Moving from alcohol to methane biofilters: an experimental study on biofilter performance and carbon distribution
Bibliographic record
Abstract
Abstract BACKGROUND Biofilters can be used to eliminate different gaseous pollutants including alcohols (e.g. methanol, ethanol) and methane (CH4) individually or in a mixture. In this regard, the biofilter adaptability to feed composition variation is an industrial requisite. Inlet gas composition changes also give a better insight about carbon input and end‐points in biofilters. In this study, the gradual conversions of two biofilters from methanol and ethanol to CH4 were investigated. RESULTS Both biofilters reached 100% removal efficiencies for the alcohols with no initial inoculation. Keeping the total inlet load constant (30 ± 1.3 g m−3 h−1), CH4 was progressively substituted in the feed with corresponding alcohol:CH4 mass ratios of 3:1, 1:1, 1:3 and 0 galcohol:gCH4. Maximum CH4 removal efficiencies of 52% and 29% were obtained (respectively) for biofilters started with methanol and ethanol. By moving from alcohols to CH4 biofilters, the gas phase output carbon increased from 273 to 666 gcarbon day−1 and from 377 to 681 gcarbon day−1 respectively for the methanol‐ and ethanol‐based biofilters. CONCLUSION This study showed a successful treatment based on inlet pollutant alteration from methanol or ethanol to CH4 in two separate biofilters. However, the methanol‐based biofilter displayed a better performance and a shorter acclimation time for CH4 conversion. © 2019 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".