Rubber Processing Effluent Treatment with Chitosan: A Natural Biopolymer in Comparison with a Synthetic Coagulant
Bibliographic record
Abstract
Chitosan, a naturally occurring biopolymer extracted from prawn heads was used in the treatment of crump rubber processing effluent for safe disposal into the environment. The triplicate analyses of the effluent samples obtained by composite sampling method indicated high levels of pollution which violated the permissible limits of environmental discharge standards; with a low DO of 0.63mg/L, BOD, 312.00±1.32mg/L and COD, 1069.58±2.42mg/L (mean±SD) respectively. Chitosan was analyzed according to standard procedures; it showed good coagulative potentials from the elemental analysis and a degree of deacetylation of 76.5%. The crump rubber processing effluent was treated with chitosan and Iron (III) Chloride respectively by coagulation and flocculation method and the results obtained via the physico-chemical characterization of the treated effluent showed that the use of chitosan as a coagulant compared favourably with Iron (III) Chloride after treatment. DO levels increased from 0.63mg/L to 3.90 mg/L. There were also remarkable reduction in the Turbidity, Biochemical Oxygen Demand (BOD) and the Chemical Oxygen Demand (COD) levels of the treated samples by over 80% respectively and 70% reduction in the nutrient levels. The efficacy of chitosan in comparison with Iron (III) Chloride suggests that it can be used as a replacement for synthetic coagulants based on its availability and environmental friendliness.
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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) | 0.001 | 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".