Effects of Biomass Growth on Gas Pressure Drop in Biofilters
Bibliographic record
Abstract
The effects of biomass accumulation and distribution on air pressure losses in biofilters were experimentally studied. Two bench-scale biofilters, one packed with inert porous pellets (Nova Inert) and the other with wood chips, were operated under similar conditions with excess nutrients to treat an airstream containing methanol, at loading rates of 100–150 g methanol/m3 bed/h. Localized biomass accumulation in the biofilter beds was the key factor increasing the pressure drop, which was caused by local bed clogging due to biomass growth. The highest pressure drops in the beds (wood chips: 2,600 Pa/m; Nova Inert: 550 Pa/m) occurred in sections where there were high biomass levels with high water content. The pressure drop varied nonlinearly with the amount of accumulated biomass and the amount of methanol consumed. Sixfold higher pressure drops were measured in the wood chip biofilter than in the Nova Inert biofilter because of more biomass growth and bed compaction. A model, based on the Ergun equation, was developed to predict biomass-affected porosity and pressure drop as a function of the biomass concentration in a bed packed with spherical pellets. A comparison of the experimental and the predicted pressure drops showed that the model provided good estimates of biomass-affected porosity and pressure drop in the biofilter packed with spherical porous pellets with even biomass distribution.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".