Utilizing ultraviolet photooxidation as a pre‐treatment of volatile organic compounds upstream of a biological gas cleaning operation
Bibliographic record
Abstract
Abstract Laboratory experiments were conducted to evaluate the potential to utilize ultraviolet (UV) photooxidation as a pre‐treatment to render recalcitrant volatile organic compounds into more biodegradable compounds. α‐Pinene was selected due to its low water solubility and low biodegradability. α‐Pinene‐contaminated gaseous streams with inlet loadings between 250 and 2500 g m−3 h−1 were passed through an annular reactor equipped with a UV lamp that emitted light at 254 nm and 185 nm wavelengths. The outlet stream containing UV photooxidation intermediates was then sparged through nanopure water that was then analyzed for its total organic carbon (TOC) content and subjected to batch biodegradability tests. UV photooxidation effectively degraded α‐pinene with a maximum removal rate of about 700 g m−3 h−1. The removal rate followed first order kinetics at low inlet loadings (less than 1200 g m−3 h−1) and approached zero order behavior at higher inlet loadings. The principal oxidizing species in the reactor was ozone. Of the total α‐pinene removed, measured as TOC, 50% was converted to water‐soluble and more biodegradable intermediates. The biodegradability of the resultant intermediates was similar to that of methyl ethyl ketone (MEK), which is 3–30 times more biodegradable than α‐pinene. These results show that the use of UV photooxidation is a promising and effective pre‐treatment technique for enhancing the biodegradability of hydrophobic and recalcitrant organic compounds such as α‐pinene. Copyright © 2004 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".