期刊
APPLIED THERMAL ENGINEERING
卷 112, 期 -, 页码 208-213出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.applthermaleng.2016.09.159
关键词
Rotating packed bed; Dust; Separation grade efficiency; ANN model
资金
- Science Foundation of North University of China [XJJ2016011]
- Natural Science Foundations of China [21376229]
- China Ministry of Science and Technology [2016YFC0204103]
- Program for the Outstanding Innovative Teams of Higher Learning Institutions of Shanxi [201316]
Artificial neural network (ANN) models, including the Cascade-forward back propagation neural network (CFBPNN), feed-forward back propagation neural network (FFBPNN) and Elman-forward back propagation neural network (EFBPNN), were proposed to predict the dust removal efficiency in rotating packed bed (RPB) to speed up its development. Total 326 data sets for separation grade efficiency had been collected from literatures for training and verifying the model. Gas Reynolds number (Re), liquid Reynolds number (Re-L), rotational Reynolds number (Re-omega), M (d(0)(2)rho(L)/dP(2)/rho(p)) and C-si/rho(G) were used as input data. While, the variable eta (separation grade efficiency) was taken as output data for each model. Various of hidden neurons were compared based on the mean square error (E-2), coefficient of determination (R-2) and residual for each model. The separation grade efficiency in RPB was also compared with other existed dust removal equipments. (C) 2016 Elsevier Ltd. All rights reserved.
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