Water and material balance at mine tailings impoundments : software program development and risk analysis
Bibliographic record
Abstract
Tailings impoundments are commonly used in the mining industry for the disposal and storage of mine wastes including tailings, waste rock and process water. The impoundments often require engineered embankment dams to facilitate containment. Failure of impoundment dams can lead to serious effects downstream due to the release of significant amounts of water and solids. Inadequate water management has been recognized as the primary cause of such failures. Tailings impoundment dam design involves estimating the site water and material balance to design appropriate impoundment structures and material management facilities. The balances are usually conducted using monthly average hydrologic values and output from the balance are the required dam crest elevations during the life of the mine. The "models" that are employed by industry and their consultants to complete these hydrologic budgets are simple and spreadsheet based, using average hydrologic values to predict required monthly dam crest elevations. The lack of flexibility and transparency in these spreadsheet balances has been identified as a problem by mining engineers. A Microsoft Windows based software program written in Visual Basic, Visual Balance, was developed as part of this study. Visual Balance is a fast, simple method of modelling the water and material balance in a single impoundment tailings disposal system and predicting required dam crest elevations. Visual Balance also includes a risk analysis module which predicts probable impoundment operation and closure conditions based on a Monte Carlo simulation of expected precipitation and surface runoff values. Water management problems identified by Visual Balance include insufficient free pond water available for reclaim, inadequate freeboard, uncontrolled release requirements, or tailings solids exposure. Knowledge and anticipation of these challenges could influence tailings impoundment site selection, design, or mine operating conditions. Planning for these conditions in impoundment and facility design could save companies considerable cost and aggravation. The results of the five Case Studies conducted as part of this study emphasized the predictive capabilities of Visual Balance. Monthly dam crest elevations similar to those previously predicted by spreadsheet based balances were modelled for the five Case Studies by Visual Balance. In the two Case Studies where actual operating conditions were available for comparison, insufficient free pond water availability and excess water leading to low freeboards experienced at each site were successfully predicted by Visual Balance.
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 | 0.000 |
| 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".