Control of acid mine drainage from an abandoned mine in Morocco by using cement kiln dust and fly ash as amendments
Bibliographic record
Abstract
Acid mine drainage (AMD) is one of the major problems of the mining industry that is causing prominent environmental damages.It occurs naturally when sulfide tailings oxidize and generate an acidic leachate containing leachate loaded with heavy metals (Pb, Fe, Zn, Cu, Cd, As ...) and other toxic compounds.During mining activities and after mine closure, storage of discharges places (waste rock piles and tailings parks) might be responsible for the production of acid leachate which will have adverse consequences on the environment.The Kettara abandoned mine (Morocco) has produced from 1965 to 1982 more than 3 Mt of mine wastes that are rich in sulfides (pyrrhotite and pyrite).The physicochemical characterization of these mine wastes confirmed their strong potential to produce Acid Mine drainage with a pH varying between 2.9 and 1.5.In order to control AMD in Kettara mine site, AMD neutralization tests were undertaken in the laboratory using two types of alkaline industrial byproducts as amendments.The latter consisted of alkaline Fly Ash (FA) from the thermal power plant of JorfLasfar in El Jadida and Cement Kiln Dust (CKD), from Lafarge cement of Bouskoura near the city of Casablanca, Morocco.The tests in leaching columns objectives are to determine the ratios of CKD, FA and residues who may neutralize the phenomenon of AMD in Kettara site.The leaching columns tests show that the use the industrial by-products allows increase the leachate pH to values of about 6.5 and 7.13 and the substantial reduction of metals concentrations such as Fe (from 0.01 to <0.12 mg/L) and Cu (<0.02mg/L).The AMD from Kettara mine tailings could be reduced by adding amendments composed of 80% of CKD and 20% of FA.This method of treatment with CKD and FA allow recycling and valorization of industrial waste cement plants and thermal power plants.
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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 | 0.001 | 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 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".