Dispersion of volatile organic compounds (VOCs) emissions from a biofilter at an electronic manufacturing facility
Bibliographic record
Abstract
Printed circuit board (PCB) manufacturing industries, known as PCB facilities, emit odorous and toxic volatile organic compounds (VOCs). Such compounds not only harm the health but also create a nuisance environment for people who live in the neighborhood of PCB facilities. Biofilter technology which is based on the bio‐oxidation of pollutants has been used by many industries to remove odorous VOCs. When the removal efficiency drops due to a design or operational deficiency, odorous VOCs are released from the biofilter and this becomes evident by the increased odor complaints. Poor biofilter performance, geographical location of the facility, and meteorological conditions contribute to increased dispersion of VOCs. This research investigates dispersion of a VOC pollutant known as propylene glycol monomethyl ether acetate (PGMEA) which is released from a commercial biofilter unit installed at a PCB facility in Ontario, Canada. A Gaussian dispersion model and California puff model (CALPUFF) were used to examine the dispersion effects due to changes in wind speed, wind direction, temperature, mixing height, atmospheric stability classes as well as biofilter performance. Simulations were done for the three modeling periods (January, May, September) to account for seasonal effects on PGEMEA dispersion and predictions from both models are compared. The contour hourly wind and concentration plots confirm that the seasonal changes have a direct impact on the PGMEA concentration and plume path. The results presented under variable meteorological conditions show that the average daily concentration of PGMEA is the highest in the month of September. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1100–1107, 2017
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".