A Correlation of Overall Mass Transfer Coefficient of Water Transport in a Hollow-Fiber Membrane Module via an Artificial Neural Network Approach
Water transport in a hollow-fiber membrane depends on mass convection around the tube, mass convection inside the tube, and water diffusion through the membrane tube. The performance of water transport is then explained by the overall mass transfer coefficient in hollow-fiber membranes. This study p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Membranes |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0375/13/1/8 |
| _version_ | 1797438900705165312 |
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| author | Xuan Linh Nguyen Ngoc Van Trinh Younghyeon Kim Sangseok Yu |
| author_facet | Xuan Linh Nguyen Ngoc Van Trinh Younghyeon Kim Sangseok Yu |
| author_sort | Xuan Linh Nguyen |
| collection | DOAJ |
| description | Water transport in a hollow-fiber membrane depends on mass convection around the tube, mass convection inside the tube, and water diffusion through the membrane tube. The performance of water transport is then explained by the overall mass transfer coefficient in hollow-fiber membranes. This study presents the prediction of overall mass transfer coefficients of water transport in a hollow-fiber membrane module by an artificial neural network (ANN) that is used for a humidifier of a vehicular fuel cell system. The input variables of ANN are collected from water transport experiments of the hollow-fiber membrane module that is composed of inlet flow rates, inlet relative humidity, system pressures, and operating temperatures. The experimental mass transfer coefficients are the targets of the training model, which are determined via the effectiveness analysis. When unknown data are applied to the ANN model, the correlation of the overall mass transfer coefficient predicts precise results with R = 0.99 (correlation coefficient). The ANN model shows good prediction capability of water transport in membrane humidifiers. |
| first_indexed | 2024-03-09T11:45:00Z |
| format | Article |
| id | doaj.art-f2f2216400874060bc47cc31de78b661 |
| institution | Directory Open Access Journal |
| issn | 2077-0375 |
| language | English |
| last_indexed | 2024-03-09T11:45:00Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Membranes |
| spelling | doaj.art-f2f2216400874060bc47cc31de78b6612023-11-30T23:25:55ZengMDPI AGMembranes2077-03752022-12-01131810.3390/membranes13010008A Correlation of Overall Mass Transfer Coefficient of Water Transport in a Hollow-Fiber Membrane Module via an Artificial Neural Network ApproachXuan Linh Nguyen0Ngoc Van Trinh1Younghyeon Kim2Sangseok Yu3Department of Mechanical Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Mechanical Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Mechanical Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of KoreaSchool of Mechanical Engineering, Chungnam National University, Daejeon 34134, Republic of KoreaWater transport in a hollow-fiber membrane depends on mass convection around the tube, mass convection inside the tube, and water diffusion through the membrane tube. The performance of water transport is then explained by the overall mass transfer coefficient in hollow-fiber membranes. This study presents the prediction of overall mass transfer coefficients of water transport in a hollow-fiber membrane module by an artificial neural network (ANN) that is used for a humidifier of a vehicular fuel cell system. The input variables of ANN are collected from water transport experiments of the hollow-fiber membrane module that is composed of inlet flow rates, inlet relative humidity, system pressures, and operating temperatures. The experimental mass transfer coefficients are the targets of the training model, which are determined via the effectiveness analysis. When unknown data are applied to the ANN model, the correlation of the overall mass transfer coefficient predicts precise results with R = 0.99 (correlation coefficient). The ANN model shows good prediction capability of water transport in membrane humidifiers.https://www.mdpi.com/2077-0375/13/1/8hollow-fiber membranemass transferartificial neural networkhumidifiervehicular fuel cell |
| spellingShingle | Xuan Linh Nguyen Ngoc Van Trinh Younghyeon Kim Sangseok Yu A Correlation of Overall Mass Transfer Coefficient of Water Transport in a Hollow-Fiber Membrane Module via an Artificial Neural Network Approach Membranes hollow-fiber membrane mass transfer artificial neural network humidifier vehicular fuel cell |
| title | A Correlation of Overall Mass Transfer Coefficient of Water Transport in a Hollow-Fiber Membrane Module via an Artificial Neural Network Approach |
| title_full | A Correlation of Overall Mass Transfer Coefficient of Water Transport in a Hollow-Fiber Membrane Module via an Artificial Neural Network Approach |
| title_fullStr | A Correlation of Overall Mass Transfer Coefficient of Water Transport in a Hollow-Fiber Membrane Module via an Artificial Neural Network Approach |
| title_full_unstemmed | A Correlation of Overall Mass Transfer Coefficient of Water Transport in a Hollow-Fiber Membrane Module via an Artificial Neural Network Approach |
| title_short | A Correlation of Overall Mass Transfer Coefficient of Water Transport in a Hollow-Fiber Membrane Module via an Artificial Neural Network Approach |
| title_sort | correlation of overall mass transfer coefficient of water transport in a hollow fiber membrane module via an artificial neural network approach |
| topic | hollow-fiber membrane mass transfer artificial neural network humidifier vehicular fuel cell |
| url | https://www.mdpi.com/2077-0375/13/1/8 |
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