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Dynamic Modelling and Analysis of Solar Powered Reverse Osmosis Desalination System for Pakistan using the Bond Graph Model

2022· article· en· W4283207848 on OpenAlexaff
Sheikh Usman Uddin, Geoff Rideout

Bibliographic record

Venue2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) · 2022
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBond graphReverse osmosisComputer scienceDesalinationPopulationSoftwareGraphMultidisciplinary approachProcess engineeringEngineeringTheoretical computer scienceMembraneProgramming languageMathematicsChemistry

Abstract

fetched live from OpenAlex

With the advancement of technology and increase in world’s population, the demand for the consumption of fresh drinking water has increased worldwide. Utilization of clean energy sources for purification of water is the need of today’s world. This paper focuses on modelling the solar powered reverse osmosis system using the bond graph modelling technique which is very effective for multidisciplinary systems. The model contains electrical, mechanical and hydraulic domains merged together using the bond graph language. It further utilizes the 20-Sim software to implement the dynamic model and simulate the system. The paper eventually provides a detailed analysis on how the system will respond to the changing system parameters.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.702
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.020
GPT teacher head0.266
Teacher spread0.246 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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