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Record W4388723690 · doi:10.1080/09593330.2023.2284688

Concept and analysis of hybrid reversal multi-stage flash and membrane distillation desalination system

2023· article· en· W4388723690 on OpenAlexaff
Emad Ali, Jamel Orfi, Hany Al‐Ansary, Saleh S. Baakeem, Ahmad S. Alsaadi, Noreddine Ghaffour

Bibliographic record

VenueEnvironmental Technology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMembrane Separation Technologies
Canadian institutionsUniversity of Calgary
FundersKing Saud University
KeywordsDesalinationBrineDistillationSeawaterLow-temperature thermal desalinationChemistryProcess engineeringEnvironmental scienceMembraneChromatographyEngineeringBiology

Abstract

fetched live from OpenAlex

The concept and analysis of integrating membrane distillations (MD) with reversal once-through Multistage Flash (RV-MSF) desalination is presented. The analysis is based on numerical simulation. The MD vessels are integrated into the terminal ends of the RV-MSF system to leverage the thermal energy associated with these terminal streams. Hybridisation at the last MSF stage, i.e. by replacing the brine cooler, contributes marginally to the overall production rate which amounts to 2%. However, it is found that hybridisation at stage one, i.e. utilising the energy of the MSF reject brine can increase the overall production rate by 65%. For seawater feed temperature of 80 oC and 24 MSF stages, 5 MD vessels in series can be integrated with the RV-MSF process. This ultimate hybridisation helped improve the recovery ratio from 7 to 23%, decreasing the specific cooling water requirement from 23 to 12 kg/kg and reducing the specific energy consumption from 129 to 41 kWh/m3 with respect to the stand-alone RV-MSF system. However, this achievement incurs an additional specific area for heat transfer which increased from 29 to 65 m2/(kg/s). This is because a large number of MD modules are incorporated into the hybridisation.

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 categoriesnone
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 score0.176
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2023
Admission routes1
Has abstractyes

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