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Record W3184849730 · doi:10.3390/membranes11080556

Feasibility of Pressure-Retarded Osmosis for Electricity Generation at Low Temperatures

2021· article· en· W3184849730 on OpenAlexafffund
Elham Abbasi-Garravand, Catherine N. Mulligan

Bibliographic record

VenueMembranes · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsConcordia University
FundersMitacsConcordia University
KeywordsOsmotic powerPressure-retarded osmosisFoulingMembraneForward osmosisSalinityElectricity generationPower densityOsmosisMembrane foulingEnvironmental scienceEnvironmental engineeringChemical engineeringOsmotic pressureMaterials scienceReverse osmosisPressure gradientElectricityChemistryPower (physics)EngineeringMechanicsThermodynamicsElectrical engineeringGeology

Abstract

fetched live from OpenAlex

A membrane-based technique for production of pressure-retarded osmosis (PRO) is salinity gradient energy. This sustainable energy is formed by combining salt and fresh waters. The membrane of the PRO process has a significant effect on controlling the salinity gradient energy or osmotic energy generation. Membrane fouling and operating conditions such as temperature have an extreme influence on the efficiency of the PRO processes because of their roles in salt and water transportation through the PRO membranes. In this study, the temperature impact on the power density and the fouling of two industrial semi-permeable membranes in the PRO system was investigated using river and synthetic sea water. Based on the findings, the power densities were 17.1 and 14.2 W/m2 at 5 °C for flat sheet and hollow fiber membranes, respectively. This is the first time that research indicates that power density at low temperature is feasible for generating electricity using PRO processes. These results can be promising for regions with high PRO potential that experience low temperatures most of the year.

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 score0.004
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)0.0010.000

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 head0.028
GPT teacher head0.265
Teacher spread0.238 · 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

Citations13
Published2021
Admission routes2
Has abstractyes

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