relation: https://khub.utp.edu.my/scholars/16829/
title: Artificial neural network (ANN) modeling for CO2 adsorption on Marcellus Shale
creator: Irfan, S.A.
creator: Abdulkareem, F.A.
creator: Radman, A.
creator: Faugere, G.
creator: Padmanabhan, E.
description: In this work, artificial neural network modeling for CO2 adsorption on various types of Marcellus shale samples is studied. The eight shale geometries are investigated for their CO2 adsorption at 298k and up to 50bar pressure utilizing a gravimetric technique and magnetic suspension balance. ANN modelling was applied to investigate three main objectives which are the impact of various training algorithms, various data initiation points, and altered training/validating ratios and number of neurons required for ANN model. The work can provide insightful knowledge linked to the impact of each of the studied parameters which play an important role in ANN modeling and training algorithms. The outcomes can provide an optimized matrix for unconventional resources, enhanced gas and oil recovery applications intend to apply the artificial intelligence modeling in their assessments. © Published under licence by IOP Publishing Ltd.
publisher: Institute of Physics
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Irfan, S.A. and Abdulkareem, F.A. and Radman, A. and Faugere, G. and Padmanabhan, E.  (2022) Artificial neural network (ANN) modeling for CO2 adsorption on Marcellus Shale.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129877760&doi=10.1088%2f1755-1315%2f1003%2f1%2f012029&partnerID=40&md5=48b0c3acec8c435d046f3c75848e3e7b
relation: 10.1088/1755-1315/1003/1/012029
identifier: 10.1088/1755-1315/1003/1/012029