Thermobioremediation of Soil Contaminated with Used Motor Oil
Bibliographic record
Abstract
A laboratory-scale bioreactor was used to investigate the effectiveness of thermobioremediation in the destruction of petroleum hydrocarbons in a soil contaminated with used engine oil. The total kjeldahl and ammonium nitrogen concentrations, moisture content, temperature, and total petroleum hydrocarbon (in fuel and lubricant ranges) were monitored. There was an initial lag period of 4 days after which the destruction of hydrocarbon took place resulting in a rise in the bioreactor temperature. The heavy range hydrocarbons (C21–C32) decreased rapidly from 17,000 ppm to 9,000 in the first 20 days (47% reduction at an average rate of 400 ppm/day) and then decreased slowly to less than 5,000 ppm at day 60 (an additional 23% reduction at an average rate of 100 ppm/day). The medium range hydrocarbons (C10–C21) decreased rapidly from 3,000 ppm to 800 ppm in the first 20 days (73% reduction at an average rate of 110 ppm/day) and then decreased slowly reaching 720 ppm by the end of the experiment (an additional 2.6% reduction at an average rate of 2 ppm/day). The results indicated that the nitrogen was not a limiting factor as the C:N ratio remained in the range of 30:1 to 10:1. The temperature appeared to be the limiting factor in the biodegradation of hydrocarbons. The results also showed significant reduction in the degradation rates during the mesophilic stage (24–35°C), which lasted from day 4 to day 16. The thermophilic stage caused substantial reduction in the moisture content. However, moisture content was not a limiting factor until day 50 when it dropped below 30%. For optimum bioremediation of hydrocarbons system, reactor temperature must be maintained at or below 35°C with moisture content in the range of 45–50%. Under these conditions, a complete destruction of petroleum hydrocarbons would be achieved in 102 days.
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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 | 0.000 |
| 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".