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...
Main Authors: | , , , , , , , , , |
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Format: | Article |
Language: | English |
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SAGE Publications
2022-01-01
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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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format | Article |
id | doaj.art-cda864508c1c421da77ae19a9dbc57a8 |
institution | Directory Open Access Journal |
issn | 2048-4038 |
language | English |
last_indexed | 2024-03-07T16:42:39Z |
publishDate | 2022-01-01 |
publisher | SAGE Publications |
record_format | Article |
series | Adsorption Science & Technology |
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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