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DC Field | Value | Language |
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dc.contributor.author | Rajesh, R | - |
dc.contributor.author | Chattopadhyay, S | - |
dc.contributor.author | Kundu, Madhusree | - |
dc.date.accessioned | 2007-06-04T06:26:01Z | - |
dc.date.available | 2007-06-04T06:26:01Z | - |
dc.date.issued | 2006 | - |
dc.identifier.citation | Proceedings of CHEMECA’06, Auckland, New Zealand from 17-20 September 2006 | en |
dc.identifier.uri | http://hdl.handle.net/2080/436 | - |
dc.description | Copyright for this article belongs to Proceedings Publisher | en |
dc.description.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. | en |
dc.format.extent | 238893 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.title | prediction of equilibrium solubility of co2 in aqueous alkanolamines through artificial neural network | en |
dc.type | Article | en |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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chattopadhyay et al_085_chemrca06.pdf | 233.29 kB | Adobe PDF | View/Open |
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