Dynamic Study of Butanol and Water Adsorption onto Oat Hull: Experimental and Simulated Breakthrough Curves
Bibliographic record
Abstract
Lignocellulosic material oat hull previously demonstrated its capability for concentrating butanol from butanol–water vapor mixtures by adsorption process. To better understand the fundamentals of water and butanol adsorption, this work further investigated the adsorption dynamics of pure butanol and water. The research was first done experimentally using the oat hull based biosorbent in a packed column system, and then the Klinkenberg model was employed to simulate the adsorption breakthrough curves obtained in water or butanol single component system. The Klinkenberg model simulated the experimental data satisfactorily. Moreover, the internal and external mass transfer resistances were estimated from the modeling results for pure water and butanol adsorption. The results demonstrated that internal mass transfer controls the adsorption kinetics. Compared to butanol, water has more favorable adsorption performance on the oat hull biosorbent. Effective separation of water from butanol using the oat hull based biosorbent is based on kinetic control. Furthermore, this study reveals that the oat hull material was stable and had been used for over 20 adsorption–desorption cycles without deteriorated quality for water adsorption.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".