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Showing 2 results for Shafaei

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.
A. Azizi, S.z. Shafaei, R. Rooki,
Volume 13, Issue 2 (June 2016)
Abstract

Nowadays steel balls wear is a major problem in mineral processing industries and forms a significant part of the grinding cost. Different factors are effective on balls wear. It is needed to find models which are capable to estimate wear rate from these factors. In this paper a back propagation neural network (BPNN) and multiple linear regression (MLR) method have been used to predict wear rate of steel balls using some significant parameters including, pH, solid content, throughout of grinding circuit, speed of mill, charge weight of balls and grinding time. The comparison between the predicted wear rates and the measured data resulted in the correlation coefficients (R), 0.977 and 0.955 for training and test data using BPNN model. However, the R values were 0.936 and 0.969 for training and test data by MLR method. In addition, the average absolute percent relative error (AAPE) obtained 2.79 and 4.18 for train and test data in BPNN model, respectively. Finally, Analysis of the predictions shows that the BPNN and MLR methods could be used with good engineering accuracy to directly predict the wear rate of steel balls.

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