RESEARCH ON SELECTION AND PERFORMANCE IMPROVEMENT OF BLAST HOLE DRILLING EQUIPMENT

This paper describes the application of geological modeling-adapted sampling to improve operating costs and efficiency in some mining applications, and productivity in drill-and-blast processes. The objectives are to reduce the number of blast holes drilled and accidental access to the geologic boundary of interest. These goals come from an economic point of view, since the cost, firstly, is directly proportional to the number of drill holes, and secondly, it is related to the efficiency of recovery of the target material associated with excavation and blast damage. Therefore, problem formulation is an incentive to learn more about lithology and drill less. The main problem in creating an accurate surface model is that the sedimentary rock mass is usually coarsely sampled by drilling widely spaced exploratory holes. Thus, interpolation does not adequately capture local variations in the underlying geology. With the recent advent of consistent and reliable real-time detection of geologic boundaries in the field using borehole measurement data, we posed the problem of local model estimation in an adaptive sampling system. The proposed selection strategy consists of two steps. First, blast holes are drilled to the geologic boundary of interest and their locations are chosen adaptively to maximize benefit in terms of incremental improvements to the evolving spatial model. The second phase relies on forecast geology and conducts expert-driven drilling at a predetermined distance from the geological boundary of interest to optimize blasting and minimize its damage. Using data from the Kalmokir Study Bench in the Republic of Uzbekistan, we show that appropriate selection of blast holes in Stage 1 can reduce the total number of holes drilled into the top of the coal seam and optimize the blasting process, as opposed to random hole selection. we will show. maintaining a reasonable compromise in the error in seam-to-stop distances. We also show that adaptive sampling requires only a fraction of the holes initially opened for this data set for accurate estimation.