Detection of ash fusion temperatures based on the image processing

The detection of ash fusion temperatures is important in the research of coal characteristics. The prevalent method is to build up ash cone with some dimension and detect the characteristic temperatures according to the morphological change. However, Conditional detection work is not accurate and brings high intensity of labor as a result of both visualization and real-time observation. According to the insufficiency of conventional method, a new method to determine ash fusion temperatures with image processing techniques is introduced in this paper. Seven techniques (image cutting, image sharpening, edge picking, open operation, dilate operation, close operation, geometrical property extraction) are used in image processing program. The processing results show that image sharpening can intensify the outline of ash cone; Prewitt operator may extract the edge well among many operators; mathematical morphology of image can filter noise effectively while filling up the crack brought by filtration, which is useful for further disposal; characteristic temperatures of ash fusion temperatures can be measured by depth-to-width ratio. Ash fusion temperatures derived from this method match normal values well, which proves that this method is feasible in detection of ash fusion temperatures.