International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 41 - Issue 9 |
Published: March 2012 |
Authors: S. L. Pandharipande, Anish M. Shah, Heena Tabassum |
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S. L. Pandharipande, Anish M. Shah, Heena Tabassum . Artificial Neural Network Modeling for Estimation of Composition of a Ternary Liquid Mixture with its Physical Properties such as Refractive Index, pH and Conductivity. International Journal of Computer Applications. 41, 9 (March 2012), 23-26. DOI=10.5120/5570-7663
@article{ 10.5120/5570-7663, author = { S. L. Pandharipande,Anish M. Shah,Heena Tabassum }, title = { Artificial Neural Network Modeling for Estimation of Composition of a Ternary Liquid Mixture with its Physical Properties such as Refractive Index, pH and Conductivity }, journal = { International Journal of Computer Applications }, year = { 2012 }, volume = { 41 }, number = { 9 }, pages = { 23-26 }, doi = { 10.5120/5570-7663 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2012 %A S. L. Pandharipande %A Anish M. Shah %A Heena Tabassum %T Artificial Neural Network Modeling for Estimation of Composition of a Ternary Liquid Mixture with its Physical Properties such as Refractive Index, pH and Conductivity%T %J International Journal of Computer Applications %V 41 %N 9 %P 23-26 %R 10.5120/5570-7663 %I Foundation of Computer Science (FCS), NY, USA
The analysis of a ternary mixture can be done by using analytical instruments like TLC, GLC, HPLC, GC etc. which is time consuming & expensive. In the present work Artificial neural network modeling has been applied to estimate composition of a ternary liquid mixture with its physical properties such as refractive index, pH & conductivity. The work is extended in developing ANN model for estimation of composition of a known ternary mixture for the experimentally determined physical properties, refractive index, pH & conductivity. Samples having known compositions of a ternary liquid mixture, acetic acid-water-ethanol have been prepared & analysed for the physical properties, refractive index, pH & conductivity. ANN models 1 & 2 with different topologies have been developed based on the generated data. The predicted & the actual values using ANN models 1 & 2 have been compared based on the % relative error. The novel feature of this work has been the development of ANN model 1 with the accuracy of prediction between 0-3 % for output parameter, mole % water & 0-5% for output parameter, mole % acetic acid for training data set & ANN model 1 having accuracy level of 0-10% for output parameter, mole % water & 0-3% for output parameter, mole % acetic acid for test data set.