Selective Water Removal by Sorption from Butanol–Water Vapor Mixtures: Analyses of Key Operating Parameters and Site Energy Distribution
Bibliographic record
Abstract
In the present paper, selective water removal from butanol–water vapor mixture was carried out in a pressure swing adsorption (PSA) system using canola meal (CM) biosorbent. Five operating parameters (temperature, pressure, feed butanol concentration, feed flow rate, and CM particle size) were studied by the orthogonal array design method and range analysis to obtain the favorable process conditions for butanol drying. The performance of butanol dehydration was evaluated using five indices: water uptake, butanol uptake, water selectivity, butanol recovery, and maximum butanol concentration in the effluent. The obtained favorable dehydration conditions resulted in the maximum effluent butanol concentration of >99 v/v %, water uptake of 0.48 g/g-ads, water separation factor of 5.4, and butanol recovery of 90%. The Dubinin–Polanyi model for large pores fit the water adsorption isotherms reasonably well. Furthermore, site energy distribution of water adsorption was also estimated. Average site energy (3.33 kJ/mol) and standard deviation of the site energy distribution (2.36 kJ/mol) were determined and applied to analyze the interaction between the biosorbent and adsorbate, and adsorbent surface energy heterogeneity. Saturated CM was regenerated at 110 °C under vacuum and reused for more than 16 cycles.
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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) | 0.000 | 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 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".