relation: https://khub.utp.edu.my/scholars/19543/
title: New correlations for screening of ionic liquids for efficient gas hydrate inhibition
creator: Sulaimon, A.A.
creator: Murungi, P.I.
creator: Mohshim, D.F.B.
description: Regression analysis techniques were employed to develop correlations for the average temperature shift of hydrates (�Tav) as a function of seven thermophysical properties of the ILs. These predictors are; hydrogen bonding energy (EHB), Vander Waal's energy (EvdW), the energy of misfit (EMF), the energy of interaction (EINT), density (�), viscosity (μ), and molecular weight (MW). Multicollinearity effects among the variables were resolved by adopting the ridge regression approach. The regression models were developed from over 200 experimental data points involving 1-methyl-3-propyl-imidazolium ILs (PMIM-ILs), 1-(3-cyanopropyl)-3-methylimidazolium ILs (CPMIM-ILs), 1,3-dimethyl-imidazolium ILs (DMIM-ILs), 1-butyl-3-methylimidazolium ILs (BMIM-ILs), and 1-Ethyl-3-methylimidazolium ILs (EMIM-ILs). The predictive models were developed in three categories: Several ILs, BMIM-ILs, and the EMIM-IL models. Analysis shows that the prediction of �Tav from models developed from the thermophysical data obtained from specific IL models is more accurate than those obtained from Several ILs models. Except for 1-Ethyl-3-methylimidazolium dicyanamide (EMIM-DCN) IL, the percentage accuracy of the models ranges between 81.56 and 99.91. The new models present a significantly simpler and more accurate way to optimize the inhibitor synthesis, selection, and validation process for practical purposes. © 2022 Taylor & Francis Group, LLC.
date: 2023
type: Article
type: PeerReviewed
identifier:   Sulaimon, A.A. and Murungi, P.I. and Mohshim, D.F.B.  (2023) New correlations for screening of ionic liquids for efficient gas hydrate inhibition.  Petroleum Science and Technology, 41 (3).  pp. 257-301.      
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129724649&doi=10.1080%2f10916466.2022.2055065&partnerID=40&md5=bef1b4723a182d49ceb9b51f40eb2f87
relation: 10.1080/10916466.2022.2055065
identifier: 10.1080/10916466.2022.2055065