eprintid: 8693
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/86/93
datestamp: 2023-11-09 16:20:36
lastmod: 2023-11-09 16:20:36
status_changed: 2023-11-09 16:13:17
type: article
metadata_visibility: show
creators_name: Garg, S.
creators_name: Shariff, A.M.
creators_name: Shaikh, M.S.
creators_name: Lal, B.
creators_name: Suleman, H.
creators_name: Faiqa, N.
title: Experimental data, thermodynamic and neural network modeling of CO2 solubility in aqueous sodium salt of l-phenylalanine
ispublished: pub
keywords: Amino acids; Carbon; Deep neural networks; Equilibrium constants; Neural networks; Salts; Solubility; Temperature, Effect of temperature; Kent-Eisenberg; l-Phenylalanine; Modeling technique; Neural network model; Pressure ranges; Solubility data; Solvent concentration, Carbon dioxide
note: cited By 59
abstract: In this study, experimental CO2 solubility in aqueous sodium salt of l-phenylalanine (Na-Phe) was investigated at concentrations (w = 0.10, 0.20, and 0.25) mass fractions. The solubility was measured in a high-pressure solubility cell at temperatures 303.15, 313.15 and 333.15 K, over a CO2 pressure range of (2-25) bar. The effect of temperature, equilibrium CO2 pressure and Na-Phe concentration on CO2 loading were examined. Two different models namely modified Kent-Eisenberg and artificial neural network (ANN) were used to correlate the CO2 solubility data. Carbamate hydrolysis and amine deprotonation equilibrium constants were estimated as a function of temperature, pressure and solvent concentration from modified Kent-Eisenberg model. Also, the comparison of prediction results obtained from both modeling techniques was carried out. It was found that ANN model performed better than modified Kent-Eisenberg model. © 2017 Elsevier Ltd. All rights reserved.
date: 2017
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016434935&doi=10.1016%2fj.jcou.2017.03.011&partnerID=40&md5=d7123aac407012796927e1e5ca6fc010
id_number: 10.1016/j.jcou.2017.03.011
full_text_status: none
publication: Journal of CO2 Utilization
volume: 19
pagerange: 146-156
refereed: TRUE
issn: 22129820
citation:   Garg, S. and Shariff, A.M. and Shaikh, M.S. and Lal, B. and Suleman, H. and Faiqa, N.  (2017) Experimental data, thermodynamic and neural network modeling of CO2 solubility in aqueous sodium salt of l-phenylalanine.  Journal of CO2 Utilization, 19.  pp. 146-156.  ISSN 22129820