Resume/ Abstract

Knowledge gaps and uncertainty exist regarding many aspects of ML/ARD. Recognizing and proactively managing the knowledge gaps and resulting uncertainty is vital for environmentally and financially sound mining. Topics covered in the presentation include properties that are difficult to measure or predict, learning from previous experience, and taking corrective actions. Examples of knowledge gaps include:

• lack of data on the evolution in drainagechemistry from different types of tailings and waste rock to use as bench marks when predicting drainage chemistry
• future maintenance and repair to sustain different mitigation measures
• effect on subaqueous covers of flora and fauna invasion
• monitoring required for proactive repair
• future reagent costs
• dewatered tailings cover performance
• oversight mechanisms for qualified professionals

Sensitivity and gap analysis and risk assessment should be conducted to identify data gaps that create significant risk.

Measures to reduce risks and safely manage uncertain aspects of ML/ARD include studies to fill knowledge gaps, conservative assumptions and additional capacity, adaptive management, and contingency plans. The measures required depend on the timing, consequences, and response requirements. Additional capacity includes redundancy and alternative transportation options, and sources of power and supplies. After state-of-the-art engineering and scientific calculations, experienced past practitioners, such as Noranda, often added additional capacity in the form of big ditches and ponds to handle unexpected or underestimated events.

Keywords - ML/ARD, knowledge, uncertainty, mitigation, challenges