Quality lignite coal detection with discrete wavelet transform, discrete fourier transform, and ANN based on k-means clustering method

Abstract

In this article, the lignite coal datas in the Kalburçayı area of the Sivas-Kangal Basin have been used. This original data obtained from Kalburçayı area of the Sivas-Kangal Basin consists of 66 observations in the lignite coal area, including lignite quality parameters such as moisture content, ash, sulfur content and calorific value. These lignite coal datas have been clustered in two group with k-means method according to calori values. This clustering lignite coal data is classified by the Artifical Neural Network (ANN) classifier. In addition, Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) have been applied to coal data for ANN classifiers. DFT_ANN, DWT_ANN, and ANN classification success results are compared. The highest classification success rate was found by DWT_ANN method.