Extraction of Cr(III) and Other Metals from Tannery Sludge by Mineral Acids
Bibliographic record
Abstract
A comprehensive investigation on the extraction conditions of Cr(III) and other metals from tannery sludge using mineral acids was performed. The effect of various factors (the extraction time, the type of mineral acid, the consumption of acid, pH, sludge solids concentration, temperature, and the type of sludge) on the leaching yield of metals was studied. The results indicate that the metal extraction time for most of the metals was 2h at 25 degrees C. The most suitable acid for Cr(III) extraction was sulphuric acid. A relationship for the acid requirement to adjust different sludge pH at varying sludge solids concentration was established. The leaching yield of chromium varied with sludge pH and the suitable pH for Cr(III) extraction was between 2.0 to 3.0. The optimum sludge solids concentration for Cr(III) extraction was 78.5 g x l(-1). The leaching yield of Cr(III) decreased with the increased temperature. The type of sludge (wet or dry) has no effect on the leaching yield of Cr(III) The metal adsorption and bonding on the insoluble organic colloid matter as well as the formation of metal precipitates may be two possible reasons for the decreased extraction yield of metals with increased solids concentration.
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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) | 0.001 | 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) | 0.000 | 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".