Optimization of the loading patterns of silica gels for o‐xylene recovery by vacuum swing adsorption
Bibliographic record
Abstract
Abstract The key step for the adsorption‐desorption engineering process is the reversible in‐situ desorption of the adsorbent without VOCs (volatile organic compounds) accumulation. Our previous results show that the mesoporous silica gel performs well in adsorbing and desorbing the VOCs due to their capillary condensation in the mesopores. However, the microporous silica performs better in adsorbing though poorer in desorbing VOCs because of the direct interaction between VOCs molecules and the adsorbent wall in the micropores. Herein, the adsorption and desorption of o‐ xylene on two silica gels (SG) with different pore size distribution by vacuum swing adsorption (VSA) has been systematically studied. The equilibrium adsorption and desorption experiments of two kinds of silica gels at different loading heights in fixed bed show that the concentration front of o‐ xylene in microporous SG disperses slowly. However, the concentration front of o‐ xylene in mesoporous SG is significantly dispersed. To improve the overall efficiency of adsorption‐desorption, the mesoporous and microporous SGs are loaded together in the fixed bed. It was found that the increase in the microporous SG loading percentage facilitated the adsorption capacity but deteriorated its desorption efficiency, while the opposite occurs to the mesoporous SG. After optimization, it was observed that minimal energy consumption (1.295 kWh/m 3 VOCs gas) was used when the adsorption column is loaded with 60% SG B at the inlet and 40% of the SG A at the outlet end for o‐ xylene vapour adsorption. This finding will be useful for designing adsorbent loading in the adsorption column.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".