Evaluation Of Safety Of Tailings Dams

Author : Haley Schafer
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (141 download)

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Download or read book Risk Management for Tailings Dam Safety written by Haley Schafer and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Post-closure tailings dams are expected to remain on mine sites in perpetuity following mine closure. Combined with the lack of knowledge regarding how tailings dams will evolve over time, these long-time frames necessitate the development of risk management practices. The purpose of this research was to develop methodology to support the process of determining and assessing the long-term risks of tailings dams in Alberta as they transition into landforms. The research project evaluated the state of knowledge on the long-term behaviour of tailings dams and demonstrated the knowledge gap (and associated uncertainty) associated with the closure phase of a tailings dam. To address this, a risk management tool, referred to as the Generalized Failure Modes Effects Analysis (G-FMEA) framework, was developed. The G-FMEA framework was applied to a case study oil sands tailings dam to illustrate an example application for practitioners. Finally, seepage analysis was conducted on the case study oil sands tailings dam to demonstrate how modelling tools can be used in conjunction with risk management tools to reduce uncertainty. To aid in the development of the G-FMEA framework, a detailed literature review and interviews with industry professionals were conducted. This process allowed for available information on the physical performance of tailings dams undergoing closure in Canada to be synthesized. Further, it allowed for the identification of hazards, triggers, failure modes, and uncertainties associated with tailings dam closure, which was a key element to the G-FMEA development. The interviews themselves clearly showed the lack of consensus amongst practitioners regarding long-term dam evolution. This emphasizes the uncertainty associated with closure and is a primary reason why comprehensive risk management practices must be developed. The developed G-FMEA framework uses an element approach to conduct the FMEA, which allows for the relationships between different elements to be established. The G-FMEA framework includes four individual charts for the drainage system, foundation, dam body, and landform to assess the failure modes, triggers/causes, and failure effects for different elements. The G-FMEA framework requires the risk assessment to be conducted at four different temporal scales (immediate term, short term, medium term, and long term) that are anchored to different periods of management of the facility. A risk matrix was developed for use with the G-FMEA, which includes a likelihood rating table, consequence rating table, and risk rating table. The developed G-FMEA framework provides a systematic method to assess the risks following closure of tailings dams. The G-FMEA framework was applied to a case study oil sands tailings dam to illustrate how the framework could be used in practice. Two example failure modes were selected to show how the risk matrix could be used to assign risk ratings. For both of the example failure modes, the risk rating increased as the temporal scale increased, which is partially attributed to the increasing uncertainty over time. Long-term seepage modelling was conducted on a case study oil sands tailings dam to evaluate how the phreatic surface may evolve in response to various factors (drain failure, pond formation, downstream slope erosion, and climate change). The results suggest that the phreatic surface has the potential to rise in the long term in response to different events, including drain failure and ponds forming on the reclamation surface. The seepage modelling was used to show how modelling could be used to inform risk management decisions by reducing uncertainty and allowed for the development of a framework for guidance on conducting long-term seepage modelling.