Carbon adsorption on waste biomass of passion fruit peel: A promising machine learning model for CO2 capture

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorRua da Escola Industrial e Comercial de Nun 'Alvares-
Autor(es): dc.contributornº 644-
Autor(es): dc.contributorUniversity of Porto-
Autor(es): dc.creatorPeres, Christiano Bruneli-
Autor(es): dc.creatorMorais, Leandro Cardoso de-
Autor(es): dc.creatorResende, Pedro Miguel Rebelo-
Data de aceite: dc.date.accessioned2025-08-21T15:25:39Z-
Data de disponibilização: dc.date.available2025-08-21T15:25:39Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.jcou.2024.102680-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/304604-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/304604-
Descrição: dc.descriptionThe alarming increase in the concentration of carbon dioxide (CO2) in the atmosphere, mainly due to human emissions, represents a significant threat to life. In this context, carbon capture and storage (CCS) technologies have emerged as promising solutions, such as adsorption on carbonaceous materials, standing out as a prominent approach. This study aims to quantify the maximum CO2 capture in the laboratory scale using functionalized activated carbon by passion fruit peel biomass (FACPFP) and to develop a simple and improved machine learning model to predict the capture of this greenhouse gas. FACPFP was successfully prepared through chemical activation with K2C2O4 and doping with ethylenediamine (EDA) at 700 °C and 1 h. The samples were thoroughly characterized by thermogravimetric analysis (TGA), scanning electron microscopy (SEM) with energy dispersive X-ray detector (EDX), Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). CO2 sorption was assessed using functional density theory (DFT). For predictive model, multiple linear regression with cross-validation was used. Under CO2 atmosphere conditions, the textural parameters allowed to see the probable presence of ultra-micropores, the BET surface area, the total pore and micropore volume were 105 m²/g, 0.03 cm³ /g and 0.06 cm³ /g, respectively. The maximum CO2 adsorption capacity in the FACPFP reached about 2.2 mmol/g at 0 °C and 1 bar. The predictive model demonstrated an improvement of CO2 adsorption precision, raising it from 53% to 61% with cross-validation. This study also aims to stimulate future investigations in the area of CO2 capture, due to the extreme relevance of this topic.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionInstitute of Science and Technology São Paulo State University (UNESP) “Júlio de Mesquita Filho”, Sorocaba Campus, Av. Três de Março, 511, Alto da Boa Vista, São Paulo-
Descrição: dc.descriptionPrometheus Polytechnic Institute of Viana do Castelo Rua da Escola Industrial e Comercial de Nun 'Alvares-
Descrição: dc.descriptionEscola Superior de Tecnologia e Gestão Instituto Politécnico de Viana do Castelo Avenida do Atlântico nº 644-
Descrição: dc.descriptionCEFT Faculty of Engineering University of Porto-
Descrição: dc.descriptionInstitute of Science and Technology São Paulo State University (UNESP) “Júlio de Mesquita Filho”, Sorocaba Campus, Av. Três de Março, 511, Alto da Boa Vista, São Paulo-
Descrição: dc.descriptionFAPESP: #2021/11104-8-
Idioma: dc.languageen-
Relação: dc.relationJournal of CO2 Utilization-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCO2 capture-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectPorous carbon-
Título: dc.titleCarbon adsorption on waste biomass of passion fruit peel: A promising machine learning model for CO2 capture-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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