Showing 2 results for Noaparast
A. Azizi, S. Z. Shafaei, M. Noaparast, M. Karamoozian,
Volume 10, Issue 4 (december 2013)
Abstract
This paper was aimed to address the modeling and optimization of factors affecting the corrosive wear of low alloy and high carbon chromium steel balls. Response surface methodology, central composite design (CCD) was employed to assess the main and interactive effects of the parameters and also to model and minimize the corrosive wear of the steels. The second-order polynomial regression model was proposed for relationship between the corrosion rates and relevant investigated parameters. Model fitted to results indicated that the linear effects of all of factors, interactive effect of pH and grinding time and the quadratic effects of pH and balls charge weight, were statistically significant in corrosive wear of low alloy steel balls. The significant parameters in the corrosive wear of high carbon chromium steel balls were the linear effects of all factors, the interactions effect of solid concentration, mill speed, mill throughout, grinding time, and the quadratic effects of pH and solid content. Also, the results showed that within the range of parameters studied, the corrosion rate of 78.38 and 40.76 could be obtained for low alloy and high carbon chromium steel balls, respectively.
B. Shahbazi, B. Rezai, S. Chehreh Chelgani, S. M. J. Koleini, M. Noaparast,
Volume 12, Issue 1 (march 2015 2015)
Abstract
Multivariable regression and artificial neural network procedures were used to modeling of the input power
and gas holdup of flotation. The stepwise nonlinear equations have shown greater accuracy than linear ones where
they can predict input power, and gas holdup with the correlation coefficients of 0.79 thereby 0.51 in the linear, and
R2=0.88 versus 0.52 in the non linear, respectively. For increasing accuracy of predictions, Feed-forward artificial
neural network (FANN) was applied. FANNs with 2-2-5-5, and 2-2-3-2-2 arrangements, were capable to estimating of
the input power and gas holdup, respectively. They were achieved quite satisfactory correlations of 0.96 in testing stage
for input power prediction, and 0.64 for gas holdup prediction
