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Centrifugal Separation

2019· other· en· W4252644795 on OpenAlexaff
Reza Sabbagh, David S. Nobes

Bibliographic record

VenueKirk-Othmer Encyclopedia of Chemical Technology · 2019
Typeother
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCentrifugeCompactionFiltration (mathematics)BottleSedimentationAbrasion (mechanical)Rotational speedCentrifugal forceRotation (mathematics)Mixing (physics)Mechanical engineeringProcess engineeringMaterials scienceEngineeringComputer scienceGeotechnical engineeringGeologyMathematicsPhysicsSediment

Abstract

fetched live from OpenAlex

Abstract Separation by density difference, compaction, and drainage is presented, along with formulas developed for bottle centrifuges, solid wall centrifuges, disk centrifuges, and hydrocyclones. The Σ‐concept development and several practical variants are discussed. Liquid–liquid separation interface location formulas are reviewed. An updated performance chart for selection and design is provided. Models for centrifuge energy consumption based on Σ‐concept are discussed. Centrifuge components, including a breakdown of power and energy losses with applicable information for centrifuges, are represented. Centrifuge materials of construction and their corrosion, erosion, and strength characteristics are reviewed. Stresses generated owing to rotation and process materials, abrasion resistance, and noise generated by centrifuges are discussed. The various types of sedimentation and filtration equipment are reviewed. Examples of use, rates, and performance of bottle; disk; decanter; preparation; zonal; tubular; perforated basket; inverting filter; conical; and continuous single‐ and multistage pusher centrifuges are reviewed. For a brief discussion of gas centrifuges and their use in uranium enrichment, please see the previous editions.

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: Other · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0380.043

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 head0.005
GPT teacher head0.254
Teacher spread0.250 · 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
GenreOther

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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Citations1
Published2019
Admission routes1
Has abstractyes

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