Artificial neural network modeling for predicting the carbon black content derived from unserviceable tires for elastomeric composite production

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorCruz, Marco Antônio Galindo-
Autor(es): dc.creatorHiranobe, Carlos Toshiyuki-
Autor(es): dc.creatorCardim, Guilherme Pina-
Autor(es): dc.creatorCabrera, Flávio Camargo-
Autor(es): dc.creatorRibeiro, Gabriel Deltrejo-
Autor(es): dc.creatorTolosa, Gabrieli Roefero-
Autor(es): dc.creatorGarcia, Rogério Eduardo-
Autor(es): dc.creatordos Santos, Renivaldo José-
Data de aceite: dc.date.accessioned2025-08-21T16:41:38Z-
Data de disponibilização: dc.date.available2025-08-21T16:41:38Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-10-05-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1002/app.55951-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/300046-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/300046-
Descrição: dc.descriptionGiven the increasing need for sustainable solutions and the large amount of improperly discarded end-of-life tires, recovered carbon black (rCB) from tire pyrolysis was investigated as a filler for rubber composites. This study considered rCB as an alternative to commercial carbon black due to its sustainability and CO2 emissions reduction. Composites with varying rCB contents (0 to 50 per 100 rubber) were produced and assessed for mechanical properties, such as hardness, abrasion resistance, and rheometric tests. The findings were used to train artificial neural networks (ANNs) with MATLAB software to predict rCB contents. Input parameters included optimal curing time, minimum and maximum torque, and results of mechanical tests like Shore A hardness and abrasion loss. The model was trained on data from 90 samples, with 10 reserved for validation. The predicted outcomes closely matched the experimental data, with a maximum prediction error of less than 3%. This indicates that ANNs are effective tools for intelligently modeling the curing process of natural rubber mixtures, minimizing material waste, optimizing production time, and determining suitable carbon black contents for desired mechanical properties.-
Descrição: dc.descriptionFaculty of Engineering and Sciences Department of Engineering Sao Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionFaculty of Science and Technology Department of Physics Sao Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionFaculty of Science and Technology Department of Mathematics and Computer Science Sao Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionFaculty of Engineering and Sciences Department of Engineering Sao Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionFaculty of Science and Technology Department of Physics Sao Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionFaculty of Science and Technology Department of Mathematics and Computer Science Sao Paulo State University (UNESP), São Paulo-
Idioma: dc.languageen-
Relação: dc.relationJournal of Applied Polymer Science-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectartificial neural networks-
Palavras-chave: dc.subjectelastomeric composites-
Palavras-chave: dc.subjectnatural rubber-
Palavras-chave: dc.subjectrecovered carbon black-
Palavras-chave: dc.subjectvulcanization-
Palavras-chave: dc.subjectwaste tires-
Título: dc.titleArtificial neural network modeling for predicting the carbon black content derived from unserviceable tires for elastomeric composite production-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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