The Effect of Switchable Water Additives on Clay Settling
Bibliographic record
Abstract
The recycling of process water from strip mining extractions is a very relevant task both industrially and environmentally. The sedimentation of fine tailings during such processes, however, can often require long periods of time and/or the addition of flocculants which make later water recycling difficult. We propose the use of switchable water additives as reversible flocculants for clay/water suspensions. Switchable water additives are compounds such as diamines that make it possible to reversibly control the ionic strength of an aqueous solution. Addition of CO(2) to such an aqueous solution causes the ionic strength to rise dramatically, and the change is reversed upon removal of the CO(2). These additives, while in the presence of CO(2), promote the aggregation of clay tailings, reduce settling times, and greatly increase the clarity of the liberated water. The removal of CO(2) from the liberated water regenerates a low ionic strength solution that does not promote clay aggregation and settling until CO(2) is added again. Such reversible behavior would be useful in applications such as oil sands separations in which the recycled water must not promote aggregation. When added to kaolinite and montmorillonite clay suspensions, switchable water provided process waters of lower turbidity than those additives from inorganic salts or by CO(2)-treatment alone. When recollected, the switchable water supernatant was shown to be recyclable over three cycles for enhanced settling of kaolinite.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 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".