An investigation of oil adsorption onto novel carbonised coconut fibres
Bibliographic record
Abstract
Removal of emulsified oil through the adsorption process using adsorbents from waste material is a cost-effective process. However, the determination of optimum conditions for the maximum removal at a minimum number of trials is the greatest challenge. The Taguchi method provides a solution for determining optimum conditions at a minimum number of trials with greater accuracy. The present research developed novel adsorbent carbonised coconut fibres prepared by thermally carbonising coconut waste for removal of emulsified oil from water. The analysis of variance revealed the influencing factors and their percentage contribution in the following order: initial concentration > pH > temperature > dose of adsorbent > contact time. The optimum conditions for maximum oil removal (about 98%) are pH of 2, dose of adsorbent of 6 g/l, temperature of 40°C, initial concentration of 500 mg/l and contact time of 180 min as per the analysis of means. The equilibrium studies suggested that the present adsorption process fitted best the Freundlich model. The adsorption capacity of carbonised coconut fibres was found to be 20.23 mg/g. The kinetic data fitted better the pseudo-second-order model, and thermodynamic enthalpy ΔH = 33.65 kJ/mol; thus, the adsorption of cutting oil is endothermic.
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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".