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Record W1993497069 · doi:10.1002/ceat.201200048

Semicontinuous Thermal Separation Systems

2012· article· en· W1993497069 on OpenAlexaff
Thomas A. Adams, Alicia Pascall

Bibliographic record

VenueChemical Engineering & Technology · 2012
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsMcMaster University
Fundersnot available
KeywordsExtractive distillationDistillationProcess engineeringSeparation (statistics)Air separationBatch distillationTernary operationProcess (computing)Separation processComputer scienceFractional distillationEngineeringChemistryChromatographyChemical engineering

Abstract

fetched live from OpenAlex

Abstract Since the year 2000, semicontinuous separation processes have been studied as a unique process intensification technique for the thermal separation of chemical mixtures. Typically, a single separation unit is used for multiple purposes with the aid of an auxiliary storage tank called a middle vessel. The system is operated cyclically but without startup or shutdown phases. As a result, multiple separation steps can be carried out with fewer capital expenses. In several cases, a significant profitability advantage over continuous or batch process alternatives has been shown, particularly for intermediate production capacities. The progress in semicontinuous systems is reviewed, including the development of semicontinuous ternary distillation, liquid‐liquid extraction, azeotrope distillation, reactive distillation, and semicontinuous systems with integrated reaction and distillation or extraction. Control and operational strategies for semicontinuous systems are reviewed, as well as heuristics for modeling and simulation.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.004
GPT teacher head0.196
Teacher spread0.192 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

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