Iranian Journal of Materials Science and Engineering

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Abstract: The specific rate of breakage is one of the most important factors in evaluation of grinding process especially in ball mill. In this article the effect of ball size and feed size on selection function were investigated using batch grinding circuit on two-iron ore anomaly (B, C) from sangan mine in north of Iran. Eight different monosize fractions were prepared between 2000 and 500 microns, using a 2 sieve series. The specific rates of breakage (Si) were determined from the size distributions at different grinding times, and the specific rates of breakage were compared for three different ball diameters (25.4, 16.6 and 9.6 mm). The results showed that the breakage function of both anomalies is normalizable (independent to the particle size) and it is independent to the ball size. But the specific rate of breakage variate with feed size and ball size. Also optimum size of balls for grinding of this feed obtained to be between 9.6-16.6mm.

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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.