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.
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.
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.
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.