Modelling of dust removal in rotating packed bed using artificial neural networks (ANN). (5th February 2017)

• Record Type: Journal Article
• Authors: Li, Weiwei; Wu, Xiaoli; Jiao, Weizhou; Qi, Guisheng; Liu, Youzhi
• Is Part Of: Applied thermal engineering. Volume 112(2017:Feb.)
• Page Start: 208
• Page End: 213
• Publication Date: 2017-02-05
• Subjects: Rotating packed bed -- Dust -- Separation grade efficiency -- ANN model; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2016.09.159
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: Modeling of separation grade efficiency by ANN were conducted in RPB. Optimal hidden neurons for CFBPNN, EBPNN and FFBPNN were 15, 14 and 8, respectively. The FFBPNN was shown better performance than EBPNN and CFBPNN. Abstract: 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 (ReG ), liquid Reynolds number (ReL ), rotational Reynolds number (Reω ), M (d0 2 ρL /dp 2 /ρp ) and Csi /ρG were used as input data. While, the variable η (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.