Determination of coal quality using Artificial Intelligence Algorithms

Abstract

The main task of coal producers is to provide for sufficient quantities of coal of required quality with minimum costs of excavation. Therefore, the prediction of energy values is the most important task aiming to secure the optimal usage of coal energy value. The goal of paper is to identify, examines and evaluate most influential artificial intelligence algorithms that have been widely used in the data mining community, on a real problem of predicting coal quality. Our researches are based on the data achieved under laboratory conditions during the period of five years (2005-2010), and include 33256 coal samples from ’’Kreka’’ Coal Mine Company. The goal of the research is to build, based on the described data, a prediction model that will be used for predicting the coal quality class of unknown samples. Four algorithms have been identified: C4.5, kNN, Naive Bayes and Multilayer Perceptron (MLP). The idea is to find the best model through the following stages: each of the algorithms is calibrated in order to find appropriate model division techniques which maximize the performance of the algorithms, to assess the importance of input attributes and ultimate comparison of the algorithm orders them with respect to their performance. The final evaluation of the outcomes allowed singling out the MLP to be the best predicting methods for the given domain with optimal structure for input, hidden and output layer.