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Article Dans Une Revue Industrial and engineering chemistry research Année : 2024

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

Résumé

We demonstrate the application of a Recurrent Neural Network to perform multistep and multivariate time-series performance predictions for stirred and static mixers as exemplars of complex multiphase systems. We employ two Long-Short-Term Memory (LSTM) frameworks in this study 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 physico-chemical properties, mixer geometry, and operating conditions. Our results demonstrate that whilst it is possible to train a LSTM with a single fully-connected layer more efficiently than a LSTM Encoderdecoder, the latter is shown to be more capable of learning the dynamics underlying dispersion metrics. Details of the methodology are presented, which include data pre-processing, LSTM model exploration, methods for model performance visualisation; 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.
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Dates et versions

hal-04647255 , version 1 (13-07-2024)

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Fuyue Liang, Juan Valdes, Sibo Cheng, Lyes Kahouadji, Seungwon Shin, et al.. Liquid–Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks. Industrial and engineering chemistry research, 2024, 63 (17), pp.7853-7875. ⟨10.1021/acs.iecr.4c00014⟩. ⟨hal-04647255⟩
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