文章基本信息

标题：REGIME IDENTIFICATION OF SLURRY TRANSPORT IN PIPELINES - A NOVEL MODELING APPROACH USING ANN AND DIFFERENTIAL EVOLUTION
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作者：SANDIP KUMAR LAHIRI ; K.C. GHANTA
期刊名称：Chemical Industry and Chemical Engineering Quarterly
印刷版ISSN：1451-9372
出版年度：2010
卷号：16
期号：04
页码：329-343
出版社：Association of the Chemical Engineers
摘要：Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipelines depending upon the average velocity of flow. In the literature, few correlations have been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite for applying different pressure drop correlations in different regimes. How¬ever, the 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 ar¬tificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and differential evolution technique (ANN-DE) 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 cor-relations in the literature showed that the developed ANN-DE method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.
关键词：artificial neural network ; differential evolution ; slurry flow regime ; slurry flow.