Multivariate Statistical Analysis of Coal in Dahor Formation, Borneo Island, Indonesia: A Comparative Study Utilizing Principal Component Analysis (PCA)

Abstract

This study focuses on the multivariate statistical analysis of coal samples from the Dahor Formation in Borneo Island, Indonesia, utilizing Principal Component Analysis (PCA). The objective is to discern the differences in coal samples based on their geological history and chemical properties. The Indonesian coal industry heavily relies on domestic consumption, with plans to increase coal usage for domestic purposes. Coal classification systems play a vital role in evaluating coal quality and determining its economic value. The geological background of the study area in Borneo Island is discussed, emphasizing relevant formations and sedimentation processes. The application of PCA in multivariate statistical analysis is explained, along with the generation of a correlation matrix to explore relationships between variables. The results reveal the presence of four distinct quadrants in the processed coal data, indicating the influence of volatile matter, total moisture, ash content, and fixed carbon. Further analysis demonstrates relatively insignificant differences in the proximate analysis parameters, except for ash content, which indicates the presence of impurity minerals. The concentration of volatile matter, ash, and moisture impacts the fixed carbon content, while volatile matter influences the combustion process. These findings provide valuable insights into coal quality and utilization. The utilization of PCA and multivariate analysis enhances understanding of coal characteristics and facilitates decision-making in coal-related industries. The study concludes with a call for further investigations and analyses to enhance our understanding of coal deposits, improve resource estimation, and develop more sustainable and efficient coal-based processes. Continued research in this field will contribute to advancements in coal science and the development of strategies for responsible coal utilization.