Unplanned dilution and ore-loss optimisation in underground mines via cooperative neuro-fuzzy network

The aim of study is to establish a proper unplanned dilution and ore-loss (UB: uneven break) management system. To achieve the goal, UB prediction and consultation systems were established using artificial neural network (ANN) and fuzzy expert system (FES). Attempts have been made to illuminate the UB mechanism by scrutinising the contributions of potential UB influence factors. Ultimately, the proposed UB prediction and consultation systems were unified as a cooperative neuro fuzzy system

Tunnel Overbreak Management System Using Overbreak Resistance Factor

When tunnel is excavated via drilling and blasting, the excessive overbreak is the primary cause of personal or equipment safety hazards and increasing the cost of the tunnel operation owing to additional ground supports such as shotcrete. The practical management of overbreak is extremely difficult due to the complex causative mechanism of it. The study examines the relationship between rock mass characteristics (unsupported face condition, uniaxial compressive strength, face weathering and alteration, discontinuities- frequency, condition and angle between discontinuities and tunnel contour) and the depth of overbreak through using feed-forward artificial neuron networks. Then, Overbreak Resistance Factor (ORF) has been developed based on the weights of rock mass parameters to the overbreak phenomenon. Also, a new concept of tunnel overbreak management system using ORF has been suggested

Unplanned dilution and ore loss prediction in longhole stoping mines via multiple regression and artificial neural network analyses

Unplanned dilution and ore loss directly influence not only the productivity of underground stopes, but also the profitability of the entire mining process. Stope dilution is a result of complex interactions between a number of factors, and cannot be predicted prior to mining. In this study, unplanned dilution and ore loss prediction models were established using multiple linear and nonlinear regression analysis (MLRA and MNRA), as well as an artificial neural network (ANN) method based on 1067 datasets with ten causative factors from three underground longhole stoping mines in Western Australia. Models were established for individual mines, as well as a general model that includes all of the mine data-sets. The correlation coefficient (R) was used to evaluate the methods, and the values for MLRA, MNRA, and ANN compared with the general model were 0.419, 0.438, and 0.719, respectively. Considering that the current unplanned dilution and ore loss prediction for the mines investigated yielded an R of 0.088, the ANN model results are noteworthy. The proposed ANN model can be used directly as a practical tool to predict unplanned dilution and ore loss in mines, which will not only enhance productivity, but will also be beneficial for stope planning and design

Visualization of Predicted Ground Vibration Induced by Blasting in Urban Quarry Site Utilizing Web-GIS

Blasting is routinely carried out at various quarries. When blasting is done in an urban area, the ground vibration induced by the operation may affect nearby residents physically and mentally. In this study, a visualization system of ground vibration induced by blasting is constructed for the purpose of reducing these adverse effects. The system consists of two phases. The first is the ground vibration prediction by using artificial intelligence, specifically an ANN (Artificial Neural Network). The second is the visualization of the predicted vibration through Web-GIS. Four prediction factors, namely MIC (Maximum Instantaneous Charge), distance, elevation difference, and direction were used and PPV (Peak Particle Velocity) was used as an index of ground vibration strength. Colored contours representing vibration intensity were generated using GIS tools based on predicted PPV. Furthermore, the contour is converted into a KMZ file and overlaid on a web-based map (Google Maps) that also displays other pertinent information about the quarry vicinity. This means that the system can be used by anyone who has an internet connection and access to a browser. The data would be available to residents, local government officers, and anyone else who wishes to use it. In addition, the ground vibration prediction data and contour maps could also be used to optimize blasting designs in advance. Through the use of this system, optimal blasting can be done, maximizing the productivity of the quarry as well as minimizing the impact on the local residences

Web-GIS Based Visualization System of Predicted Ground Vibration Induced by Blasting in Urban Quarry Sites

Blasting is routinely carried out at various resource extraction sites, even in urban areas. As a consequence of this, residents around urban quarry sites are affected by ground vibration induced by blasting on a regular basis. In this study, a prediction and visualization system for ground vibrations is developed for the purpose of reducing the adverse psychological effects of blasting. The system consists of predicting ground vibration using an Artificial Neural Network (ANN) and visualizing it on an online map using Web-GIS. A prediction model using ANN that learned the optimum weight by taking 50 sets of data indicated a regression value of 0.859 and a Mean Square Error (MSE) of 0.0228. Compared with previous researches, these values are not bad results. Peak Particle Velocity (PPV) was used as a metric to measure ground vibration intensity. A color contour is generated using GIS tools based on the PPV value of each prediction point. The system is completed by overlaying the contour onto a basic map in a website. The basic map shows the surrounding area through the use of Google Maps data. This system can be used by anyone with access to the internet and a browser, requiring no special software or hardware. In addition, mining operations can utilize the data to modify blasting design and planning to minimize ground vibration. In conclusion, this system has the potential to alleviate the worries of surrounding residents caused by ground vibrations from blasting due to the fact that they can personally check the predicted vibration around their locale. Furthermore, since this data will be publicly available on the internet, it is also possible that this system can contribute to research in other fields

Development of a Remote Rock Fragmentation Size Distribution Measurement System for Surface Mines Using 3D Photogrammetry

One of the factors that can affect the efficiency of a mining operation is the fragmentation size distribution of blasted rock. A consistent fragmentation size allows the company to streamline its process, and more importantly, minimize costs. In order to maintain this fragmentation size, monitoring must be done regularly so that adjustments can be made. Traditional methods such as manual sieving and visual estimation are have been used for this purpose, but limitations on sampling procedure and bias make these methods relatively inefficient. One of the solutions that were developed was to use digital image-based particle size analysis. The study proposes a cloud-based 3D photogrammetry rock fragmentation size distribution system that will make use of multiple images to create 3D models that can then be analyzed and segmented to provide a fragmentation size distribution. Several pictures of a muckpile using a smartphone are taken from an angle and compiled into a dataset. This is used as input for a Structure-from-Motion algorithm, which can create a 3D point cloud from the image data. This point cloud is then subjected to clustering so that the individual fragments can be represented and their dimensions could be measured. Finally, from these dimensions, a fragmentation size distribution can be created. As the system requires a large amount of computing power, it can be implemented in a remote server so that it can be accessible in the field. This system could provide surface mine operators an easy way to estimate size distribution using only a smartphone

Development of Auto Scaling Method for 3D Rock Fragmentation Measurement System

Fragmentation Distribution is one of the important aspects of mining operations as it affects productivities on the majority of Mine-to-Mill operations. Nevertheless the significance of fragmentation management, the mining industry has relied on 2D image based fragmentation measurement system which poses many downsides. To overcome the drawbacks of current 2D fragmentation measurement system, 3D Rock Fragmentation Measurement System has been proposed with using 3D photogrammetry technologies. One of the common difficulty of fragmentation measurement system is scaling of the object, which is an essential component to secure the accuracy of particle size distribution. In this study, the actual scales and size information of objects have been obtained by measuring the acceleration when moving between the photographing points and giving the information of the distance obtained from the acceleration. The developed system would be equipped with the 3D Rock Fragmentation Measurement System

University Students’ Preferences for Labour Conditions at a Mining Site: Evidence from Two Australian Universities

The mining industry makes up a large portion of the gross domestic product (GDP) in Australia, although securing human resources remains a problem in that field. The aim of this paper is to identify Australian university mining students’ preferences, considering it as potential employees’ preferences, for labour conditions at mining sites by means of a discrete choice experiment to promote efficient improvements in labour conditions in the mining industry. The data of 93 respondents analysed in this paper was collected by survey carried out in two universities in Australia. The result of the study showed that students have preferences on several factors such as wage, fatality rate, working position, commuting style, and company. Students having specific sociodemographic characters were found to show specific preferences on labour conditions. The results of this study indicate the potential average of appropriate monetary compensation for each factor

A Case Study of Assessing Button Bits Failure through Wavelet Transform Using Rock Drilling Induced Noise Signals

Finding the precise moment of button breakage of bits during drilling, with the experience of drill rig operators is a serious concern for modern vibrant mining industry. This research proposed a new methodology to find the failure of button using the sound generated by rock-bit interactions. The experiment is conducted by the video and sound data recorded during a drilling process in an underground mine, that uses a Sandvik AXERA7 twin boom jumbo drill rig and Polycrystalline diamond (PCD) tapered button bits. Signal analysis techniques such as Fourier transform and Wavelet transform are utilised to analyse the hectic noise signal recorded. The analysed results are shown that Wavelet Transform is much more effective in finding singularity points such as chipping or breakage of a button in compared to the Fourier Transform. The outcome of this analysis, which is the peak intensity at the breakage point, was correlated to the average intensity of the sound wave using moving average method. The results suggest that the noise generated during the drilling process can be used to detect the condition of the drill bit

3-dimensional Modeling and Mining Analysis for Open-pit Limestone Mine Stope Using a Rotary-wing Unmanned Aerial Vehicle

The purpose of this study is to show the possibility of 3-dimensional modeling of open-pit limestone mine by using a rotary-wing unmanned aerial vehicle, a drone, and to estimate the amount of mining before and after mining of limestone by explosive blasting. Analysis of the image duplication of the mine has shown that it is possible to achieve high image quality. Analysis of each axis error at the shooting position after analyzing the distortions through camera calibration was shown the allowable range. As a result of estimating the amount of mining before and after explosive blasting, it was possible to estimate the amount of mining of a wide range quickly and accurately in a relatively short time. In conclusion, it is considered that the drone of a rotary-wing unmanned aerial vehicle can be usefully used for the monitoring of open-pit limestone mines and the estimation of the amount of mining. Furthermore, it is expected that this method will be utilized for periodic monitoring of construction sites and road slopes as well as open-pit mines in the future