Carbon adsorption on waste biomass of passion fruit peel: A promising machine learning model for CO2 capture
The 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 materia...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Journal of CO2 Utilization |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2212982024000155 |
| _version_ | 1827347507264356352 |
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| author | Christiano Bruneli Peres Leandro Cardoso de Morais Pedro Miguel Rebelo Resende |
| author_facet | Christiano Bruneli Peres Leandro Cardoso de Morais Pedro Miguel Rebelo Resende |
| author_sort | Christiano Bruneli Peres |
| collection | DOAJ |
| description | The 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. |
| first_indexed | 2024-03-07T23:51:38Z |
| format | Article |
| id | doaj.art-00d5cc71dbdf46f6a5e4dc40ec4357a4 |
| institution | Directory Open Access Journal |
| issn | 2212-9839 |
| language | English |
| last_indexed | 2024-03-07T23:51:38Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of CO2 Utilization |
| spelling | doaj.art-00d5cc71dbdf46f6a5e4dc40ec4357a42024-02-19T04:13:31ZengElsevierJournal of CO2 Utilization2212-98392024-02-0180102680Carbon adsorption on waste biomass of passion fruit peel: A promising machine learning model for CO2 captureChristiano Bruneli Peres0Leandro Cardoso de Morais1Pedro Miguel Rebelo Resende2Institute of Science and Technology, São Paulo State University (UNESP) “Júlio de Mesquita Filho”, Sorocaba Campus, Av. Três de Março, 511, Alto da Boa Vista, Sorocaba 18087-180, São Paulo, Brazil; Corresponding author.Institute of Science and Technology, São Paulo State University (UNESP) “Júlio de Mesquita Filho”, Sorocaba Campus, Av. Três de Março, 511, Alto da Boa Vista, Sorocaba 18087-180, São Paulo, BrazilPrometheus, Polytechnic Institute of Viana do Castelo, Rua da Escola Industrial e Comercial de Nun 'Alvares, 4900-347 Viana do Castelo, Portugal; Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Viana do Castelo, Avenida do Atlântico, nº 644, 4900-348 Viana do Castelo, Portugal; CEFT, Faculty of Engineering, University of Porto, 4200-465 Porto, PortugalThe 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.http://www.sciencedirect.com/science/article/pii/S2212982024000155CO2 captureMachine learningPorous carbon |
| spellingShingle | Christiano Bruneli Peres Leandro Cardoso de Morais Pedro Miguel Rebelo Resende Carbon adsorption on waste biomass of passion fruit peel: A promising machine learning model for CO2 capture Journal of CO2 Utilization CO2 capture Machine learning Porous carbon |
| title | Carbon adsorption on waste biomass of passion fruit peel: A promising machine learning model for CO2 capture |
| title_full | Carbon adsorption on waste biomass of passion fruit peel: A promising machine learning model for CO2 capture |
| title_fullStr | Carbon adsorption on waste biomass of passion fruit peel: A promising machine learning model for CO2 capture |
| title_full_unstemmed | Carbon adsorption on waste biomass of passion fruit peel: A promising machine learning model for CO2 capture |
| title_short | Carbon adsorption on waste biomass of passion fruit peel: A promising machine learning model for CO2 capture |
| title_sort | carbon adsorption on waste biomass of passion fruit peel a promising machine learning model for co2 capture |
| topic | CO2 capture Machine learning Porous carbon |
| url | http://www.sciencedirect.com/science/article/pii/S2212982024000155 |
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