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Record W2588012120 · doi:10.1109/ecce.2016.7855379

Real-time emulation of a pressure retarded osmosis power generation system

2016· article· en· W2588012120 on OpenAlexafffund
R. Sudharshan Kaarthik, Jonathan Maisonneuve, Pragasen Pillay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsConcordia University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsPressure-retarded osmosisOsmotic powerElectric power systemElectricity generationEmulationPower stationEngineeringTurbineControl engineeringComputer scienceForward osmosisPower (physics)Electrical engineeringMechanical engineeringChemistry

Abstract

fetched live from OpenAlex

Power production by conversion of salt gradient energy (osmotic power production) has potential for global commercialization. Research on the net-power output and plant configuration provide viable methods for efficient plant operation. In this paper, a novel equivalent electric-circuit model of the pressure retarded osmosis process (PRO) is described and, is used to develop a power hardware-in-the-loop (PHIL) emulator to represent the osmotic power plant which includes impulse turbine and synchronous generator for supplying power to off-grid or isolated loads. The response of the hydraulic, mechanical and electrical components of the system is observed given changes in the source flow-rate and electrical loading. The proposed PHIL emulator provides insight into the operational dynamics and behavior of the PRO system. The proposed real-time emulator is a powerful tool which can advance research and development of pressure retarded osmotic power generation system. Simulation and experimental results are presented in the paper to validate the operation of the proposed PHIL PRO emulator.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2016
Admission routes2
Has abstractyes

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