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Artificial Neural Network (ANN) approach for modeling experimental data of viscosity and density of a ternary solution

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Type: Conference paper
Language: English
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NTOGP03_016

Index date: 24 June 2022

Artificial Neural Network (ANN) approach for modeling experimental data of viscosity and density of a ternary solution abstract

The Artificial neural network (ANN) approach was used to model experimental viscosity anddensity data for ternary aqueous solutions of calcium chloride and potassium chloride. Then, the ANNmodel was compared with the previously investigated models e.g., modified Jones-Dole, Hu,Exponential and GF models used for the same dataset. In the present study, the Levenberg-Marquardtalgorithm or "trainlm" command was selected as the training algorithm. Subsequently, differentconfigurations of the network were compared and the optimal multi-layer perceptron (MLP) networkwas designed with 3 hidden layers and [8 4 3] neurons since it showed better performance. Moreover,80% of the dataset for network training, 10% for validation and the rest for network testing wererandomly selected. Amid investigated models, ANN obtained the minimum mean square error (MSE) of6.2008×10-5 and maximum R2 of 0.9997 while at best modifies Jones-Dole could achieve a MSE of2.768×10-5 and R2 of 0.9996. suggesting that the ANN model is the most optimal model for modelingthe viscosity and density of this ternary solution.

Artificial Neural Network (ANN) approach for modeling experimental data of viscosity and density of a ternary solution Keywords:

Artificial Neural Network (ANN) approach for modeling experimental data of viscosity and density of a ternary solution authors

Saeed Ghasemzade Bariki

School of Chemical, Petroleum and Gas Engineering, Iran university of science and technology, ۱۶۸۴۶-۱۳۱۱۴, Tehran, Iran

Salman Movahedirad

School of Chemical, Petroleum and Gas Engineering, Iran university of science and technology, ۱۶۸۴۶-۱۳۱۱۴, Tehran, Iran

Ali Esmaeeli

UNESCO Chair on Water Reuse, School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran