Determination of Volumetric Mass Transfer Coefficient in Gas-Solid-Liquid Stirred Vessels Handling High Solids Concentrations: Experiment and Modeling
Rigorous analysis of the determinants of volumetric mass transfer coefficient (kLa) and its accurate forecasting are of vital importance for effectively designing and operating stirred reactors. Majority of the available literature is limited to systems with low solids concentration, while there has...
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| Format: | Article |
| Language: | English |
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Iranian Institute of Research and Development in Chemical Industries (IRDCI)-ACECR
2018-06-01
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| Series: | Iranian Journal of Chemistry & Chemical Engineering |
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| Online Access: | http://www.ijcce.ac.ir/article_34210_9bdd490572273e14174f5f153d7b7fef.pdf |
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| author | Meysam Davoody Abdul Aziz Abdul Raman Seyedali Asgharzadeh Ahmadi Shaliza Binti Ibrahim Rajarathinam Parthasarathy |
| author_facet | Meysam Davoody Abdul Aziz Abdul Raman Seyedali Asgharzadeh Ahmadi Shaliza Binti Ibrahim Rajarathinam Parthasarathy |
| author_sort | Meysam Davoody |
| collection | DOAJ |
| description | Rigorous analysis of the determinants of volumetric mass transfer coefficient (kLa) and its accurate forecasting are of vital importance for effectively designing and operating stirred reactors. Majority of the available literature is limited to systems with low solids concentration, while there has always been a need to investigate the gas-liquid hydrodynamics in tanks handling high solid loadings. Several models have been proposed for predicting kLa values, but the application of neuro-fuzzy logic for modelingkLa based on combined operational and geometrical conditions is still unexplored. In this paper, an ANFIS (adaptive neuro-fuzzy inference system) model was designed to map three operational parameters (agitation speed (RPS), solid concentration, superficial gas velocity (cm/s)) and one geometrical parameter (number of curved blades) as input data, to kLa as output data. Excellent performance of ANFIS’s model in predicting kLa values was demonstrated by various performance indicators with a correlation coefficient of 0.9941. |
| first_indexed | 2024-12-14T01:42:32Z |
| format | Article |
| id | doaj.art-a0be7d01c6b94b03ba04e53ce0ee9eb8 |
| institution | Directory Open Access Journal |
| issn | 1021-9986 1021-9986 |
| language | English |
| last_indexed | 2024-12-14T01:42:32Z |
| publishDate | 2018-06-01 |
| 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-a0be7d01c6b94b03ba04e53ce0ee9eb82022-12-21T23:21:40ZengIranian Institute of Research and Development in Chemical Industries (IRDCI)-ACECRIranian Journal of Chemistry & Chemical Engineering1021-99861021-99862018-06-0137319521234210Determination of Volumetric Mass Transfer Coefficient in Gas-Solid-Liquid Stirred Vessels Handling High Solids Concentrations: Experiment and ModelingMeysam Davoody0Abdul Aziz Abdul Raman1Seyedali Asgharzadeh Ahmadi2Shaliza Binti Ibrahim3Rajarathinam Parthasarathy4Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, MALAYSIADepartment of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, MALAYSIADepartment of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, MALAYSIADepartment of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, MALAYSIASchool of Civil, Environmental, and Chemical Engineering, RMIT University, City Campus 3001, AUSTRALIARigorous analysis of the determinants of volumetric mass transfer coefficient (kLa) and its accurate forecasting are of vital importance for effectively designing and operating stirred reactors. Majority of the available literature is limited to systems with low solids concentration, while there has always been a need to investigate the gas-liquid hydrodynamics in tanks handling high solid loadings. Several models have been proposed for predicting kLa values, but the application of neuro-fuzzy logic for modelingkLa based on combined operational and geometrical conditions is still unexplored. In this paper, an ANFIS (adaptive neuro-fuzzy inference system) model was designed to map three operational parameters (agitation speed (RPS), solid concentration, superficial gas velocity (cm/s)) and one geometrical parameter (number of curved blades) as input data, to kLa as output data. Excellent performance of ANFIS’s model in predicting kLa values was demonstrated by various performance indicators with a correlation coefficient of 0.9941.http://www.ijcce.ac.ir/article_34210_9bdd490572273e14174f5f153d7b7fef.pdfartificial intelligence-based modelingadaptive neuro-fuzzy inference systemartificial neural networksvolumetric mass transfer coefficientstirred vessels |
| spellingShingle | Meysam Davoody Abdul Aziz Abdul Raman Seyedali Asgharzadeh Ahmadi Shaliza Binti Ibrahim Rajarathinam Parthasarathy Determination of Volumetric Mass Transfer Coefficient in Gas-Solid-Liquid Stirred Vessels Handling High Solids Concentrations: Experiment and Modeling Iranian Journal of Chemistry & Chemical Engineering artificial intelligence-based modeling adaptive neuro-fuzzy inference system artificial neural networks volumetric mass transfer coefficient stirred vessels |
| title | Determination of Volumetric Mass Transfer Coefficient in Gas-Solid-Liquid Stirred Vessels Handling High Solids Concentrations: Experiment and Modeling |
| title_full | Determination of Volumetric Mass Transfer Coefficient in Gas-Solid-Liquid Stirred Vessels Handling High Solids Concentrations: Experiment and Modeling |
| title_fullStr | Determination of Volumetric Mass Transfer Coefficient in Gas-Solid-Liquid Stirred Vessels Handling High Solids Concentrations: Experiment and Modeling |
| title_full_unstemmed | Determination of Volumetric Mass Transfer Coefficient in Gas-Solid-Liquid Stirred Vessels Handling High Solids Concentrations: Experiment and Modeling |
| title_short | Determination of Volumetric Mass Transfer Coefficient in Gas-Solid-Liquid Stirred Vessels Handling High Solids Concentrations: Experiment and Modeling |
| title_sort | determination of volumetric mass transfer coefficient in gas solid liquid stirred vessels handling high solids concentrations experiment and modeling |
| topic | artificial intelligence-based modeling adaptive neuro-fuzzy inference system artificial neural networks volumetric mass transfer coefficient stirred vessels |
| url | http://www.ijcce.ac.ir/article_34210_9bdd490572273e14174f5f153d7b7fef.pdf |
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