Large-scale bioventing degradation rates of petroleum hydrocarbons and determination of scale-up factors
Bibliographic record
Abstract
Bioventing is a cutting edge, nondestructive treatment method that uses indigenous soil microorganisms in situ to remediate petroleum hydrocarbons in the unsaturated soil zone. Transferring the application of this technology to a field environment still has some uncertainties due to scale-up challenges. In order to identify the scale-up factor, a 80-kg soil reactor system was developed, consisting of a custom-made reactor, climate chamber, low-flow venting system, and an off-gas capture device. Sandy and clayey soils were tested with known concentrations of spiked synthetic gasoline. Various environmental conditions were monitored, which included moisture levels, pH, microbial levels, and nutrient and oxygen levels. Results show a second-stage degradation rate similar to the degradation rate obtained from research conducted with a 4-kg reactor, giving an average scale-up factor of 2.3 ± 0.4. The completed research shows that working with a 80-kg laboratory reactor is feasible, yet not always necessary for the development of scale-up factors. A complimentary study with aged soil contaminants was performed and yielded degradation rates that were significantly reduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".