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Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines
Chemical Product and Process Modeling (2012)
  • Sandip K Lahiri, National Institute of Technology, Durgapur, India
  • Kartik Chandra Ghanta, National Institute of Technology, Durgapur, India
Abstract
Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.
Keywords
  • artificial neural network,
  • genetic algorithm,
  • slurry flow regime,
  • slurry flow
Publication Date
April 21, 2012
Citation Information
Sandip K Lahiri and Kartik Chandra Ghanta. "Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines" Chemical Product and Process Modeling Vol. 4 Iss. 1 (2012)
Available at: http://works.bepress.com/sandip_lahiri/31/