Artificial neural network model with the parameter tuning assisted by a differential evolution technique: The study of the hold up of the slurry flow in a pipeline | Zendy

Sandip Kumar Lahiri | Zendy; Kartik Chandra Ghanta | Zendy

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Artificial neural network model with the parameter tuning assisted by a differential evolution technique: The study of the hold up of the slurry flow in a pipeline

Author(s) -

Sandip Kumar Lahiri,

Kartik Chandra Ghanta

Publication year - 2009

Publication title -

chemical industry and chemical engineering quarterly

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.189

H-Index - 26

eISSN - 2217-7434

pISSN - 1451-9372

DOI - 10.2298/ciceq0902103l

Subject(s) - artificial neural network , differential evolution , pipeline (software) , range (aeronautics) , slurry , flow (mathematics) , process (computing) , computer science , differential (mechanical device) , engineering , artificial intelligence , mathematics , geometry , environmental engineering , programming language , aerospace engineering , operating system

This paper describes a robust hybrid artificial neural network (ANN) methodo- logy which can offer a superior performance for the important process engine- ering problems. The method incorporates a hybrid artificial neural network and differential evolution technique (ANN-DE) for the efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of the hold up of the solid liquid slurry flow. A comparison with selected correlations in the lite- rature showed that the developed ANN correlation noticeably improved the prediction of hold up over a wide range of operating conditions, physical pro- perties, and pipe diameters.

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