Experimental Analysis of Semi-autogenous Grinding Mill Characteristics Under Different Working Conditions

In the present work, vibration, acoustic and thermal signals were correlated to the semi-autogenous grinding mill working parameters such as total power and inlet water flow rate, and then these parameters were monitored using vibration, acoustic and thermal analyses. Next, the influential controlling parameters were obtained to monitor the mill conditions via SPSS software and afterward by exploiting neural network method, the modified controlling parameters were acquired for the development of mill performance. It was found that the mill vibration and the bearing temperature increased with motors power. Also, the results revealed that the mill noise reduced with the increase in water flow rate. Finally, an analytical relationship between the controlling parameters and the noise as a measurable parameter was proposed.