Hydrocarbon biodegradation in oxygen-limited sequential batch reactors by consortium from weathered, oil-contaminated soil
Bibliographic record
Abstract
We studied the use of sequential batch reactors under oxygen limitation to improve and maintain consortium ability to biodegrade hydrocarbons. Air-agitated tubular reactors (2.5 L) were operated for 20 sequential 21-day cycles. Maya crude oil-paraffin mixture (13,000 mg/L) was used as the sole carbon source. The reactors were inoculated with a consortium from the rhizosphere of Cyperus laxus, a native plant that grows naturally in weathered, contaminated soil. Oxygen limitation was induced in the tubular reactor by maintaining low oxygen transfer coefficients (k(L)a < 20.6 h(-1)). The extent and biodegradation rates increased significantly up to the fourth cycle, maintaining values of about 66.33% and 460 mg x L(-1) x d(-1), respectively. Thereafter, sequential batch reactor operation exhibited a pattern with a constant general trend of biodegradation. The effect of oxygen limitation on consortium activity led to a low biomass yield and non-soluble metabolite (0.45 g SS/g hydrocarbons consumed). The average number of hydrocarbon-degrading microorganisms increased from 6.5 x 10(7) (cycles 1-3) to 2.2 x 10(8) (cycles 4-20). Five bacterial strains were identified: Achromobacter (Alcaligenes) xylosoxidans, Bacillus cereus, Bacillus subtilis, Brevibacterium luteum, and Pseudomonas pseudoalcaligenes. Asphaltene-free total petroleum hydrocarbons, extracted from a weathered, contaminated soil, were also biodegraded (97.1 mg x L(-1) x d(-1)) and mineralized (210.48 mg CO2 x L(-1) x d(-1)) by the enriched consortium without inhibition. Our results indicate that sequential batch reactors under oxygen limitation can be used to produce consortia with high and constant biodegradation ability for industrial applications of bioremediation.
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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 | 0.001 | 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".