Development and simulation studies of an unsteady state biofilter model for the treatment of cyclic air emissions of an α‐pinene gas stream
Bibliographic record
Abstract
Abstract This paper describes the development and simulation of an unsteady state biofilter model used to predict dynamic behaviour of cyclically‐operated biofilters and compares it with experimental results obtained from three, parallel, bench‐scale biofilters treating both periodically fluctuating concentrations and constant concentrations of an α‐pinene‐laden gas stream. The dynamic model, using kinetic parameters estimated from the constant concentration biofilter, was able to predict the performance of cyclic biofilters operating at short cycle periods (ie, in the order of minutes and hours). Steady state kinetic data from a constant concentration biofilter can be used to predict unsteady state biofilter operation. At a 24 h cycle period, the dynamic model compared well with experimental results. For long cycle periods (ie, hours and days), removal efficiency decreased after periods of non‐loading: the longer the period of non‐loading, the poorer the biofilter's performance at the re‐commencement of pollutant loading. At longer time scales the model did not effectively predict transient behaviour, as adsorption and changes in kinetic parameters were not accounted for. Modelling results showed that similar biofiltration performance for the cyclic and constant concentration biofiltration of α‐pinene is expected for biofilters operating solely in the first order kinetics regime. Poorer performance for cyclic biofilters following Monod kinetics spanning the entire kinetics range is expected as the cycle amplitude increases. The most important parameters affecting the performance of a cyclically‐operated biofilter with short cycle periods are: amplitude of cyclic fluctuations, Cg, max/Cg, relative value of the half‐saturation constant in the Monod expression, Ks, and effective diffusivity of α‐pinene in the biofilm, De. Copyright © 2005 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 | 0.001 |
| 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 | 0.001 | 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".