Artificial neural network model with parameter tuning assisted by genetic algorithm technique: study of critical velocity of slurry flow in pipeline

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 genetic algorithm technique (ANN–GA) for efficient tuning of ANN meta ‐parameters. The algorithm has been applied for prediction of critical velocity of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved prediction of critical velocity over a wide range of operating conditions, physical properties, and pipe diameters.