Automated coal characterization using computational intelligence and image analysis techniques

The coal petrologist looks to focus the petrographic attributes of natural and inorganic coal constituents and their parallel and vertical varieties inside of a solitary coal sample of a specific coal field. Conventional investigation of coal by a petrologists are subjected to diverse insufficiencies like inter and intra observation throughout screen analysis and various machine usage, slowness, need of experienced petrologists and tiredness. In chemical examination, usage of conventional analyzers is unrestrained for characterization technique. In this paper, image analysis serves as an incredible computerized characterization procedure of subtyping the coal, according to their textural, and color features. Coal characterization is imperative for the right use of coal in the power and steel industries etc. Henceforth, in this paper, endeavors are made to devise a methodology for an automated characterization and sub typing of different grades of coal samples using image processing and standard neural network techniques.