This thesis investigates the application of pattern recognition techniques to rock type recognition using monitoring-while-drilling data. The research is focused on data from a large electric blasthole drill operating in an open-pit coal mine. Pre-processing and normalization techniques are applied to minimize potential misclassification issues. Both supervised and unsupervised learning is employed in the classifier design: back-propagation neural networks are used for the supervised learning, while self-organizing maps are used for unsupervised learning. A variety of combinations of drilling data and geophysical data are investigated as inputs to the classifiers. The outputs from these classifiers are evaluated relative to the rock classification made by a commercially available rock type recognition system, as well as relative to independent labelling by a geologist. Classifier performance is improved when drilling data used as inputs are augmented with geophysical data inputs. By using supervised learning with both drilling and geophysical data as inputs, the misclassification of coal, as well as of the non-coal rock types, is reduced compared to results of current commercial recognition methods. Moreover, rock types which were not detected by the previous methods were successfully classified by the supervised models. / Thesis (Master, Mining Engineering) -- Queen's University, 2007-11-28 15:22:17.454 / I would like to thank the financial support provided by the George C. Bateman and J. J. Denny Graduate Fellowship, as well as funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) provided via NSERC grant support to Dr. Daneshmend.

This thesis investigated the monitoring and analysis of signals related to the vibration of large rotary blasthole drills. The research focused on a machine with electric drilling actuators: such machines are used as primary production equipment in the drill‐and‐blast cycle of the surface (open‐pit) mining process. The performance of such machines is limited by the onset of severe vibrations, which can arise due to the interaction of geology, bit, drill string, machine structure, and control settings. Experimental data for the thesis were obtained during field periods at an iron ore mine in Minnesota’s Mesabi Iron Range. The data acquisition and signal analysis techniques which were utilized are presented, including smoothing of signals and calculated variables such as specific energy. Ambient vibration sources and vibration aliasing issues are investigated. Results from analyzing structural response tests indicate that, as expected, the natural frequency of the drill mast decreases with increasing bit depth – although the mounting position of accelerometers distorts this trend. The pull down force (weight on bit) is shown to have no appreciable impact on the mast’s natural frequency, nor on the mast’s damping ratio. A strong relationship between rotary speed and the dominant vibration frequency peaks at 3x and 6x rotary speed is demonstrated, and a physical explanation of the 6x vibration peak is postulated. The rotary motor current is shown to consistently exhibit frequency peaks at 3x and 6x rotation speed, indicating that this variable is a good candidate for use either as a substitute for accelerometer feedback, or as an auxiliary signal to detect down‐the‐hole vibration when it is not manifested by the mast mounted accelerometers. System identification is used to demonstrate that the dynamic relationship between vibration and rotary current, while it can be modeled locally, varies with depth and geology and hence is essentially a time‐varying process. This results in the amplitude of rotary current not being usable as a proxy for vibration amplitude. Nonetheless, it is demonstrated that the root‐mean‐square (RMS) of the low frequency current oscillations, in a nonlinear combination with the RMS of the current signal as a whole, may be able to serve as a proxy for the RMS of the vibration signal. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2010-09-27 12:15:07.644

The ability of an explosive to break rock is influenced considerably by the extent of confinement in the blasthole and it is believed that confinement is improved by the use of stemming. The aim of this paper is to present the first and second stages of results in developing a stemming performance testing and evaluation facility for small diameter boreholes. The results showed that different stemming products have differences in terms of their functionality, which can have a major impact on the efficiency of rock breaking. Two test procedures were used, one through the exclusive use of compressed air and the second using a purpose built high pressure test rig with small quantities of explosives. Both tests were used to identify and evaluate the ability of various stemming products to resist the escape of explosive gas through the collar of a blasthole. Extensive research was conducted to determine the types of stemming products most commonly used in South African underground hard rock mines, and the differences in design between the various products are discussed. The first stage of tests using compressed air only did not prove adequate to predict with certainty the pressure behaviour in the borehole of a particular product under high pressure conditions. The purpose built high pressure test rig did not prove to be a very effective tool to test stemming products under high pressure conditions. The test rig only incorporated the effect of gas pressure on the stemming product and in doing so omitted to take the effect of the shock wave into account. This study proved that to only take the gas pressure generated in the blasthole into account in not sufficient to effectively test stemming product design. A more comprehensive study should include the effect of gas pressure in the borehole, shock waves generated by the explosive and also the coefficient of friction of both the surface of the stemming product as well as the inside of the blasthole. / Dissertation (MEng)--University of Pretoria, 2009. / Mining Engineering / unrestricted