Amirkabir University of Technology Amirkabir Journal of Civil Engineering 2588-297X 50 3 2018 08 23 Perdition of Semi-autogenous mill Power Using Radial Artificial Neural Network Based on Principal Component Perdition of Semi-autogenous mill Power Using Radial Artificial Neural Network Based on Principal Component 511 518 1952 10.22060/ceej.2017.12599.5240 FA F.S. Hoseinian Department of Mining & Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran B. Rezai Department of Mining & Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran S. Soltani-Mohammadi Department of Mining Engineering, University of Kashan, Kashan, Iran 0000-0002-8390-1917 Journal Article 2017 03 11 Providing of semi-autogenous (SAG) mill models for prediction of its effectiveness is one of the most useful tools for better design of grinding circuit. Many SAG mill models have been presented in the literature, but in most of them have not been predicted the mill performance in industrial scale. Semi-autogenous mill power has an effective impact on the mill performance. So in this study, a new model based on combination of radial artificial neural network and principal component is presented to predict semi-autogenous mill power. The feed moisture, mass flowrate, mill load cell weight, SAG mill solid percent, inlet and outlet water to the SAG mill and work index selected as input variables and evaluated the effect of them on the mill power. The results showed that the trained hybrid model of artificial neural network and principal component with R=0.8456 and RMSE= 68.0752 can be used to predict the semi-autogenous mill power in industrial scale. The sensitivity analysis results showed that all model input parameters had a significant effect on the output. Providing of semi-autogenous (SAG) mill models for prediction of its effectiveness is one of the most useful tools for better design of grinding circuit. Many SAG mill models have been presented in the literature, but in most of them have not been predicted the mill performance in industrial scale. Semi-autogenous mill power has an effective impact on the mill performance. So in this study, a new model based on combination of radial artificial neural network and principal component is presented to predict semi-autogenous mill power. The feed moisture, mass flowrate, mill load cell weight, SAG mill solid percent, inlet and outlet water to the SAG mill and work index selected as input variables and evaluated the effect of them on the mill power. The results showed that the trained hybrid model of artificial neural network and principal component with R=0.8456 and RMSE= 68.0752 can be used to predict the semi-autogenous mill power in industrial scale. The sensitivity analysis results showed that all model input parameters had a significant effect on the output. https://ceej.aut.ac.ir/article_1952_1113d7a76ffceca1bb350bfe145467c6.pdf