Evidence map›Paper›PMID 42209571›Full record

ArticleScientific reports2026

Physics-informed machine learning for cross-study prediction of oil-water membrane fouling.

Carine Lourenco Alves, Silvio Edegar Weschenfelder, Maria Alice Prado Cechinel, Mariane Carolina Proner, Regina de Fátima, Peralta Muniz Moreira, Alan Ambrosi, Marco Di Luccio, Sergio Yesid Gómez González

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In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Carine Lourenco AlvesLaboratory of Energy and Environment - LEMA, Department of Chemical and Food Engineering, Federal University of Santa Catarina, Florianópolis, 88040-970, SC, Brazil. carine.lourenco.alves@gmail.com.
Silvio Edegar WeschenfelderPetrobras Research Center - CENPES, Rio de Janeiro, 21941-915, RJ, Brazil.
Maria Alice Prado CechinelLaboratory of Energy and Environment - LEMA, Department of Chemical and Food Engineering, Federal University of Santa Catarina, Florianópolis, 88040-970, SC, Brazil.
Mariane Carolina PronerLaboratory of Energy and Environment - LEMA, Department of Chemical and Food Engineering, Federal University of Santa Catarina, Florianópolis, 88040-970, SC, Brazil.
Regina de FátimaLaboratory of Energy and Environment - LEMA, Department of Chemical and Food Engineering, Federal University of Santa Catarina, Florianópolis, 88040-970, SC, Brazil.
Peralta Muniz MoreiraLaboratory of Energy and Environment - LEMA, Department of Chemical and Food Engineering, Federal University of Santa Catarina, Florianópolis, 88040-970, SC, Brazil.
Alan AmbrosiLaboratory of Membrane Processes - LABSEM, Department of Chemical and Food Engineering, Federal University of Santa Catarina, Florianópolis, 88040-970, SC, Brazil.
Marco Di LuccioLaboratory of Membrane Processes - LABSEM, Department of Chemical and Food Engineering, Federal University of Santa Catarina, Florianópolis, 88040-970, SC, Brazil.
Sergio Yesid Gómez GonzálezLaboratory of Mass Transfer, Department of Chemical and Food Engineering, Numerical Simulation of Chemical Systems - LABSIN-LABMASSA, Federal University of Santa Catarina, Florianópolis, 88040- 970, SC, Brazil.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico 315722/2025-8Petrobras - Project EO&M, Process: 2023/00226-0,
6 · The paper itself

Abstract

Performance decay driven by coupled transport, accumulation, and removal processes remains a central challenge in many chemical engineering systems. Membrane fouling in oil-water separation is examined here as a representative case. While numerical and empirical models can capture specific cases, their predictive performance across independent studies remains limited. In this work, we combine a reduced-order finite-element model of transport and fouling resistance with a data-driven framework that maps experimental metadata to flux-decline behavior across 112 datasets involving various membranes, oils, and operating conditions. The reduced representation reproduces flux-time trajectories with high fidelity (average R

Identifiers

PMID42209571
PMCPMC13447941

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.