Flow Enhancement of Mineral Pastes to Increase Water Recovery in Tailings: A Matlab-Based Imaging Processing Tool
Bibliographic record
Abstract
The rate of growth of mining copper industry in Chile requires higher consumption of water, which is a resource limited in quality and quantity and a major point of concern in present times. In addition, the efficient use of water is restricted due to high levels of evaporation (10 to 15 (l/m2) per day), in particular at the north highland mining sites (Chile). On the contrary, the final disposal of tailings is mainly on pond, which loses water by evaporation and in some cases by percolation. An alternative are the paste thickeners, which generate stable paste (70% solids), reducing evaporation and percolation and therefore reducing water make up. Water is a resource with more demand as the industries are expanding, making the water recovery processes more of a necessity than a simple upgrade in efficiency. This technology was developed in Canada (early 80s) and it has widely been used in Australia (arid zones with similar weather conditions to Chile), although few plants are using this technology. The tendency in the near future is to move from open ponds to paste thickeners. One of the examples of this is Minera El Tesoro. This scenario requires developing technical capacity in both paste flow characterization and rheology modifiers (fluidity enhancer) in order to make possible the final disposal of this paste. In this context, a new technique is introduced and experimental results of fluidity modifiers are discussed. This study describes how water content affects the flow behavior and depositional geometry of tailings and silica flour pastes. The depositional angle determined from the flume tests, and the yield stresses is determined from slump test and a rheological model. Both techniques incorporate digital video and image analysis. The results indicate that the new technique can be incorporated in order to determine the proper solid content and modifiers to a given fluidity requirement. In addition, the experimental results showed that the pH controls strongly the fluid paste behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".