Numerical and experimental modelling to support climate change adaptation of tailing management facilities in cold regions.
Notice bibliographique
Résumé
Climate change, induced by increased anthropogenic emission of greenhouse gases, is one of today’s grand challenges. Changes are being experienced particularly intensely in the high latitudes, where permafrost degradation associated with warmer temperatures, and increasing intensity, duration and frequency of extreme events, are having significant impacts on engineering systems, including mines and mine life-cycle. This thesis addresses some of the critical knowledge gaps related to climate-mine interactions and adaptation strategies for Canada's high latitude regions, through advanced numerical and experimental modelling approaches, with a specific focus on mine tailings, i.e., waste generated by the mechanical and chemical processes involved in the extraction and separation of the desired mine ore in a processing plant. The main objective of this thesis unfolds in three main phases. The first phase of this thesis identifies potentially vulnerable mines and phases of the mine life cycle from climate change perspective for the Canadian permafrost regions. This involves utilizing an ensemble of climate change simulations performed using a state-of-the-art regional climate model GEM (Global Environmental Multiscale), at 50 km horizontal resolution. The second phase of this thesis assesses the climate resiliency of the Mont-Wright mine tailings management facility (TMF), specifically the potential for tailings erosion and flooding in a warmer future climate. This is done through climate simulations at a 1 km horizontal resolution, covering the life span of the mine, coupled with advanced diagnostics. The third phase investigates the properties of Mont-Wright mine tailings, for innovating an effective climate change adaptation strategy. Laboratory experiments and lab-scale and field-scale numerical models are developed to comprehensively explore the mechanical, thermophysical, and rheological properties of Mont-Wright tailings (i.e., with 20 and 30% water contents), and their suitability for the application of frozen paste surface disposal method.Results from the first phase identifies northernmost and northeastern mines to be more vulnerable, with air/soil temperature, precipitation and wind speed being the most influential climate variables, especially for managing various types of TMFs. Focused investigation of the Mont-Wright TMF in phase two suggests that higher wind magnitudes could potentially lead to slight increases in tailings internal erosion rate by up to 6% for a high emission scenario. Furthermore, results also suggest future increases in flooding, estimated in terms of changes to the probable maximum flood (PMF), with summer/fall PMF increases of up to 20%, which is larger than that for spring PMF. According to the laboratory investigation of the properties of Mont-Wright tailings, the unconfined compressive strength (UCS) of the tailings is found to be in the 0.26 MPa to 0.93 MPa range depending on the water content, ambient temperature, and number of freeze-thaw cycles (FTCs), with 30% water content resulting in higher strength compared to 20%. Additionally, investigation shows that the use of paste tailings with 30% water content provide enhanced rheological properties, where viscosity is 140% lower, compared with paste with 20% water content, favoring workability and pumpability. The experiments and numerical modelling lead to the conclusion that Mont-Wright tailings are suited for surface disposal in a frozen paste state.This thesis contributes significantly to the Canadian mining sector by quantifying climate change impacts on mines located in the Canadian permafrost region, for the first time. The developed actionable high-resolution climate projections provide unprecedented insights into the climate resiliency of Mont-Wright's tailings management facility. In addition, this thesis lays a strong foundation for the innovative use of frozen paste tailings surface disposal at Mont-Wright mine
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».