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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.

chemical industry and chemical engineering quarterly

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