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

Definitions

• 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.
• An object 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.
• a method of characterizing molten phases using principal components analysis of image data taken from the surface of molten phases involves (a) developing a standard and (b) using the standard to identify and quantify an online image data.
• the procedure developed consists of the following steps:
• Figure 1 depicts a schematic diagram of the online measurement of molten phases.
• 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 (t j versus t j ) 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 injection time;
• Figure 6 is a plot correlating the temperature of the bath and the average second principal component, ,, for slag properties.
• FIG. 1 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 is capturing image data of the slag surface using the digital camera 24 in RGB (Red-Green-Blue) format.
• RGB Red-Green-Blue
• 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.
• RGB red, green, and blue
• Such an image may be schematically represented as a stack of three congruent n x m pixel images.
• the image can be viewed as a matrix, I m , with dimension n x m x 3, as shown in Figure 3.
• I m a matrix
• n x m x 3 dimension of a steel making ladle
• PCA principal component analysis
• 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].
• PCA principal component analysis
• PLS partial least squares
• the three-way matrix I m(mxnx3 of Figure 3 is unfolded into an extended two-way matrix X ((n . m)X3) , as illustrated in Figure 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 is an unfolded version of I m ; T is a score matrix; P is a loading matrix; and E is a residual matrix.
• the score vectors, t i5 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.
• the combination of the first two score vectors (t x and tj) 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.
• the average of the pixel intensities at each wavelength is represented by t l3 whilst the contrast or difference among the pixel intensities at various wavelengths is represented by ⁇ [Bharati and MacGregor, 1998].
• the average value of t x or t j may be used to characterize the property of an image, such as to determine the temperature.
• 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 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
• a scatter plot of the first two score vectors (t x versus t ⁇ ) 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.
• the results from this process can be used to delineate the pixel class.
• the combination values oft ! and t 2 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.
• 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 m 2 .
• Table 2 Mapping of the first two principal components to information in original image.
• 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.
• 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.
• the second principal component, ⁇ 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 intensity will also be a function of the reflecting properties of the material, which in part is a function of ladle chemistry.
• Figure 6 shows a con-elation between temperature of the bath and the average second principal component, t 2 , for various slag grades .
• the temperature of the bath can be represented by the average value of the second principal component, t 2 .
• the temperature of molten phases, . including slags, fluxes, metal, and matte can be determined using the average value of t 2 .

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• 2003
  • 2003-07-10 CN CNA038165376A patent/CN1668920A/zh active Pending
  • 2003-07-10 EP EP03763539A patent/EP1552291A2/de not_active Withdrawn
  • 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 WO PCT/CA2003/001053 patent/WO2004008135A2/en not_active Application Discontinuation
  • 2003-07-10 US US10/520,953 patent/US20060220281A1/en not_active Abandoned
  • 2003-07-10 JP JP2004520233A patent/JP2005532557A/ja active Pending

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