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Record W4401280150 · doi:10.1002/cjce.25427

Scaling in reverse osmosis seawater desalination: Mechanism and prevention—A literature review

2024· article· en· W4401280150 on OpenAlexvenueno aff
Jiaxuan Shen, Xiaodong Wang, Xiaoyi Zhu, Bojin Tang, Cong Liu, Wan Li, Xueqiang Gao

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
FundersNational Key Research and Development Program of China
KeywordsFoulingNanofiltrationDesalinationReverse osmosisBiofoulingMembrane foulingEnvironmental scienceEnvironmental engineeringSeawaterMembrane technologyMembraneWaste managementPulp and paper industryChemistryEngineeringGeology

Abstract

fetched live from OpenAlex

Abstract There is currently a huge imbalance between the demand and supply of freshwater resources. The shortage of fresh water can be mitigated by seawater desalination. Reverse osmosis (RO) is currently the most popular desalination technology around the world. Despite its various advantages, fouling has been one of its major limitations of RO. Membrane fouling can be divided into four categories: colloidal fouling, inorganic fouling, organic fouling, and biofouling. Precipitation of inorganic salts of small solubility, among which CaCO 3 , CaSO 4 , BaSO 4 , and SiO 2 are the most common ones, are the cause of inorganic fouling, which is commonly referred to as scaling. Pretreatment technologies for prevention or mitigation of scaling in the RO process can be classified as conventional pretreatment technologies, which include water softening and scale inhibitors, and membrane‐based pretreatment technologies which include nanofiltration, forward osmosis, and membrane surface modification.

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 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 score0.088
Threshold uncertainty score0.182

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 integrity0.0000.000
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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations9
Published2024
Admission routes1
Has abstractyes

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