Kinetics, equilibrium isotherm and neural network modeling studies for the sorption of hexavalent chromium from aqueous solution by quartz/feldspar/wollastonite

RSC Advances ◽  
2016 ◽  
Vol 6 (7) ◽  
pp. 5837-5847 ◽  
Author(s):  
B. Kavitha ◽  
D. Sarala Thambavani
Keyword(s):  
Neural Network ◽  
Aqueous Solution ◽  
Artificial Neural Network ◽  
Adsorption Process ◽  
Network Modeling ◽  
Back Propagation ◽  
Neural Network Modeling ◽  
Training Algorithm ◽  
Equilibrium Isotherm ◽  
Artificial Neural Network Ann

A three layer feed forward artificial neural network (ANN) with back propagation training algorithm was developed to model the adsorption process of Cr(vi) in aqueous solution using riverbed sand containing quartz/feldspar/wollastonite (QFW) as adsorbent.

Prediction of Cutting Temperatures by Using Back Propagation Neural Network Modeling when Cutting Hardened H-13 Steel in CNC End Milling

2012 ◽  
Vol 576 ◽  
pp. 91-94 ◽  
Author(s):  
Erry Yulian Triblas Adesta ◽  
Muataz H.F. Al Hazza ◽  
M.Y. Suprianto ◽  
Muhammad Riza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
End Milling ◽  
Back Propagation ◽  
Cutting Temperature ◽  
Neural Network Modeling ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Cnc End Milling

Machining of hardened steel at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality and cutting tool life. Thus, predicting the temperature in early stage becomes utmost importance. This research presents a neural network model for predicting the cutting temperature in the CNC end milling process. The Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the cutting temperature. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the cutting temperature. The artificial neural network (ANN) was applied to predict the cutting temperature. Twenty hidden layer has been used with feed forward back propagation hierarchical neural networks were designed with Matlab2009b Neural Network Toolbox. The results show a high correlation between the predicted and the observed temperature which indicates the validity of the models.

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