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.

Indonesian coal production nowadays has reached 63% of total production, which means this high demand will also produce a lot of data. This high demand needs to be innovated as a new alternative energy based on coal production, Underground Coal Gasification (UCG). The coal in this alternative energy source is used to turn the solid coal into gas. Coal mining data has a lot of variables that might be difficult to process manually. Our automatic system will help the users, especially the geologists, identify which coal seams have the potential to be developed as the UCG. We developed the system using a python-based coding system and required data standardization to ease the built-in code reading and process all the required steps to identify the UCG. We implemented the calculation and characterization regarding the calorific value (ADB), proximate, and ultimate analysis from the provided data to find the needed variables for the UCG analytics system. The automatic system will allow the user to choose the interesting borehole that they want to identify. Our system then shows the initial UCG recommendation layer for the next analysis. From our experiment, our system finally found that at the depth of 260 meters, Borehole MJ02 has the potential as the initial guest of the recommendation layer of the UCG development. Doi: 10.28991/CEJ-SP2021-07-012 Full Text: PDF