Multi-object optimization of Navy-blue anodic oxidation via response surface models assisted with statistical and machine learning techniques.

Khan, Hammad; Wahab, Fazal; Hussain, Sajjad; Khan, Sabir; Rashid, Muhammad

Khan, Hammad; Wahab, Fazal; Hussain, Sajjad; Khan, Sabir; Rashid, Muhammad.

Afiliação

Khan H; Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, KP, Pakistan. Electronic address: hammad@giki.edu.pk.
Wahab F; Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, KP, Pakistan.
Hussain S; Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, KP, Pakistan.
Khan S; São Paulo State University (UNESP), Institute of Chemistry, Araraquara. 55 Prof. Francisco Degni St, Araraquara, SP, 14800-060, Brazil.
Rashid M; Faculty of Fisheries and Wildlife, University of Veterinary and Animal Sciences, Lahore, Pakistan.

Chemosphere ; 291(Pt 2): 132818, 2022 Mar.

Article em En | MEDLINE | ID: mdl-34780736

Assuntos

Poluentes Químicos da Água; Eletrodos; Eletrólise; Aprendizado de Máquina; Oxirredução; Poluentes Químicos da Água/análise

Palavras-chave

ANN; Electrochemical degradation; MLR; Navy blue; Nb/BDD; SVR

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Poluentes Químicos da Água Tipo de estudo: Prognostic_studies Idioma: En Revista: Chemosphere Ano de publicação: 2022 Tipo de documento: Article

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