Liquid-Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networksopen access
- Authors
- Liang, Fuyue; Valdes, Juan P.; Cheng, Sibo; Kahouadji, Lyes; Shin, Seungwon; Chergui, Jalel; Juric, Damir; Arcucci, Rossella; Matar, Omar K.
- Issue Date
- 1-May-2024
- Publisher
- American Chemical Society
- Citation
- Industrial and Engineering Chemistry Research, v.63, no.17, pp 7853 - 7875
- Pages
- 23
- Journal Title
- Industrial and Engineering Chemistry Research
- Volume
- 63
- Number
- 17
- Start Page
- 7853
- End Page
- 7875
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/33210
- DOI
- 10.1021/acs.iecr.4c00014
- ISSN
- 0888-5885
1520-5045
- Abstract
- We demonstrate the application of a recurrent neural network (RNN) to perform multistep and multivariate time-series performance predictions for stirred and static mixers as exemplars of complex multiphase systems. We employ two network architectures in this study, fitted with either long short-term memory and gated recurrent unit cells, which are trained on high-fidelity, three-dimensional, computational fluid dynamics simulations of the mixer performance, in the presence and absence of surfactants, in terms of drop size distributions and interfacial areas as a function of system parameters; these include physicochemical properties, mixer geometry, and operating conditions. Our results demonstrate that while it is possible to train RNNs with a single fully connected layer more efficiently than with an encoder-decoder structure, the latter is shown to be more capable of learning long-term dynamics underlying dispersion metrics. Details of the methodology are presented, which include data preprocessing, RNN model exploration, and methods for model performance visualization; an ensemble-based procedure is also introduced to provide a measure of the model uncertainty. The workflow is designed to be generic and can be deployed to make predictions in other industrial applications with similar time-series data. © 2024 The Authors. Published by American Chemical Society.
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Collections - College of Engineering > Department of Mechanical and System Design Engineering > 1. Journal Articles
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