Bio-ethanol production to be blended with gasoline: Improvements in energy use by adsorption
Bibliographic record
Abstract
Batch adsorption experiments were conducted to examine the liquid-phase adsorption of ethanol from ethanol–water solutions. Experiments performed established the kinetic and equilibrium behaviour of the various adsorbents in solution. The experiments with the ZSM-5 adsorbents indicate that the silica to alumina ratio had little effect on the ethanol–water separation at low ethanol concentrations. In general, ZSM-5 adsorbents were outperformed by the activated carbon adsorbents, which showed higher adsorption capacities. The capacity of activated carbon adsorbents correlated strongly with cumulative pore volume and Brunauer, Emmet and Teller (BET) surface area. Particle size was found to be the most influential factor in the ethanol uptake rate. The large pellets showed sluggish kinetics when compared to their powdered counterparts. When considering kinetic performance and adsorption capacity XTRUSORB A754 and M-30 activated carbon show the most potential for the selective adsorption of ethanol. The adsorbent screening performed herein applies to the energy efficient production of bio-ethanol via adsorption. Copyright © 2007 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".