Applying a Generalized FMEA Framework to an Oil Sands Tailings Dam Closure Plan in Alberta, Canada
Bibliographic record
Abstract
Historically, tailings facilities have been designed primarily with consideration of the mine’s active life. This is problematic, as the lifespan of a tailings dam may far exceed the life of the mine. Over time, it is expected that these structures will transform into a mine waste structure and then eventually a landform. In Alberta, Canada, dam owners can submit a decommissioning, closure, and abandonment (DCA) plan and completion reports to apply for the facility to be de-registered as a dam. If successful, the structure would be considered a solid waste structure and may be reclassified and regulated as a mine waste dump. The Alberta Energy Regulator expects DCAs to be accompanied and supported by risk assessments that consider long-term physical failure modes, including failure modes that may not be applicable during operations, in accordance with Manual 019. To help support the process of de-registering a tailings dam, a risk management tool, referred to as a Generalized Failure Modes Effects (G-FMEA) framework, was developed and presented in the Journal of Minerals in the Special Issue Tailings Dams: Design, Characterization, Monitoring, and Risk Assessment. The G-FMEA was designed to be used for assessing risks of an external tailings facility in closure, with the goal of assessing the long-term risk of geotechnical failure to support the process of de-registration. In Alberta, a number of tailings dams are undergoing closure and reclamation activities. This paper applies the developed G-FMEA framework to an oil sands tailings dam in Alberta to demonstrate the application of the framework. The paper assesses two specific failure modes of two different elements, including clogging of the drains and surface erosion of the berm. The failure modes are assessed over different timescales to demonstrate how the consequence, likelihood, and risk rating may change over time. The results of this process are discussed in the context of the potential for the facility to be de-registered as a dam.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".