Predicting Carbon Residual in Biomass Wastes Using Soft Computing Techniques

In recent decades, the development of complex materials developed a class of biomass waste-derived porous carbons (BWDPCs), which are used for carbon capture and sustainable waste management. It is difficult in understanding the adsorption mechanism of CO2 in the air as it has a wide range of proper...

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Main Authors: Preety Verma, J. Godwin Ponsam, Rajeev Shrivastava, Ajay Kushwaha, Neelabh Sao, AL Chockalingam, Leena Bojaraj, null JaikumarR, S. Chandragandhi, Assefa Alene
Format: Article
Language:English
Published: SAGE Publications 2022-01-01
Series:Adsorption Science & Technology
Online Access:http://dx.doi.org/10.1155/2022/8107196
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author Preety Verma
J. Godwin Ponsam
Rajeev Shrivastava
Ajay Kushwaha
Neelabh Sao
AL Chockalingam
Leena Bojaraj
null JaikumarR
S. Chandragandhi
Assefa Alene
author_facet Preety Verma
J. Godwin Ponsam
Rajeev Shrivastava
Ajay Kushwaha
Neelabh Sao
AL Chockalingam
Leena Bojaraj
null JaikumarR
S. Chandragandhi
Assefa Alene
author_sort Preety Verma
collection DOAJ
description In recent decades, the development of complex materials developed a class of biomass waste-derived porous carbons (BWDPCs), which are used for carbon capture and sustainable waste management. It is difficult in understanding the adsorption mechanism of CO2 in the air as it has a wide range of properties associated with its diverse textures, functional group existence, pressure, and temperature of varying range. These properties influence diversely the adsorption mechanism of CO2 and pose serious challenges in the process. To resolve this multiobjective formulation, we use a machine learning classifier that maps systematically the CO2 adsorption as a function of compositional and textural properties and adsorption parameters. The machine learning classifier helps in the classification of various porous carbon materials during the time of training and testing. The results of the simulation show that the proposed method is more efficient in classifying the porous nature of the CO2 adsorption materials than other methods.
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spelling doaj.art-cda864508c1c421da77ae19a9dbc57a82024-03-03T07:32:25ZengSAGE PublicationsAdsorption Science & Technology2048-40382022-01-01202210.1155/2022/8107196Predicting Carbon Residual in Biomass Wastes Using Soft Computing TechniquesPreety Verma0J. Godwin Ponsam1Rajeev Shrivastava2Ajay Kushwaha3Neelabh Sao4AL Chockalingam5Leena Bojaraj6null JaikumarR7S. Chandragandhi8Assefa Alene9Department of Computer Science & EngineeringDepartment of Networking and CommunicationsPrinceton Institute of Engineering and Technology for WomenComputer Science and EngineeringComputer Science and EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electronics and Communications EngineeringDepartment of ECEDepartment of Computer Science and EngineeringDepartment of Chemical EngineeringIn recent decades, the development of complex materials developed a class of biomass waste-derived porous carbons (BWDPCs), which are used for carbon capture and sustainable waste management. It is difficult in understanding the adsorption mechanism of CO2 in the air as it has a wide range of properties associated with its diverse textures, functional group existence, pressure, and temperature of varying range. These properties influence diversely the adsorption mechanism of CO2 and pose serious challenges in the process. To resolve this multiobjective formulation, we use a machine learning classifier that maps systematically the CO2 adsorption as a function of compositional and textural properties and adsorption parameters. The machine learning classifier helps in the classification of various porous carbon materials during the time of training and testing. The results of the simulation show that the proposed method is more efficient in classifying the porous nature of the CO2 adsorption materials than other methods.http://dx.doi.org/10.1155/2022/8107196
spellingShingle Preety Verma
J. Godwin Ponsam
Rajeev Shrivastava
Ajay Kushwaha
Neelabh Sao
AL Chockalingam
Leena Bojaraj
null JaikumarR
S. Chandragandhi
Assefa Alene
Predicting Carbon Residual in Biomass Wastes Using Soft Computing Techniques
Adsorption Science & Technology
title Predicting Carbon Residual in Biomass Wastes Using Soft Computing Techniques
title_full Predicting Carbon Residual in Biomass Wastes Using Soft Computing Techniques
title_fullStr Predicting Carbon Residual in Biomass Wastes Using Soft Computing Techniques
title_full_unstemmed Predicting Carbon Residual in Biomass Wastes Using Soft Computing Techniques
title_short Predicting Carbon Residual in Biomass Wastes Using Soft Computing Techniques
title_sort predicting carbon residual in biomass wastes using soft computing techniques
url http://dx.doi.org/10.1155/2022/8107196
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