Removal efficiency of heavy oil by free and immobilised microorganisms on laboratory‐scale
Bibliographic record
Abstract
Abstract This study explored free and immobilised microorganisms to degrade heavy oil. Two oil‐degrading bacterial strains (W‐1 and W‐2) were isolated from heavy oil wastewater samples collected from Shengli Oil Field in China. W‐1 and W‐2, identified as Rhosococcus sp. and Bacillus cereus sp., respectively, were tested for their growth behaviour and optimal growth conditions in the laboratory. The obtained results showed that the optimal growth conditions for W‐1 and W‐2 were identified as pH of 8, temperature of 40°C, and salinity of 2% and 4%, respectively. The environmental conditions affecting oil‐degrading efficiency by W‐1 and W‐2 were optimised in the media containing 0.3% heavy oil. The results showed that the optimal degradation and optimal growth conditions were similar, and the oil degradation rates of W‐1 and W‐2 were about 34.6% and 45.3%, respectively after 5 days. W‐1 and W‐2 capable of degrading oil was immobilised in calcium alginate gel beads containing active carbon and used for degradation of heavy oil. The heavy oil biodegradability of immobilised bacteria improved dramatically, compared with that of the free ones. The heavy oil biodegradation rates of immobilised W‐2 were found to be maximal at the same optimal growth conditions of pH, temperature, and salinity as the free ones. The best biodegradation rate of immobilised W‐2 reached above 78%, which is 33% than that of the free W‐2. © 2011 Canadian Society for Chemical Engineering
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".