A Textural Characterization of Coal SEM Images Using Functional Link Artificial Neural Network

In an absolute characterization trial, there are no substitutes for the final subtyping of coal quality independent of chemical analysis. Petrology is a specialty that deals with the understanding of the essential characteristics of the coal through appropriate chemical, morphological, or porosity analysis. Conventional analysis of coal by a petrologists is subjected to various shortcomings like inter-observer variations during screen analysis and due to different machine utilization, time consuming, highly skilled operator experience, and tiredness. In chemical analysis, use of conventional analyzers is expensive for characterization process. Thus, image analysis serves as an impressive automated characterization procedure of subtyping the coal, according to their textural, morphological, color, etc., features. Coal characterization is necessary for the proper utilization of coal in the power generation, steel, and several manufacturing industries. Thus, in this paper, attempts are made to devise the methodology for an automated characterization and subclassification of different grades of coal samples using image processing and computational intelligence techniques.