Evaluation of the performance of catalytic oxidation of <scp>VOCs</scp> by a mixed oxide at a semi‐pilot scale<sup>†</sup>
Bibliographic record
Abstract
Abstract Volatile organic compounds (VOCs) are one of the main contributors to air pollution. To reduce anthropogenic emissions, it is necessary to improve existing techniques such as catalytic oxidation through the development of new cost‐effective catalysts. Although many studies deal with the development and testing of new materials, most are performed at laboratory scale, of which only a few study mixtures of VOCs. To assess their viability for industrial applications, further tests are required, namely, mixture tests at intermediate scale in relevant environment and extrapolated on an industrial scale. In this work, the catalytic performance of a new mixed oxide Co‐Al‐Ce was investigated towards the oxidation of the n‐butanol and toluene on a semi‐pilot scale (TRL 4). Single component and mixture experiments were performed for several concentrations at a fixed flow rate. A commercial catalyst Pd/γ‐Al2O3 was used as the benchmark to evaluate the performance of the mixed oxide. The Co‐Al‐Ce catalyst enables complete oxidation of n‐butanol at the same temperature as the reference catalyst. Moreover, it provides a better selectivity for n‐butanol, while providing an equivalent one for the oxidation of toluene. In mixtures, the presence of n‐butanol promotes the oxidation of toluene for both catalysts but more significantly for the Co‐Al‐Ce catalyst. The presence of toluene inhibits the oxidation of n‐butanol for the Co‐Al‐Ce and promotes it for high conversions of n‐butanol for the Pd/γ‐Al2O3 catalyst.
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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 | 0.000 | 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".