Stochastic orebody modelling and stochastic long-term production sheduling for an iron ore deposit in Northern Quebec
Notice bibliographique
Résumé
The process of mine planning, from prospection of orebody deposits defining its extension, location and value, until obtaining minerals and their extraction sequence in time, requires mathematical optimization to determine the size and grade (grades) of the deposit and finally define a proper mine schedule to obtain the maximum earnings from it, at the lowest possible cost, in order to fulfill the business targets. In general, a mining project passes through a set of stages in order to evaluate its viability. One of these stages is the "feasibility stage" where the gathered information from mining studies is used to determine the economics and practical aspects of the ore deposit. This identifies, early on, whether further investment in estimation and engineering studies are required, and identifies areas for further work and development. The KéMag iron ore deposit owned by New Millennium Limited in northern Quebec, Canada, is in feasibility stage. The KéMag iron ore deposit is a taconite type, where the iron content is present as finely dispersed magnetite between 20 and 35 % iron (Fe) in a sedimentary rock interlayered with quartz, chert, and carbonate. This thesis focuses on a set of methodologies to develop a whole process of stochastic orebody modelling and stochastic strategic mine planning for the KéMag deposit, aiming to generate a modelling and optimization methodology that integrates geological uncertainty and manages risk in the mine schedule. This mathematical framework has been successfully implemented in the last two decades allowing modelling and integration of geological uncertainty to mine design, production scheduling and valuation of mining projects. From the application, several cases have shown an increment on the value of the production schedules up to 25%, and a reduction of deviation from production targets from 9% to 0.2%. For the KéMag deposit a set of fifteen realizations of nine lithological units (layers) were simulated using WAVESIM which is a multiple point simulation method combined to an image compression procedure to allow faster simulations. The orebody simulations obtained through WAVESIM serve as geological boundaries to integrate the variability of the four grades of interest using DBMAFSIM this method allows the simulation of correlated variables directly at block support using min/max autocorrelation factors MAF. The final result is a series of equally probable representations of the deposit that incorporate both grade and tonnage uncertainty. These simulations of the KéMag deposit were validated in terms of histograms, variograms (low order statistics) and high-order statistics through 3rd order cumulants maps for the boundary limits only. Geological uncertainty can then, be managed by directly incorporating stochastic simulations within the mine scheduling framework. To achieve this, one flexible method for long-term production scheduling based on Stochastic Integer Programming (SIP) was applied with an acceleration methodology based on a heuristic algorithm called Topological Sort Algorithm (TSA) to reduce the computational time required to solve the problem of production scheduling. The result of the stochastic mine planning framework is a single schedule robust enough to account for geological uncertainty of the KéMag deposit giving valuable information for the conceptual stage of the project, in terms of silica content, iron production and expected cash flows per year.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».