a characterizing property to areas of the on-line image data according to said standard values of t1 and t2; and viii) creating an output of said characterizing property whereby phases are identified and quantified:
i) taking a digital image of an exposed surface area of a molten phase product to produce a standard image data;
ii) performing principal component analysis on the standard image data to define score vectors t1 and t2 characterizing the standard image data;
iii) correlating values of the score vectors t1 and t2 with characterizing properties of the molten phase product to define standard values of t1 and t2;
and the evaluation is performed using the following steps:
iv) taking a.digital image of an exposed surface area of a molten phase product to produce on-line image data;
v) performing principal component analysis on the on-line image data to define score vectors t1 and t2 characterizing the on-line image data;
vi) assigning a characterizing property to areas of the on-line image data according to said standard values of t1 and t2; and viii) creating an output of said characterizing property whereby phases are identified and quantified:
2. Method according to Claim 1 in which the molten phases include any one of the following: slag, flux, metal, matte, and glass.
3. Method according to Claim 1 in which the digital image is taken in the visible spectrum.
4. Method according to Claim 1 in which the digital image consists of an array of pixel elements of measured intensity values in at least three wavelength ranges.
5. Method according to Claim 4 in which the pixel elements of the digital image have varying intensities of the colours red, green, and blue.
6. Method according to Claim 1 in which the characterizing property which is corrected with the score vectors t1 and t2 is selected from the following group: phase identification of molten phase product; surface area occupied by each identified phase; temperature of each identified phase.
7. A method of monitoring a steelmaking ladle having high temperature molten phases to discriminate between areas of the ladle having any bare metal, bare metal covered with slag and fluid slag, the method comprising the steps of:
a) developing a standard for on-line evaluation of digital images and b) performing said evaluation on-line, in which the standard is developed using the following steps:
i) taking a digital image of an exposed surface area of a steelmaking ladle to produce a standard image data;
ii) performing principal component analysis on the standard image data to define score vectors t1 and t2 characterizing the standard image data;
iii) correlating values of the score vectors t1 and t2 with characterizing properties of the molten phase product to define standard values of t1 and t2;
and the evaluation is performed using the following steps:
iv) taking a digital image of an exposed surface area of a molten phase product to produce on-line image data;
v) performing principal component analysis on the on-line image data to define score vectors t1 and t2 characterizing the on-line image data;
vi) assigning a characterizing property to areas of the on-line image data according to said standard values of t1 and t2; and viii) creating an output of said characterizing property whereby phases are identified and quantified.
a) developing a standard for on-line evaluation of digital images and b) performing said evaluation on-line, in which the standard is developed using the following steps:
i) taking a digital image of an exposed surface area of a steelmaking ladle to produce a standard image data;
ii) performing principal component analysis on the standard image data to define score vectors t1 and t2 characterizing the standard image data;
iii) correlating values of the score vectors t1 and t2 with characterizing properties of the molten phase product to define standard values of t1 and t2;
and the evaluation is performed using the following steps:
iv) taking a digital image of an exposed surface area of a molten phase product to produce on-line image data;
v) performing principal component analysis on the on-line image data to define score vectors t1 and t2 characterizing the on-line image data;
vi) assigning a characterizing property to areas of the on-line image data according to said standard values of t1 and t2; and viii) creating an output of said characterizing property whereby phases are identified and quantified.
8. Method according to Claim 7 in which the characterizing property which is corrected with the score vectors t1 and t2 is selected from the following group: phase identification; surface area occupied by each identified phase; temperature of each identified phase.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US39507902P | 2002-07-11 | 2002-07-11 | |
| US60/395,079 | 2002-07-11 | ||
| PCT/CA2003/001053 WO2004008135A2 (en) | 2002-07-11 | 2003-07-10 | Method for online measurement of molten phases |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2491646A1 true CA2491646A1 (en) | 2004-01-22 |
Family
ID=30115807
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA002491646A Abandoned CA2491646A1 (en) | 2002-07-11 | 2003-07-10 | Method for online measurement of molten phases |
Country Status (7)
| Country | Link |
|---|---|
| US (1) | US20060220281A1 (en) |
| EP (1) | EP1552291A2 (en) |
| JP (1) | JP2005532557A (en) |
| CN (1) | CN1668920A (en) |
| AU (1) | AU2003249798A1 (en) |
| CA (1) | CA2491646A1 (en) |
| WO (1) | WO2004008135A2 (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105562630A (en) * | 2016-02-29 | 2016-05-11 | 宝钢工程技术集团有限公司 | Detection device and detection method for melting conditions of crystallizer mold fluxes |
| CN107590838A (en) * | 2017-08-18 | 2018-01-16 | 陕西维视数字图像技术有限公司 | A kind of metal surface colour vision detecting system |
| CN112091206A (en) * | 2019-05-31 | 2020-12-18 | 宝山钢铁股份有限公司 | Safe and reliable molten iron pretreatment automatic slag skimming method and system |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5736938B2 (en) * | 2011-04-28 | 2015-06-17 | Ｊｆｅスチール株式会社 | Thermoelectric power generation apparatus and thermoelectric power generation method using the same |
| MY181827A (en) * | 2014-06-17 | 2021-01-08 | Suntory Holdings Ltd | Resin cap |
| CN105698870B (en) * | 2016-03-25 | 2017-11-21 | 辽宁科技学院 | A kind of contactless temperature-measuring determines carbon device and its assay method |
| US11208197B2 (en) | 2017-03-31 | 2021-12-28 | Heka Aero LLC | Gimbaled fan |
| CN108052950B (en) * | 2017-12-08 | 2021-06-11 | 东北大学 | MIA-based fused magnesia furnace dynamic flame segmentation and feature extraction method |
| KR101956168B1 (en) * | 2018-04-24 | 2019-03-08 | 한국산업기술대학교산학협력단 | Method for testing slag dissolution behavior |
| CN110434478B (en) * | 2018-04-28 | 2021-11-23 | 大族激光科技产业集团股份有限公司 | Treatment method and device for laser cutting slag spraying |
| KR102299562B1 (en) * | 2020-06-22 | 2021-09-07 | 현대제철 주식회사 | Method for measuring melt layer of powder and electronic device thereof |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4749171A (en) * | 1984-09-06 | 1988-06-07 | Nippon Steel Corporation | Method and apparatus for measuring slag-foam conditions within a converter |
| FR2710154B1 (en) * | 1993-09-14 | 1995-12-08 | Ascometal Sa | Method of analysis and quantification of perlite bands in ferritoperlitic steels. |
| US6197086B1 (en) * | 1997-11-13 | 2001-03-06 | Bethlehem Steel Corporation | System and method for minimizing slag carryover during the production of steel |
| US6562285B1 (en) * | 2000-11-15 | 2003-05-13 | Metallurgical Sensors, Inc. | Method and apparatus for detecting slag carryover |
-
2003
- 2003-07-10 EP EP03763539A patent/EP1552291A2/en not_active Withdrawn
- 2003-07-10 WO PCT/CA2003/001053 patent/WO2004008135A2/en not_active Application Discontinuation
- 2003-07-10 CN CNA038165376A patent/CN1668920A/en active Pending
- 2003-07-10 AU AU2003249798A patent/AU2003249798A1/en not_active Abandoned
- 2003-07-10 CA CA002491646A patent/CA2491646A1/en not_active Abandoned
- 2003-07-10 US US10/520,953 patent/US20060220281A1/en not_active Abandoned
- 2003-07-10 JP JP2004520233A patent/JP2005532557A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105562630A (en) * | 2016-02-29 | 2016-05-11 | 宝钢工程技术集团有限公司 | Detection device and detection method for melting conditions of crystallizer mold fluxes |
| CN107590838A (en) * | 2017-08-18 | 2018-01-16 | 陕西维视数字图像技术有限公司 | A kind of metal surface colour vision detecting system |
| CN112091206A (en) * | 2019-05-31 | 2020-12-18 | 宝山钢铁股份有限公司 | Safe and reliable molten iron pretreatment automatic slag skimming method and system |
| CN112091206B (en) * | 2019-05-31 | 2021-07-16 | 宝山钢铁股份有限公司 | Safe and reliable molten iron pretreatment automatic slag skimming method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| AU2003249798A8 (en) | 2004-02-02 |
| WO2004008135A3 (en) | 2004-04-08 |
| JP2005532557A (en) | 2005-10-27 |
| US20060220281A1 (en) | 2006-10-05 |
| WO2004008135A2 (en) | 2004-01-22 |
| CN1668920A (en) | 2005-09-14 |
| EP1552291A2 (en) | 2005-07-13 |
| AU2003249798A1 (en) | 2004-02-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP3597439B2 (en) | Diagnosis method for paint deterioration of painted steel | |
| US6950545B1 (en) | Nondestructive inspection method and apparatus | |
| CA2491646A1 (en) | Method for online measurement of molten phases | |
| JP3884834B2 (en) | Defect inspection method and apparatus | |
| WO2000073974A1 (en) | Method and system for identifying an image feature | |
| JP3581149B2 (en) | Method and apparatus for identifying an object using a regular sequence of boundary pixel parameters | |
| US20120189189A1 (en) | Optical inspection optimization | |
| CA2384340A1 (en) | Animal carcase analysis | |
| US6562285B1 (en) | Method and apparatus for detecting slag carryover | |
| JP3361768B2 (en) | X-ray fluorescence analyzer and X-ray irradiation position confirmation method | |
| Yoo et al. | Extraction of colour information from digital images towards cultural heritage characterisation applications | |
| EP3443899A1 (en) | Method for evaluation of site of color irregularity and color irregularity site evaluation device | |
| JP2007147448A (en) | Oil film detector and oil film detection method | |
| US9668653B2 (en) | Quantification of under-eye skin color | |
| JP5727709B2 (en) | Residue measuring method and residue measuring apparatus | |
| US5517237A (en) | Video photometric color system for processing color specific streams | |
| JPH06116914A (en) | Film deterioration diagnostic method and device thereof | |
| JPS59204741A (en) | Automatic hardness measuring device | |
| KR102554824B1 (en) | Plating thickness measurement system and method | |
| Legeard et al. | Real-time quality evaluation of pork hams by color machine vision | |
| CN117635734A (en) | System and method for online recognition of chromaticity of wastewater based on image processing | |
| KR20030008300A (en) | Method to Measure a Brightness of an Display Device | |
| JP2002160161A (en) | Peeling efficiency evaluating method for blast device | |
| WO2021046759A1 (en) | Apparatus, system and method for quality control of polymer pellets and method for controlling production line for producing polymer pellets | |
| Sekerin | Segmentation of computer-processed images of tested surfaces obtained using the dye-penetrant testing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FZDE | Discontinued |