Treatment of tannery wastewater using hybrid upflow anaerobic sludge blanket reactor
Bibliographic record
Abstract
In India, and in the State of Tamilnadu, tanning is a small-scale industry. During the tanning process, a huge volume of wastewater is generated and in most cases it pollutes the environment. In the present study, an attempt has been made to treat the tannery wastewater by using hybrid upflow anaerobic sludge blanket (hybrid UASB) reactors that offer the advantages of both fixed film and upflow sludge blanket treatment over a period of 370 d, at two different hydraulic retention times (HRT) viz., 60 and 70 h. The average concentration of COD and tannin in the influent tannery wastewater used was 14 000 mg/L and 1987 mg/L, respectively. The reactor performed to its maximum at an organic loading rate (OLR) of 2.74 kg COD m–3 d–1 and 3.14 kg COD m–3 d–1 at a HRT of 70 and 60 h, respectively. Increase in OLR beyond 2.74 kg COD m–3 d–1 and 3.14 kg COD m–3 d–1 caused a gradual decrease in COD removal efficiency. The degradation of inhibitor substance such as tannin during the anaerobic digestion was investigated and it was in the range of 65–91% at a HRT of 70 h and 63–89% at a HRT of 60 h. The performance of the reactor with reference to COD, TS removal, and biogas production were also evaluated.Key words: anaerobic treatment, hybrid-UASB, tannery wastewater, biogas.
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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) | 0.000 | 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".