Model based process optimization of nanosuspension preparation via wet stirred media milling

2018 ◽  
Vol 331 ◽  
pp. 146-154 ◽  
Author(s):  
Frederik Flach ◽  
Sandra Breitung-Faes ◽  
Arno Kwade
2016 ◽  
Vol 88 (9) ◽  
pp. 1245-1245
Author(s):  
M. N. Cruz Bournazoua ◽  
M. Gruber ◽  
S. Kamel ◽  
R. Giessman ◽  
A. Wagner ◽  
...  

Desalination ◽  
2018 ◽  
Vol 436 ◽  
pp. 125-143 ◽  
Author(s):  
I. Hitsov ◽  
K. De Sitter ◽  
C. Dotremont ◽  
I. Nopens

2011 ◽  
Author(s):  
Rafael Aldana ◽  
Venu Vellanki ◽  
Wenjin Shao ◽  
Ronald Goossens ◽  
Zongchang Yu ◽  
...  

Author(s):  
Yang Ban ◽  
Kara Kearney ◽  
Bryan Sundahl ◽  
Leandro Medina ◽  
Roger T. Bonnecaze ◽  
...  

2009 ◽  
Author(s):  
Tsung-Chih Chien ◽  
C. Y. Shih ◽  
R. C. Peng ◽  
H. H. Liu ◽  
Y. C. Chen ◽  
...  

2012 ◽  
Vol 538-541 ◽  
pp. 439-443
Author(s):  
Zhi Jun Lv ◽  
Qian Xiang ◽  
Jian Guo Yang

The yarn production is a complex industrial process, and the relation between the spinning variables and the yarn properties has not been established conclusively so far. In fact, the existing process cases which were recorded to ensure the ability to trace production steps can also be used to optimize the process itself. This paper presents a novel process decision model based on CBR and SVM hybird intelligence for optimization of large numbers of spnning parameters. The applied cases are demonstrated that the intelligent model to optimizing the spinning process is promising.


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