Predictive Control Method of Reaming up in the Raise Boring Process Using Kernel Based Extreme Learning Machine

Raise boring is an important method to construct the underground shafts of mines and other infrastructures, by drilling down pilot hole then reaming up desired diameter. Seriously different from operations mechanical parts in mechanized mass production, it very difficult obtain a good consistency construction environments each raise or shaft, be more exact, every process highly customized. The bottom-up impossible observed directly, rock breaking effect measured real-time, due debris freely falling under excavated shaft. optimal configurations operational parameters working pressures, torque, rotation speed penetration speed, mainly depend on accumulation experience empirical models. To this end, we presented machine learning method, based extreme machine, determine relationships between performance parameters, physical-mechanical properties geologic zones, aiming at higher production excavation rate, safer operation minimum ground disturbance. This research brings out new possibilities revolutionize planning paradigm that traditionally depends subject matter expertise.