MétaCan
Menu
Back to cohort

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.002

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; both teacher heads agree on what is shown here.

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueKirk-Othmer Encyclopedia of Chemical TechnologySame topicMinerals Flotation and Separation TechniquesFrench-language works237,207