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Record W2889253485 · doi:10.3390/membranes8030072

Submerged Osmotic Processes: Design and Operation to Mitigate Mass Transfer Limitations

2018· article· en· W2889253485 on OpenAlexfundno aff
Gaëtan Blandin, Ignasi Rodríguez‐Roda, Joaquím Comas

Bibliographic record

VenueMembranes · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsnot available
FundersFP7 People: Marie-Curie ActionsAgència per a la Competitivitat de l’EmpresaGeneralitat de CatalunyaEuropean CommissionCanadian Institute for Advanced Research
KeywordsForward osmosisMass transferConcentration polarizationProcess engineeringCoalescence (physics)BioreactorMechanicsViscosityMechanical engineeringEngineeringEnvironmental engineeringEnvironmental scienceMaterials scienceChemistryReverse osmosisMembranePhysics

Abstract

fetched live from OpenAlex

Submerged forward osmosis (FO) is of high interest for bioreactors, such as osmotic membrane bioreactor, microalgae photobioreactor, food or bioproduct concentration where pumping through pressurized modules is a limitation due to viscosity or breakage of fragile components. However, so far, most FO efforts have been put towards cross flow configurations. This study provides, for the first time, insights on mass transfer limitations in the operation of submerged osmotic systems and offer recommendations for optimized design and operation. It is demonstrated that operation of the submerged plate and frame FO module requires draw circulation in the vacuum mode (vacuum assisted osmosis) that is in favor of the permeation flux. However, high pressure drops and dead zones occurring in classical U-shape FO draw channel strongly disadvantage this design; straight channel design proves to be more effective. External concentration polarization (ECP) is also a crucial element in the submerged FO process since mixing of the feed solution is not as optimized as in the cross flow module unless applying intense stirring. Among the mitigation techniques tested, air scouring proves to be more efficient than feed solution circulation. However, ECP mitigation methodology has to be adapted to application specificities with regards to combined/synergetic effects with fouling mitigation.

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 score0.101
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.040
GPT teacher head0.246
Teacher spread0.206 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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