Title of Invention

AN EXPERT SYSTEM FOR ORE SORTING USING IMAGE PROCESSING AND ARTIFICIAL NEURAL NETWORK

Abstract

The invention relates to Expert system for ore sorting. It is a system or a process for on-line quality control, separation and classification of ores, ore-blends and rocks to improve the quality of ore feed to a plant. It consists of digital camera used to produce digital images of ores etc. and an artificial neural network which is an inert connected group of artificial neurons that uses a computational model for information processing based on a connectionist approach to computation.

Full Text

FIELD OF INVENTION The invention relates to an expert system for ore sorting and ore classification to improve the quality of ferruginous manganese ore feed to a ferromanganese plant. NOVELTY OF INNOVATION • Developed an innovative methodology for on-line ore sorting and ore classification using image processing and radial bases neural network techniques. This was used to develop an expert system for on-line ore sorting and ore classification for ferruginous manganese ore feed to a ferromanganese plant. • Developed a cost effective process for on-line ore sorting and ore classification. BACKGROUND OF THE INVENTION Most of Ferro-manganese plant in India utilizes lumpy (-75, + 10 mm) ferruginous manganese ore of higher grade ( Mn : > 46%; Mn/Fe ratio : > 6; AI2O3 : sedimentary deposits and shows significant difference in color due to variation in ore composition. Ore lumps of high manganese (> 46%) and high Mn/Fe ratio (> 6) shows steel gray color. Ore containing low manganese and high iron with low Mn/Fe ratio shows reddish brown color. Ores having higher alumina with low Mn/Fe ratio shows white color. To improve the ore grade and to maintain the Mn/Fe ratio for Ferromanganese making ore sorting and ore blending is most common method. Most common existing method to improve the ore quality is manual ore sorting and ore blending. X-ray based on line ore sorting systems needs highly complicated system with high investment. So, any system for on line ore sorting and ore classification for better blending can be of immense significance. A US Patent Document No. 6748354 which discloses a teaching related to a signal processing system comprising wave form shape descriptions were classified by Artificial Neural Network after TESPAR coading to monitor the performance of machinery involved in crushing of ores by using a digital camera. In Instant specification, image features discloses a teaching of color, histogram, texture (energy, entropy, contrast, homogeneity) are being used to classify different kind of manganese ores (ferruginous, low grade and high grade) by Artificial Neural network to monitor the quality of ores by using a digital camera. SUMMARY OF THE INVENTION Image processing and artificial neural network are two most emerging technologies to develop the machine vision systems. Digital image (photographs is a representation of a two-dimensional image as a finite set of digital valves, called pixels. This representation of image is processed by various operations to extract useful features from an image. An artificial neural network (ANN) is an interconnected group of artificial neurons that uses a computational model for information processing based on a connectionist approach to computation. It can learn from examples and a trained network can recognize the unknown examples. These two techniques were used to develop this methodology and process steps are explained and described here after. Process steps: There are four major steps involved in this methodology and high speed computing is required in this system. Step 1: Image acquisition: In this step various kind of ores and ore blends are selected from feed material of the ferromanganese plant to produce digital images. Digital images of these ores were produced by using digital camera (Example: SANYO 0.8 Mega-pixel). The illumination at the site is to be sufficient to support the image producing instrument for desired operation. Step 2: Image processing: In this step various image produced were categorized as high grade ore (Images of manganese enriched ores), ferruginous ore (Images of Iron enriched ores) and low grade ores (Images of alumina enriched ores). These images are pre-processed to remove the noise and to improve the contrast for better visibility of objects. It can be done by using computer and high level computer language (C, C ++, Java etc.). a digital image is a matrix representing the pixel intensities. The value of any matrix element represents the brightness of that point. In most of method minimum brightness is called black and stands for zero (0) and maximum intensity is white and stands for 225. The extracted useful features { RGB color model, histogram, image texture (Entropy, contrast, energy homogeneity)} are elaborately described in step 2.1 to 2.3. Step 2.1: RGB Color Model: Useful features of Image data represents physical quantities such as chromatics and luminance. Chromaticity is the color quantity defined by its wave length while luminance is the amount of light. This information can be expressed such as attributes like color and brightness. The most common color models are RGB (red, green and blue), HSV (hue, saturation, value), and CMYK (cyan, magenta, yellow, black). A color model is a method to represent color and their relationship to each other RGB color model was selected in our system because of its simplicity and ore quality can be easily distinguished in these colors. It was confirmed by chemical analysis that the color difference in the ores is depending on the chemical composition and reddish color is due to iron minerals and white color is due to alumina minerals and steel gray color is due to manganese minerals. In RGB color (Red, Green, and Blue) analysis significant difference was found in all three kinds of ore images. Results are shown in Figure 1. Step - 2.2. Histogram Analysis: An image histogram is a device that shows the distribution of intensities in an image and simply a count of the gray levels (A single value extracted by operation from different color values (R, G, B) of a pixel) in the image. The image histograms of three ore categories are shown in Figure 2. Image histogram analysis of three different kind images show the difference in occurrences of different gray level intensities and it was shifting towards left to right for steel gray manganese ores, reddish brown ferruginous ore and white alumina rich low grade ores respectively due to variation in the increased frequency of high gray level values in the images (Figure 2). Step 2.3. Image texture analysis: Image texture represents the orientation of gray values in a image. There are various method to quantify the image texture and we select the oldest and most reliable method suggested by Haralic et. al. Four textual features (Entropy, Energy, homogeneity and contrast) were calculated from gray level co-occurrence matrix and these play a significant role in ore type recognition and ore classification (Table 1). A co-occurrence matrix is a square matrix with elements corresponding to the relative frequency of occurrence of pairs of gray laval of pixels separated by a certain distance in a given direction. Formally, the elements of a GxG gray level co-occurrence matrix Pa for a displacement vector d = (dx, dy) is defined at: where I (.,.) denote an image of size NxN with G gray values, (r, s), (t, v) εNxN, (t, v) = (r + dx, s + dy) and 1.1 is the cardinality of a sat. Haralick, Shanmugan and Dinstein proposed fourteen measures of textural features which are derived from the co-occurrence matrices, and each represents certain image properties as coarseness, contrast, and homogeneity and texture complexity. Gray laval co- occurrence matrix was derived from the images and given textural features was extracted. (1) Entropy: It givas a measure of complexity of the image. Complex textures tend to have higher entropy. (2) Contrast: It is a measure of the amount of local variations present in an image. The higher the value of contrast is the sharper the structural variations in the image. (3) Energy: It is a measure of the homogeneity of an image and a suitable measure for detection of disorders in textures. For homogeneous textures value of energy turns out to be small compared to non- homogenous ones. (4) Homogeneity: It is a parameter which represents monotonic of image texture. In Eqs. (2) - (5), P (i, j) refers to the normalized entry of the co-occurrence matrices. That is p (i, j) - Pd (i, j) / R where R is the total number of pixel pairs (i, j). For a displacement vector d = (dx, dy) and image of size NxM. R is given by (N - dx) (M-dy). A data base was prepared for various ore categories on the basis of the results shown in the table 1. Step Step 3: Ore Class Reegaanization by Artificial Neural Netwerk: Artificial Neural Networks are relatively crude electronic models based on the neural structure of the brain. It learns from examples and used for nonlinear function approximation, classification and pattern reorganization (figure 3). A data base prepared containing (average color values (red, green and blue), entropy, contrast, homogeneity and energy) for three ore categories using various images of ores with small variations in ore quality. This database was used to train the artificial neural network. The trained neural network is able to detect new images of various categories upto a satisfactory accuracy. Overall system accuracy was 88.71 % and descriptive results are given in table 2. Table 2: Neural network classification Step 4: Expert System Development: An expert system was developed by combining the all three steps explained in previous section. A digital camera is needed to for image producing and image processing algorithms to extract the useful features from the images. The prepared datasets used to train the artificial neural network. The trained neural network can label the new unknown image of the feed ore. On the basis of this classification the ore blending can be controlled to achieve desired grade of ores. Process methodology and expert system for online implementations is shown in Figure 4 and 5. It's Utlity: The developed method and system will be suitable for developing cost effective online ore sorting and ore classification system to improve the ore quality and ore blending operation in ferruginous manganese ores. New Obvieus: The same process and the system can be used in much other type of ores and rocks fix on line quality control, separation and classification. Estimated market valve if pateated: High for all the Indian ferromanganese producers which produce alloy from ferruginous lumpy manganese ores. Many other mineral industries which need ore sorting operation at various stages of the process. WE CLAIM 1. An expert system for ore sorting using image processing and artificial neural network for on-line quality control, separation and classification of ores, ore-blends and rocks to improve the quality of ore feed to a plant such as ferromanganese plant, consisting of digital camera used to produce digital images of ores, ore blends, and rocks and an artificial neural network (ANN) which is an interconnected group of artificial neurons that uses a computational model for information processing based on a connectionist approach to computation. 2. A system as claimed in claim 1, wherein the digital image is a matrix representing a two dimensional image as a finite set of digital values, called pixels and the value of any matrix represents the brightness of that point. 3. A system as claimed in claim 2, wherein the said representation of image is processed by various operations to extract useful features from an image and the image is pre-processed to remove the noise and to improve the contrast for better visibility of object. 4. A system as claimed in claim 3, wherein the said pre-processing is done by using computer and high level of computer language (C,C++Java etc.) 5. A system as claimed in claim 1, wherein the said artificial neural network is a crude electronic model based on the neural structure of the brain, and is trained using the data base which is prepared containing, average colour values-red, green and blue and entropy, contrast, homogeneity and energy. 6. A system as claimed in claim 5, wherein the trained neural network is used to detect new images of various categories upto a satisfactory accuracy. 7. A system as claimed in any of the claims is directed mainly to be used for sorting and classification of ore to improve the quality of ferruginous manganese ore feed to a ferromanganese plant. 8. A process for on-line quality control, separation and classification of ores, ore blends, rocks used in a plant such as ferromanganese plant consisting of acquisition of digital image of ores, ore blends, and rocks selected from feed material by using digital camera, categorizing the image as of high grade ore, ferruginous ore and low grade ore, pre-processing the image to remove the noise and to improve the contrast for better visibility of objects, and getting the result in respect of quality, grade, classification and pattern re-organization of the ore, ore-blend and rock by using artificial neural network. 9. The process as claimed in claim 8, wherein the pre-procesing to remove the noise and to improve the contrast for better visibility of objects is done by computer using any software or programming of any high level computer language (C, C++, Java etc.). 10.The process as claimed in claim 9, wherein the images are processed to measure the color quantity which is defined by its wave length and the amount of light that is brightness. 11.The process as claimed in claim 9, wherein the distribution of intensities in an image is measured by operation from different colour valves in the image and it is a count of gray levels in the image and the orientation of gray valves in an image is also measured. 12.The process as claimed in claim 8, wherein Artificial Neural Networks are trained by using the data base which is prepared containing average colour valves, entropy, contrast, homogeneity and energy for various ore categories using various images of ores with small variations in ore quality. 13.The process as claimed in claim 12, wherein the trained neural network is used to detect new images of various categories of ores up to satisfactory accuracy. The invention relates to Expert system for ore sorting. It is a system or a process for on-line quality control, separation and classification of ores, ore-blends and rocks to improve the quality of ore feed to a plant. It consists of digital camera used to produce digital images of ores etc. and an artificial neural network which is an inert connected group of artificial neurons that uses a computational model for information processing based on a connectionist approach to computation.

Documents:

90-KOL-2006-(26-08-2011)-CORRESPONDENCE.pdf

90-KOL-2006-(26-08-2011)-PA.pdf

90-KOL-2006-FORM 15.pdf

90-kol-2006-granted-abstract.pdf

90-kol-2006-granted-claims.pdf

90-kol-2006-granted-correspondence.pdf

90-kol-2006-granted-description (complete).pdf

90-kol-2006-granted-drawings.pdf

90-kol-2006-granted-examination report.pdf

90-kol-2006-granted-form 1.pdf

90-kol-2006-granted-form 18.pdf

90-kol-2006-granted-form 2.pdf

90-kol-2006-granted-form 3.pdf

90-kol-2006-granted-gpa.pdf

90-kol-2006-granted-reply to examination report.pdf

90-kol-2006-granted-specification.tif

90-KOL-2006-PA.pdf