Abstract

The intrusion of wet muck and fines and the potential of mud rushes pose safety risks for workers, equipment and infrastructure at El Teniente. Wet muck can also result in the loss of reserves because of the need to close drawpoints when large amounts of fine materials and moisture are observed. This paper presents the analysis and the development of a mathematical model to estimate wet muck entry for long-term planning applications at El Teniente. The models have been imbedded in BCRisk®, which is a machine-learning software that estimates hazards associated with the extraction process for underground mines. Four basins of El Teniente were included in the study of wet muck control: North, Center, South, and Reno. Each basin has mines with different characteristics in each exploitation sector. Consequently, models were built for each of the basins to represent its distinct reality. Several variables were investigated to define which determine the phenomenon. The variables include tonnage extracted or draw rate, amount of water entering the cave, season of the year, presence of mud in neighbouring drawpoints or sectors that have been closed due to wet muck above, and changes in surface or depressions. In addition, flow variables such as fragmentation and lithologies have been included and estimated with FlowSim 6.3® for increased precision. Results indicate that the classification models can reproduce the phenomenon with an acceptable precision of 71% and an average tonnage error per drawpoint of 7 to 10%. These results have been useful for long-term planning at El Teniente mine to predict wet muck entry and define when and where autonomous LHD may be required for the extraction of wet muck in the future.