Neural Networks and Support Vector Machine Models Applied to Energy Consumption Optimization in Semiautogeneous Grinding

@inproceedings{Curilem2011NeuralNA,
  title={Neural Networks and Support Vector Machine Models Applied to Energy Consumption Optimization in Semiautogeneous Grinding},
  author={Millaray Curilem and Gonzalo Acu{\~n}a and F. Cubillos and Eduardo Vyhmeister},
  year={2011}
}
Semiautogenous (SAG) mills for ore grinding are large energy consumption equipments. The SAG energy consumption is strongly related to the fill level of the mill. Hence, on-line information of the mill fill level is a relevant state variable to monitor and drive in SAG operations. Unfortunately, due to the prevailing conditions in a SAG mill, it is difficult to measure and represent from first principle model the state of the mill fill level. Alternative approaches to tackle this problem… CONTINUE READING