Bioscrubbing as a treatment for a complex mixture of volatile organic compounds: influence of the absorption column characteristics on performanceThis article is one of a selection of papers published in this Special Issue on Biological Air Treatment.
Bibliographic record
Abstract
A semi-industrial bioscrubber was developed to treat a complex mixture of volatile organic compounds (VOCs), including oxygenated, aromatic, and chlorinated compounds. Packed and atomizing columns were used for the bioscrubber. For an applied inlet load of around 850–870 g VOCs·m–3 packing material·h–1, the various performances of the bioscrubber were equivalent (around 50%), regardless of the selected absorption column. Only the removal efficiency of oxygenated compounds was found to be reasonable, close to 80%–85%. For the bioscrubber equipped with an atomizing column, a doubling of the inlet load involved a decrease in the removal efficiency of the VOC mixture (35%) and oxygenated compounds (from 80% to 55%). As the transferred compounds were biodegraded, the limits could be explained by the hydrodynamic characteristics of the atomizing column. Furthermore, these results demonstrate the necessity of optimizing the gas–liquid mass transfer step. Two-phase partitioning bioreactors seem to represent an attractive option, even though knowledge of the transfer of compounds and biodegradation mechanisms is required prior to scale-up and industrial use.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".