Elimination of Volatile Organic Compounds by Bioflltration: A Review
Bibliographic record
Abstract
Volatile organic compounds (VOCs) are pollutants that are responsible for the formation of the tropospheric ozone, one of the precursors of smog. VOCs are emitted by various industries including chemical plants, pulp and paper mills, pharmaceuticals, cosmetics, electronics and agri-food industries. Some VOCs cause odor pollution while many of them are harmful to environment and human or animal health. For the removal of VOCs, biofiltration, a biological process, has proved to be reliable when properly operated. This process has therefore been widely applied in Europe and North America. The main advantages associated with the use of biofiltration are related to its set-up, maintenance, and operating costs which are usually lower than those related to other VOCs control technologies and because it is less harmful for the environment than conventional processes like incineration. In the present paper, the main parameters (type, moisture, pH, and temperature of filter bed, microbial population, nutrients concentrations, and VOCs' inlet load) to be controlled during the biofiltration are identified and described in detail. The main phenomena involved in biofiltration are also discussed. For improving the efficiency of VOC control biotechnology, new techniques are now proposed that include the use of membranes, biphasic reactors, UV photolysis, and many others.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".