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Liquid-Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks.

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Liang, Fuyue 
Cheng, Sibo 
Kahouadji, Lyes 
Shin, Seungwon 


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.


Publication status: Published


40 Engineering, 34 Chemical Sciences, Bioengineering

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Ind Eng Chem Res

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American Chemical Society (ACS)
Engineering and Physical Sciences Research Council (EP/K003976/1)
Engineering and Physical Sciences Research Council (EP/T000414/1)
Ministerio de Ciencia, Tecnología e Innovación (NA)