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Record W3030769667 · doi:10.2166/wst.2020.256

Process water recovery via forward osmosis: membrane and integrated process development

2020· article· en· W3030769667 on OpenAlexaff
Jeffrey T. Martin, Γεώργιος Κολλιόπουλος, Vladimiros G. Papangelakis

Bibliographic record

VenueWater Science & Technology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsForward osmosisEnergy consumptionOsmosisProcess engineeringVolumetric flow rateWastewaterReverse osmosisWork (physics)Process (computing)MembraneEnvironmental scienceEnvironmental engineeringChemistryEngineeringComputer scienceThermodynamicsMechanical engineering

Abstract

fetched live from OpenAlex

Abstract This work reports on efforts to develop an integrated continuous forward osmosis system for the recovery of water from wastewater streams, highlighting critical process parameters to minimize energy consumption. Forward osmosis experiments were performed using NaCl draw solutions of various concentrations and the intrinsic membrane parameters (water permeability, draw solution permeability, and structural parameter) were then determined via nonlinear regression using MATLAB. The experimental data was then used to validate a theoretical water flux model, which was subsequently applied to simulate the forward osmosis performance under different hydrodynamic conditions using both NaCl and TMA-CO2-H2O (TMA: trimethylamine) draw solutions. Analysis of the energy efficiency of the TMA-CO2 draw solution regeneration stage revealed that the draw solution flow rate has a significant impact on energy consumption. Also, increasing the feed flow rate was found to slightly enhance the water flux up to 2.5%, while having a negligible impact on the downstream regeneration process energy consumption.

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 categoriesInsufficient payload (model declined to judge)
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 score0.024
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
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.229
Teacher spread0.219 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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