Utilization of corncob as adsorbent to remove oil and grease from produced water
Bibliographic record
Abstract
The hazardous composition of produced water (PW) places it as a dangerous contamination agent, which must be treated to meet environmental and operational requirements before disposal, reuse, or reinjection. The current work evaluated an adsorption treatment using raw and pretreated corncob samples to remove total oil and grease (TOG) from PW. The influence of adsorbent dosage (1.0, 2.5, and 5.0 g), particle size (0.5, 1.0, and 2.0 mm), and contact time (60, 120, and 240 min) were tested in a batch system, showing that the lowest oil concentration was achieved with the smallest particle size (0.5 mm) and highest contact time (240 min) and adsorbent dosage (5.0 g). Using a 20 cm secondary partition in the fixed-bed column system was more efficient for TOG removal than a 10 cm one: the raw corncob removed 85.23% of TOG against 17.41% pretreated biomass. Comparative studies showed that the adsorption performance of untreated corncob was superior to that observed for walnut shells (69%), a widely used commercial absorbent. Results indicated corncob’s environmental and economic potential as a natural and cost-effective adsorbent.
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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) | 0.001 | 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".