Semiempirical Quantum Chemical Method and Artificial Neural Networks Applied for λmaxComputation of Some Azo Dyes

2004 ◽  
Vol 44 (6) ◽  
pp. 2047-2050 ◽  
Author(s):  
Guo-Zheng Li ◽  
Jie Yang ◽  
Hai-Feng Song ◽  
Shang-Sheng Yang ◽  
Wen-Cong Lu ◽  
...  
2006 ◽  
Vol 12 (4) ◽  
pp. 521-527 ◽  
Author(s):  
Jinwei Gao ◽  
Xueye Wang ◽  
Xinliang Yu ◽  
Xiaobing Li ◽  
Hanlu Wang

2006 ◽  
Vol 12 (4) ◽  
pp. 513-520 ◽  
Author(s):  
Jinwei Gao ◽  
Xueye Wang ◽  
Xiaobing Li ◽  
Xinliang Yu ◽  
Hanlu Wang

Author(s):  
Les M. Sztandera ◽  
Charles Bock ◽  
Mendel Trachtman ◽  
Janardhan Velga

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Masroor Mohajerani ◽  
Mehrab Mehrvar ◽  
Farhad Ein-Mozaffari

One-hidden-layer artificial neural networks (ANNs) using a back-propagation structure have been trained on different sets of experimental data to identify and evaluate the degradation of different azo dyes (Reactive Yellow 84, Reactive Blue 19, Direct Red 23, Direct Red 28, and Acid Blue 193) by photo-Fenton process and combined ozonation and ultrasonolysis processes. Different input variables such as pH, initial concentrations of dyes and ozone, reaction time, ultrasonic power density, and initial concentrations of hydrogen peroxide and ferrous in aqueous solution were employed to model the degradation rates of azo dyes based on the decolorization efficiency and the removal rate using chemical oxygen demand (COD) and total organic carbon (TOC). A new model expression is developed to find the effect of individual parameters and their interactions on the efficiency of organic degradation by advanced oxidation processes.


1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner

Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  

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