Pattern Recognition Based on the Nonparametric Kernel Regression Method in A-share Market

2013 ◽  
pp. 309-314 ◽  
Author(s):  
Huaiyu Sun ◽  
Mi Zhu ◽  
Feng He
Keyword(s):  
Pattern Recognition ◽  
Kernel Regression ◽  
Regression Method ◽  
Nonparametric Kernel ◽  
Nonparametric Kernel Regression ◽  
A Share

Nonparametric modeling of mass transfer coefficients for air stripping packed towers

1996 ◽  
Vol 23 (2) ◽  
pp. 549-559 ◽  
Author(s):  
Roberto M. Narbaitz ◽  
Yassine Djebbar
Keyword(s):  
Mass Transfer ◽  
Transfer Coefficient ◽  
Mass Transfer Coefficient ◽  
Kernel Regression ◽  
Transfer Coefficients ◽  
Nonparametric Modeling ◽  
Air Stripping ◽  
Volatile Organic ◽  
Nonparametric Kernel ◽  
Nonparametric Kernel Regression

Existing parametric correlations have been found to have difficulties in predicting the removal of trace levels of volatile organic chemicals by modern air stripping towers. In this study, a new approach using a nonparametric kernel regression method was used to predict the mass transfer coefficient, KLa, of air stripping towers. Although only four variables were used, the predictions are already improved more than 50% as compared with Onda correlation, the best existing parametric correlation. The proposed technique shows a dependency of KLa on the liquid flow rate which is in good agreement with established theory. Previous parametric approaches were unable to model this relationship correctly. Key words: mass transfer coefficient, air stripping tower, volatile organic compound, nonparametric kernel regression.

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