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Record W4394876234 · doi:10.1016/j.seppur.2024.127562

Mathematical and statistical modeling of glucose permeation through ultrafiltration system

2024· article· en· W4394876234 on OpenAlexaff
Saleha Al-Mardeai, Emad Elnajjar, Raed Hashaikeh, Boguslaw Kruczek, Bart Van der Bruggen, Sulaiman Al‐Zuhair

Bibliographic record

VenueSeparation and Purification Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Ottawa
FundersUnited Arab Emirates University
KeywordsUltrafiltration (renal)PermeationChemistryChromatographyChemical engineeringProcess engineeringMembraneEngineeringBiochemistry

Abstract

fetched live from OpenAlex

Membrane bioreactors (MBRs) have been recently proposed for enhancing enzymatic hydrolysis of lignocelluloses by simultaneously and selectively removing the produced sugars from the reaction system. An inverted dead-end MBR with polyethersulfone membrane was used to investigate glucose permeation. The effects of glucose concentration, water flowrate, and membrane molecular weight cut-offs (MWCO), were investigated. The developed diffusion-convective model predicted glucose permeation with R 2 value of 0.96. The statistical analysis showed that the effects of glucose concentration and water flowrate were significant, with P-value less than 0.05 for both, whereas that of the MWCO was insignificant (P-value of 0.66). This is the first attempt to mathematically describe the behavior of glucose across ultrafiltration membrane, while taking into consideration both molecular diffusion and convective flow. The findings of this work are essential for understanding the behavior and enhancing the performance of solute permeation through ultrafiltration membrane, which is the heart of many processes.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score0.358

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)0.0000.000

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 head0.009
GPT teacher head0.247
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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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