Shrimp shell waste – a sustainable green solution in industrial effluent treatment
Bibliographic record
Abstract
ABSTRACT In this study, the capacity of wasted natural material Fenneropenaeus indicus (Shrimp) shell, as a coagulant, was appraised in the treatment of simulated paint factory effluent (SPFE) by colour and turbidity. The study was conducted by varying different operational parameters. The proposed case to treat a litre of SPFE was 400 mL of eluate made from 4% (wt/vol) of shrimp shell powder (SSP) and 3 N NaCl, at its own initial pH (8.4–8.6). The outcome was 93.67% (colour) and 81.77% (turbidity). The optimized conditions were applied on real paint factory effluent. The evaluated sludge volume (SV f ) and sludge volume index were boosted and the hindered settling velocity (V HS ) was in declined trend with the upgrade in initial concentration of effluents, in the settling studies. The final volume of sludge was ranged between 190 and 260 mL/L and its dry weight was between 32.7 and 38.1 g/L, respectively. The presence of chitosan, an active component, responsible for coagulation was confirmed by Fourier transform infrared spectroscopy. The results were contrasted with chemical coagulant chitosan and it confessed that, being a biodegradable and universally abundant, the SSP has a capacity to become a sustainable green alternate for chemical coagulants in the paint factory effluent treatment.
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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) | 0.001 | 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".