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Record W2167930136 · doi:10.1002/er.1317

Bio-ethanol production to be blended with gasoline: Improvements in energy use by adsorption

2007· article· en· W2167930136 on OpenAlexaff
Richard A. Jones, F. Handan Tezel, Jules Thibault, Jeffrey S. Tolan

Bibliographic record

VenueInternational Journal of Energy Research · 2007
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsIogen CorporationUniversity of Ottawa
Fundersnot available
KeywordsAdsorptionActivated carbonChemistryEthanolGasolineEthanol fuelChemical engineeringPelletsParticle sizeCarbon fibersPowdered activated carbon treatmentKineticsChromatographyOrganic chemistryMaterials scienceComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.312
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2007
Admission routes1
Has abstractyes

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