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Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks

It is believe that 80% industrial of carbon dioxide can be controlled by separation and storage technologies which use the blended ionic liquids absorber. Among the blended absorbers, the mixture of water, N-methyldiethanolamine (MDEA) and guanidinium trifluoromethane sulfonate (gua) has presented t...

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Main Authors: Abdollahi, Yadollah, Sairi, Nor Asrina, Mohd Said, Suhana, Abouzari-lotf, Ebrahim, Zakaria, Azmi, Mohd Sabri, Mohd Faizul, Islam, Aminul, Alias, Yatimah
Format: Article
Language:English
Published: Elsevier 2015
Online Access:http://psasir.upm.edu.my/id/eprint/46222/1/Scheduling%20the%20blended%20solution%20as%20industrial%20CO2%20absorber%20in%20separation%20process%20by%20back-propagation%20artificial%20neural%20networks.pdf
http://psasir.upm.edu.my/id/eprint/46222/
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spelling my.upm.eprints.462222018-03-30T13:06:05Z http://psasir.upm.edu.my/id/eprint/46222/ Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks Abdollahi, Yadollah Sairi, Nor Asrina Mohd Said, Suhana Abouzari-lotf, Ebrahim Zakaria, Azmi Mohd Sabri, Mohd Faizul Islam, Aminul Alias, Yatimah It is believe that 80% industrial of carbon dioxide can be controlled by separation and storage technologies which use the blended ionic liquids absorber. Among the blended absorbers, the mixture of water, N-methyldiethanolamine (MDEA) and guanidinium trifluoromethane sulfonate (gua) has presented the superior stripping qualities. However, the blended solution has illustrated high viscosity that affects the cost of separation process. In this work, the blended fabrication was scheduled with is the process arranging, controlling and optimizing. Therefore, the blend’s components and operating temperature were modeled and optimized as input effective variables to minimize its viscosity as the final output by using back-propagation artificial neural network (ANN). The modeling was carried out by four mathematical algorithms with individual experimental design to obtain the optimum topology using root mean squared error (RMSE), R-squared (R2) and absolute average deviation (AAD). As a result, the final model (QP-4-8-1) with minimum RMSE and AAD as well as the highest R2 was selected to navigate the fabrication of the blended solution. Therefore, the model was applied to obtain the optimum initial level of the input variables which were included temperature 303–323 K, x[gua], 0–0.033, x[MDAE], 0.3–0.4, and x[H2O], 0.7–1.0. Moreover, the model has obtained the relative importance ordered of the variables which included x[gua] > temperature > x[MDEA] > x[H2O]. Therefore, none of the variables was negligible in the fabrication. Furthermore, the model predicted the optimum points of the variables to minimize the viscosity which was validated by further experiments. The validated results confirmed the model schedulability. Accordingly, ANN succeeds to model the initial components of the blended solutions as absorber of CO2 capture in separation technologies that is able to industries scale up. Elsevier 2015-11 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/46222/1/Scheduling%20the%20blended%20solution%20as%20industrial%20CO2%20absorber%20in%20separation%20process%20by%20back-propagation%20artificial%20neural%20networks.pdf Abdollahi, Yadollah and Sairi, Nor Asrina and Mohd Said, Suhana and Abouzari-lotf, Ebrahim and Zakaria, Azmi and Mohd Sabri, Mohd Faizul and Islam, Aminul and Alias, Yatimah (2015) Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 150. pp. 892-901. ISSN 1386-1425; ESSN: 1873-3557 10.1016/j.saa.2015.06.036
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description It is believe that 80% industrial of carbon dioxide can be controlled by separation and storage technologies which use the blended ionic liquids absorber. Among the blended absorbers, the mixture of water, N-methyldiethanolamine (MDEA) and guanidinium trifluoromethane sulfonate (gua) has presented the superior stripping qualities. However, the blended solution has illustrated high viscosity that affects the cost of separation process. In this work, the blended fabrication was scheduled with is the process arranging, controlling and optimizing. Therefore, the blend’s components and operating temperature were modeled and optimized as input effective variables to minimize its viscosity as the final output by using back-propagation artificial neural network (ANN). The modeling was carried out by four mathematical algorithms with individual experimental design to obtain the optimum topology using root mean squared error (RMSE), R-squared (R2) and absolute average deviation (AAD). As a result, the final model (QP-4-8-1) with minimum RMSE and AAD as well as the highest R2 was selected to navigate the fabrication of the blended solution. Therefore, the model was applied to obtain the optimum initial level of the input variables which were included temperature 303–323 K, x[gua], 0–0.033, x[MDAE], 0.3–0.4, and x[H2O], 0.7–1.0. Moreover, the model has obtained the relative importance ordered of the variables which included x[gua] > temperature > x[MDEA] > x[H2O]. Therefore, none of the variables was negligible in the fabrication. Furthermore, the model predicted the optimum points of the variables to minimize the viscosity which was validated by further experiments. The validated results confirmed the model schedulability. Accordingly, ANN succeeds to model the initial components of the blended solutions as absorber of CO2 capture in separation technologies that is able to industries scale up.
format Article
author Abdollahi, Yadollah
Sairi, Nor Asrina
Mohd Said, Suhana
Abouzari-lotf, Ebrahim
Zakaria, Azmi
Mohd Sabri, Mohd Faizul
Islam, Aminul
Alias, Yatimah
spellingShingle Abdollahi, Yadollah
Sairi, Nor Asrina
Mohd Said, Suhana
Abouzari-lotf, Ebrahim
Zakaria, Azmi
Mohd Sabri, Mohd Faizul
Islam, Aminul
Alias, Yatimah
Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks
author_facet Abdollahi, Yadollah
Sairi, Nor Asrina
Mohd Said, Suhana
Abouzari-lotf, Ebrahim
Zakaria, Azmi
Mohd Sabri, Mohd Faizul
Islam, Aminul
Alias, Yatimah
author_sort Abdollahi, Yadollah
title Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks
title_short Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks
title_full Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks
title_fullStr Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks
title_full_unstemmed Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks
title_sort scheduling the blended solution as industrial co2 absorber in separation process by back-propagation artificial neural networks
publisher Elsevier
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/46222/1/Scheduling%20the%20blended%20solution%20as%20industrial%20CO2%20absorber%20in%20separation%20process%20by%20back-propagation%20artificial%20neural%20networks.pdf
http://psasir.upm.edu.my/id/eprint/46222/
_version_ 1643833746188664832
score 13.211869

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