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This paper describes a robust hybrid artificial neural network (ANN) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid artificial neural network and differential evolution technique (ANN-DE) for efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of pressure drop of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of pressure drops over a wide range of operating conditions, physical properties, and pipe diameters.

Artificial Neural Network Model with Parameter Tuning Assisted by Differential Evolution Technique: Study of Pressure Drop of Slurry Flow in Pipeline