Prediction of CO2 Mass Transfer Flux in Aqueous Amine Solutions Using Artificial Neural Networks
In the present research, neural networks were applied to predict mass transfer flux of CO2 in aqueous amine solutions. Buckingham π theorem was used to determine the effective dimensionless parameters on CO2 mass transfer flux in reactive separation processes. The dimensionless parameters including...
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| Format: | Article |
| Language: | English |
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Iranian Institute of Research and Development in Chemical Industries (IRDCI)-ACECR
2020-08-01
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| Series: | Iranian Journal of Chemistry & Chemical Engineering |
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| Online Access: | http://www.ijcce.ac.ir/article_31858_a8e8be3b782f1be0e444b132858e4894.pdf |
| _version_ | 1818888377170132992 |
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| author | Ahad Ghaemi Zahra Jafari Edris Etemad |
| author_facet | Ahad Ghaemi Zahra Jafari Edris Etemad |
| author_sort | Ahad Ghaemi |
| collection | DOAJ |
| description | In the present research, neural networks were applied to predict mass transfer flux of CO2 in aqueous amine solutions. Buckingham π theorem was used to determine the effective dimensionless parameters on CO2 mass transfer flux in reactive separation processes. The dimensionless parameters including CO2 loading, the ratio of CO2 diffusion coefficient of gas to a liquid, the ratio of the CO2 partial pressure to the total pressure, the ratio of film thickness of gas to liquid and film parameter as input variables and mass transfer flux of CO2 as output variables were in the modeling. A multilayer perceptron network was used in the prediction of CO2 mass transfer flux.As a case study, experimental data of CO2 absorption into Piperazine solutions were used in the learning, testing, and evaluating steps of the multilayer perceptron. The optimal structure of the multilayer perceptron contains 21 and 17 neurons in two hidden layers. The predicting results of the network indicated that the mean square error for mass transfer flux was 4.48%. In addition, the results of the multilayer perceptron were compared with the predictions of other researchers’ results. The findings revealed that the artificial neural network computes the mass transfer flux of CO2 more accurately and more quickly. |
| first_indexed | 2024-12-19T16:52:09Z |
| format | Article |
| id | doaj.art-c47d4d15784c40ed998beec96de4db35 |
| institution | Directory Open Access Journal |
| issn | 1021-9986 1021-9986 |
| language | English |
| last_indexed | 2024-12-19T16:52:09Z |
| publishDate | 2020-08-01 |
| publisher | Iranian Institute of Research and Development in Chemical Industries (IRDCI)-ACECR |
| record_format | Article |
| series | Iranian Journal of Chemistry & Chemical Engineering |
| spelling | doaj.art-c47d4d15784c40ed998beec96de4db352022-12-21T20:13:30ZengIranian Institute of Research and Development in Chemical Industries (IRDCI)-ACECRIranian Journal of Chemistry & Chemical Engineering1021-99861021-99862020-08-0139426928010.30492/ijcce.2018.3185831858Prediction of CO2 Mass Transfer Flux in Aqueous Amine Solutions Using Artificial Neural NetworksAhad Ghaemi0Zahra Jafari1Edris Etemad2School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, P.O. Box 16765-163, Tehran, I.R. IRANSchool of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, P.O. Box 16765-163, Tehran, I.R. IRANSchool of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, P.O. Box 16765-163, Tehran, I.R. IRANIn the present research, neural networks were applied to predict mass transfer flux of CO2 in aqueous amine solutions. Buckingham π theorem was used to determine the effective dimensionless parameters on CO2 mass transfer flux in reactive separation processes. The dimensionless parameters including CO2 loading, the ratio of CO2 diffusion coefficient of gas to a liquid, the ratio of the CO2 partial pressure to the total pressure, the ratio of film thickness of gas to liquid and film parameter as input variables and mass transfer flux of CO2 as output variables were in the modeling. A multilayer perceptron network was used in the prediction of CO2 mass transfer flux.As a case study, experimental data of CO2 absorption into Piperazine solutions were used in the learning, testing, and evaluating steps of the multilayer perceptron. The optimal structure of the multilayer perceptron contains 21 and 17 neurons in two hidden layers. The predicting results of the network indicated that the mean square error for mass transfer flux was 4.48%. In addition, the results of the multilayer perceptron were compared with the predictions of other researchers’ results. The findings revealed that the artificial neural network computes the mass transfer flux of CO2 more accurately and more quickly.http://www.ijcce.ac.ir/article_31858_a8e8be3b782f1be0e444b132858e4894.pdfpredictionabsorptionmass transfer fluxco2piperazinemultilayer perceptron |
| spellingShingle | Ahad Ghaemi Zahra Jafari Edris Etemad Prediction of CO2 Mass Transfer Flux in Aqueous Amine Solutions Using Artificial Neural Networks Iranian Journal of Chemistry & Chemical Engineering prediction absorption mass transfer flux co2 piperazine multilayer perceptron |
| title | Prediction of CO2 Mass Transfer Flux in Aqueous Amine Solutions Using Artificial Neural Networks |
| title_full | Prediction of CO2 Mass Transfer Flux in Aqueous Amine Solutions Using Artificial Neural Networks |
| title_fullStr | Prediction of CO2 Mass Transfer Flux in Aqueous Amine Solutions Using Artificial Neural Networks |
| title_full_unstemmed | Prediction of CO2 Mass Transfer Flux in Aqueous Amine Solutions Using Artificial Neural Networks |
| title_short | Prediction of CO2 Mass Transfer Flux in Aqueous Amine Solutions Using Artificial Neural Networks |
| title_sort | prediction of co2 mass transfer flux in aqueous amine solutions using artificial neural networks |
| topic | prediction absorption mass transfer flux co2 piperazine multilayer perceptron |
| url | http://www.ijcce.ac.ir/article_31858_a8e8be3b782f1be0e444b132858e4894.pdf |
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