Stochastic orebody modelling and stochastic long-term production sheduling for an iron ore deposit in Northern Quebec
Bibliographic record
Abstract
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".