Neural network prediction of residence time distribution for quasi-2D pebble flow

2021 ◽  
pp. 117363
Author(s):  
Yujia Liu ◽  
Jeremy Marquardt ◽  
Sifan Peng ◽  
Liang Ge ◽  
Nan Gui ◽  
...  
2008 ◽  
Vol 2 (1) ◽  
pp. 73-78 ◽  
Author(s):  
V.K. Pareek ◽  
R. Sharma ◽  
C.G. Cooper ◽  
A.A. Adesina

Residence time distribution (RTD) study of solids in a three-phase pilot-scale bubble column photoreactor has been carried out in order to provide data for the development of an artificial neural network model usable for process optimisation. The experimental data indicated that the RTD of solids was a complex nonlinear function of gas and liquid velocities as well as the contacting pattern (co-current and countercurrent flow of gas and liquid). In this study, the solid particle RTD data were modeled using feed forward artificial neural networks (ANN). The networks were trained with 250- sets of input-output patterns using back-propagation algorithm. The trained networks were tested using 50-sets of RTD data previously unknown to the networks. Out of several configurations, a 3-layered network with 6-neurons in its hidden layer yielded optimal results with respect to the validation data. The optimal model and empirical data exhibited good agreement with a correlation coefficient of 0.995.


Author(s):  
Naohisa NISHIDA ◽  
Tatsumi OBA ◽  
Yuji UNAGAMI ◽  
Jason PAUL CRUZ ◽  
Naoto YANAI ◽  
...  

2021 ◽  
Vol 32 (2) ◽  
pp. 611-618
Author(s):  
Atena Dehghani Kiadehi ◽  
Mikel Leturia ◽  
Franco Otaola ◽  
Aissa Ould-Dris ◽  
Khashayar Saleh

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