Effect of Sulphur Concentration on Bioleaching of Cr(III) and Other Metals from Tannery Sludge by Indigenous Sulphur-Oxidizing Bacteria
Bibliographic record
Abstract
An investigation on the effect of elemental sulphur concentration on bioleaching of Cr(III) and other metals from tannery sludge by sludge indigenous sulphur-oxidizing bacteria was performed. Elemental sulphur concentrations ranging from 5 to 40 g/L were tested during this study. The sludge solids concentration was fixed at 40 g/L. The results showed that the sludge pH decrease was faster and sharper with increased sulphur concentration. The time required to achieve the final pH (the lowest pH) was about 15 days irrespective of sulphur concentration. The leaching efficiencies of Cr(III), Al, and Fe, and SO42- production increased with increasing initial sulphur concentration. However, the leaching efficiencies of Zn, Ca, and Mg were less affected by the initial sulphur concentration. The preferred sulphur concentration for Cr leaching was 20 g/L, from an economic viewpoint. The total and volatile suspended solids decreased during the process. The measurements of sulphur-oxidizing bacteria showed that less-acidophilic thiobacilli as well as acidophilic thiobacilli contributed to the pH reduction in tannery sludge. The sulphur-oxidizing bacteria could survive in tannery sludge at a pH as low as 0.5 at 21°C
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".