BibTex format
@article{Liang:2024:10.1021/acs.iecr.4c00014,
author = {Liang, F and Valdes, JP and Cheng, S and Kahouadji, L and Shin, S and Chergui, J and Juric, D and Arcucci, R and Matar, OK},
doi = {10.1021/acs.iecr.4c00014},
journal = {Industrial and Engineering Chemistry Research},
pages = {7853--7875},
title = {Liquid-liquid dispersion performance prediction and uncertainty quantification using recurrent neural networks},
url = {http://dx.doi.org/10.1021/acs.iecr.4c00014},
volume = {63},
year = {2024}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - 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.
AU - Liang,F
AU - Valdes,JP
AU - Cheng,S
AU - Kahouadji,L
AU - Shin,S
AU - Chergui,J
AU - Juric,D
AU - Arcucci,R
AU - Matar,OK
DO - 10.1021/acs.iecr.4c00014
EP - 7875
PY - 2024///
SN - 0888-5885
SP - 7853
TI - Liquid-liquid dispersion performance prediction and uncertainty quantification using recurrent neural networks
T2 - Industrial and Engineering Chemistry Research
UR - http://dx.doi.org/10.1021/acs.iecr.4c00014
UR - https://www.ncbi.nlm.nih.gov/pubmed/38706982
UR - https://pubs.acs.org/doi/10.1021/acs.iecr.4c00014
VL - 63
ER -