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

This paper describes a robust hybrid artificial neural network (ANN) methodology which can offer a superior performance for the Important process engineering 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 literature showed that the developed ANN correlation noticeably improved the prediction of hold up over a wide range of operating conditions, physical properties, and pipe diameters.