Project Details

Project Details:

An inrush event is a sudden and uncontrolled inflow of material into an underground mine excavation, typically from an extraction-level drawpoint when in a cave mine setting. Inrushes can be dry, albeit these are rare, or in most cases wet, creating a more mobile flow material (also referred to as mud rushes or wet muck spills) (Figure 1) (Hubert et al. 2000). Inrushes pose a major risk to mine safety, assets, and production by causing equipment damage, ore handling difficulties, production delays, unplanned dilution and, in extreme cases, fatalities (Jakubec et al. 2012).

Understanding inrush causative factors and triggers has gained particular attention in the past decade, with caving operations increasingly being impacted by the hazard. The major reason for this trend is that operating caves have either extended to greater depths (with greater column heights), are maturing, and/or have broken through to surface or into an overlying older cave.

Identification of an inrush hazard level generally relies on classification, examining the percentage of fines present in the muck and its water saturation through drawpoint sampling (Widijanto et al. 2006). Once identified, various mitigation measures are taken as part of the inrush hazard management plan, the most effective being the utilization of remote equipment (e.g., semi-autonomous loaders).

Automation has significantly improved operational safety when dealing with inrush hazards. However, mines can face severe interruptions in daily production due to equipment damage, the lower capacity of remote equipment and reduced drawpoint availability due to inrush events (Edgar et al. 2020). Prediction of these interruptions is challenging due to uncertainties associated with the spatial distribution, frequency, and severity of inrush events.

Figure 1. Wet muck at the DOZ mine (http://ikata.or.id)

The key objective of this project is to identify the significant factors influencing inrush susceptibility (event likelihood) and inrush severity (inrush volume and runout distance).  In this project, the research questions are being addressed mainly by the utilization of statistical models and machine learning methods applied to databases compiled by underground mines (Figure 2).

The knowledge gained from historical inrush events will be used to develop tools to predict the spatial and temporal pattern of inrush events and provide estimates of inrush severity.  An example of a wet muck spill susceptibility map developed by Varian (2022) for the Deep Ore Zone (DOZ) mine is shown in Figure 3.

Figure 2 – Supervised machine learning algorithms for inrush susceptibility and severity assessment

Figure 3. Example of spill susceptibility map (related to the DOZ mine) developed based on results from machine learning algorithms (Varian 2022)

In this project, special focus has been placed on how inrush susceptibility and severity are affected by draw strategies. For example, Figure 4 shows how the probability of a high-volume wet muck spill event changes at different ranges of two draw-related variables: draw rate ( ) and differential draw index (DI); DI is a new parameter proposed in this project for quantification of differential draw.

Incorporating effective draw-related variables into an inrush susceptibility/severity assessment tool gives the tool the capability of significantly outperforming the previously developed tools, by being able to guide prediction of the temporal pattern of inrush events and guide risk-informed draw optimization.

Figure 4 – Empirical probability of a high-volume wet muck spill event (Vspill > 500 m3) at the DOZ mine at different ranges of draw rate and differential draw index values (Ghadirianniari et al. 2022)

References

• Edger, I., Prasetyo, R., Wilkinson, M. , Deep Ore Zone mine wet ore mining empirical learnings, mining process evolution and development pathway, in Massmin. 2020.
• Hubert, G., et al., Tele-operation at Freeport to reduce wet muck Hazards. MassMin 2000: p. 173-179.
• Jakubec, J., R. Clayton, and A. Guest. Mudrush risk evaluation. in Proceedings of the sixth international conference and exhibition on mass mining, Canadian Institute of Mining, Metallurgy and Petroleum. 2012.
• Ghadirianniari S., McDougall S., Eberhardt E., Campbell R., Llewelyn K. and Moss A., Impact of draw strategy on wet muck spill severity at the DOZ mine. In: Caving 2022: Fifth International Conference on Block and Sublevel Caving, Adelaide.
• Varian J., McDougall S., Ghadirianniari S., Campbell R., Llewelyn K., Eberhardt E. and Moss A., Development of a wet muck spill susceptibility tool for short-term prediction through a logistic regression approach. In: Caving 2022: Fifth International Conference on Block and Sublevel Caving, Adelaide
• Widijanto, E., et al. Lessons learned in wet muck management in Ertsberg East Skarn System of PT Freeport Indonesia. in Proceedings of the fifth international conference and exhibition on mass mining, Canadian Institute of Mining, Metallurgy and Petroleum. 2012.