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Record W4403491304 · doi:10.1002/cjce.25526

A comprehensive review of cyclone separator technology

2024· review· en· W4403491304 on OpenAlexvenueno aff
Ece Aylı, Eyup Koçak

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2024
Typereview
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCyclonic separationSeparator (oil production)Cyclone (programming language)Environmental scienceComputer scienceEngineeringMechanical engineeringPhysicsEmbedded system

Abstract

fetched live from OpenAlex

Abstract This review article examines the working principles, optimal dimensions, effects of key parameters, and the results of experimental/numerical studies on cyclone separators. Investigations have been conducted on the effects of parameters such as vortex finder diameter, conical part diameter, cyclone separator diameter, cylinder height, inlet height, inlet width, vortex finder length, and cyclone total length on efficiency, performance, and pressure drop. Furthermore, the article explores current modifications and efforts to improve efficiency. These modifications include adding water nozzles, inserting ribs, employing double‐stage cyclones, incorporating additional inlets, using finned cylinder bodies, adding extra top inlets, introducing liquid jets, employing helical roof inlets, adding laminarizers, incorporating internal spiral vanes, and employing slotted vortex finders. While serving as a guide to optimize the design and performance of cyclone separators, this article emphasizes new and innovative approaches to enhance their industrial applicability. By compiling studies conducted from conceptual birth to the present, the aim of this article is to serve as a guidebook.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2024
Admission routes1
Has abstractyes

Explore more

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