Fuzzy machine learning predictions of settling velocity based on fractal aggregate physical features in water treatment

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Birmingham (UoB)-
Autor(es): dc.creatorBressane, Adriano-
Autor(es): dc.creatorMelo, Carrie Peres-
Autor(es): dc.creatorSharifi, Soroosh-
Autor(es): dc.creatorda Silva, Pedro Grava-
Autor(es): dc.creatorToda, Daniel Hiroshi Rufino-
Autor(es): dc.creatorMoruzzi, Rodrigo-
Data de aceite: dc.date.accessioned2025-08-21T20:25:34Z-
Data de disponibilização: dc.date.available2025-08-21T20:25:34Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.jwpe.2024.106138-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/304836-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/304836-
Descrição: dc.descriptionThe dynamics of gravitational sedimentation in water treatment are crucial for optimising particulate matter removal. This study addresses the effect of fractal aggregate features on settling velocity and explores fuzzy machine learning (ML) for predicting this phenomenon. Particle image velocimetry determined aggregate velocities within a sedimentation column, with features identified concurrently. Using a comprehensive methodological framework, significant predictors were selected through various statistical analyses. The fuzzy ML model, developed with the ‘FisPro’ package in R, incorporated a hierarchical partitioning scheme using Lukasiewicz and Sum operators for conjunction and disjunction processes, respectively. The Wang-Mendel method extracted fuzzy rules, with defuzzification achieved using the maximum crisp operator. Hyperparameter optimization was conducted through grid search techniques, and model performance was evaluated using 3-fold cross-validation. The findings reveal that Margination, Radius, and Clumpiness significantly impact settling velocity. The model demonstrates exceptional predictive accuracy (R2 = 0.923) across both training and validation datasets, highlighting its potential for forecasting terminal velocity in water treatment. This research suggests that precise predictions of sedimentation dynamics can improve particulate matter removal efficiency and encourages further investigations into diverse aggregate types and environmental scenarios, advocating for integrating physics-informed ML approaches to enhance the model.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionSão Paulo State University (UNESP) Institute of Science and Technology-
Descrição: dc.descriptionGraduate Program in Civil and Environmental Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionSchool of Civil Engineering University of Birmingham (UoB)-
Descrição: dc.descriptionSão Paulo State University (UNESP) Institute of Science and Technology-
Descrição: dc.descriptionGraduate Program in Civil and Environmental Engineering São Paulo State University (UNESP)-
Descrição: dc.descriptionFAPESP: 2023/08052-1-
Descrição: dc.descriptionCNPq: 309788/2021-8-
Descrição: dc.descriptionCNPq: 441591/2023-0-
Descrição: dc.descriptionCAPES: 88887.310463/2018-00-
Idioma: dc.languageen-
Relação: dc.relationJournal of Water Process Engineering-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectFractal aggregate-
Palavras-chave: dc.subjectFuzzy-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectSettling velocity-
Palavras-chave: dc.subjectWater treatment-
Título: dc.titleFuzzy machine learning predictions of settling velocity based on fractal aggregate physical features in water treatment-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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