Fabrication modeling of industrial CO2 ionic liquids absorber by artificial neural networks
The fabrication of industrial CO2 blended solution absorber was modeled by artificial neutral network. First the generated model had been statistically evaluated and then its ability of prediction was confirmed by validation test. The validated model was used to predict the desirable density and rel...
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my.um.eprints.157632019-03-21T06:55:16Z http://eprints.um.edu.my/15763/ Fabrication modeling of industrial CO2 ionic liquids absorber by artificial neural networks Abdollahi, Y. Sairi, N.A. Aroua, M.K. Masoumi, H.R.F. Jahangirian, H. Alias, Y. T Technology (General) TP Chemical technology The fabrication of industrial CO2 blended solution absorber was modeled by artificial neutral network. First the generated model had been statistically evaluated and then its ability of prediction was confirmed by validation test. The validated model was used to predict the desirable density and relative importance of the fabrication's effective variables. In conclusion, the importance included xH2O, 36.18%, xgua, 25.37%, xMDEA, 25.34% and temperature, 13.11% which showed none of them is negligible as well as the density (g cm(-3)) was validated by further experiment that showed the actual density, 1.101, was quite close to the predicted value, 1.017. (C) 2014 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved. Elsevier 2015-05-25 Article PeerReviewed application/pdf en http://eprints.um.edu.my/15763/1/Fabrication_modeling_of_industrial_CO2_ionic_liquids_absorber_by_artificial_neural_networks.pdf Abdollahi, Y. and Sairi, N.A. and Aroua, M.K. and Masoumi, H.R.F. and Jahangirian, H. and Alias, Y. (2015) Fabrication modeling of industrial CO2 ionic liquids absorber by artificial neural networks. Journal of Industrial and Engineering Chemistry, 25. pp. 168-175. ISSN 1226-086X http://www.sciencedirect.com/science/article/pii/S1226086X14005188 10.1016/j.jiec.2014.10.029 |
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T Technology (General) TP Chemical technology Abdollahi, Y. Sairi, N.A. Aroua, M.K. Masoumi, H.R.F. Jahangirian, H. Alias, Y. Fabrication modeling of industrial CO2 ionic liquids absorber by artificial neural networks |
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The fabrication of industrial CO2 blended solution absorber was modeled by artificial neutral network. First the generated model had been statistically evaluated and then its ability of prediction was confirmed by validation test. The validated model was used to predict the desirable density and relative importance of the fabrication's effective variables. In conclusion, the importance included xH2O, 36.18%, xgua, 25.37%, xMDEA, 25.34% and temperature, 13.11% which showed none of them is negligible as well as the density (g cm(-3)) was validated by further experiment that showed the actual density, 1.101, was quite close to the predicted value, 1.017. (C) 2014 The Korean Society of Industrial and Engineering Chemistry. Published by Elsevier B.V. All rights reserved. |
format |
Article |
author |
Abdollahi, Y. Sairi, N.A. Aroua, M.K. Masoumi, H.R.F. Jahangirian, H. Alias, Y. |
author_facet |
Abdollahi, Y. Sairi, N.A. Aroua, M.K. Masoumi, H.R.F. Jahangirian, H. Alias, Y. |
author_sort |
Abdollahi, Y. |
title |
Fabrication modeling of industrial CO2 ionic liquids absorber by artificial neural networks |
title_short |
Fabrication modeling of industrial CO2 ionic liquids absorber by artificial neural networks |
title_full |
Fabrication modeling of industrial CO2 ionic liquids absorber by artificial neural networks |
title_fullStr |
Fabrication modeling of industrial CO2 ionic liquids absorber by artificial neural networks |
title_full_unstemmed |
Fabrication modeling of industrial CO2 ionic liquids absorber by artificial neural networks |
title_sort |
fabrication modeling of industrial co2 ionic liquids absorber by artificial neural networks |
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Elsevier |
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2015 |
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http://eprints.um.edu.my/15763/1/Fabrication_modeling_of_industrial_CO2_ionic_liquids_absorber_by_artificial_neural_networks.pdf http://eprints.um.edu.my/15763/ http://www.sciencedirect.com/science/article/pii/S1226086X14005188 |
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