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By J Taylor, M Smith, A Tunnadine, D Nikolakopoulos

As Olympic Dam rapidly expands into the Southern Mine Area (SMA) the Mine Geology team has concurrently ramped up underground diamond drilling to sustain rates of above 175 km/a. With >10 underground rigs double-shifting, traditional paper sections used for the management of drill programs were no longer optimal due to scale of operations. An opportunity was identified to modernise monitoring of all underground diamond drill programs, through the integration of logged copper sulfide estimates with Olympic Dam’s first-fully integrated deposit-scale geological model in Leapfrog Geo. This paper describes the transition within the Mine Geology team from reliance on 2D paper sections, towards near-real time 3D geological modelling for optimising end-of-hole decisions in the field. This transition was enabled by innovative database solutions informing locally constrained implicit models, limited by a distance buffer around active underground fan drilling. Due to the high volume of historic drilling, there were significant computational challenges with the implementation of daily production style implicit models at Olympic Dam and initial testing of regular updates to implicit gradeshells and sulfide domains required in excess of 12 hrs to run. The enabling solution was to optimise the interfaces between the AcQuire database, Deswik MDM and Leapfrog Geo to create a dynamic, streamlined workflow that was easily completed by Mine Geologists in accordance with daily production duties. Geologists are now building rapid implicit models for a range of variables from an integrated data set of logged sulfide estimates and existing assays, spatially constrained by 150 m buffers around active drilling using a unique drill fan identifier flagged during the planning phase. Once the live-implicit models are updated, they are exported for visualisation on field tablets to be used during logging and core checks at the start and end of every shift. To manage the risk of ‘going digital’ interpretation remains at the forefront of the drill section close-out process, with Mine Geologists critically reviewing the finished sections on a standardised template with new learnings wireframed and communicated to stakeholders. The Live Field Model has enabled a step-change in the geological effectiveness of diamond drilling, reducing over – and under-drilling ore-waste at Olympic Dam continuing to push drilling effectiveness towards 20 per cent of intercepts >2 per cent Cu. In addition, upside or risk identified during the proactive management of drilling execution can be rapidly capitalised or mitigated.

Mar 22, 2022

By A Whaanga

The use of photogrammetry has greatly increased the accuracy of 3D mapping underground. However, this has not reduced the need or desire for geologists to produce plans and sections showing an interpretation of the mapped geology. At the Waihi Underground Gold Mine, a hybrid solution has been developed that utilises the accuracy of photogrammetry and mapping onto face photos with the CAD-based Deswik.MappingTM functions to create plan and section maps. These have the benefit of looking like traditional paper maps but are more spatially accurate and richly attributed with metadata that can be filtered and exported to downstream processes. Deswik.MappingTM is a tablet-based solution that allows the geologist to map digitally with a stylus. Automation is utilised for the updating of survey as-builts, approved designs, drill holes, geological mapping, vein interpretations and mine work areas, reducing the requirement to print out maps at various locations and scales. Having 3D mine software on a tablet device provides a wealth of features not available when using paper: scaling is a problem of the past; information can be turned on and off; features can be viewed in 3D; and measurements between points of interest can be calculated in 3D space, for example, between vein wireframes and drill hole sample information. A suite of custom scripts has been developed that take the spatially accurate, registered and attributed polyline data from photogrammetry scans and converts them to a geological mapping legend with vein textures, structural information, and lithological boundaries. These 3D maps can be plotted in plan or section view and satisfy the requirements of the most ardent paper map supporter.

Mar 22, 2022

By J Ashford, Minniakhmetov

Geostatistical simulations are the best practice for uncertainty quantification in non-linear systems. Simulations are currently undertaken on an ad hoc basis in the mining industry due to high requirements in computing environments and advanced geostatistics. In contrast to the estimation process, multivariate simulations can be performed using different methods, tools, and computational environments, similar to data science. For large companies with multiple commodities it is important to have a standard framework to test, build, and run workflows using different methods, applications and computational environments. This work presents an auditable, modular and scalable framework to create geostatistical simulation workflows where the workflow is abstracted from the implementation. Key aspects: automation – workflows should be able to run automatically given updated data (if hands on modelling is not required); auditability – workflows should document key considerations and parameters; modularisation – workflows should permit swapping out components, for example substituting domaining in Leapfrog software with automated domaining using machine learning scripts; scalability – easy, repeatable, portable deployments on diverse infrastructure (for example, experimenting on a laptop, then moving to an on premise cluster or to the Cloud); reproducibility – parameters should be archived to permit re-running and reproduction of workflows in future years; and sensitivity analysis – any component of a workflow should be able to run with multiple scenarios of parameters and data to enable global uncertainty quantification and sensitivity analysis. The presented framework can be used to create different workflows for geostatistical simulations in multiple assets to quantify uncertainty in the value chain for resource categorisation, reserves uncertainty, value of information and robust mine planning amongst others.

Mar 22, 2022

By B Haase, R Saha

Conditional simulation (CS) has been extensively used in metal industry to assess uncertainty of geo metallurgical variables to build robust distributions around grade tonnages across different time horizons. Coal’s entry to nonlinear stochastic modelling is relatively new and this paper summarises the work done at BHP to enable its practical usage from production time frames to Life of Asset planning. CS in coal using Sequential Gaussian Simulation (SGS) is used to understand resource confidence around specific variables within pre-defined boundaries of spatially continuous domains for each coal seam. The variation between simulations for a given parameter reflects uncertainty. This paper demonstrates the use of CS to profile resource based upon agreed seam/parameter uncertainty thresholds which affect planning, forecasting, extraction and inform identification of key production risks and defining mitigation strategies. The end goal is to de-risk long-term plans by optimising infill data collection and improve stability in short-term planning through increased understanding of confidences locally rather than at global resource definition stages. The paper also briefly discusses the use of automated workflows and systems to minimise the time spent on the process to enable use in decision-making.

Mar 22, 2022

By S Sullivan

A recent review of resource reports lodged to statutory bodies found geological models with oversmoothed or unrealistic lithological boundaries, many of which do not represent geological appearance in the field, pit or underground drive. The underlying data is often too complex or messy to be incorporated into the resource model and is thus a simplification of reality, to the detriment of the mining operation and its shareholders. Volumes can be over- or underestimated and subsequent grade predictions can be misplaced, combining to cause over- or understatement in resource reporting to stakeholders and statutory authorities. Domain models can be improved with new modelling techniques such as machine learning. However, with poor data or improper use of machine learning techniques, the conclusions can be just as misleading as with current techniques. Machine learning rarely offers a single ‘right’ outcome, rather a range of possible outcomes from the data provided. But importantly it can take advantage of the full richness of data for more informed models. Machine learning also provides confidence in its predictions so that uncertainty in the resultant models can be quantified and passed through for risk assessment during mine planning and operation. This paper will discuss the factors accentuating complexity in deposit modelling: data diversity, structural controls, chemistry, data volumes, process workflows and external non-geological constraints. A case study illustrates the risk of reducing or ignoring complexity, which can result in an overly simplified geological model.

Mar 22, 2022

By E J. Hill, F Huang, A Otto

Geochemical data from drill core samples is used in mineral exploration to enable geologists to understand subsurface geology. However, it is challenging to analyse these data sets rigorously and consistently. Manual data interpretation relies on the knowledge and experience of logging geologists. When multiple geologists are involved, it is possible to introduce inconsistencies in logged lithology type and scale of logging (‘splitters’ versus ‘lumpers’). Machine learning (ML) is a powerful tool for analysing high dimensional data (ie data with many variables) and large data sets (ie data with many samples). However, ML methods applied to drill hole samples do not incorporate spatial information and this can result in high misclassification rates and small-scale ‘noisy’ results. We demonstrate the use of multiscale spatial analysis (wavelet tessellation) as a pre-ML step to analyse spatial information. This step helps reduce misclassification and filter out unwanted small-scale variation in the data. In an experiment, we used wavelet tessellation and supervised ML to predict litho-geochemical rock types from geochemistry data. The data set is from the Valhalla uranium deposit near Mount Isa, northern Queensland. First, geochemical knowledge and statistical analysis was used to select appropriate geochemical elements for litho-geochemical classification. Second, the elements were processed using multiscale spatial analysis. Third, we trained a Random Forest model with the data set and found that the model performs worse in predicting rare rock samples than common ones. We applied over-sampling and down-sampling methods in aim to overcome this issue. As a conclusion, the combination of multiscale spatial analysis (to define rock unit boundaries) and Random Forests (to classify rock units) is a good method for rapid and accurate analysis of drill hole geochemistry.

Mar 22, 2022

By A. Juganda, H. PINHEIRO, J. Brune, N. SANDOVAL, G. Bogin, K. Gallagher, F Wilson

The present work discusses the nuances of scaling and flow similarity to develop a 1/40th scale version of a longwall coal mine. Details on the geometric, kinematic, and dynamic scaling of the physical model are presented and discussed. This works also discusses using a 30% CO2 – 70% He gas mixture as a surrogate for methane. It also presents the implications of various Reynolds numbers (ranging from 7,000 to 140,000) and rotation of the shearer drums in the dynamic representation of the airflow and gas mixtures in the physical scaled model.

Mar 19, 2022

By Jessica M. Wempen, William G. Pariseau, Dallan J. Coons, Michael K. McCarter, Erik C. Westman

The abstract discusses the importance of proper pillar sizing in underground mining for safety and efficiency. It highlights the use of theoretical models, geotechnical measurements, and numerical modeling in predicting pillar behavior. The research focuses on using seismic tomography to image stress changes in a yield pillar at a longwall mine as the longwall face approaches. The study aims to compare the stress distribution imaged using tomography with theoretical models, geotechnical measurements, and numerical models.

Mar 18, 2022

By J. R. R. Bowling, A. McFadden, M. Francoeur

The abstract discusses the increasing use of extensive duct-fan systems in underground hard-rock mine development. It highlights the challenges of designing these systems, particularly the risk of partial vacuum and the need for careful booster fan positioning. The paper explores factors influencing the design of these systems, including component limitations, operational wear margins, and power constraints. It also defines the concept of partial vacuum and its impact on ventilation ducts. The authors propose an algorithmic approach for specifying the location and blade pitch of in-line booster fans to prevent system failure.

Mar 17, 2022

By Qingwen Shi, Brijes Mishra

The abstract discusses the use of numerical simulations to investigate roof fall in underground mines. It highlights the advantages of the discrete element method (DEM) in simulating large strain problems and fracturing processes, while acknowledging its computational limitations for large-scale problems. The study presents a 3D numerical model coupling the finite difference method (FDM) and DEM to simulate laminated roof failure in a coal mine. The model represents the laminated roof with bonded particles and discontinuities, while the surrounding rock is represented by continuum zones. The research analyzes the fracturing of the laminated roof as the entry advances, demonstrating the coupled method's ability to reproduce laminated roof failure.

Mar 17, 2022

By ETHAN FUSTER, Richard Ray Jr, STEPHEN LEE

The abstract states that horizontally-mounted jet fans are commonly used to ventilate vehicular tunnels due to their space and cost savings compared to fully ducted systems. It highlights the successful use of vertically-mounted jet fans in exhaust ventilation shafts for a passenger rail overbuild in New York City. The paper compares shaft dimensions, jet fan placement, and measured airflows, also comparing these measurements to predictions from computational fluid dynamics (CFD) simulations.

Mar 16, 2022

By Jared Winkler, Jon A. Hummer, Aleksandar D. Bugarski, Shad Peters, Steven E. Mischler, Steve Cochrane, Shawn Vanderslice, Teresa Barone

The case study assessed submicron aerosols at a continuous miner (CM) section, diesel aerosol concentrations at the longwall (LW) section, and elemental carbon (EC) and total carbon (TC) exposures. Aerosols at CM sections were a mix of freshly generated and those from main drifts. Low ambient concentrations and personal exposures suggest effective control strategies, with further reductions possible by curbing emissions from high-emitting light-duty vehicles.

Mar 16, 2022

By Yuanyuan Wang, XUEWEN WANG, Jinzhi Wei, Ligang Wen, Muxin Jia, Zhengyao Li

The abstract discusses the use of disodium carboxymethyl trithiocarbonate (D8) as an effective depressant in the flotation separation of stibnite and pyrite. The study investigates the mechanism behind D8's selective inhibition of pyrite, utilizing techniques like micro-flotation experiments, contact angle measurements, infrared spectroscopy, adsorption capacity analysis, and zeta potential measurements. The findings reveal that D8 physically adsorbs onto both pyrite and stibnite, but its adsorption capacity on pyrite is significantly higher. This adsorption enhances the hydrophilicity of the pyrite surface, hindering the adsorption of the collector and effectively depressing pyrite flotation, while having minimal impact on stibnite floatability.

Mar 14, 2022

By SONGHE WANG, YI XUE, PENG HOU, CAIHUI ZHU, XUANYE JIAO, Feng Gao

The abstract discusses the challenges of coalbed methane development in China due to low coal seam permeability. It highlights protective layer mining as a method to reduce in-situ stress and enhance permeability for improved gas drainage and disaster prevention. The study involves experimental and numerical simulations to analyze gas migration and permeability changes during stress release. It includes a stress-strain-seepage coupled test on coal, development of a mathematical model for coal seepage under mining unloading, and a numerical model based on the Wulan coal mine to explore in-situ stress release, permeability evolution, and gas drainage in an unloaded coal seam. The aim is to demonstrate the effectiveness of combining protective layer mining and gas drainage technology for gas outburst risk elimination.

Mar 14, 2022

By Dongjie Zhang, Shuxin Liu, Jianduo Wang

Broken rock mass and unreasonable stope structure parameters can cause excessive ore loss and dilution in sublevel caving; this is the main problem faced by caving mining in recent years. This paper proposes a stope structure parameter optimization method suitable for steeply inclined medium-thick broken ore bodies and evaluates the reliability and effectiveness through case studies. To solve the problem of large ore loss and dilution in the high-end wall sublevel caving method of Xilinhot fluorite mine, it is necessary to select proper stope structure parameters. Based on the theory of random media, an end-wall ore drawing experiment was carried out, and the bulk flow parameters needed to optimization of stope structure parameters were determined. In the experiment, it was found the draw-out body of high-end wall ore drawing showed a fine top and a thick bottom; the granular media had good fluidity. The reasonable caving step is determined to be 1.2 m instead of the 1.6 m originally used. Through the optimization experiment of the mining route position along the vein, the study found that the best route position is different with the ore body inclination. When the inclination angle of the ore body was 90°, the reasonable route was located in the center of the ore body. When the inclination angle of the ore body was between 85° and 90°, the route moved from the center of the ore body to the footwall at a rate of 22.5%. When the inclination angle of the ore body was between 80° and 85°, the route moved to the footwall at a rate of 54.8%. When the inclination angle of the ore body is not greater than 80°, the route should be arranged on the boundary of the footwall. Field application results showed the dilution rate reduced by 7.66% and the recovery rate increased by 7.31%. Field test results verify the effectiveness and applicability of the optimized stope structure, which corroborates the effectiveness and applicability of the optimized stope structure.

Mar 10, 2022

By L. Zhou, L. Yuan, R. A. Thomas, D. Bahrami

The abstract emphasizes the importance of a calibrated ventilation network model for mine ventilation planning, optimization, and fire simulation. It highlights the impact of continuous airflow monitoring on improving model calibration. The paper discusses an experimental study at the Safety Research Coal Mine (SRCM) to understand the use of continuous airflow data for model improvement. It also addresses practical problems in calibration and tuning using this data, including analysis of air velocity sensor fluctuations and application of sensor location correction factors for accurate average air velocity.

Mar 7, 2022

By Yousef Ghorbani, Abolfazl Danesh, Fatemeh Kazemi, Ataallah Bahrami, Morteza Abdollahi

The abstract highlights the impact of flocculants used in the middle thickener of a copper-molybdenum plant on both process water quality and molybdenite floatability. The study investigates the effect of a starch-based flocculant (wheat starch) on the efficiency of the middle thickener and molybdenite flotation. The findings indicate that the presence of starch influences the settling behavior of particles with different shapes, and the dosage of starch affects both molybdenum recovery and the presence of copper minerals in the molybdenum concentrate.

Mar 7, 2022

By Brian Y. Lattimer, Kray D. Luxbacher, Jonathan L. Hodges, Manuel J. Barros-Daza

The abstract discusses the importance of predicting fire response time (the time before conditions become untenable for firefighters) in underground coal mines. It introduces a data-driven approach using artificial neural networks (ANNs) to predict response time and fire size based on measurable parameters like CO concentration, mine geometry, air velocity, and time since fire detection. The approach was tested using simulations from fire models (FDS and FSSIM), showing promising results for real-time predictions during mine fires.

Mar 6, 2022

By K. Luxbacher, D. T. Hristopulos, S. Schafrik, J. Diaz, Z. Agioutantis

Big data is generated from both surface and underground mining operations. Such data contain a wealth of information concerning safety and health in the workplace and production parameters. This paper will discuss the progress towards developing an accurate forecasting model for methane gas concentration based on the analysis of data collected from Atmospheric Monitoring Systems in underground coal mines employing time series models. Several procedures need to be applied to raw data, such as data cleaning and filtering for outlier removal, data homogenization and segmentation, and exploratory statistical analysis. The data analyzed were retrieved from two coal operations in the Eastern US. A negative correlation between barometric pressure and methane gas concentration was established, at least for certain data segments. Such correlations raise the possibility that barometric pressure data can predict variations of gas concentration in the mine. The datasets will be further investigated to establish the robustness of barometric pressure and gas concentration correlation and explore the dependence of gas concentration on other factors related to mine design and operations

Mar 2, 2022

By T. Doshi, S. Ennis

The interactions of mineralogy, chemistry and mechanics in an ore concentrator are highly complex, so it can be particularly hard to measure the impact of a specific plant change. This paper describes a method for modeling a baseline metal recovery so that the value of a major process improvement can be measured. This method only considers variables that cannot be optimized, such as mineralogy or equipment availability. Linear regression, random forests, extreme gradient boosting (XGB), and simple two-layer neural network models of process value were tested as candidate baseline model types to assess the impact on throughput and recovery in a copper concentrator. XGB performed the best in terms of accuracy for both throughput and recovery models. Using an XGB model of nonoptimizable process variables provides an accurate, dynamic baseline that can be compared with continuous production data to determine the value added by major concentrator improvements in real time.

Mar 2, 2022