V. Nikkhah Rashidabad

Publications

4 Investigation Bubble Growth and Nucleation Rates during the Pool Boiling Heat Transfer of Distilled Water Using Population Balance Model

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

Abstract:

In this research, the changes in bubbles diameter and  number that may occur due to the change in heat flux of pure water  during pool boiling process. For this purpose, test equipment was  designed and developed to collect test data. The bubbles were graded  using Caliper Screen software. To calculate the growth and  nucleation rates of bubbles under different fluxes, population balance  model was employed. The results show that the increase in heat flux  from q=20 kw/m2 to q= 102 kw/m2 raised the growth and nucleation  rates of bubbles.

 

Keywords: Heat Flux, bubble growth, bubble nucleation, population balance model

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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 Modification of Rk Equation of State for Liquid and Vapor of Ammonia by Genetic Algorithm

Authors: S. Mousavian, F. Mousavian, V. Nikkhah Rashidabad

Abstract:

Cubic equations of state like Redlich–Kwong (RK)  EOS have been proved to be very reliable tools in the prediction of  phase behavior. Despite their good performance in compositional  calculations, they usually suffer from weaknesses in the predictions  of saturated liquid density. In this research, RK equation was  modified. The result of this study show that modified equation has  good agreement with experimental data.

 

Keywords: Modification, Genetic Algorithm, Equation of state, Ammonia

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1 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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Abstracts

4 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
3 Investigation Bubble Growth and Nucleation Rates during the Pool Boiling Heat Transfer of Distilled Water Using Population Balance Model

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

Abstract:

In this research, the changes in bubbles diameter and number that may occur due to the change in heat flux of pure water during pool boiling process. For this purpose, test equipment was designed and developed to collect test data. The bubbles were graded using Caliper Screen software. To calculate the growth and nucleation rates of bubbles under different fluxes, population balance model was employed. The results show that the increase in heat flux from q=20 kw/m2 to q=102 kw/m2 raised the growth and nucleation rates of bubbles.

Keywords: Heat Flux, bubble growth, bubble nucleation, population balance model

Procedia PDF Downloads 294
2 Modification of Rk Equation of State for Liquid and Vapor of Ammonia by Genetic Algorithm

Authors: S. Mousavian, F. Mousavian, V. Nikkhah Rashidabad

Abstract:

Cubic equations of state like Redlich–Kwong (RK) EOS have been proved to be very reliable tools in the prediction of phase behavior. Despite their good performance in compositional calculations, they usually suffer from weaknesses in the predictions of saturated liquid density. In this research, RK equation was modified. The result of this study shows that modified equation has good agreement with experimental data.

Keywords: Modification, Genetic Algorithm, Equation of state, Ammonia

Procedia PDF Downloads 226
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