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Record W2961623335 · doi:10.1021/acs.iecr.9b02639

Finding Better Limit Cycles of Semicontinuous Distillation. 1. Back Stepping Design Methodology

2019· article· en· W2961623335 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2019
Typearticle
Languageen
FieldEngineering
TopicProcess Optimization and Integration
Canadian institutionsMcMaster University
Fundersnot available
KeywordsZeotropic mixtureDistillationLimit (mathematics)Process engineeringTernary operationProcess (computing)Computer scienceFractionating columnLimit cycleMathematicsThermodynamicsChemistryEngineeringChromatographyHeat exchangerPhysics

Abstract

fetched live from OpenAlex

Semicontinuous ternary zeotropic distillation is a periodic process that is carried out in a single distillation column and a tightly integrated external middle vessel. In the state-of-the-art design procedure of this process, a continuous distillation process that separates the top and bottom products to the desired purity is used to generate an arbitrary initial state for simulating the dynamics of the semicontinuous distillation process. Although this method is useful in estimating the limit cycle, it was later found that the operation of the process in this limit cycle was economically suboptimal. In this study, a new algorithmic design procedure, called the back-stepping design methodology, is proposed to find better limit cycles for zeotropic ternary semicontinuous distillation using the aspenONE Engineering suite. The proposed methodology was applied to two different case studies using feed mixtures with different chemical components. A comparison with the current design procedure for the two case studies indicates that the new method outperforms the state-of-the-art by finding limit cycles that were 4–16% lower in separating cost, which was the chosen measure of cycle performance.

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.

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)0.001
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 score0.115
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.223
GPT teacher head0.344
Teacher spread0.121 · 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