MétaCan
Menu
Back to cohort
Record W2967666161 · doi:10.51584/ijrias.2024.907025

Pressure-Driven Functional Polymeric Membrane Technology as Athermal Separation Unit Operation in Chemical Engineering : A Review

2024· review· en· W2967666161 on OpenAlexaboutno aff

Bibliographic record

VenueInternational Journal of Research and Innovation in Applied Science · 2024
Typereview
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsnot available
Fundersnot available
KeywordsUnit (ring theory)Separation (statistics)MembraneMembrane technologyMaterials scienceProcess engineeringChemical engineeringNanotechnologyComputer scienceEngineeringChemistryMathematicsMathematics education

Abstract

fetched live from OpenAlex

This manuscript provides an overview of published scientific and trade literatures and a collection of useful reference information on all aspects of membrane science and technology, selected result of experiment applications, and more recent developments in pressure-driven flat sheet membrane (PDFSM) processes as new frontier and athermal separation unit operation in chemical engineering covering general description of the basic principles of membrane separation processes, benefits and drawbacks, and future trends of membrane developments. Meanwhile, experiment results of selected applications of membranes are removing and/or reducing bacteria, and recovering high protein and low fat from skim milk by microfiltration (MF) membrane (Fluoro polymer, 0.45 µm, Alfa Laval) as an alternative to replace heat sterilization, separating and/or concentrating protease enzyme by ultrafiltration (UF) membrane (Polysulphone, 20000 MWCO, DSS), separating target and valuable components in corn steeping water by using nanofiltration (NF) membrane (Thin Film Composite on Polyester, DSS), and separating and/or purifiing ions component in brackish water and sea water by reverse osmosis (RO) membrane (Thin film composite on Polypropylene, DSS).

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.965
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.007
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.435
Teacher spread0.363 · 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 teacher head, 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Research and Innovation in Applied ScienceSame topicExtraction and Separation ProcessesFrench-language works237,207