Numerous mining activities revolve around processes, often codified through flowcharts, trigger action response plans (TARPs), and similar tools. Tailings deposition is a critical process within the management of tailings storage facilities (TSFs), directly influencing the stability, operability, and environmental compliance of the facility. This has been shown by various forensic analyses of major failures around the world. Traditionally, tailings deposition processes have relied on qualitative assessments and empirical operational experience, often leading to uncertainty in their reliability. Our experience shows that this is true even with the most modern treatment techniques, such as dewatering. While industry guidelines provide general principles for tailings management, they often lack site-specific tailings deposition criteria and what-if scenarios to estimate the likelihood of inappropriate deposition. This makes it challenging for operators to implement quantitative informed decision-making frameworks. Information related to the deposition process is generally found in the mine’s Operation, Maintenance, and Surveillance manuals (OMS). However, those manuals often do not cover cases deviating from normal conditions and do not provide mechanisms to cope with production fluctuations or extreme meteorological events (such as wet or dry conditions). In our day-to-day practice, we have reviewed flowcharts designed to support mine personnel in their deposition decisions and found that uncertainties related to deposition sometimes generate a disproportionate level of risk.
This paper examines a subset of filtered, compacted, and dewatered tailings deposition procedures, emphasizing how seemingly minor decision biases can significantly influence the process outcomes. It introduces a probabilistic risk assessment framework to evaluate the likelihood of inappropriate deposition in a real-world operational mine, treating compacted filtered tailings deposition as a complex process with interdependent failure nodes. The study quantifies the “failure probabilities” within the decision-making scheme, which contribute to overall system reliability and may lead to operational disruptions, including cascading failure effects on the tailings storage facility (TSF) medium and long-term performance.
By adopting a quantitative reliability framework, this approach enhances transparency, supports more informed engineering and operational decisions, and optimizes tailings deposition strategies, ultimately improving long-term TSF performance. The approach described in this paper can be extended to any flowchart, process description, or TARP definition, bringing value to operations.
