University Students\u27 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\u27 preferences, considering it as potential employees\u27 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

Model Scaling in Smartphone GNSS-Aided Photogrammetry for Fragmentation Size Distribution Estimation

Fragmentation size distribution estimation is a critical process in mining operations that employ blasting. In this study, we aim to create a low-cost, efficient system for producing a scaled 3D model without the use of ground truth data, such as GCPs (Ground Control Points), for the purpose of improving fragmentation size distribution measurement using GNSS (Global Navigation Satellite System)-aided photogrammetry. However, the inherent error of GNSS data inhibits a straight-forward application in Structure-from-Motion (SfM). To overcome this, the study proposes that, by increasing the number of photos used in the SfM process, the scale error brought about by the GNSS error will proportionally decrease. Experiments indicated that constraining camera positions to locations, relative or otherwise, improved the accuracy of the generated 3D model. In further experiments, the results showed that the scale error decreased when more images from the same dataset were used. The proposed method is practical and easy to transport as it only requires a smartphone and, optionally, a separate camera. In conclusion, with some modifications to the workflow, technique, and equipment, a muckpile can be accurately recreated in scale in the digital world with the use of positional data

Optimizing overbreak prediction based on geological parameters comparing multiple regression analysis and artificial neural network

Underground mining becomes more efficient due to the technological advancements of drilling & blasting methods and the developing of highly productive mining methods that facilitate easier access to ore. In the perspective of maximizing productivity in underground mining by drilling and blasting methods, overbreak control is an essential component. The causing factors of overbreak can simply divided as blasting and geological parameters and all of the factors are nonlinearly correlated. In this paper, the blasting design of the tunnel was fixed as the standard blasting pattern and the research focus on effects of geological parameters to the overbreak phenomenon. 49 sets of rock mass rating (RMR) and overbreak data were applied to linear and nonlinear multiple regression analysis (LMRA & NMRA) and artificial neural network (ANN) to predict overbreak as input and output parameters respectively. The performance of LMRA, NMRA and ANN models were evaluated by comparing coefficient correlations (R2) and their values are 0.694, 0.704 and 0.945 respectively which means that the relatively high level of accuracy of the ANN in comparison of LMRA and NMRA. The developed optimum overbreak predicting ANN model is suitable for establishing an overbreak warning and preventing system and it will utilize as a foundation reference for a practical drift blasting reconciliation at mines for operation improvements

Prediction of Particle Size Distribution of Mill Products Using Artificial Neural Networks

High energy consumption in size reduction operations is one of the most significant issues concerning the sustainability of raw material beneficiation. Thus, process optimization should be done to reduce energy consumption. This study aimed to investigate the applicability of artificial neural networks (ANNs) to predict the particle size distributions (PSDs) of mill products. PSD is one of the key sources of information after milling since it significantly affects the subsequent beneficiation processes. Thus, precise PSD prediction can contribute to process optimization and energy consumption reduction by avoiding over-grinding. In this study, coal particles (-2 mm) were ground with a rod mill under different conditions, and their PSDs were measured. The variables studied included volume% (vol.%) of feed (coal particle), vol.% rod load, and grinding time. Our supervised ANN models were developed to predict PSDs and trained by experimental data sets. The trained models were verified with the other experimental data sets. The results showed that the PSDs predicted by ANN fitted very well with the experimental data after the training. Root mean squared error (RMSE) was calculated for each milling condition, with results between 0.165 and 0.965. Also, the developed ANN models can predict the PSDs of ground products under different milling conditions (i.e., vol.% feed, vol.% rod load, and grinding time). The results confirmed the applicability of ANNs to predict PSD and, thus the potential contribution to reducing energy consumption by optimizing the grinding conditions.Validerad;2023;Nivå 2;2023-01-16 (joosat);Licens fulltext: CC BY License</p

Practical Models To Distinguish Between Seismic Events And Blast Signals

The seismic events are contaminated with the blast/noise signals in microseismic monitoring of the underground excavations, negatively affecting the interpretation and detection of high-stress zones. This study proposes explicit and comprehensible classifiers by hybridizing the principal component analysis (PCA) with genetic programming (GP) and classification and regression tree (CART) algorithms. Six discriminant parameters representing the spectrum and source characteristics of the signals were used as input variables. PCA reduced the problem's dimensionality to two components, which were then fed into GP and CART algorithms as the new input variables. A systematic hyperparameter tuning procedure was employed to find the optimum values of the controlling parameters of the algorithms. The hybrid PCA-GP and PCA-CART classifiers provided practical mathematical equations and tree structures, respectively, capable of distinguishing between the signal types with high accuracy. However, the PCA-GP model outperformed the PCA-CART model based on the performance indices

An investigation of underground monitoring and communication system based on radio waves attenuation using ZigBee

In the challenging environment and changing topology of a mine, reliable and effective communication is a high-stake issue along with the objectives of safe and efficient mining operations. Automation by remote and automatic systems have improved workplace health and safety for employees, cost-effectiveness, management of technical problems, energy saving, real-time response to events. In response to these challenges, wireless sensor networks (WSNs) have been widely employed in underground monitoring and communication systems for the purpose of environmental monitoring, positioning of workers and equipment, operational monitoring and communication system. Considering the capabilities of WSNs, ZigBee network is adapted. In this study, common WSNs are evaluated for application in underground mines and demonstrated why ZigBee network performance is suitable for such environments. ZigBee radio waves attenuation is investigated to evaluate stable communication range between ZigBee nodes at straight and curved tunnels in a real mine scenario. Moreover, experimental measurements of ZigBee radio waves attenuation are validated by simulation results. Based on the analysis of the experimental and simulation results, the effective factors on the radio waves attenuation in the junctions, curvatures and fields near and far from the source are assessed. Finally, stable wireless communication ranges between developed ZigBee nodes in the underground Angas Zinc Mine is concluded 100 m and 70 m for straight and curved tunnels, respectively. The development of ZigBee network application compared to other WSNs in underground mines is also approved

Illumination of contributing parameters of uneven break in narrow vein mine

One of the principal challenge facing the stope production in underground mining is the overbreak and underbreak (UB: uneven break). Although the UB features a critical economic fallout to the entire mining process, it is much inevitable and usually left as an unpredictable phenomenon in underground mines. The complex mechanism of UB must be examined to minimize the UB phenomenon. In this study, the contribution of ten primary UB causative parameters is scrutinized investigating a published UB prediction ANN model. The inputs (UB causative factors) contributions to the output (percentage of UB) of the ANN model were analyzed using Profile methodology (PM). The results PM revealed the essential importance of geological parameters to UB phenomenon as the calculated contributions of adjusted Q-rate (GAQ) and average horizontal to vertical stress ratio (GSK) are 20.48% and 18.12% respectively. Also, the trends of the other eight UB causative factors were investigated. The findings of this study can be used as a reference in stope design and reconciliation processes to maximize the productivity of the underground mine

Support characteristics of eco-spiral pile with respect to twisting angle and ratio of borehole diameter to pile width

This study numerically analysed the characteristics of eco-spiral piles (Eco-SPs) as multi-purpose geotechnical supports. The behaviour of eco-spiral piles in the ground was studied with respect to their twisting angle and the ratio of borehole diameter to eco-spiral pile width using FLAC-3D based on the finite-difference method. A cement mortar grout was modelled to sustain the eco-spiral pile in a sandy soil. Relationships were found among the eco-spiral pile’s twisting angle, pull-out load and maximum shear stress. The results should aid the further design of the bolts for application in a range of circumstances