Liquid–Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks-Reference-Cited by-同舟云学术

Liquid–Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks

Published:2024-04-22 Issue:17 Volume:63 Page:7853-7875
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Liang Fuyue¹,Valdes Juan P.¹^ORCID,Cheng Sibo²,Kahouadji Lyes¹,Shin Seungwon³,Chergui Jalel⁴,Juric Damir⁴⁵,Arcucci Rossella⁶,Matar Omar K.¹^ORCID

Affiliation:

1. Department of Chemical Engineering, Imperial College London, London SW7 2AZ, U.K.

2. CEREA, École des Ponts ParisTech-EdF R&D, Champs-sur-Marne 77455, France

3. Department of Mechanical and System Design Engineering, Hongik University, Seoul 04066, Republic of Korea

4. Centre National de la Recherche Scientifique (CNRS), Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), Université Paris Saclay, Orsay 91400, France

5. Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, U.K.

6. Department of Earth Science & Engineering, Imperial College London, London SW7 2AZ, U.K.

Funder

Engineering and Physical Sciences Research Council

Ministerio de Ciencia, Tecnolog?a e Innovaci?n

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.4c00014

Reference70 articles.

1. Current advances in liquid–liquid mixing in static mixers: A review

2. Leng, D. E.; Calabrese, R. V. Handbook of Industrial Mixing - Science and Practice. Chapter 12; John Wiley & Sons, 2004, pp 639–755.

3. Drop Size Distributions Produced by Turbulent Pipe Flow of Immiscible Fluids through a Static Mixer

4. Drop breakup in turbulent stirred-tank contactors. Part I: Effect of dispersed-phase viscosity

5. Correlation of mean drop size and minimum drop size with the turbulence energy dissipation and the flow in an agitated tank

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