Effects of Residence Time Distribution and Packing on Methanol Oxidation in Biotrickling Filter
Bibliographic record
Abstract
The effects of residence time distribution (RTD) on biotrickling filter systems and the comparison of the maximum elimination capacity (EC) and poisoning limits as functions of loadings of two packing media, Celite Biocatalyst Carrier R-635 and a subituminous coal, were studied. To alter the RTD patterns in the two reactor columns, two baffle designs were chosen. The RTD tests were done under dry conditions, over a range of airflow rates, with zero baffle, one baffle, and two baffles added into each column. Mixed culture from compost was used to acclimate the bed for the methanol removal efficiency study. No nutrients were added in the coal column. To study the poisoning limit, the inlet methanol concentration was randomly increased until a severe drop in removal efficiency occurred. From the RTD tests and the removal efficiency runs, which did not result in 100% conversion, number of tank-in-series (N) values, maximum EC values, and rate constants of each column with different baffle configurations could be obtained. Results from duplicate runs showed that addition of baffles decreased the N values of the columns and increased the back mixing in both systems. Maximum EC values, critical loadings, and poisoning limits also increased with increasing back mixing. Coal was superior to Celite Biocatalyst Carrier R-635 because it gave good conversions without additional nutrients. In all runs, the rate of methanol removal was controlled by a zero order process.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".