DSpace@nitr >
National Institue of Technology- Rourkela >
Conference Papers >

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/436

Full metadata record

DC FieldValueLanguage
contributor.authorRajesh, R-
contributor.authorChattopadhyay, S-
contributor.authorKundu, Madhusree-
date.accessioned2007-06-04T06:26:01Z-
date.available2007-06-04T06:26:01Z-
date.issued2006-
identifier.citationProceedings of CHEMECA’06, Auckland, New Zealand from 17-20 September 2006en
identifier.urihttp://hdl.handle.net/2080/436-
descriptionCopyright for this article belongs to Proceedings Publisheren
description.abstractThe 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.en
format.extent238893 bytes-
format.mimetypeapplication/pdf-
language.isoen-
titleprediction of equilibrium solubility of co2 in aqueous alkanolamines through artificial neural networken
typeArticleen
Appears in Collections:Conference Papers

Files in This Item:

File Description SizeFormat
chattopadhyay et al_085_chemrca06.pdf233KbAdobe PDFView/Open

Show simple item record

All items in DSpace are protected by copyright, with all rights reserved.

 

Powered by DSpace Feedback