Development of a hybrid support vector machine and genetic algorithm model for regime identification of slurry transport in pipelines

作者：Lahiri Sandip Kumar^*; Ghanta Kartik Chandra

来源：Asia-Pacific Journal of Chemical Engineering, 2010, 5(6): 847-861.

DOI：10.1002/apj.410

• support vector machine
• genetic algorithm
• slurry flow regime
• slurry flow

Four distinct regimes (namely sliding bed, saltation, heterogeneous suspension, and homogeneous suspension) were found existent in slurry flow in a pipeline depending upon the average velocity of flow. In the literature, a few numbers of correlations have been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as it is the prerequisite to apply different pressure drop correlations in different regimes. 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 support vector machine (SVM) modeling. The method incorporates hybrid support vector machine and genetic algorithm technique (SVM-GA) for efficient tuning of SVM meta-parameters. Statistical analysis showed that the proposed method has an average misclassification error (AARE) of 0.03%. A comparison with selected correlations in the literature showed that the developed SVM-GA method noticeably improved prediction of regimes over a wide range of operating conditions, physical properties, and pipe diameters.

• 出版日期2010-12