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Record W2319839505 · doi:10.1021/ie4012744

Semicontinuous Separation of Bio-Dimethyl Ether from a Vapor–Liquid Mixture

2013· article· en· W2319839505 on OpenAlexaff
Alicia Pascall, Thomas A. Adams

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2013
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCondenser (optics)Dimethyl etherProcess engineeringDistillationExtractive distillationSeparation (statistics)Materials scienceChemical engineeringChromatographyComputer scienceChemistryOrganic chemistryMethanolEngineeringPhysics

Abstract

fetched live from OpenAlex

In this work, a semicontinuous system is investigated for the separation of biomass-derived dimethyl ether from a vapor–liquid mixture. This novel semicontinuous system operates in a cyclic campaign and utilizes one partial condenser column coupled with a middle vessel to achieve the separation typically carried out using two continuous distillation columns. The performance of six control configurations based on composition or temperature inferential control were analyzed. Dynamic simulation results demonstrate the effectiveness of the semicontinuous system and its temperature inferential control configuration in achieving the separation objectives, remaining within operational limits and rejecting fresh feed disturbances. Finally, the semicontinuous system is compared to the traditional continuous system from an economic standpoint with the sensitivity of operating cost to middle vessel product target purity highlighted.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.302
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2013
Admission routes1
Has abstractyes

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