Soft sensors design in a petrochemical process using an evolutionary algorithm

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorMorais, Gustavo A. P. de-
Autor(es): dc.creatorBarbosa, Bruno H. G.-
Autor(es): dc.creatorFerreira, Danton D.-
Autor(es): dc.creatorPaiva, Leonardo S.-
Data de aceite: dc.date.accessioned2026-02-09T12:37:14Z-
Data de disponibilização: dc.date.available2026-02-09T12:37:14Z-
Data de envio: dc.date.issued2020-03-30-
Data de envio: dc.date.issued2020-03-30-
Data de envio: dc.date.issued2019-11-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/39547-
Fonte completa do material: dc.identifierhttps://www.sciencedirect.com/science/article/pii/S0263224119307778-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1164959-
Descrição: dc.descriptionThe downhole pressure is an important variable used to optimize the oil production in deep-water oil wells. However, due to its localization at the seabed, its sensor breaks down easily. Thus, a parameter-less Evolutionary Algorithm, called Evolutionary Algorithm with Numerical Differentiation (EAND), is proposed in this work for designing soft sensors to predict the downhole pressure. Results show that the EAND performs good balance between local and global searches, providing the best results in 17 out of the 20 optimization problems, and achieving the fastest convergence in 16 simulated problems. The proposed algorithm yielded the best soft sensors under the five offshore oil wells studied when compared to other identification methods. Three kinds of nonlinear models for prediction were implemented, and ensembles composed of decision trees (Random Forest) obtained the best results. The Mean Absolute Percentage Errors (MAPE) found when predicting the downhole pressure by the identified soft sensors ranged from 0.1453% to 0.788%, which are very satisfactory.-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceMeasurement-
Palavras-chave: dc.subjectEvolutionary algorithm-
Palavras-chave: dc.subjectSoft sensors-
Palavras-chave: dc.subjectSystems identification-
Palavras-chave: dc.subjectOffshore oil extraction-
Palavras-chave: dc.subjectDownhole pressure-
Título: dc.titleSoft sensors design in a petrochemical process using an evolutionary algorithm-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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