Experimental and modeling evaluation of droplet size in immiscible liquid-liquid stirred vessel using various impeller designs
•Experimentally determination of drop sizes through different designs of impellers.•Drop size analysis through image processing tools.•Develop a model to predict d32.by using ANFIS-Fuzzy C-means (ANFIS-FCM).•Compare the prediction capability of the ANFIS-FCM and the empirical correlations. The prese...
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| Published in | Journal of the Taiwan Institute of Chemical Engineers Vol. 100; pp. 26 - 36 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.07.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1876-1070 1876-1089 |
| DOI | 10.1016/j.jtice.2019.04.005 |
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| Abstract | •Experimentally determination of drop sizes through different designs of impellers.•Drop size analysis through image processing tools.•Develop a model to predict d32.by using ANFIS-Fuzzy C-means (ANFIS-FCM).•Compare the prediction capability of the ANFIS-FCM and the empirical correlations.
The present study investigates the effects of impeller design and dispersed phase volume ratio on mean drop sizes (d32) in immiscible liquid-liquid stirred vessel through experimental and modeling approaches. Various impeller designs including conventional and new impeller designs were employed to cover both radial and axial flow impellers. The microscopic method associated with image processing tools was used for the drop size analysis. The results showed the hydrofoil impeller produced the largest drop sizes while the double-curved blade turbine produced the smallest drop sizes, corresponding to about 37% difference. Increasing the dispersed phase volume ratio from 1% to 10%) increased the d32 by approximately 20–40%. Adaptive neuro-fuzzy inference system based on fuzzy C–means (ANFIS-FCM) clustering algorithm was used to develop a model to predict drop sizes, and its validation and accuracy were examined by comparing the results to the experimental data. The results also proved the superior prediction capability of the ANFIS-FCM method over the empirical correlations for the most cases. |
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| AbstractList | •Experimentally determination of drop sizes through different designs of impellers.•Drop size analysis through image processing tools.•Develop a model to predict d32.by using ANFIS-Fuzzy C-means (ANFIS-FCM).•Compare the prediction capability of the ANFIS-FCM and the empirical correlations.
The present study investigates the effects of impeller design and dispersed phase volume ratio on mean drop sizes (d32) in immiscible liquid-liquid stirred vessel through experimental and modeling approaches. Various impeller designs including conventional and new impeller designs were employed to cover both radial and axial flow impellers. The microscopic method associated with image processing tools was used for the drop size analysis. The results showed the hydrofoil impeller produced the largest drop sizes while the double-curved blade turbine produced the smallest drop sizes, corresponding to about 37% difference. Increasing the dispersed phase volume ratio from 1% to 10%) increased the d32 by approximately 20–40%. Adaptive neuro-fuzzy inference system based on fuzzy C–means (ANFIS-FCM) clustering algorithm was used to develop a model to predict drop sizes, and its validation and accuracy were examined by comparing the results to the experimental data. The results also proved the superior prediction capability of the ANFIS-FCM method over the empirical correlations for the most cases. |
| Author | Bostanci, Huseyin Afshar Ghotli, Reza Bagheri, AmirHossein Abbasi, Mohammad Reza Ibrahim, Shaliza Raman, Abdul Aziz Abdul |
| Author_xml | – sequence: 1 givenname: Reza orcidid: 0000-0002-3264-9328 surname: Afshar Ghotli fullname: Afshar Ghotli, Reza email: reza.afshar@um.edu.my organization: Institute of Ocean and Earth Sciences (IOES), Research Management and Innovation Centre, University of Malaya, 50603 Kuala Lumpur, Malaysia – sequence: 2 givenname: Mohammad Reza orcidid: 0000-0002-3721-6054 surname: Abbasi fullname: Abbasi, Mohammad Reza email: m_abbasi@ifac-mail.com organization: Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia – sequence: 3 givenname: AmirHossein orcidid: 0000-0003-1295-4330 surname: Bagheri fullname: Bagheri, AmirHossein email: amirhossein.bagheri@unt.edu organization: Department of Mechanical and Energy Engineering, University of North Texas, Denton, TX 76207, USA – sequence: 4 givenname: Abdul Aziz Abdul orcidid: 0000-0001-7586-1903 surname: Raman fullname: Raman, Abdul Aziz Abdul email: azizraman@um.edu.my organization: Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia – sequence: 5 givenname: Shaliza surname: Ibrahim fullname: Ibrahim, Shaliza email: shaliza@um.edu.my organization: Institute of Ocean and Earth Sciences (IOES), Research Management and Innovation Centre, University of Malaya, 50603 Kuala Lumpur, Malaysia – sequence: 6 givenname: Huseyin surname: Bostanci fullname: Bostanci, Huseyin email: huseyin.bostanci@unt.edu organization: Department of Engineering Technology, University of North Texas, Denton, TX 76207, USA |
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| Title | Experimental and modeling evaluation of droplet size in immiscible liquid-liquid stirred vessel using various impeller designs |
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