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Record W1953081547 · doi:10.2166/wst.2016.014

Application of forward osmosis membrane technology for oil sands process-affected water desalination

2016· article· en· W1953081547 on OpenAlexaff
Yaxin Jiang, Jiaming Liang, Yang Liu

Bibliographic record

VenueWater Science & Technology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsCanadian Natural ResourcesUniversity of Alberta
Fundersnot available
KeywordsOil sandsDesalinationTailingsForward osmosisFoulingCellulose triacetateAsphaltReverse osmosisBrineExtraction (chemistry)Waste managementEnvironmental scienceMembraneMembrane technologyEnvironmental engineeringChemistryEngineeringMaterials scienceChromatography

Abstract

fetched live from OpenAlex

The extraction process used to obtain bitumen from the oil sands produces large volumes of oil sands process-affected water (OSPW). As a newly emerging desalination technology, forward osmosis (FO) has shown great promise in saving electrical power requirements, increasing water recovery, and minimizing brine discharge. With the support of this funding, a FO system was constructed using a cellulose triacetate FO membrane to test the feasibility of OSPW desalination and contaminant removal. The FO systems were optimized using different types and concentrations of draw solution. The FO system using 4 M NH4HCO3 as a draw solution achieved 85% water recovery from OSPW, and 80 to 100% contaminant rejection for most metals and ions. A water backwash cleaning method was applied to clean the fouled membrane, and the cleaned membrane achieved 77% water recovery, a performance comparable to that of new FO membranes. This suggests that the membrane fouling was reversible. The FO system developed in this project provides a novel and energy efficient strategy to remediate the tailings waters generated by oil sands bitumen extraction and processing.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.089
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.004
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.248
Teacher spread0.242 · 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.

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

Citations30
Published2016
Admission routes1
Has abstractyes

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