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Title: prediction of equilibrium solubility of co2 in aqueous alkanolamines through artificial neural network
Authors: Rajesh, R
Chattopadhyay, S
Kundu, Madhusree
Issue Date: 2006
Citation: Proceedings of CHEMECA’06, Auckland, New Zealand from 17-20 September 2006
Abstract: The removal of acid gases from gas streams by using suitable solvent like alkanolamine, commonly referred to as gas sweetening, is a technology that has been in use industrially for over half a century. In this work artificial neural network (ANN) has been used to predict the equilibrium solubility of CO2 over the alkanolamine solvents N-methyldiethanolamine (MDEA) and 2-amino-2-methyl-1-propanol (AMP) instead of using any thermodynamic model. A multilayer feed forward network with back propagation training algorithm has been used here in an effort to predict the VLE data of CO2-MDEA-H2O and CO2-AMP-H2O system with a comparable accuracy to those predictions based on rigorous thermodynamic model. It has been found that the predictions are within accuracy of 5% for 95 % of the data.
Description: Copyright for this article belongs to Proceedings Publisher
Appears in Collections:Conference Papers

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