Coal rank data analytic for ASTM and PSDBMP classification

Abstract

The large amount of coal production in Indonesia generates a significant amount of data that can be used to understand the rank of the coal. To effectively process and interpret this data, our study employs the use of big data techniques, including big data management and big data analysis. Big data management allows us to organize and understand the data patterns, while big data analysis is used to gain insights and knowledge about the data, such as coal rank analysis and identifying the type of coal. Our study uses a python-based approach to define variables and automatically classify the coal rank based on the threshold values obtained from the two basic analyses described earlier. Our results show that this method is able to accurately classify the coal according to the given threshold. We found that according to the Indonesian Coal Standardization based on Pusat Sumber Daya Mineral Batubara dan Panas Bumi (PSDBMP) standard, the calorific value (in adb) is dominated in low to medium calories for 14 boreholes. The coal rank in American Standard Testing and Material (ASTM) analysis is dominated by Lignite A and B for 14 boreholes. The last analysis, according to the atomic ratio, shows that the coal can be classified as Lignite and Subbituminous Coal. Thus, by implementing the big data concept, we can easily analyze the coal classification with comprehensive and large amount of data.