Case study: From benches to zones: Unlocking the potential of a complex coal resource with Python and LIMN modelling

Coal processing plants are typically designed to treat specific run-of-mine (ROM) feed to meet market specifications. This design is closely linked to mining and sales strategies to optimise the exploitation of the orebody. At Exxaro's Grootegeluk Coal Complex (GGC), a significant shift from bench-based to zone-based geological modelling introduced additional granularity in coal characteristics. This new, more detailed information provided an opportunity to re-evaluate and confirm whether the current exploitation strategy remains the most optimal approach. This paper provides a detailed description of the orebody characteristics and highlights innovative means to determine optimal exploitation of an orebody. Given the limitations of Excel in handling large geospatial datasets, Python Programming language and LIMN simulation software were employed. This method does not supersede current geological and mining processes or modelling but serves as a supplementary tool to work jointly while executing the exploitation strategy, complementing the process with geological, mining, and metallurgical knowledge and guidance. Python was utilised to process vast amounts of data efficiently and to perform robust data validation of the geological model. Limn was used to model different plant configurations. This study focuses on the processing of coal to achieve a desired mass percent ash in the products. Other deleterious contaminants, however, such as moisture content, volatile matter, free swelling index, sulphur content, and phosphorus are also considered. The orebody, with 11 distinct zones, highlights its complex nature and the multitude of viable mining and processing options that can be applied across these zones. These zones were modelled through various plant configurations. Normalised and site-specific processing costs were applied to evaluate the best product options per zone, assuming the entire ore is mined. Product revenue was normalised based on quality. The outcomes validate certain processing routes and identify potential alternative mining and processing strategies which require further evaluation.