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Conceptual analysis of single-feed heterogeneous distillation columns

2015· article· en· W1919733160 on OpenAlexaff
Carlos Arturo Martinez Riascos, Libardo Estupiñan Pérez

Bibliographic record

VenueIngeniería e Investigación · 2015
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDistillationBoiling pointProcess engineeringInitializationBoilingFractionating columnExtractive distillationProcess (computing)Vapor–liquid equilibriumReactive distillationChemistryHomogeneousWork (physics)Separation processThermodynamicsChromatographyComputer scienceEngineeringOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Separation in heterogeneous distillation columns is attained by interaction of two liquid and one vapor phases, interaction of three phases involves complexities due to the determination of vapor-liquid-liquid equilibrium and hence, in the design of separation units. Nevertheless, the liquid-liquid equilibrium allows developing separations that may be unfeasible by vapor-liquid equilibrium. In this way, heterogeneous azeotropic distillation is a useful operation for the separation of azeotropic and close-boiling mixtures. In this work, a new methodology for evaluating the feasibility of this process is developed. This methodology is an extension of that proposed by Castillo et al. (1998) for homogeneous systems. Operation leaves for heterogeneous systems are calculated using the concept of pinch point curves in order to establish the process feasibility. Heterogeneous columns with external decanter are considered as the only heterogeneous stage (OHED: only heterogeneous external decanter). The initialization process for the column calculation requires the selection of the distillate composition using thermodynamic criteria in order to guarantee homogeneous phases within the column. A system with industrial and academic relevance was considered as case study: water-acetic acid-amyl acetate. Results show that the developed shortcut method allows evaluating process feasibility and estimating design parameters, without the use of trial and error procedures implemented, with the aid of simulation tools.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.044
GPT teacher head0.235
Teacher spread0.191 · 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 designSimulation or modeling
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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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