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

• Main Authors: S. K. Lahiri, K. C. Ghanta
• Format: Article
• Language: English
• Published: Association of the Chemical Engineers of Serbia 2009-05-01
• Series: Chemical Industry and Chemical Engineering Quarterly
• Subjects: artificial neural network; differential evolution; slurry hold up; slurry flow
• Online Access: http://www.ache.org.rs/CICEQ/2009/No2/CICEQ_Vol15_%20No2_pp103-117_Apr-Jun_2009.pdf

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.