Develop and validate a mathematical model to estimate the removal of indoor VOCs by carbon filters
Bibliographic record
Abstract
Adsorption-based air filters, especially carbon-based ones, are usually employed to remove VOCs from indoor air . Conducting tests at ppm level, which is substantially higher than the actual indoor concentration of VOCs, is recommended for reducing the test duration. Consequently, developing a model for estimating the service life of adsorptive filters under actual conditions is imperative. A comprehensive model that considers axially dispersed plug flow for interpellet mass transfer and pore surface diffusion model (PSDM) with variable surface diffusivity for intrapellet mass transfer was used to predict the performance of two filters exposed to three VOCs. First, the results of experiments conducted at concentrations ranging from 9 to 90 ppm in a bench-scale setup were used to compute the Dubinin-Radushkevich (D-R) isotherm parameters, which are concentration-independent. Then, the surface diffusivities at zero loading for various adsorbate-adsorbent systems were determined by fitting the developed model into the results of the experimental data performed at concentrations of 9 ppm or 30 ppm. Finally, the model was validated using experimental data which were conducted at lower concentrations, a higher velocity and on a full-scale setup. The inter-model comparison was carried out by comparing the comprehensive model with four other models to study the importance of various mass transfer steps. Finally, in sensitivity analysis, five dimensionless parameters ( Pe , St, E d p , E d s and D g ) were examined to investigate their impact on the filter's efficiency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".