Biodegradation of diesel oil in a baffled roller bioreactor
Bibliographic record
Abstract
Abstract BACKGROUND: Ex situ bioremediation is a feasible and economical way to remove petroleum pollutants from contaminated soil or water. A baffled roller bioreactor was shown to be effective for biodegradation of diesel oil as a model petroleum pollutant. Microorganisms enriched from an industrially contaminated soil with heavy hydrocarbons were shown to be the best inoculum source for diesel biodegradation. RESULTS: The baffled roller bioreactor demonstrated better performance than control (roller bioreactor without baffles) or bead mill roller (control bioreactor filled partially with spherical beads) bioreactors. Biodegradation consisted of both fast and slow stages for degradation of light and heavy compounds, respectively. Among the tested temperatures ranging from 15 to 35 °C, room temperature (23 °C) was found to be the optimum temperature for biodegradation. The values of maximum specific growth rate and substrate yield (µmax and YXS) for the indigenous microorganisms in the baffled roller bioreactor at room temperature were found to be 0.72 ± 0.08 h−1 and (7.0 ± 1.0) × 107 cells mg−1 diesel, respectively. Biodegradation of diesel concentrations up to 200 g L−1 was achieved with the highest biodegradation rate of 266 mg L−1 h−1 at the highest rotation rate of 45 rpm in the baffled roller bioreactor. CONCLUSION: Using indigenous bacteria enriched from industrial contaminated soil at room temperature, a baffled roller bioreactor is able to biodegrade high diesel oil concentrations at high biodegradation rates. Copyright © 2008 Society of Chemical Industry
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".