Stochastic long-term production scheduling of the LabMag iron ore deposit in Labrador, Canada
Bibliographic record
Abstract
In long-term production scheduling, which is of vital importance to a project's success and profitability, the goal is to determine a feasible extraction sequence that maximizes the discounted cash flows of a mine while also ensuring the target ore quantities and qualities are met. There is risk of the actual production deviating from what is planned due to geological variability, which is not considered by conventional mine designs and production schedules that are based on a single estimated ore body model. In order to address this issue, multiple simulations of an orebody can be created to represent its geological variability and allow for quantifying expected bounds, instead of single estimates, for grades, tonnages, and financial results. Beyond simply quantifying the geological uncertainty, a mine production schedule can be optimized while directly considering simulations in order to manage the geological risk.In this study, a set of geological simulations of the LabMag iron ore deposit in Labrador, Canada is generated in order to quantify the geological variability in an existing mining schedule and assess the schedule's performance. The ‘DBMAFSIM' algorithm is used to provide joint geostatistical simulation of spatially correlated variables of interest. First, a novel application of the method is used to jointly simulate the thicknesses of seven lithological layers, and then four correlated grades within each lithology are jointly simulated. The variability in an existing production schedule, designed based on a single deterministic geological model, is then evaluated using the simulations. This evaluation quantifies the potential deviations from expected production target grades and tonnages as well as the associated financial impact of these deviations. Subsequently, a production schedule optimization based on stochastic integer programming (SIP) is presented that aims to improve mine profitability while simultaneously managing the risk of production tonnage and quality deviations. In addition, the formulation has components for equipment and waste material management: the truck fleet requirements are minimized while ensuring that the number of required trucks is an increasing function to avoid unnecessary peaks; and the evolution of the pit is controlled so that space within the mined out pit is continuously provided to allow for tailings and waste rock to be replaced, thus minimizing the project's environmental footprint.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".