Time series prediction of hydrate dynamics on flow assurance using PCA and Recurrent neural networks with iterative transfer learning

Author:

Lee Nayoung,Kim Hyunho,Jung JongYeon,Park Ki-Heum,Linga Praveen,Seo Yutaek

Funder

Ministry of Land, Infrastructure and Transport

Publisher

Elsevier BV

Subject

Applied Mathematics,Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Reference52 articles.

1. Hydrate formation and particle distributions in gas–water systems;Akhfash;Chem. Eng. Sci.,2013

2. Alon, J., Sclaroff, S., Kollios, G., Pavlovic, V., 2003. Discovering clusters in motion time-series data. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. IEEE, pp. I-I.

3. Methane hydrate bed formation in a visual autoclave: cold restart and Reynolds number dependence;Aman;J. Chem. Eng. Data,2015

4. Hydrate formation and deposition in a gas-dominant flowloop: Initial studies of the effect of velocity and subcooling;Aman;J. Nat. Gas Sci. Eng.,2016

5. Deep learning for time series forecasting: predict the future with MLPs, CNNs and LSTMs in Python;Brownlee,2018

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