Water balance of metal mining tailings management facilities : influence of climate conditions and tailings management options
Bibliographic record
Abstract
The objective of this research was done to review and compare available methods for Tailings Management Facilities (TMFs) water balance; to develop deterministic and probabilistic models; and to compare the impacts of different tailings management options and climate conditions. The developed models were spreadsheet based. Mount Polley operational data were used. Water balance models were created for lined and unlined impoundments in both wet and dry climates. Wet condition climate data were extracted from ClimateBC (a University of British Columbia Software Program) using the location of the Kerr-Sulphurets-Mitchell (KSM) project in British Columbia. Climatic data from the Cerro Negro mine site in Argentina were used to simulate the dry condition. After developing a deterministic model, Monte Carlo simulation computational algorithm was used to develop the probabilistic evaluations. Simulations were carried out using the Oracle Crystal Ball Excel add-in. Evaluations were done on four management options (slurry, thickened, paste, and filtered tailings) by changing the tailings solids content. Results confirmed that entrainment and evaporation were the biggest water losses in TMF. For slurry tailings, entrainment loss was more than 80% of the total water loss in the wet condition and more than 50% of the total water loss in the dry condition. The reported average mine water consumption for slurry tailings in arid climate is between 0.4 and 0.7 m³/tonne. The estimated mean required make up water from the developed model in this reaserch was 0.70 m³/tonne. Water withdrawal in dry climate conditions can decrease to 0.18m³/tonne when a filtered tailings option is implemented. The average water surplus in wet climate conditions for an unlined impoundment varied between 0.83 and 1.12 m³/tonne for solids contents between 45% (slurry tailings) and 80% (filtered tailings). The corresponding values for a lined impoundment were 0.86 and 1.16 m³/tonne. Implementing dewatered tailings is not recommended in wet climates. In contrast, paste tailings and filtered tailings are good options in arid areas for proper-size operations. TMFs are site-specific complex systems. Results presented here are only examples to outline how the mining industry can work toward reducing water losses by using dewatering and tailings management technologies.
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 | 0.001 |
| 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".