Controlling Calcium Sulphate Scale Formation In Acid Mine Waters
Bibliographic record
Abstract
Membrane technologies are capable of treating mine waste waters to produce clean permeate water for reuse and a concentrate stream that can be used for valuable metals recovery.Reverse Osmosis (RO), Nano-filtration (NF) and Ultra-filtration (UF) technology is increasingly being adopted.In precious metal mines, waste water can be concentrated using membrane plant so additional metals can be recovered from barren liquor.Acid mine drainage (AMD) is increasingly treated and then reused or sent off site as a valuable resource for domestic and agricultural use.The use of membrane technology is hampered by the potential for rapid membrane fouling and calcium sulphate (gypsum) scale deposition.The authors have embarked on research project to investigate calcium sulphate scale formation in acidic pH and in the presence of a variety of soluble metals.New antiscalant chemistry for preventing sulphate scale formation in acidic conditions with metals have been investigated and the results are presented.A series of experiments were performed to assess calcium sulphate scale formation and inhibition in the presence of metals at low pH.The dissociation of sulphate and bi-sulphate ions at low pH was investigated, followed by Threshold jar tests to screen water chemistries at different conditions and finally using actual membrane coupons with a Flat Sheet Test rig to assess membrane performance and scaling inhibition.
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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) | 0.000 | 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) | 0.001 | 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".