Modeling and optimization of high chromium alloy wear in phosphate laboratory grinding mill with fuzzy logic and particle swarm optimization technique

This study evaluated the potential of fuzzy logic as an alternative method to the traditional statistical regression techniques, which were employed in a previous publication (Chen et al., 2006), for predicting the wear rate of high chromium alloy during phosphate grinding. Moreover an attempt has been made to use a novel particle swarm optimization technique to determine the optimum process parameters for minimum wear rate. The comparison of a fuzzy model and a regression model, which was assessed by various measures (i.e., root mean square error (RMSE), R2 and the percentage of predicted values that are within the 5% tolerance of the corresponding actual values), showed the superiority of the developed fuzzy model. The optimization results indicated that the minimum liner wear rate can be obtained with solution pH of 10.5, a rotation speed of 52 rpm, a solid concentration of 63% and a crop load 70%.