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

Abstract

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