Modeling Competitive Adsorption of Mixtures of Volatile Organic Compounds in a Fixed-Bed of Beaded Activated Carbon
Bibliographic record
Abstract
A two-dimensional mathematical model was developed to study competitive adsorption of n-component mixtures in a fixed-bed adsorber. The model consists of an isotherm equation to predict adsorption equilibria of n-component volatile organic compounds (VOCs) mixture from single component isotherm data, and a dynamic adsorption model, the macroscopic mass, energy and momentum conservation equations, to simulate the competitive adsorption of the n-components onto a fixed-bed of adsorbent. The model was validated with experimentally measured data of competitive adsorption of binary and eight-component VOCs mixtures onto beaded activated carbon (BAC). The mean relative absolute error (MRAE) was used to compare the modeled and measured breakthrough profiles as well as the amounts of adsorbates adsorbed. For the binary and eight-component mixtures, the MRAE of the breakthrough profiles was 13 and 12%, respectively, whereas, the MRAE of the adsorbed amounts was 1 and 2%, respectively. These data show that the model provides accurate prediction of competitive adsorption of multicomponent VOCs mixtures and the competitive adsorption isotherm equation is able to accurately predict equilibrium adsorption of VOCs mixtures.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".