D. Ashouri

Publications

3 Modeling and Prediction of Zinc Extraction Efficiency from Concentrate by Operating Condition and Using Artificial Neural Networks

Authors: D. Ashouri, S. Mousavian, F. Mousavian, V. Nikkhah Rashidabad, N. Ghazinia

Abstract:

PH, temperature and time of extraction of each stage,  agitation speed and delay time between stages effect on efficiency of  zinc extraction from concentrate. In this research, efficiency of zinc  extraction was predicted as a function of mentioned variable by  artificial neural networks (ANN). ANN with different layer was  employed and the result show that the networks with 8 neurons in  hidden layer has good agreement with experimental data.

 

Keywords: Neural Networks, Efficiency, zinc extraction, operating condition

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2 Application of Neural Networks to Predict Changing the Diameters of Bubbles in Pool Boiling Distilled Water

Authors: D. Ashouri, S. Mousavian, V. Nikkhah Rashidabad, M. Manteghian, M. Masoumi

Abstract:

In this research, the capability of neural networks in  modeling and learning complicated and nonlinear relations has been  used to develop a model for the prediction of changes in the diameter  of bubbles in pool boiling distilled water. The input parameters used  in the development of this network include element temperature, heat  flux, and retention time of bubbles. The test data obtained from the  experiment of the pool boiling of distilled water, and the  measurement of the bubbles form on the cylindrical element. The  model was developed based on training algorithm, which is  typologically of back-propagation type. Considering the correlation  coefficient obtained from this model is 0.9633. This shows that this  model can be trusted for the simulation and modeling of the size of  bubble and thermal transfer of boiling.

Keywords: Neural Network, Heat Flux, bubble diameter, training algorithm

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1 Investigation of Artificial Neural Networks Performance to Predict Net Heating Value of Crude Oil by Its Properties

Authors: Mousavian, M. Moghimi Mofrad, M. H. Vakili, D. Ashouri, R. Alizadeh

Abstract:

The aim of this research is to use artificial neural networks computing technology for estimating the net heating value (NHV) of crude oil by its Properties. The approach is based on training the neural network simulator uses back-propagation as the learning algorithm for a predefined range of analytically generated well test response. The network with 8 neurons in one hidden layer was selected and prediction of this network has been good agreement with experimental data.

Keywords: Experimental, Modeling, Neural Network, crude oil, Net Heating Value

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Abstracts

2 Application of Neural Networks to Predict Changing the Diameters of Bubbles in Pool Boiling Distilled Water

Authors: D. Ashouri, S. Mousavian, V. Nikkhah Rashidabad, M. Manteghian, M. Masoumi

Abstract:

In this research, the capability of neural networks in modeling and learning complicated and nonlinear relations has been used to develop a model for the prediction of changes in the diameter of bubbles in pool boiling distilled water. The input parameters used in the development of this network include element temperature, heat flux, and retention time of bubbles. The test data obtained from the experiment of the pool boiling of distilled water, and the measurement of the bubbles form on the cylindrical element. The model was developed based on training algorithm, which is typologically of back-propagation type. Considering the correlation coefficient obtained from this model is 0.9633. This shows that this model can be trusted for the simulation and modeling of the size of bubble and thermal transfer of boiling.

Keywords: Neural Network, Heat Flux, bubble diameter, training algorithm

Procedia PDF Downloads 322
1 Modeling and Prediction of Zinc Extraction Efficiency from Concentrate by Operating Condition and Using Artificial Neural Networks

Authors: D. Ashouri, S. Mousavian, F. Mousavian, V. Nikkhah Rashidabad, N. Ghazinia

Abstract:

PH, temperature, and time of extraction of each stage, agitation speed, and delay time between stages effect on efficiency of zinc extraction from concentrate. In this research, efficiency of zinc extraction was predicted as a function of mentioned variable by artificial neural networks (ANN). ANN with different layer was employed and the result show that the networks with 8 neurons in hidden layer has good agreement with experimental data.

Keywords: Neural Networks, Efficiency, zinc extraction, operating condition

Procedia PDF Downloads 381