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CO2 capture by benzene‐based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology
In this research, porous benzene‐based hypercrosslinked polymeric adsorbents with different morphological properties were synthesized through Friedel–Crafts alkylation reaction. The resulting samples were applied for CO2 capture at different operational conditions. Two modelling approaches, includin...
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Published in: | Canadian journal of chemical engineering 2023-10, Vol.101 (10), p.5621-5642 |
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creator | Moradi, Mohammad Reza Ramezanipour Penchah, Hamid Ghaemi, Ahad |
description | In this research, porous benzene‐based hypercrosslinked polymeric adsorbents with different morphological properties were synthesized through Friedel–Crafts alkylation reaction. The resulting samples were applied for CO2 capture at different operational conditions. Two modelling approaches, including artificial neural network (radial basis function [RBF] and multi layer perceptron [MLP]) and response surface methodology (RSM), were employed to investigate the effect of independent parameters on adsorption capacity. A semi‐empirical quadratic model for adsorption capacity was presented based on RSM‐central composite design technique. Additionally, the optimal structure of RBF was determined with 200 neurons, and the optimal structure of MLP was determined with three hidden layers and 10, 8, and 7 neurons. The modelling results demonstrate the better prediction of MLP and RBF approaches than the RSM method with correlation coefficient values of 0.999, 0.989, and 0.931, respectively. Finally, process optimization was carried out using RSM optimization module and the optimized values of synthesis time, crosslinker ratio (formaldehyde dimethyl acetal [FDA]/benzene), adsorption time, pressure, and temperature were obtained at 10.11 h, 1, 220 s, 9 bar, and 55°C, respectively. The optimum value of CO2 uptake capacity was obtained around 167 (mg/g). |
doi_str_mv | 10.1002/cjce.24887 |
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The resulting samples were applied for CO2 capture at different operational conditions. Two modelling approaches, including artificial neural network (radial basis function [RBF] and multi layer perceptron [MLP]) and response surface methodology (RSM), were employed to investigate the effect of independent parameters on adsorption capacity. A semi‐empirical quadratic model for adsorption capacity was presented based on RSM‐central composite design technique. Additionally, the optimal structure of RBF was determined with 200 neurons, and the optimal structure of MLP was determined with three hidden layers and 10, 8, and 7 neurons. The modelling results demonstrate the better prediction of MLP and RBF approaches than the RSM method with correlation coefficient values of 0.999, 0.989, and 0.931, respectively. Finally, process optimization was carried out using RSM optimization module and the optimized values of synthesis time, crosslinker ratio (formaldehyde dimethyl acetal [FDA]/benzene), adsorption time, pressure, and temperature were obtained at 10.11 h, 1, 220 s, 9 bar, and 55°C, respectively. The optimum value of CO2 uptake capacity was obtained around 167 (mg/g).</description><identifier>ISSN: 0008-4034</identifier><identifier>EISSN: 1939-019X</identifier><identifier>DOI: 10.1002/cjce.24887</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Adsorbents ; Adsorption ; Alkylation ; Artificial neural networks ; Benzene ; Carbon dioxide ; Carbon sequestration ; CO2 capture ; Correlation coefficients ; Dimethyl acetals ; Hydrocarbons ; hypercrosslinked polymers ; Modelling ; Multilayer perceptrons ; multi‐layer perceptron ; Neural networks ; Neurons ; Optimization ; Radial basis function ; Response surface methodology ; Surface chemistry</subject><ispartof>Canadian journal of chemical engineering, 2023-10, Vol.101 (10), p.5621-5642</ispartof><rights>2023 Canadian Society for Chemical Engineering.</rights><rights>2023 Canadian Society for Chemical Engineering</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Moradi, Mohammad Reza</creatorcontrib><creatorcontrib>Ramezanipour Penchah, Hamid</creatorcontrib><creatorcontrib>Ghaemi, Ahad</creatorcontrib><title>CO2 capture by benzene‐based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology</title><title>Canadian journal of chemical engineering</title><description>In this research, porous benzene‐based hypercrosslinked polymeric adsorbents with different morphological properties were synthesized through Friedel–Crafts alkylation reaction. The resulting samples were applied for CO2 capture at different operational conditions. Two modelling approaches, including artificial neural network (radial basis function [RBF] and multi layer perceptron [MLP]) and response surface methodology (RSM), were employed to investigate the effect of independent parameters on adsorption capacity. A semi‐empirical quadratic model for adsorption capacity was presented based on RSM‐central composite design technique. Additionally, the optimal structure of RBF was determined with 200 neurons, and the optimal structure of MLP was determined with three hidden layers and 10, 8, and 7 neurons. The modelling results demonstrate the better prediction of MLP and RBF approaches than the RSM method with correlation coefficient values of 0.999, 0.989, and 0.931, respectively. Finally, process optimization was carried out using RSM optimization module and the optimized values of synthesis time, crosslinker ratio (formaldehyde dimethyl acetal [FDA]/benzene), adsorption time, pressure, and temperature were obtained at 10.11 h, 1, 220 s, 9 bar, and 55°C, respectively. The optimum value of CO2 uptake capacity was obtained around 167 (mg/g).</description><subject>Adsorbents</subject><subject>Adsorption</subject><subject>Alkylation</subject><subject>Artificial neural networks</subject><subject>Benzene</subject><subject>Carbon dioxide</subject><subject>Carbon sequestration</subject><subject>CO2 capture</subject><subject>Correlation coefficients</subject><subject>Dimethyl acetals</subject><subject>Hydrocarbons</subject><subject>hypercrosslinked polymers</subject><subject>Modelling</subject><subject>Multilayer perceptrons</subject><subject>multi‐layer perceptron</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Optimization</subject><subject>Radial basis function</subject><subject>Response surface methodology</subject><subject>Surface chemistry</subject><issn>0008-4034</issn><issn>1939-019X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNotkNtKw0AQhhdRsFZvfIIFr1P3kGQ33pVQTxR6o-Ddstmd2rRpNu4mlAiCj-Az-iSmrTDwzwwfM_AhdE3JhBLCbs3awITFUooTNKIZzyJCs7dTNCKEyCgmPD5HFyGsh5GRmI7QV75g2Oim7TzgoscF1J9Qw-_3T6EDWLzqG_DGuxCqst4Mi8ZV_RY81jY4P9DtHZ76tlyWptQVrqHzh2h3zm-wri32EBpXB8Ch80ttAG-hXTnrKvfeX6Kzpa4CXP3nGL3ez17yx2i-eHjKp_OoYSwRUSKEsVRnJjWUM633JQkkglsNVibUmkwbaWMmEyFTmQBnIuXaWE7AEsPH6OZ4t_Huo4PQqrXrfD28VEymhKWSCTpQ9Ejtygp61fhyq32vKFF7t2rvVh3cqvw5nx06_gejmHLj</recordid><startdate>202310</startdate><enddate>202310</enddate><creator>Moradi, Mohammad Reza</creator><creator>Ramezanipour Penchah, Hamid</creator><creator>Ghaemi, Ahad</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>7SR</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>L7M</scope></search><sort><creationdate>202310</creationdate><title>CO2 capture by benzene‐based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology</title><author>Moradi, Mohammad Reza ; Ramezanipour Penchah, Hamid ; Ghaemi, Ahad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p2257-577cd1a9c6c132aa2aa280e573daed851dc9ac8d428578685e32763acd30ed0c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adsorbents</topic><topic>Adsorption</topic><topic>Alkylation</topic><topic>Artificial neural networks</topic><topic>Benzene</topic><topic>Carbon dioxide</topic><topic>Carbon sequestration</topic><topic>CO2 capture</topic><topic>Correlation coefficients</topic><topic>Dimethyl acetals</topic><topic>Hydrocarbons</topic><topic>hypercrosslinked polymers</topic><topic>Modelling</topic><topic>Multilayer perceptrons</topic><topic>multi‐layer perceptron</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Optimization</topic><topic>Radial basis function</topic><topic>Response surface methodology</topic><topic>Surface chemistry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Moradi, Mohammad Reza</creatorcontrib><creatorcontrib>Ramezanipour Penchah, Hamid</creatorcontrib><creatorcontrib>Ghaemi, Ahad</creatorcontrib><collection>Engineered Materials Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Canadian journal of chemical engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Moradi, Mohammad Reza</au><au>Ramezanipour Penchah, Hamid</au><au>Ghaemi, Ahad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CO2 capture by benzene‐based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology</atitle><jtitle>Canadian journal of chemical engineering</jtitle><date>2023-10</date><risdate>2023</risdate><volume>101</volume><issue>10</issue><spage>5621</spage><epage>5642</epage><pages>5621-5642</pages><issn>0008-4034</issn><eissn>1939-019X</eissn><abstract>In this research, porous benzene‐based hypercrosslinked polymeric adsorbents with different morphological properties were synthesized through Friedel–Crafts alkylation reaction. The resulting samples were applied for CO2 capture at different operational conditions. Two modelling approaches, including artificial neural network (radial basis function [RBF] and multi layer perceptron [MLP]) and response surface methodology (RSM), were employed to investigate the effect of independent parameters on adsorption capacity. A semi‐empirical quadratic model for adsorption capacity was presented based on RSM‐central composite design technique. Additionally, the optimal structure of RBF was determined with 200 neurons, and the optimal structure of MLP was determined with three hidden layers and 10, 8, and 7 neurons. The modelling results demonstrate the better prediction of MLP and RBF approaches than the RSM method with correlation coefficient values of 0.999, 0.989, and 0.931, respectively. Finally, process optimization was carried out using RSM optimization module and the optimized values of synthesis time, crosslinker ratio (formaldehyde dimethyl acetal [FDA]/benzene), adsorption time, pressure, and temperature were obtained at 10.11 h, 1, 220 s, 9 bar, and 55°C, respectively. The optimum value of CO2 uptake capacity was obtained around 167 (mg/g).</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/cjce.24887</doi><tpages>22</tpages></addata></record> |
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subjects | Adsorbents Adsorption Alkylation Artificial neural networks Benzene Carbon dioxide Carbon sequestration CO2 capture Correlation coefficients Dimethyl acetals Hydrocarbons hypercrosslinked polymers Modelling Multilayer perceptrons multi‐layer perceptron Neural networks Neurons Optimization Radial basis function Response surface methodology Surface chemistry |
title | CO2 capture by benzene‐based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology |
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