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Record W7117650539 · doi:10.3390/membranes16010014

Dewatering Hypersaline Na2SO4 and NaCl via Commercial Forward Osmosis Module

2025· article· en· W7117650539 on OpenAlexafffund
Noel Devaere, Vladimiros G. Papangelakis

Bibliographic record

VenueMembranes · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsForward osmosisDewateringOsmosisReverse osmosisMembraneWater treatmentFiltration (mathematics)

Abstract

fetched live from OpenAlex

Efficient water recycling in the hydrometallurgical industry requires the dewatering of hypersaline Na2SO4 or similar brines via non-evaporative methods. Unfortunately, many non-evaporative methods require the use of specific solutes and are not compatible with complex hydrometallurgical effluents. Forward Osmosis (FO) uses a draw solution to link known non-evaporative water recycling methods with feed solutions that are otherwise incompatible. There is minimal experimental data on the dewatering performance of today’s available commercial FO membranes, especially with hypersaline concentrations (>70,000 mg/L total dissolved solids). This study tests the commercially available Aquaporin HFFO2 hollow fibre FO membrane module with hypersaline Na2SO4 or NaCl feed solutions versus a MgCl2 draw solution. It identifies a key requirement to maintain water flux above a certain threshold to prevent a decrease in Na Rejection or an increase in Mg reverse flux. It also defines a minimum osmotic differential that can be used to parameterize water flux, similar to the temperature of approach in heat exchangers, but to determine the extent of water removal in FO. We demonstrate that even under mildly acidic conditions, existing FO membranes can concentrate Na2SO4 to saturation, paving the way for their use in the hydrometallurgical industry.

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 score0.140
Threshold uncertainty score0.592

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)0.0010.000

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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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