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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".