Open Collections

Varian, Jovian

University of British Columbia

Wet muck (also known as mud rush) can be described as the sudden flow of fragmented rock into a drawpoint or other underground mine opening, exposing the mine to safety and operational risks. This hazard is analogous to an underground debris flow and is most commonly encountered in cave mines. Numerous fatalities, infrastructure damage, loss of reserves, and operational delays, have been reported in various caving operations. To better understand and manage this hazard, this thesis uses data and experiences from the PT Freeport Indonesia, Deep Ore Zone (DOZ) block cave mine in Indonesia where the ground conditions and operational factors that both increase the susceptibility of a drawpoint and act to trigger a wet muck event. Spatio-temporal relationships are drawn from this data, recognizing that the probability of wet muck events tends to increase as a cave matures, with increasing draw column heights contributing to increase secondary fragmentation and the generation of fines. Other contributing factors included in the analysis are extraction rate, uniformity of draw, Height of Draw (HoD), and drawpoint condition. Univariate and multivariate logistic regression models are developed, with the goal of improving prediction and mitigation of these events to improve safety and productivity in caving operations. Although the consequences of wet muck spill events are high, they are still relatively rare, resulting in an imbalanced dataset. Cost-sensitive learning is incorporated into the logistic regression models to address this technical challenge. These methods are used in this thesis to develop a spreadsheet-based wet muck susceptibility tool, which includes implementation guidelines and Python scripts. The concepts, methodologies and tools developed from this research are not restricted to the DOZ but can also be implemented in other caving operations that are susceptible to wet muck spills.

Text

2022-03-22

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/81000

Geological Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/